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1.  Human genome meeting 2016 
Srivastava, A. K. | Wang, Y. | Huang, R. | Skinner, C. | Thompson, T. | Pollard, L. | Wood, T. | Luo, F. | Stevenson, R. | Polimanti, R. | Gelernter, J. | Lin, X. | Lim, I. Y. | Wu, Y. | Teh, A. L. | Chen, L. | Aris, I. M. | Soh, S. E. | Tint, M. T. | MacIsaac, J. L. | Yap, F. | Kwek, K. | Saw, S. M. | Kobor, M. S. | Meaney, M. J. | Godfrey, K. M. | Chong, Y. S. | Holbrook, J. D. | Lee, Y. S. | Gluckman, P. D. | Karnani, N. | Kapoor, A. | Lee, D. | Chakravarti, A. | Maercker, C. | Graf, F. | Boutros, M. | Stamoulis, G. | Santoni, F. | Makrythanasis, P. | Letourneau, A. | Guipponi, M. | Panousis, N. | Garieri, M. | Ribaux, P. | Falconnet, E. | Borel, C. | Antonarakis, S. E. | Kumar, S. | Curran, J. | Blangero, J. | Chatterjee, S. | Kapoor, A. | Akiyama, J. | Auer, D. | Berrios, C. | Pennacchio, L. | Chakravarti, A. | Donti, T. R. | Cappuccio, G. | Miller, M. | Atwal, P. | Kennedy, A. | Cardon, A. | Bacino, C. | Emrick, L. | Hertecant, J. | Baumer, F. | Porter, B. | Bainbridge, M. | Bonnen, P. | Graham, B. | Sutton, R. | Sun, Q. | Elsea, S. | Hu, Z. | Wang, P. | Zhu, Y. | Zhao, J. | Xiong, M. | Bennett, David A. | Hidalgo-Miranda, A. | Romero-Cordoba, S. | Rodriguez-Cuevas, S. | Rebollar-Vega, R. | Tagliabue, E. | Iorio, M. | D’Ippolito, E. | Baroni, S. | Kaczkowski, B. | Tanaka, Y. | Kawaji, H. | Sandelin, A. | Andersson, R. | Itoh, M. | Lassmann, T. | Hayashizaki, Y. | Carninci, P. | Forrest, A. R. R. | Semple, C. A. | Rosenthal, E. A. | Shirts, B. | Amendola, L. | Gallego, C. | Horike-Pyne, M. | Burt, A. | Robertson, P. | Beyers, P. | Nefcy, C. | Veenstra, D. | Hisama, F. | Bennett, R. | Dorschner, M. | Nickerson, D. | Smith, J. | Patterson, K. | Crosslin, D. | Nassir, R. | Zubair, N. | Harrison, T. | Peters, U. | Jarvik, G. | Menghi, F. | Inaki, K. | Woo, X. | Kumar, P. | Grzeda, K. | Malhotra, A. | Kim, H. | Ucar, D. | Shreckengast, P. | Karuturi, K. | Keck, J. | Chuang, J. | Liu, E. T. | Ji, B. | Tyler, A. | Ananda, G. | Carter, G. | Nikbakht, H. | Montagne, M. | Zeinieh, M. | Harutyunyan, A. | Mcconechy, M. | Jabado, N. | Lavigne, P. | Majewski, J. | Goldstein, J. B. | Overman, M. | Varadhachary, G. | Shroff, R. | Wolff, R. | Javle, M. | Futreal, A. | Fogelman, D. | Bravo, L. | Fajardo, W. | Gomez, H. | Castaneda, C. | Rolfo, C. | Pinto, J. A. | Akdemir, K. C. | Chin, L. | Futreal, A. | Patterson, S. | Statz, C. | Mockus, S. | Nikolaev, S. N. | Bonilla, X. I. | Parmentier, L. | King, B. | Bezrukov, F. | Kaya, G. | Zoete, V. | Seplyarskiy, V. | Sharpe, H. | McKee, T. | Letourneau, A. | Ribaux, P. | Popadin, K. | Basset-Seguin, N. | Chaabene, R. Ben | Santoni, F. | Andrianova, M. | Guipponi, M. | Garieri, M. | Verdan, C. | Grosdemange, K. | Sumara, O. | Eilers, M. | Aifantis, I. | Michielin, O. | de Sauvage, F. | Antonarakis, S. | Likhitrattanapisal, S. | Lincoln, S. | Kurian, A. | Desmond, A. | Yang, S. | Kobayashi, Y. | Ford, J. | Ellisen, L. | Peters, T. L. | Alvarez, K. R. | Hollingsworth, E. F. | Lopez-Terrada, D. H. | Hastie, A. | Dzakula, Z. | Pang, A. W. | Lam, E. T. | Anantharaman, T. | Saghbini, M. | Cao, H. | Gonzaga-Jauregui, C. | Ma, L. | King, A. | Rosenzweig, E. Berman | Krishnan, U. | Reid, J. G. | Overton, J. D. | Dewey, F. | Chung, W. K. | Small, K. | DeLuca, A. | Cremers, F. | Lewis, R. A. | Puech, V. | Bakall, B. | Silva-Garcia, R. | Rohrschneider, K. | Leys, M. | Shaya, F. S. | Stone, E. | Sobreira, N. L. | Schiettecatte, F. | Ling, H. | Pugh, E. | Witmer, D. | Hetrick, K. | Zhang, P. | Doheny, K. | Valle, D. | Hamosh, A. | Jhangiani, S. N. | Akdemir, Z. Coban | Bainbridge, M. N. | Charng, W. | Wiszniewski, W. | Gambin, T. | Karaca, E. | Bayram, Y. | Eldomery, M. K. | Posey, J. | Doddapaneni, H. | Hu, J. | Sutton, V. R. | Muzny, D. M. | Boerwinkle, E. A. | Valle, D. | Lupski, J. R. | Gibbs, R. A. | Shekar, S. | Salerno, W. | English, A. | Mangubat, A. | Bruestle, J. | Thorogood, A. | Knoppers, B. M. | Takahashi, H. | Nitta, K. R. | Kozhuharova, A. | Suzuki, A. M. | Sharma, H. | Cotella, D. | Santoro, C. | Zucchelli, S. | Gustincich, S. | Carninci, P. | Mulvihill, J. J. | Baynam, G. | Gahl, W. | Groft, S. 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T. | Liang, T. | Pham, K. | Saghbini, M. | Dzakula, Z. | Chee-Wei, Y. | Dongsheng, L. | Lai-Ping, W. | Lian, D. | Hee, R. O. Twee | Yunus, Y. | Aghakhanian, F. | Mokhtar, S. S. | Lok-Yung, C. V. | Bhak, J. | Phipps, M. | Shuhua, X. | Yik-Ying, T. | Kumar, V. | Boon-Peng, H. | Campbell, I. | Young, M. -A. | James, P. | Rain, M. | Mohammad, G. | Kukreti, R. | Pasha, Q. | Akilzhanova, A. R. | Guelly, C. | Abilova, Z. | Rakhimova, S. | Akhmetova, A. | Kairov, U. | Trajanoski, S. | Zhumadilov, Z. | Bekbossynova, M. | Schumacher, C. | Sandhu, S. | Harkins, T. | Makarov, V. | Doddapaneni, H. | Glenn, R. | Momin, Z. | Dilrukshi, B. | Chao, H. | Meng, Q. | Gudenkauf, B. | Kshitij, R. | Jayaseelan, J. | Nessner, C. | Lee, S. | Blankenberg, K. | Lewis, L. | Hu, J. | Han, Y. | Dinh, H. | Jireh, S. | Walker, K. | Boerwinkle, E. | Muzny, D. | Gibbs, R. | Hu, J. | Walker, K. | Buhay, C. | Liu, X. | Wang, Q. | Sanghvi, R. | Doddapaneni, H. | Ding, Y. | Veeraraghavan, N. | Yang, Y. | Boerwinkle, E. | Beaudet, A. L. | Eng, C. M. | Muzny, D. M. | Gibbs, R. A. | Worley, K. C. C. | Liu, Y. | Hughes, D. S. T. | Murali, S. C. | Harris, R. A. | English, A. C. | Qin, X. | Hampton, O. A. | Larsen, P. | Beck, C. | Han, Y. | Wang, M. | Doddapaneni, H. | Kovar, C. L. | Salerno, W. J. | Yoder, A. | Richards, S. | Rogers, J. | Lupski, J. R. | Muzny, D. M. | Gibbs, R. A. | Meng, Q. | Bainbridge, M. | Wang, M. | Doddapaneni, H. | Han, Y. | Muzny, D. | Gibbs, R. | Harris, R. A. | Raveenedran, M. | Xue, C. | Dahdouli, M. | Cox, L. | Fan, G. | Ferguson, B. | Hovarth, J. | Johnson, Z. | Kanthaswamy, S. | Kubisch, M. | Platt, M. | Smith, D. | Vallender, E. | Wiseman, R. | Liu, X. | Below, J. | Muzny, D. | Gibbs, R. | Yu, F. | Rogers, J. | Lin, J. | Zhang, Y. | Ouyang, Z. | Moore, A. | Wang, Z. | Hofmann, J. | Purdue, M. | Stolzenberg-Solomon, R. | Weinstein, S. | Albanes, D. | Liu, C. S. | Cheng, W. L. | Lin, T. T. | Lan, Q. | Rothman, N. | Berndt, S. | Chen, E. S. | Bahrami, H. | Khoshzaban, A. | Keshal, S. Heidari | Bahrami, H. | Khoshzaban, A. | Keshal, S. Heidari | Alharbi, K. K. R. | Zhalbinova, M. | Akilzhanova, A. | Rakhimova, S. | Bekbosynova, M. | Myrzakhmetova, S. | Matar, M. | Mili, N. | Molinari, R. | Ma, Y. | Guerrier, S. | Elhawary, N. | Tayeb, M. | Bogari, N. | Qotb, N. | McClymont, S. A. | Hook, P. W. | Goff, L. A. | McCallion, A. | Kong, Y. | Charette, J. R. | Hicks, W. L. | Naggert, J. K. | Zhao, L. | Nishina, P. M. | Edrees, B. M. | Athar, M. | Al-Allaf, F. A. | Taher, M. M. | Khan, W. | Bouazzaoui, A. | Harbi, N. A. | Safar, R. | Al-Edressi, H. | Anazi, A. | Altayeb, N. | Ahmed, M. A. | Alansary, K. | Abduljaleel, Z. | Kratz, A. | Beguin, P. | Poulain, S. | Kaneko, M. | Takahiko, C. | Matsunaga, A. | Kato, S. | Suzuki, A. M. | Bertin, N. | Lassmann, T. | Vigot, R. | Carninci, P. | Plessy, C. | Launey, T. | Graur, D. | Lee, D. | Kapoor, A. | Chakravarti, A. | Friis-Nielsen, J. | Izarzugaza, J. M. | Brunak, S. | Chakraborty, A. | Basak, J. | Mukhopadhyay, A. | Soibam, B. S. | Das, D. | Biswas, N. | Das, S. | Sarkar, S. | Maitra, A. | Panda, C. | Majumder, P. | Morsy, H. | Gaballah, A. | Samir, M. | Shamseya, M. | Mahrous, H. | Ghazal, A. | Arafat, W. | Hashish, M. | Gruber, J. J. | Jaeger, N. | Snyder, M. | Patel, K. | Bowman, S. | Davis, T. | Kraushaar, D. | Emerman, A. | Russello, S. | Henig, N. | Hendrickson, C. | Zhang, K. | Rodriguez-Dorantes, M. | Cruz-Hernandez, C. D. | Garcia-Tobilla, C. D. P. | Solorzano-Rosales, S. | Jäger, N. | Chen, J. | Haile, R. | Hitchins, M. | Brooks, J. D. | Snyder, M. | Jiménez-Morales, S. | Ramírez, M. | Nuñez, J. | Bekker, V. | Leal, Y. | Jiménez, E. | Medina, A. | Hidalgo, A. | Mejía, J. | Halytskiy, V. | Naggert, J. | Collin, G. B. | DeMauro, K. | Hanusek, R. | Nishina, P. M. | Belhassa, K. | Belhassan, K. | Bouguenouch, L. | Samri, I. | Sayel, H. | moufid, FZ. | El Bouchikhi, I. | Trhanint, S. | Hamdaoui, H. | Elotmani, I. | Khtiri, I. | Kettani, O. | Quibibo, L. | Ahagoud, M. | Abbassi, M. | Ouldim, K. | Marusin, A. V. | Kornetov, A. N. | Swarovskaya, M. | Vagaiceva, K. | Stepanov, V. | De La Paz, E. M. Cutiongco | Sy, R. | Nevado, J. | Reganit, P. | Santos, L. | Magno, J. D. | Punzalan, F. E. | Ona, D. | Llanes, E. | Santos-Cortes, R. L. | Tiongco, R. | Aherrera, J. | Abrahan, L. | Pagauitan-Alan, P. | Morelli, K. H. | Domire, J. S. | Pyne, N. | Harper, S. | Burgess, R. | Zhalbinova, M. | Akilzhanova, A. | Rakhimova, S. | Bekbosynova, M. | Myrzakhmetova, S. | Gari, M. A. | Dallol, A. | Alsehli, H. | Gari, A. | Gari, M. | Abuzenadah, A. | Thomas, M. | Sukhai, M. | Garg, S. | Misyura, M. | Zhang, T. | Schuh, A. | Stockley, T. | Kamel-Reid, S. | Sherry, S. | Xiao, C. | Slotta, D. | Rodarmer, K. | Feolo, M. | Kimelman, M. | Godynskiy, G. | O’Sullivan, C. | Yaschenko, E. | Xiao, C. | Yaschenko, E. | Sherry, S. | Rangel-Escareño, C. | Rueda-Zarate, H. | Tayubi, I. A. | Mohammed, R. | Ahmed, I. | Ahmed, T. | Seth, S. | Amin, S. | Song, X. | Mao, X. | Sun, H. | Verhaak, R. G. | Futreal, A. | Zhang, J. | Whiite, S. J. | Chiang, T. | English, A. | Farek, J. | Kahn, Z. | Salerno, W. | Veeraraghavan, N. | Boerwinkle, E. | Gibbs, R. | Kasukawa, T. | Lizio, M. | Harshbarger, J. | Hisashi, S. | Severin, J. | Imad, A. | Sahin, S. | Freeman, T. C. | Baillie, K. | Sandelin, A. | Carninci, P. | Forrest, A. R. R. | Kawaji, H. | Salerno, W. | English, A. | Shekar, S. N. | Mangubat, A. | Bruestle, J. | Boerwinkle, E. | Gibbs, R. A. | Salem, A. H. | Ali, M. | Ibrahim, A. | Ibrahim, M. | Barrera, H. A. | Garza, L. | Torres, J. A. | Barajas, V. | Ulloa-Aguirre, A. | Kershenobich, D. | Mortaji, Shahroj | Guizar, Pedro | Loera, Eliezer | Moreno, Karen | De León, Adriana | Monsiváis, Daniela | Gómez, Jackeline | Cardiel, Raquel | Fernandez-Lopez, J. C. | Bonifaz-Peña, V. | Rangel-Escareño, C. | Hidalgo-Miranda, A. | Contreras, A. V. | Polfus, L. | Wang, X. | Philip, V. | Carter, G. | Abuzenadah, A. A. | Gari, M. | Turki, R. | Dallol, A. | Uyar, A. | Kaygun, A. | Zaman, S. | Marquez, E. | George, J. | Ucar, D. | Hendrickson, C. L. | Emerman, A. | Kraushaar, D. | Bowman, S. | Henig, N. | Davis, T. | Russello, S. | Patel, K. | Starr, D. B. | Baird, M. | Kirkpatrick, B. | Sheets, K. | Nitsche, R. | Prieto-Lafuente, L. | Landrum, M. | Lee, J. | Rubinstein, W. | Maglott, D. | Thavanati, P. K. R. | de Dios, A. Escoto | Hernandez, R. E. Navarro | Aldrate, M. E. Aguilar | Mejia, M. R. Ruiz | Kanala, K. R. R. | Abduljaleel, Z. | Khan, W. | Al-Allaf, F. A. | Athar, M. | Taher, M. M. | Shahzad, N. | Bouazzaoui, A. | Huber, E. | Dan, A. | Al-Allaf, F. A. | Herr, W. | Sprotte, G. | Köstler, J. | Hiergeist, A. | Gessner, A. | Andreesen, R. | Holler, E. | Al-Allaf, F. | Alashwal, A. | Abduljaleel, Z. | Taher, M. | Bouazzaoui, A. | Abalkhail, H. | Al-Allaf, A. | Bamardadh, R. | Athar, M. | Filiptsova, O. | Kobets, M. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Filiptsova, O. | Kobets, M. N. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Filiptsova, O. | Kobets, M. N. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Al-allaf, F. A. | Mohiuddin, M. T. | Zainularifeen, A. | Mohammed, A. | Abalkhail, H. | Owaidah, T. | Bouazzaoui, A.
Human Genomics  2016;10(Suppl 1):12.
Table of contents
O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder
A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson
O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents
R. Polimanti, J. Gelernter
O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort
X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group
O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus
A. Kapoor, D. Lee, A. Chakravarti
O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells
C. Maercker, F. Graf, M. Boutros
O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies
G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis
O7 Role of microRNA in LCL to IPSC reprogramming
S. Kumar, J. Curran, J. Blangero
O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease
S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti
O9 Metabolomic profiling for the diagnosis of neurometabolic disorders
T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea
O10 A novel causal methylation network approach to Alzheimer’s disease
Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett
O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway
A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni
O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types
B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest
O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types
C. A. Semple
O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer
E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project
O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer
F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu
O16 Modeling genetic interactions associated with molecular subtypes of breast cancer
B. Ji, A. Tyler, G. Ananda, G. Carter
O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors
H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski
O18 Predictive biomarkers to metastatic pancreatic cancer treatment
J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman
O19 DDIT4 gene expression as a prognostic marker in several malignant tumors
L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto
O20 Spatial organization of the genome and genomic alterations in human cancers
K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group
O21 Landscape of targeted therapies in solid tumors
S. Patterson, C. Statz, S. Mockus
O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma
S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis
O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis
S. Likhitrattanapisal
O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study
S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen
O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array
T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada
O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation
A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics
O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4
C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung
O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13
K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone
O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data
N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh
O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review
S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs
O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio
S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle
O32 Legal interoperability: a sine qua non for international data sharing
A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group
O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target
H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci
O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs
J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio
O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes
R. Ghosh, S. Plon
O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing
S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs
O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma
T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman
O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver
E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker
O40 A general statistic framework for genome-based disease risk prediction
M. Xiong, L. Ma, N. Lin, C. Amos
O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies
N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong
O42 Big data and NGS data analysis: the cloud to the rescue
O. Dobretsberger, M. Egger, F. Leimgruber
O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing
S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance
O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data
V. A. A. Antonio, N. Ono, Clark Kendrick C. Go
O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data
Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar
O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans
C. Zeng, J. Shao
O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations
H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula
O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing
Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng
O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes?
I. Campbell, M.-A. Young, P. James, Lifepool
O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS
C. Schumacher, S. Sandhu, T. Harkins, V. Makarov
O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform
H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs
O55 Rapid capture methods for clinical sequencing
J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs
O56 A diploid personal human genome model for better genomes from diverse sequence data
K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs
O57 Development of PacBio long range capture for detection of pathogenic structural variants
Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs
O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans
R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers
O59 Assessing RNA structure disruption induced by single-nucleotide variation
J. Lin, Y. Zhang, Z. Ouyang
P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number
A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt
P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility
E. S. Chen
P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients
H. Bahrami, A. Khoshzaban, S. Heidari Keshal
P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population
K. K. R. Alharbi
P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding
M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova
P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population
M. Matar
P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization
N. Mili, R. Molinari, Y. Ma, S. Guerrier
P9 Vulnerability of genetic variants to the risk of autism among Saudi children
N. Elhawary, M. Tayeb, N. Bogari, N. Qotb
P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction
S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion
P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3rd7 mice
Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina
P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients
B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel
P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments
A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey
P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome
D. Graur
P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients
J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak
P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns
B. S. Soibam
P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer
D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder
P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes
J. J. Gruber, N. Jaeger, M. Snyder
P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors
K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson
P23 RNA sequencing identifies gene mutations for neuroblastoma
K. Zhang
P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines
M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales
P25 Targeted Methylation Sequencing of Prostate Cancer
N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder
P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico
S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía
P28 Genetic modifiers of Alström syndrome
J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina
P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos
E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group
P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D
K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess
P34 Molecular regulation of chondrogenic human induced pluripotent stem cells
M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah
P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting
M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid
P36 Accessing genomic evidence for clinical variants at NCBI
S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko
P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA
C. Xiao, E. Yaschenko, S. Sherry
P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling
C. Rangel-Escareño, H. Rueda-Zarate
P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr
S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang
P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data
S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs
P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells
T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium
P43 Rapid and scalable typing of structural variants for disease cohorts
W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs
P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population
A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim
P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations
J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras
P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits
L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups
P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study
S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu
P49 Common variants in casr gene are associated with serum calcium levels in koreans
S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung
P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions
Y. Zhou, S. Xu
P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies
X. Wang, V. Philip, G. Carter
P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment
A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol
P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling
A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar
P54 Direct enrichment for the rapid preparation of targeted NGS libraries
C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel
P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System
R. Nitsche, L. Prieto-Lafuente
P57 ClinVar: a multi-source archive for variant interpretation
M. Landrum, J. Lee, W. Rubinstein, D. Maglott
P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome
Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad
P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation
A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler
P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia
F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar
doi:10.1186/s40246-016-0063-5
PMCID: PMC4896275  PMID: 27294413
2.  Novel Roles for Selected Genes in Meiotic DNA Processing 
PLoS Genetics  2007;3(12):e222.
High-throughput studies of the 6,200 genes of Saccharomyces cerevisiae have provided valuable data resources. However, these resources require a return to experimental analysis to test predictions. An in-silico screen, mining existing interaction, expression, localization, and phenotype datasets was developed with the aim of selecting minimally characterized genes involved in meiotic DNA processing. Based on our selection procedure, 81 deletion mutants were constructed and tested for phenotypic abnormalities. Eleven (13.6%) genes were identified to have novel roles in meiotic DNA processes including DNA replication, recombination, and chromosome segregation. In particular, this analysis showed that Def1, a protein that facilitates ubiquitination of RNA polymerase II as a response to DNA damage, is required for efficient synapsis between homologues and normal levels of crossover recombination during meiosis. These characteristics are shared by a group of proteins required for Zip1 loading (ZMM proteins). Additionally, Soh1/Med31, a subunit of the RNA pol II mediator complex, Bre5, a ubiquitin protease cofactor and an uncharacterized protein, Rmr1/Ygl250w, are required for normal levels of gene conversion events during meiosis. We show how existing datasets may be used to define gene sets enriched for specific roles and how these can be evaluated by experimental analysis.
Author Summary
Since the genome of S. cerevisiae was sequenced in 1996, a major objective has been to characterize its 6,200 genes. Important contributions to this have been made using high-throughput screens. These have provided a vast quantity of information, but many genes remain minimally characterized, and the high-throughput data are necessarily superficial and not always reliable. We aimed to bridge the gap between the high-throughput data and detailed experimental analysis. Specifically, we have developed a strategy of combining different sources of high-throughput data to predict minimally characterized genes that might be implicated in DNA processing. From this we have gone on to test the involvement of these genes in meiosis using detailed experimental analysis. In a sense, we have turned high-throughput analysis on its head and used it to return to low-throughput experimental analysis. Using this strategy we have obtained evidence that 16 out of 81 genes selected (20%) are indeed involved in DNA processing and 13 of these genes (16%) are involved in meiotic DNA processing. Our selection strategy demonstrates that different sources of high-throughput data can successfully be combined to predict gene function. Thus, we have used detailed experimental analysis to validate the predictions of high-throughput analysis.
doi:10.1371/journal.pgen.0030222
PMCID: PMC2134943  PMID: 18069899
3.  The essential genome of a bacterium 
This study reports the essential Caulobacter genome at 8 bp resolution determined by saturated transposon mutagenesis and high-throughput sequencing. This strategy is applicable to full genome essentiality studies in a broad class of bacterial species.
The essential Caulobacter genome was determined at 8 bp resolution using hyper-saturated transposon mutagenesis coupled with high-throughput sequencing.Essential protein-coding sequences comprise 90% of the essential genome; the remaining 10% comprising essential non-coding RNA sequences, gene regulatory elements and essential genome replication features.Of the 3876 annotated open reading frames (ORFs), 480 (12.4%) were essential ORFs, 3240 (83.6%) were non-essential ORFs and 156 (4.0%) were ORFs that severely impacted fitness when mutated.The essential elements are preferentially positioned near the origin and terminus of the Caulobacter chromosome.This high-resolution strategy is applicable to high-throughput, full genome essentiality studies and large-scale genetic perturbation experiments in a broad class of bacterial species.
The regulatory events that control polar differentiation and cell-cycle progression in the bacterium Caulobacter crescentus are highly integrated, and they have to occur in the proper order (McAdams and Shapiro, 2011). Components of the core regulatory circuit are largely known. Full discovery of its essential genome, including non-coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of this bacterial cell. We have identified all the essential coding and non-coding elements of the Caulobacter chromosome using a hyper-saturated transposon mutagenesis strategy that is scalable and can be readily extended to obtain rapid and accurate identification of the essential genome elements of any sequenced bacterial species at a resolution of a few base pairs.
We engineered a Tn5 derivative transposon (Tn5Pxyl) that carries at one end an inducible outward pointing Pxyl promoter (Christen et al, 2010). We showed that this transposon construct inserts into the genome randomly where it can activate or disrupt transcription at the site of integration, depending on the insertion orientation. DNA from hundred of thousands of transposon insertion sites reading outward into flanking genomic regions was parallel PCR amplified and sequenced by Illumina paired-end sequencing to locate the insertion site in each mutant strain (Figure 1). A single sequencing run on DNA from a mutagenized cell population yielded 118 million raw sequencing reads. Of these, >90 million (>80%) read outward from the transposon element into adjacent genomic DNA regions and the insertion site could be mapped with single nucleotide resolution. This yielded the location and orientation of 428 735 independent transposon insertions in the 4-Mbp Caulobacter genome.
Within non-coding sequences of the Caulobacter genome, we detected 130 non-disruptable DNA segments between 90 and 393 bp long in addition to all essential promoter elements. Among 27 previously identified and validated sRNAs (Landt et al, 2008), three were contained within non-disruptable DNA segments and another three were partially disruptable, that is, insertions caused a notable growth defect. Two additional small RNAs found to be essential are the transfer-messenger RNA (tmRNA) and the ribozyme RNAseP (Landt et al, 2008). In addition to the 8 non-disruptable sRNAs, 29 out of the 130 intergenic essential non-coding sequences contained non-redundant tRNA genes; duplicated tRNA genes were non-essential. We also identified two non-disruptable DNA segments within the chromosomal origin of replication. Thus, we resolved essential non-coding RNAs, tRNAs and essential replication elements within the origin region of the chromosome. An additional 90 non-disruptable small genome elements of currently unknown function were identified. Eighteen of these are conserved in at least one closely related species. Only 2 could encode a protein of over 50 amino acids.
For each of the 3876 annotated open reading frames (ORFs), we analyzed the distribution, orientation, and genetic context of transposon insertions. There are 480 essential ORFs and 3240 non-essential ORFs. In addition, there were 156 ORFs that severely impacted fitness when mutated. The 8-bp resolution allowed a dissection of the essential and non-essential regions of the coding sequences. Sixty ORFs had transposon insertions within a significant portion of their 3′ region but lacked insertions in the essential 5′ coding region, allowing the identification of non-essential protein segments. For example, transposon insertions in the essential cell-cycle regulatory gene divL, a tyrosine kinase, showed that the last 204 C-terminal amino acids did not impact viability, confirming previous reports that the C-terminal ATPase domain of DivL is dispensable for viability (Reisinger et al, 2007; Iniesta et al, 2010). In addition, we found that 30 out of 480 (6.3%) of the essential ORFs appear to be shorter than the annotated ORF, suggesting that these are probably mis-annotated.
Among the 480 ORFs essential for growth on rich media, there were 10 essential transcriptional regulatory proteins, including 5 previously identified cell-cycle regulators (McAdams and Shapiro, 2003; Holtzendorff et al, 2004; Collier and Shapiro, 2007; Gora et al, 2010; Tan et al, 2010) and 5 uncharacterized predicted transcription factors. In addition, two RNA polymerase sigma factors RpoH and RpoD, as well as the anti-sigma factor ChrR, which mitigates rpoE-dependent stress response under physiological growth conditions (Lourenco and Gomes, 2009), were also found to be essential. Thus, a set of 10 transcription factors, 2 RNA polymerase sigma factors and 1 anti-sigma factor are the core essential transcriptional regulators for growth on rich media. To further characterize the core components of the Caulobacter cell-cycle control network, we identified all essential regulatory sequences and operon transcripts. Altogether, the 480 essential protein-coding and 37 essential RNA-coding Caulobacter genes are organized into operons such that 402 individual promoter regions are sufficient to regulate their expression. Of these 402 essential promoters, the transcription start sites (TSSs) of 105 were previously identified (McGrath et al, 2007).
The essential genome features are non-uniformly distributed on the Caulobacter genome and enriched near the origin and the terminus regions. In contrast, the chromosomal positions of the published E. coli essential coding sequences (Rocha, 2004) are preferentially located at either side of the origin (Figure 4A). This indicates that there are selective pressures on chromosomal positioning of some essential elements (Figure 4A).
The strategy described in this report could be readily extended to quickly determine the essential genome for a large class of bacterial species.
Caulobacter crescentus is a model organism for the integrated circuitry that runs a bacterial cell cycle. Full discovery of its essential genome, including non-coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of a bacterial cell. Using hyper-saturated transposon mutagenesis coupled with high-throughput sequencing, we determined the essential Caulobacter genome at 8 bp resolution, including 1012 essential genome features: 480 ORFs, 402 regulatory sequences and 130 non-coding elements, including 90 intergenic segments of unknown function. The essential transcriptional circuitry for growth on rich media includes 10 transcription factors, 2 RNA polymerase sigma factors and 1 anti-sigma factor. We identified all essential promoter elements for the cell cycle-regulated genes. The essential elements are preferentially positioned near the origin and terminus of the chromosome. The high-resolution strategy used here is applicable to high-throughput, full genome essentiality studies and large-scale genetic perturbation experiments in a broad class of bacterial species.
doi:10.1038/msb.2011.58
PMCID: PMC3202797  PMID: 21878915
functional genomics; next-generation sequencing; systems biology; transposon mutagenesis
4.  Discovering Transcription Factor Binding Sites in Highly Repetitive Regions of Genomes with Multi-Read Analysis of ChIP-Seq Data 
PLoS Computational Biology  2011;7(7):e1002111.
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis (ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome (uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome (multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments.
Author Summary
Annotating repetitive regions of genomes experimentally is a challenging task. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) provides valuable data for characterizing repetitive regions of genomes in terms of transcription factor binding. Although ChIP-seq technology has been maturing, available ChIP-seq analysis methods and software rely on discarding sequence reads that map to multiple locations on the reference genome (multi-reads), thereby generating a missed opportunity for assessing transcription factor binding to highly repetitive regions of genomes. We develop a computational algorithm that takes multi-reads into account in ChIP-seq analysis. We show with computational experiments that multi-reads lead to significant increase in sequencing depths and identification of binding regions that are otherwise not identifiable when only reads that uniquely map to the reference genome (uni-reads) are used. In particular, we show that the number of binding regions identified can increase up to 36%. We support our computational predictions with independent quantitative real-time ChIP validation of binding regions identified only when multi-reads are incorporated in the analysis of a mouse GATA1 ChIP-seq experiment.
doi:10.1371/journal.pcbi.1002111
PMCID: PMC3136429  PMID: 21779159
5.  BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses 
BMC Genomics  2015;16:814.
Background
Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one’s biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions.
Result
We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes.
Conclusions
BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/applications/BubbleGUM/index.html.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-2012-4) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-015-2012-4
PMCID: PMC4617899  PMID: 26481321
Gene set enrichment analysis; Transcriptomic signatures; Comparative transcriptomics; Integrative representation
6.  Transcriptional landscape of repetitive elements in normal and cancer human cells 
BMC Genomics  2014;15(1):583.
Background
Repetitive elements comprise at least 55% of the human genome with more recent estimates as high as two-thirds. Most of these elements are retrotransposons, DNA sequences that can insert copies of themselves into new genomic locations by a “copy and paste” mechanism. These mobile genetic elements play important roles in shaping genomes during evolution, and have been implicated in the etiology of many human diseases. Despite their abundance and diversity, few studies investigated the regulation of endogenous retrotransposons at the genome-wide scale, primarily because of the technical difficulties of uniquely mapping high-throughput sequencing reads to repetitive DNA.
Results
Here we develop a new computational method called RepEnrich to study genome-wide transcriptional regulation of repetitive elements. We show that many of the Long Terminal Repeat retrotransposons in humans are transcriptionally active in a cell line-specific manner. Cancer cell lines display increased RNA Polymerase II binding to retrotransposons than cell lines derived from normal tissue. Consistent with increased transcriptional activity of retrotransposons in cancer cells we found significantly higher levels of L1 retrotransposon RNA expression in prostate tumors compared to normal-matched controls.
Conclusions
Our results support increased transcription of retrotransposons in transformed cells, which may explain the somatic retrotransposition events recently reported in several types of cancers.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-583) contains supplementary material, which is available to authorized users.
doi:10.1186/1471-2164-15-583
PMCID: PMC4122776  PMID: 25012247
Retrotransposon; Transposable element; Prostate cancer; LINE-1; L1; LTR; HERV; Repetitive element; RNA-seq; ChIP-seq
7.  Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping 
PLoS Computational Biology  2015;11(10):e1004491.
Segmental duplications and other highly repetitive regions of genomes contribute significantly to cells’ regulatory programs. Advancements in next generation sequencing enabled genome-wide profiling of protein-DNA interactions by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq). However, interactions in highly repetitive regions of genomes have proven difficult to map since short reads of 50–100 base pairs (bps) from these regions map to multiple locations in reference genomes. Standard analytical methods discard such multi-mapping reads and the few that can accommodate them are prone to large false positive and negative rates. We developed Perm-seq, a prior-enhanced read allocation method for ChIP-seq experiments, that can allocate multi-mapping reads in highly repetitive regions of the genomes with high accuracy. We comprehensively evaluated Perm-seq, and found that our prior-enhanced approach significantly improves multi-read allocation accuracy over approaches that do not utilize additional data types. The statistical formalism underlying our approach facilitates supervising of multi-read allocation with a variety of data sources including histone ChIP-seq. We applied Perm-seq to 64 ENCODE ChIP-seq datasets from GM12878 and K562 cells and identified many novel protein-DNA interactions in segmental duplication regions. Our analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.
Author Summary
Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is widely used for studying in vivo protein-DNA interactions genome-wide. The applicability of this method for profiling repetitive regions of the genome is limited due to short read sizes dominating ChIP-seq applications. We present Perm-seq, which implements a novel generative model for mapping short reads to repetitive regions of genomes. Perm-seq introduces a new class of read alignment algorithms that can combine data from multiple sources. We show with both computational experiments and the analysis of large volumes of ENCODE ChIP-seq data that utilizing DNase-seq derived priors in Perm-seq is especially powerful in mapping protein-DNA interactions in segmental duplication regions. This general approach enables the use of any number of histone ChIP-seq data alone or together with DNase data to supervise read allocation. Our large scale analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.
doi:10.1371/journal.pcbi.1004491
PMCID: PMC4618727  PMID: 26484757
8.  Microarray Analysis of LTR Retrotransposon Silencing Identifies Hdac1 as a Regulator of Retrotransposon Expression in Mouse Embryonic Stem Cells 
PLoS Computational Biology  2012;8(4):e1002486.
Retrotransposons are highly prevalent in mammalian genomes due to their ability to amplify in pluripotent cells or developing germ cells. Host mechanisms that silence retrotransposons in germ cells and pluripotent cells are important for limiting the accumulation of the repetitive elements in the genome during evolution. However, although silencing of selected individual retrotransposons can be relatively well-studied, many mammalian retrotransposons are seldom analysed and their silencing in germ cells, pluripotent cells or somatic cells remains poorly understood. Here we show, and experimentally verify, that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. This computational approach to genome-wide retrotransposon expression has allowed us to identify the histone deacetylase Hdac1 as a component of the retrotransposon silencing machinery in mouse embryonic stem cells, and to determine the retrotransposon targets of Hdac1 in these cells. We also identify retrotransposons that are targets of other retrotransposon silencing mechanisms such as DNA methylation, Eset-mediated histone modification, and Ring1B/Eed-containing polycomb repressive complexes in mouse embryonic stem cells. Furthermore, our computational analysis of retrotransposon silencing suggests that multiple silencing mechanisms are independently targeted to retrotransposons in embryonic stem cells, that different genomic copies of the same retrotransposon can be differentially sensitive to these silencing mechanisms, and helps define retrotransposon sequence elements that are targeted by silencing machineries. Thus repeat annotation of gene expression microarray data suggests that a complex interplay between silencing mechanisms represses retrotransposon loci in germ cells and embryonic stem cells.
Author Summary
Repetitive DNA sequences make up almost half the mammalian genome. A large proportion of mammalian repetitive DNA sequences use RNA intermediates to amplify and insert themselves into new locations in the genome. Mammalian genomes contain hundreds of different types of these mutagenic retrotransposons, but the mechanisms that host cells use to silence most of these elements are poorly understood. Here we describe a computational approach to monitoring expression of hundreds of different retrotransposons in gene expression microarray datasets. This approach reveals new retrotransposon targets for silencing mechanisms such as DNA methylation, histone modification and polycomb repression in mouse embryonic stem cells, and identifies the histone deacetylase Hdac1 as a regulator of retrotransposons in this cell type. These computational predictions are verified experimentally by qRT-PCR in Dnmt1−/− Dnmt3a−/− Dnmt3b−/− embryonic stem cells, Ring1B−/− embryonic stem cells, and Hdac1−/− embryonic stem cells. We also use microarray analysis of retrotransposon expression to show that the pluripotency-associated Tex19.1 gene has exquisite specificity for MMERVK10C elements in developing male germ cells. Importantly, our computational analysis also suggests that different genomic copies of individual retrotransposons can be differentially regulated, and helps identify the sequences in these retrotransposons that are being targeted by the host cell's silencing mechanisms.
doi:10.1371/journal.pcbi.1002486
PMCID: PMC3343110  PMID: 22570599
9.  Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens 
BMC Bioinformatics  2008;9:264.
Background
The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.
Results
Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.
Conclusion
We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
doi:10.1186/1471-2105-9-264
PMCID: PMC2443381  PMID: 18534020
10.  Evolutionary rates and patterns for human transcription factor binding sites derived from repetitive DNA 
BMC Genomics  2008;9:226.
Background
The majority of human non-protein-coding DNA is made up of repetitive sequences, mainly transposable elements (TEs). It is becoming increasingly apparent that many of these repetitive DNA sequence elements encode gene regulatory functions. This fact has important evolutionary implications, since repetitive DNA is the most dynamic part of the genome. We set out to assess the evolutionary rate and pattern of experimentally characterized human transcription factor binding sites (TFBS) that are derived from repetitive versus non-repetitive DNA to test whether repeat-derived TFBS are in fact rapidly evolving. We also evaluated the position-specific patterns of variation among TFBS to look for signs of functional constraint on TFBS derived from repetitive and non-repetitive DNA.
Results
We found numerous experimentally characterized TFBS in the human genome, 7–10% of all mapped sites, which are derived from repetitive DNA sequences including simple sequence repeats (SSRs) and TEs. TE-derived TFBS sequences are far less conserved between species than TFBS derived from SSRs and non-repetitive DNA. Despite their rapid evolution, several lines of evidence indicate that TE-derived TFBS are functionally constrained. First of all, ancient TE families, such as MIR and L2, are enriched for TFBS relative to younger families like Alu and L1. Secondly, functionally important positions in TE-derived TFBS, specifically those residues thought to physically interact with their cognate protein binding factors (TF), are more evolutionarily conserved than adjacent TFBS positions. Finally, TE-derived TFBS show position-specific patterns of sequence variation that are highly distinct from random patterns and similar to the variation seen for non-repeat derived sequences of the same TFBS.
Conclusion
The abundance of experimentally characterized human TFBS that are derived from repetitive DNA speaks to the substantial regulatory effects that this class of sequence has on the human genome. The unique evolutionary properties of repeat-derived TFBS are perhaps even more intriguing. TE-derived TFBS in particular, while clearly functionally constrained, evolve extremely rapidly relative to non-repeat derived sites. Such rapidly evolving TFBS are likely to confer species-specific regulatory phenotypes, i.e. divergent expression patterns, on the human evolutionary lineage. This result has practical implications with respect to the widespread use of evolutionary conservation as a surrogate for functionally relevant non-coding DNA. Most TE-derived TFBS would be missed using the kinds of sequence conservation-based screens, such as phylogenetic footprinting, that are used to help characterize non-coding DNA. Thus, the very TFBS that are most likely to yield human-specific characteristics will be neglected by the comparative genomic techniques that are currently de rigeur for the identification of novel regulatory sites.
doi:10.1186/1471-2164-9-226
PMCID: PMC2397414  PMID: 18485226
11.  Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes 
PLoS Computational Biology  2015;11(8):e1004429.
Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.
Author Summary
Interactions between proteins and DNA trigger many important biological processes. Therefore, to fully understand how the information encoded on the DNA transcribes into RNA, which in turn translates into proteins in the cell, we need to unravel the molecular design principles of protein-DNA interactions. It is known that many interactions occur when a protein is attracted to a specific short segment on the DNA called a specific protein-DNA binding motif. Strikingly, recent experiments revealed that many regulatory proteins reproducibly bind to different regions on the DNA lacking such specific motifs. This suggests that fundamental molecular mechanisms responsible for protein-DNA recognition specificity are not fully understood. Here, using high-throughput protein-DNA binding data obtained by two entirely different methods for ~100 TFs in each case, we show that DNA regions possessing certain repetitive sequence elements exert the statistical attractive potential on DNA-binding proteins, and as a result, such DNA regions are enriched in bound proteins. This is in agreement with our previous analysis performed for the yeast genome. We use the term nonconsensus protein-DNA binding in order to describe protein-DNA interactions that occur in the absence of specific protein-DNA binding motifs. Here we demonstrate that the identified nonconsensus effect is highly significant for a variety of organismal genomes and it affects protein-DNA binding preferences and nucleosome occupancy at the genome-wide level.
doi:10.1371/journal.pcbi.1004429
PMCID: PMC4540582  PMID: 26285121
12.  A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis 
Human Genomics  2016;10(Suppl 2):20.
Background
Innumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct erroneous NGS reads before further analysis, but independent evaluation of the impact of such dataset features as read length, genome size, and coverage depth on their performance is lacking. This comparative study aims to investigate the strength and weakness as well as limitations of some newest k-spectrum-based methods and to provide recommendations for users in selecting suitable methods with respect to specific NGS datasets.
Methods
Six k-spectrum-based methods, i.e., Reptile, Musket, Bless, Bloocoo, Lighter, and Trowel, were compared using six simulated sets of paired-end Illumina sequencing data. These NGS datasets varied in coverage depth (10× to 120×), read length (36 to 100 bp), and genome size (4.6 to 143 MB). Error Correction Evaluation Toolkit (ECET) was employed to derive a suite of metrics (i.e., true positives, false positive, false negative, recall, precision, gain, and F-score) for assessing the correction quality of each method.
Results
Results from computational experiments indicate that Musket had the best overall performance across the spectra of examined variants reflected in the six datasets. The lowest accuracy of Musket (F-score = 0.81) occurred to a dataset with a medium read length (56 bp), a medium coverage (50×), and a small-sized genome (5.4 MB). The other five methods underperformed (F-score < 0.80) and/or failed to process one or more datasets.
Conclusions
This study demonstrates that various factors such as coverage depth, read length, and genome size may influence performance of individual k-spectrum-based error correction methods. Thus, efforts have to be paid in choosing appropriate methods for error correction of specific NGS datasets. Based on our comparative study, we recommend Musket as the top choice because of its consistently superior performance across all six testing datasets. Further extensive studies are warranted to assess these methods using experimental datasets generated by NGS platforms (e.g., 454, SOLiD, and Ion Torrent) under more diversified parameter settings (k-mer values and edit distances) and to compare them against other non-k-spectrum-based classes of error correction methods.
doi:10.1186/s40246-016-0068-0
PMCID: PMC4965716  PMID: 27461106
Next-generation sequencing (NGS); k-mer; k-spectrum; Error correction; Sequence analysis; Bloom filter
13.  Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space 
PLoS Computational Biology  2013;9(4):e1003010.
For the vast majority of species – including many economically or ecologically important organisms, progress in biological research is hampered due to the lack of a reference genome sequence. Despite recent advances in sequencing technologies, several factors still limit the availability of such a critical resource. At the same time, many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map. We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples, such as in this case gene-bearing minimum-tiling-path BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones (deconvolution) so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is very accurate, and the resulting BAC assemblies have high quality. Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality. While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project, we show that it is quite successful in reconstructing the gene sequences within BACs. In the case of plants such as barley, this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.
Author Summary
The problem of obtaining the full genomic sequence of an organism has been solved either via a global brute-force approach (called whole-genome shotgun) or by a divide-and-conquer strategy (called clone-by-clone). Both approaches have advantages and disadvantages in terms of cost, manual labor, and the ability to deal with sequencing errors and highly repetitive regions of the genome. With the advent of second-generation sequencing instruments, the whole-genome shotgun approach has been the preferred choice. The clone-by-clone strategy is, however, still very relevant for large complex genomes. In fact, several research groups and international consortia have produced clone libraries and physical maps for many economically or ecologically important organisms and now are in a position to proceed with sequencing. In this manuscript, we demonstrate the feasibility of this approach on the gene-space of a large, very repetitive plant genome. The novelty of our approach is that, in order to take advantage of the throughput of the current generation of sequencing instruments, we pool hundreds of clones using a special type of “smart” pooling design that allows one to establish with high accuracy the source clone from the sequenced reads in a pool. Extensive simulations and experimental results support our claims.
doi:10.1371/journal.pcbi.1003010
PMCID: PMC3617026  PMID: 23592960
14.  Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling 
PLoS Computational Biology  2010;6(9):e1000945.
Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.
Author Summary
To model accurate protein networks we need to extend our knowledge of protein associations in molecular systems much further. Biologists believe that high-throughput experiments will fill the gaps in our knowledge. However, if these approaches perform biased screenings, leaving important areas poorly characterized, success in modelling protein networks will require additional approaches to explore these ‘dark’ areas. We assess the value of integrating bio-computational approaches to build accurate and comprehensive network models for human and yeast proteomes and compare these models with models derived by combining multiple experimental datasets. We show that the predicted networks resemble the topological and error features of the experimental networks, and contain information on true protein associations within and beyond their constitutive first order binary predictions. We suggest that the majority of predicted network space is dark matter containing important functional areas, elusive to current experimental designs. Until novel experimental designs emerge as effective tools to screen these hidden regions, computational predictions will be a valuable approach for exploring them.
doi:10.1371/journal.pcbi.1000945
PMCID: PMC2944794  PMID: 20885791
15.  A Feature-Based Approach to Modeling Protein–DNA Interactions 
PLoS Computational Biology  2008;4(8):e1000154.
Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/.
Author Summary
Transcription factor (TF) protein binding to its DNA target sequences is a fundamental physical interaction underlying gene regulation. Characterizing the binding specificities of TFs is essential for deducing which genes are regulated by which TFs. Recently, several high-throughput methods that measure sequences enriched for TF targets genomewide were developed. Since TFs recognize relatively short sequences, much effort has been directed at developing computational methods that identify enriched subsequences (motifs) from these sequences. However, little effort has been directed towards improving the representation of motifs. Practically, available motif finding software use the position specific scoring matrix (PSSM) model, which assumes independence between different motif positions. We present an alternative, richer model, called the feature motif model (FMM), that enables the representation of a variety of sequence features and captures dependencies that exist between binding site positions. We show how FMMs explain TF binding data better than PSSMs on both synthetic and real data. We also present a motif finder algorithm that learns FMM motifs from unaligned promoter sequences and show how de novo FMMs, learned from binding data of the human TFs c-Myc and CTCF, reveal intriguing insights about their binding specificities.
doi:10.1371/journal.pcbi.1000154
PMCID: PMC2516605  PMID: 18725950
16.  Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015) 
Shay, Jerry W. | Homma, Noriko | Zhou, Ruyun | Naseer, Muhammad Imran | Chaudhary, Adeel G. | Al-Qahtani, Mohammed | Hirokawa, Nobutaka | Goudarzi, Maryam | Fornace, Albert J. | Baeesa, Saleh | Hussain, Deema | Bangash, Mohammed | Alghamdi, Fahad | Schulten, Hans-Juergen | Carracedo, Angel | Khan, Ishaq | Qashqari, Hanadi | Madkhali, Nawal | Saka, Mohamad | Saini, Kulvinder S. | Jamal, Awatif | Al-Maghrabi, Jaudah | Abuzenadah, Adel | Chaudhary, Adeel | Al Qahtani, Mohammed | Damanhouri, Ghazi | Alkhatabi, Heba | Goodeve, Anne | Crookes, Laura | Niksic, Nikolas | Beauchamp, Nicholas | Abuzenadah, Adel M. | Vaught, Jim | Budowle, Bruce | Assidi, Mourad | Buhmeida, Abdelbaset | Al-Maghrabi, Jaudah | Buhmeida, Abdelbaset | Assidi, Mourad | Merdad, Leena | Kumar, Sudhir | Miura, Sayaka | Gomez, Karen | Carracedo, Angel | Rasool, Mahmood | Rebai, Ahmed | Karim, Sajjad | Eldin, Hend F. Nour | Abusamra, Heba | Alhathli, Elham M. | Salem, Nada | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Faheem, Hossam | Agarwa, Ashok | Nieschlag, Eberhard | Wistuba, Joachim | Damm, Oliver S. | Beg, Mohd A. | Abdel-Meguid, Taha A. | Mosli, Hisham A. | Bajouh, Osama S. | Abuzenadah, Adel M. | Al-Qahtani, Mohammed H. | Coskun, Serdar | Abu-Elmagd, Muhammad | Buhmeida, Abdelbaset | Dallol, Ashraf | Al-Maghrabi, Jaudah | Hakamy, Sahar | Al-Qahtani, Wejdan | Al-Harbi, Asia | Hussain, Shireen | Assidi, Mourad | Al-Qahtani, Mohammed | Abuzenadah, Adel | Ozkosem, Burak | DuBois, Rick | Messaoudi, Safia S. | Dandana, Maryam T. | Mahjoub, Touhami | Almawi, Wassim Y. | Abdalla, S. | Al-Aama, M. Nabil | Elzawahry, Asmaa | Takahashi, Tsuyoshi | Mimaki, Sachiyo | Furukawa, Eisaku | Nakatsuka, Rie | Kurosaka, Isao | Nishigaki, Takahiko | Nakamura, Hiromi | Serada, Satoshi | Naka, Tetsuji | Hirota, Seiichi | Shibata, Tatsuhiro | Tsuchihara, Katsuya | Nishida, Toshirou | Kato, Mamoru | Mehmood, Sajid | Ashraf, Naeem Mahmood | Asif, Awais | Bilal, Muhammad | Mehmood, Malik Siddique | Hussain, Aadil | Jamal, Qazi Mohammad Sajid | Siddiqui, Mughees Uddin | Alzohairy, Mohammad A. | Al Karaawi, Mohammad A. | Nedjadi, Taoufik | Al-Maghrabi, Jaudah | Assidi, Mourad | Al-Khattabi, Heba | Al-Ammari, Adel | Al-Sayyad, Ahmed | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Zitouni, Hédia | Raguema, Nozha | Ali, Marwa Ben | Malah, Wided | Lfalah, Raja | Almawi, Wassim | Mahjoub, Touhami | Elanbari, Mohammed | Ptitsyn, Andrey | Mahjoub, Sana | El Ghali, Rabeb | Achour, Bechir | Amor, Nidhal Ben | Assidi, Mourad | N’siri, Brahim | Morjani, Hamid | Nedjadi, Taoufik | Al-Ammari, Adel | Al-Sayyad, Ahmed | Salem, Nada | Azhar, Esam | Al-Maghrabi, Jaudah | Chayeb, Vera | Dendena, Maryam | Zitouni, Hedia | Zouari-Limayem, Khedija | Mahjoub, Touhami | Refaat, Bassem | Ashshi, Ahmed M. | Batwa, Sarah A. | Ramadan, Hazem | Awad, Amal | Ateya, Ahmed | El-Shemi, Adel Galal Ahmed | Ashshi, Ahmad | Basalamah, Mohammed | Na, Youjin | Yun, Chae-Ok | El-Shemi, Adel Galal Ahmed | Ashshi, Ahmad | Basalamah, Mohammed | Na, Youjin | Yun, Chae-Ok | El-Shemi, Adel Galal | Refaat, Bassem | Kensara, Osama | Abdelfattah, Amr | Dheeb, Batol Imran | Al-Halbosiy, Mohammed M. F. | Al lihabi, Rghad Kadhim | Khashman, Basim Mohammed | Laiche, Djouhri | Adeel, Chaudhary | Taoufik, Nedjadi | Al-Afghani, Hani | Łastowska, Maria | Al-Balool, Haya H. | Sheth, Harsh | Mercer, Emma | Coxhead, Jonathan M. | Redfern, Chris P. F. | Peters, Heiko | Burt, Alastair D. | Santibanez-Koref, Mauro | Bacon, Chris M. | Chesler, Louis | Rust, Alistair G. | Adams, David J. | Williamson, Daniel | Clifford, Steven C. | Jackson, Michael S. | Singh, Mala | Mansuri, Mohmmad Shoab | Jadeja, Shahnawaz D. | Patel, Hima | Marfatia, Yogesh S. | Begum, Rasheedunnisa | Mohamed, Amal M. | Kamel, Alaa K. | Helmy, Nivin A. | Hammad, Sayda A. | Kayed, Hesham F. | Shehab, Marwa I. | El Gerzawy, Assad | Ead, Maha M. | Ead, Ola M. | Mekkawy, Mona | Mazen, Innas | El-Ruby, Mona | Shahid, S. M. A. | Jamal, Qazi Mohammad Sajid | Arif, J. M. | Lohani, Mohtashim | Imen, Moumni | Leila, Chaouch | Houyem, Ouragini | Kais, Douzi | Fethi, Chaouachi Dorra Mellouli | Mohamed, Bejaoui | Salem, Abbes | Faggad, Areeg | Gebreslasie, Amanuel T. | Zaki, Hani Y. | Abdalla, Badreldin E. | AlShammari, Maha S. | Al-Ali, Rhaya | Al-Balawi, Nader | Al-Enazi, Mansour | Al-Muraikhi, Ali | Busaleh, Fadi | Al-Sahwan, Ali | Borgio, Francis | Sayyed, Abdulazeez | Al-Ali, Amein | Acharya, Sadananda | Zaki, Maha S. | El-Bassyouni, Hala T. | Shehab, Marwa I. | Elshal, Mohammed F. | M., Kaleemuddin | Aldahlawi, Alia M. | Saadah, Omar | McCoy, J. Philip | El-Tarras, Adel E. | Awad, Nabil S. | Alharthi, Abdulla A. | Ibrahim, Mohamed M. M. | Alsehli, Haneen S. | Dallol, Ashraf | Gari, Abdullah M. | Abbas, Mohammed M. | Kadam, Roaa A. | Gari, Mazen M. | Alkaff, Mohmmed H. | Abuzenadah, Adel M. | Gari, Mamdooh A. | Abusamra, Heba | Karim, Sajjad | eldin, Hend F. Nour | Alhathli, Elham M. | Salem, Nada | Kumar, Sudhir | Al-Qahtani, Mohammed H. | Moradi, Fatima A. | Rashidi, Omran M. | Awan, Zuhier A. | Kaya, Ibrahim Hamza | Al-Harazi, Olfat | Colak, Dilek | Alkousi, Nabila A. | Athanasopoulos, Takis | Bahmaid, Afnan O. | Alhwait, Etimad A. | Gari, Mamdooh A. | Alsehli, Haneen S. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Kadam, Roaa | Dallol, Ashraf | Kalamegam, Gauthaman | Eldin, Hend F. Nour | Karim, Sajjad | Abusamra, Heba | Alhathli, Elham | Salem, Nada | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Alsayed, Salma N. | Aljohani, Fawziah H. | Habeeb, Samaher M. | Almashali, Rawan A. | Basit, Sulman | Ahmed, Samia M. | Sharma, Rakesh | Agarwal, Ashok | Durairajanayagam, Damayanthi | Samanta, Luna | Abu-Elmagd, Muhammad | Abuzenadah, Adel M. | Sabanegh, Edmund S. | Assidi, Mourad | Al-Qahtani, Mohammed | Agarwal, Ashok | Sharma, Rakesh | Samanta, Luna | Durairajanayagam, Damayanthi | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Abuzenadah, Adel M. | Sabanegh, Edmund S. | Samanta, Luna | Agarwal, Ashok | Sharma, Rakesh | Cui, Zhihong | Assidi, Mourad | Abuzenadah, Adel M. | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Alboogmi, Alaa A. | Alansari, Nuha A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Jamal, Hasan S. | Rozi, Abdullraheem | Mirza, Zeenat | Abuzenadah, Adel M. | Karim, Sajjad | Al-Qahtani, Mohammed H. | Karim, Sajjad | Schulten, Hans-Juergen | Al Sayyad, Ahmad J. | Farsi, Hasan M. A. | Al-Maghrabi, Jaudah A. | Mirza, Zeenat | Alotibi, Reem | Al-Ahmadi, Alaa | Alansari, Nuha A. | Albogmi, Alaa A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Al-Qahtani, Mohammed H. | Ebiya, Rasha A. | Darwish, Samia M. | Montaser, Metwally M. | Abusamra, Heba | Bajic, Vladimir B. | Al-Maghrabi, Jaudah | Gomaa, Wafaey | Hanbazazh, Mehenaz | Al-Ahwal, Mahmoud | Al-Harbi, Asia | Al-Qahtani, Wejdan | Hakamy, Saher | Baba, Ghali | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Al-Maghrabi, Jaudah | Al-Harbi, Abdullah | Al-Ahwal, Mahmoud | Al-Harbi, Asia | Al-Qahtani, Wejdan | Hakamy, Sahar | Baba, Ghalia | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Alhathli, Elham M. | Karim, Sajjad | Salem, Nada | Eldin, Hend Nour | Abusamra, Heba | Kumar, Sudhir | Al-Qahtani, Mohammed H. | Alyamani, Aisha A. | Kalamegam, Gauthaman | Alhwait, Etimad A. | Gari, Mamdooh A. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Alsehli, Haneen S. | Kadam, Roaa A. | Al-Qahtani, Mohammed | Gadi, Rawan | Buhmeida, Abdelbaset | Assidi, Mourad | Chaudhary, Adeel | Merdad, Leena | Alfakeeh, Saadiah M. | Alhwait, Etimad A. | Gari, Mamdooh A. | Abbas, Mohammed M. | Alkaf, Mohammed H. | Alsehli, Haneen S. | Kadam, Roaa | Kalamegam, Gauthaman | Ghazala, Rubi | Mathew, Shilu | Hamed, M. Haroon | Assidi, Mourad | Al-Qahtani, Mohammed | Qadri, Ishtiaq | Mathew, Shilu | Mira, Lobna | Shaabad, Manal | Hussain, Shireen | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Mathew, Shilu | Shaabad, Manal | Mira, Lobna | Hussain, Shireen | Assidi, Mourad | Abu-Elmagd, Muhammad | Al-Qahtani, Mohammed | Rebai, Ahmed | Assidi, Mourad | Buhmeida, Abdelbaset | Abu-Elmagd, Muhammad | Dallol, Ashraf | Shay, Jerry W. | Almutairi, Mikhlid H. | Ambers, Angie | Churchill, Jennifer | King, Jonathan | Stoljarova, Monika | Gill-King, Harrell | Assidi, Mourad | Abu-Elmagd, Muhammad | Buhmeida, Abdelbaset | Al-Qatani, Muhammad | Budowle, Bruce | Abu-Elmagd, Muhammad | Ahmed, Farid | Dallol, Ashraf | Assidi, Mourad | Almagd, Taha Abo | Hakamy, Sahar | Agarwal, Ashok | Al-Qahtani, Muhammad | Abuzenadah, Adel | Karim, Sajjad | Schulten, Hans-Juergen | Al Sayyad, Ahmad J. | Farsi, Hasan M. A. | Al-Maghrabi, Jaudah A. | Buhmaida, Abdelbaset | Mirza, Zeenat | Alotibi, Reem | Al-Ahmadi, Alaa | Alansari, Nuha A. | Albogmi, Alaa A. | Al-Quaiti, Maha M. | Ashgan, Fai T. | Bandah, Afnan | Al-Qahtani, Mohammed H. | Satar, Rukhsana | Rasool, Mahmood | Ahmad, Waseem | Nazam, Nazia | Lone, Mohamad I. | Naseer, Muhammad I. | Jamal, Mohammad S. | Zaidi, Syed K. | Pushparaj, Peter N. | Jafri, Mohammad A. | Ansari, Shakeel A. | Alqahtani, Mohammed H. | Bashier, Hanan | Al Qahtani, Abrar | Mathew, Shilu | Nour, Amal M. | Alkhatabi, Heba | Zenadah, Adel M. Abu | Buhmeida, Abdelbaset | Assidi, Mourad | Al Qahtani, Muhammed | Faheem, Muhammad | Mathew, Shilu | Mathew, Shiny | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Alhadrami, Hani A. | Dallol, Ashraf | Abuzenadah, Adel | Hussein, Ibtessam R. | Chaudhary, Adeel G. | Bader, Rima S. | Bassiouni, Randa | Alquaiti, Maha | Ashgan, Fai | Schulten, Hans | Alama, Mohamed Nabil | Al Qahtani, Mohammad H. | Lone, Mohammad I. | Nizam, Nazia | Ahmad, Waseem | Jafri, Mohammad A. | Rasool, Mahmood | Ansari, Shakeel A. | Al-Qahtani, Muhammed H. | Alshihri, Eradah | Abu-Elmagd, Muhammad | Alharbi, Lina | Assidi, Mourad | Al-Qahtani, Mohammed | Mathew, Shilu | Natesan, Peter Pushparaj | Al Qahtani, Muhammed | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Khan, Fazal | Kadam, Roaa | Ahmed, Farid | Assidi, Mourad | Sait, Khalid Hussain Wali | Anfinan, Nisreen | Al Qahtani, Mohammed | Naseer, Muhammad I. | Chaudhary, Adeel G. | Jamal, Mohammad S. | Mathew, Shilu | Mira, Lobna S. | Pushparaj, Peter N. | Ansari, Shakeel A. | Rasool, Mahmood | AlQahtani, Mohammed H. | Naseer, Muhammad I. | Chaudhary, Adeel G. | Mathew, Shilu | Mira, Lobna S. | Jamal, Mohammad S. | Sogaty, Sameera | Bassiouni, Randa I. | Rasool, Mahmood | AlQahtani, Mohammed H. | Rasool, Mahmood | Ansari, Shakeel A. | Jamal, Mohammad S. | Pushparaj, Peter N. | Sibiani, Abdulrahman M. S. | Ahmad, Waseem | Buhmeida, Abdelbaset | Jafri, Mohammad A. | Warsi, Mohiuddin K. | Naseer, Muhammad I. | Al-Qahtani, Mohammed H. | Rubi | Kumar, Kundan | Naqvi, Ahmad A. T. | Ahmad, Faizan | Hassan, Md I. | Jamal, Mohammad S. | Rasool, Mahmood | AlQahtani, Mohammed H. | Ali, Ashraf | Jarullah, Jummanah | Rasool, Mahmood | Buhmeida, Abdelbasit | Khan, Shahida | Abdussami, Ghufrana | Mahfooz, Maryam | Kamal, Mohammad A. | Damanhouri, Ghazi A. | Jamal, Mohammad S. | Jarullah, Bushra | Jarullah, Jummanah | Jarullah, Mohammad S. S. | Ali, Ashraf | Rasool, Mahmood | Jamal, Mohammad S. | Assidi, Mourad | Abu-Elmagd, Muhammad | Bajouh, Osama | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Abuzenadah, Adel | Jamal, Mohammad S. | Jarullah, Jummanah | Mathkoor, Abdulah E. A. | Alsalmi, Hashim M. A. | Oun, Anas M. M. | Damanhauri, Ghazi A. | Rasool, Mahmood | AlQahtani, Mohammed H. | Naseer, Muhammad I. | Rasool, Mahmood | Sogaty, Sameera | Chudhary, Adeel G. | Abutalib, Yousif A. | Merico, Daniele | Walker, Susan | Marshall, Christian R. | Zarrei, Mehdi | Scherer, Stephen W. | Al-Qahtani, Mohammad H. | Naseer, Muhammad I. | Faheem, Muhammad | Chaudhary, Adeel G. | Rasool, Mahmood | Kalamegam, Gauthaman | Ashgan, Fai Talal | Assidi, Mourad | Ahmed, Farid | Zaidi, Syed Kashif | Jan, Mohammed M. | Al-Qahtani, Mohammad H. | Al-Zahrani, Maryam | Lary, Sahira | Hakamy, Sahar | Dallol, Ashraf | Al-Ahwal, Mahmoud | Al-Maghrabi, Jaudah | Dermitzakis, Emmanuel | Abuzenadah, Adel | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Al-refai, Abeer A. | Saleh, Mona | Yassien, Rehab I. | Kamel, Mahmmoud | Habeb, Rabab M. | Filimban, Najlaa | Dallol, Ashraf | Ghannam, Nadia | Al-Qahtani, Mohammed | Abuzenadah, Adel Mohammed | Bibi, Fehmida | Akhtar, Sana | Azhar, Esam I. | Yasir, Muhammad | Nasser, Muhammad I. | Jiman-Fatani, Asif A. | Sawan, Ali | Lahzah, Ruaa A. | Ali, Asho | Hassan, Syed A. | Hasnain, Seyed E. | Tayubi, Iftikhar A. | Abujabal, Hamza A. | Magrabi, Alaa O. | Khan, Fazal | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abuzenada, Adel | Kumosani, Taha Abduallah | Barbour, Elie | Al-Qahtani, Mohammed | Shabaad, Manal | Mathew, Shilu | Dallol, Ashraf | Merdad, Adnan | Buhmeida, Abdelbaset | Al-Qahtani, Mohammed | Assidi, Mourad | Abu-Elmagd, Muhammad | Gauthaman, Kalamegam | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Hassan, Syed A. | Tayubi, Iftikhar A. | Aljahdali, Hani M. A. | Al Nono, Reham | Gari, Mamdooh | Alsehli, Haneen | Ahmed, Farid | Abbas, Mohammed | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Mathew, Shilu | Khan, Fazal | Rasool, Mahmood | Jamal, Mohammed Sarwar | Naseer, Muhammad Imran | Mirza, Zeenat | Karim, Sajjad | Ansari, Shakeel | Assidi, Mourad | Kalamegam, Gauthaman | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Abu-Elmagd, Muhammad | Kalamegam, Gauthaman | Kadam, Roaa | Alghamdi, Mansour A. | Shamy, Magdy | Costa, Max | Khoder, Mamdouh I. | Assidi, Mourad | Pushparaj, Peter Natesan | Gari, Mamdooh | Al-Qahtani, Mohammed | Kharrat, Najla | Belmabrouk, Sabrine | Abdelhedi, Rania | Benmarzoug, Riadh | Assidi, Mourad | Al Qahtani, Mohammed H. | Rebai, Ahmed | Dhamanhouri, Ghazi | Pushparaj, Peter Natesan | Noorwali, Abdelwahab | Alwasiyah, Mohammad Khalid | Bahamaid, Afnan | Alfakeeh, Saadiah | Alyamani, Aisha | Alsehli, Haneen | Abbas, Mohammed | Gari, Mamdooh | Mobasheri, Ali | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Faheem, Muhammad | Mathew, Shilu | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Mathew, Shilu | Faheem, Muhammad | Mathew, Shiny | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad H. | Jamal, Mohammad Sarwar | Zaidi, Syed Kashif | Khan, Raziuddin | Bhatia, Kanchan | Al-Qahtani, Mohammed H. | Ahmad, Saif | AslamTayubi, Iftikhar | Tripathi, Manish | Hassan, Syed Asif | Shrivastava, Rahul | Tayubi, Iftikhar A. | Hassan, Syed | Abujabal, Hamza A. S. | Shah, Ishani | Jarullah, Bushra | Jamal, Mohammad S. | Jarullah, Jummanah | Sheikh, Ishfaq A. | Ahmad, Ejaz | Jamal, Mohammad S. | Rehan, Mohd | Abu-Elmagd, Muhammad | Tayubi, Iftikhar A. | AlBasri, Samera F. | Bajouh, Osama S. | Turki, Rola F. | Abuzenadah, Adel M. | Damanhouri, Ghazi A. | Beg, Mohd A. | Al-Qahtani, Mohammed | Hammoudah, Sahar A. F. | AlHarbi, Khalid M. | El-Attar, Lama M. | Darwish, Ahmed M. Z. | Ibrahim, Sara M. | Dallol, Ashraf | Choudhry, Hani | Abuzenadah, Adel | Awlia, Jalaludden | Chaudhary, Adeel | Ahmed, Farid | Al-Qahtani, Mohammed | Jafri, Mohammad A. | Abu-Elmagd, Muhammad | Assidi, Mourad | Al-Qahtani, Mohammed | khan, Imran | Yasir, Muhammad | Azhar, Esam I. | Al-basri, Sameera | Barbour, Elie | Kumosani, Taha | Khan, Fazal | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abuzenada, Adel | Kumosani, Taha Abduallah | Barbour, Elie | EL Sayed, Heba M. | Hafez, Eman A. | Schulten, Hans-Juergen | Elaimi, Aisha Hassan | Hussein, Ibtessam R. | Bassiouni, Randa Ibrahim | Alwasiyah, Mohammad Khalid | Wintle, Richard F. | Chaudhary, Adeel | Scherer, Stephen W. | Al-Qahtani, Mohammed | Mirza, Zeenat | Pillai, Vikram Gopalakrishna | Karim, Sajjad | Sharma, Sujata | Kaur, Punit | Srinivasan, Alagiri | Singh, Tej P. | Al-Qahtani, Mohammed | Alotibi, Reem | Al-Ahmadi, Alaa | Al-Adwani, Fatima | Hussein, Deema | Karim, Sajjad | Al-Sharif, Mona | Jamal, Awatif | Al-Ghamdi, Fahad | Al-Maghrabi, Jaudah | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Schulten, Hans-Juergen | Al-Qahtani, Mohammed | Faheem, Muhammad | Pushparaj, Peter Natesan | Mathew, Shilu | Kumosani, Taha Abdullah | Kalamegam, Gauthaman | Al-Qahtani, Mohammed | Al-Allaf, Faisal A. | Abduljaleel, Zainularifeen | Alashwal, Abdullah | Taher, Mohiuddin M. | Bouazzaoui, Abdellatif | Abalkhail, Halah | Ba-Hammam, Faisal A. | Athar, Mohammad | Kalamegam, Gauthaman | Pushparaj, Peter Natesan | Abu-Elmagd, Muhammad | Ahmed, Farid | Sait, Khalid HussainWali | Anfinan, Nisreen | Gari, Mamdooh | Chaudhary, Adeel | Abuzenadah, Adel | Assidi, Mourad | Al-Qahtani, Mohammed | Mami, Naira Ben | Haffani, Yosr Z. | Medhioub, Mouna | Hamzaoui, Lamine | Cherif, Ameur | Azouz, Msadok | Kalamegam, Gauthaman | Khan, Fazal | Mathew, Shilu | Nasser, Mohammed Imran | Rasool, Mahmood | Ahmed, Farid | Pushparaj, Peter Natesan | Al-Qahtani, Mohammed | Turkistany, Shereen A. | Al-harbi, Lina M. | Dallol, Ashraf | Sabir, Jamal | Chaudhary, Adeel | Abuzenadah, Adel | Al-Madoudi, Basmah | Al-Aslani, Bayan | Al-Harbi, Khulud | Al-Jahdali, Rwan | Qudaih, Hanadi | Al Hamzy, Emad | Assidi, Mourad | Al Qahtani, Mohammed | Ilyas, Asad M. | Ahmed, Youssri | Gari, Mamdooh | Ahmed, Farid | Alqahtani, Mohammed | Salem, Nada | Karim, Sajjad | Alhathli, Elham M. | Abusamra, Heba | Eldin, Hend F. Nour | Al-Qahtani, Mohammed H. | Kumar, Sudhir | Al-Adwani, Fatima | Hussein, Deema | Al-Sharif, Mona | Jamal, Awatif | Al-Ghamdi, Fahad | Al-Maghrabi, Jaudah | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Al-Qahtani, Mohammed | Schulten, Hans-Juergen | Alamandi, Alaa | Alotibi, Reem | Hussein, Deema | Karim, Sajjad | Al-Maghrabi, Jaudah | Al-Ghamdi, Fahad | Jamal, Awatif | Baeesa, Saleh S. | Bangash, Mohammed | Chaudhary, Adeel | Schulten, Hans-Juergen | Al-Qahtani, Mohammed | Subhi, Ohoud | Bagatian, Nadia | Karim, Sajjad | Al-Johari, Adel | Al-Hamour, Osman Abdel | Al-Aradati, Hosam | Al-Mutawa, Abdulmonem | Al-Mashat, Faisal | Al-Maghrabi, Jaudah | Schulten, Hans-Juergen | Al-Qahtani, Mohammad | Bagatian, Nadia | Subhi, Ohoud | Karim, Sajjad | Al-Johari, Adel | Al-Hamour, Osman Abdel | Al-Mutawa, Abdulmonem | Al-Aradati, Hosam | Al-Mashat, Faisal | Al-Qahtani, Mohammad | Schulten, Hans-Juergen | Al-Maghrabi, Jaudah | shah, Muhammad W. | Yasir, Muhammad | Azhar, Esam I | Al-Masoodi, Saad | Haffani, Yosr Z. | Azouz, Msadok | Khamla, Emna | Jlassi, Chaima | Masmoudi, Ahmed S. | Cherif, Ameur | Belbahri, Lassaad | Al-Khayyat, Shadi | Attas, Roba | Abu-Sanad, Atlal | Abuzinadah, Mohammed | Merdad, Adnan | Dallol, Ashraf | Chaudhary, Adeel | Al-Qahtani, Mohammed | Abuzenadah, Adel | Bouazzi, Habib | Trujillo, Carlos | Alwasiyah, Mohammad Khalid | Al-Qahtani, Mohammed | Alotaibi, Maha | Nassir, Rami | Sheikh, Ishfaq A. | Kamal, Mohammad A. | Jiffri, Essam H. | Ashraf, Ghulam M. | Beg, Mohd A. | Aziz, Mohammad A. | Ali, Rizwan | Rasool, Mahmood | Jamal, Mohammad S. | Samman, Nusaibah | Abdussami, Ghufrana | Periyasamy, Sathish | Warsi, Mohiuddin K. | Aldress, Mohammed | Al Otaibi, Majed | Al Yousef, Zeyad | Boudjelal, Mohamed | Buhmeida, Abdelbasit | Al-Qahtani, Mohammed H. | AlAbdulkarim, Ibrahim | Ghazala, Rubi | Mathew, Shilu | Hamed, M. Haroon | Assidi, Mourad | Al-Qahtani, Mohammed | Qadri, Ishtiaq | Sheikh, Ishfaq A. | Abu-Elmagd, Muhammad | Turki, Rola F. | Damanhouri, Ghazi A. | Beg, Mohd A. | Suhail, Mohd | Qureshi, Abid | Jamal, Adil | Pushparaj, Peter Natesan | Al-Qahtani, Mohammad | Qadri, Ishtiaq | El-Readi, Mahmoud Z. | Eid, Safaa Y. | Wink, Michael | Isa, Ahmed M. | Alnuaim, Lulu | Almutawa, Johara | Abu-Rafae, Basim | Alasiri, Saleh | Binsaleh, Saleh | Nazam, Nazia | Lone, Mohamad I. | Ahmad, Waseem | Ansari, Shakeel A. | Alqahtani, Mohamed H.
BMC Genomics  2016;17(Suppl 6):487.
Table of contents
O1 Regulation of genes by telomere length over long distances
Jerry W. Shay
O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy
Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa
O3 Integration of metagenomics and metabolomics in gut microbiome research
Maryam Goudarzi, Albert J. Fornace Jr.
O4 A unique integrated system to discern pathogenesis of central nervous system tumors
Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri
O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis
Heba Alkhatabi
O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service
Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp
O7 Targeted sequencing panels and their utilization in personalized medicine
Adel M. Abuzenadah
O8 International biobanking in the era of precision medicine
Jim Vaught
O9 Biobank and biodata for clinical and forensic applications
Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida
O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors
Jaudah Al-Maghrabi
O11 The CEGMR biobanking unit: achievements, challenges and future plans
Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad
O12 Phylomedicine of tumors
Sudhir Kumar, Sayaka Miura, Karen Gomez
O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment
Angel Carracedo, Mahmood Rasool
O14 From association to causality: translation of GWAS findings for genomic medicine
Ahmed Rebai
O15 E-GRASP: an interactive database and web application for efficient analysis of disease-associated genetic information
Sajjad Karim, Hend F Nour Eldin, Heba Abusamra, Elham M Alhathli, Nada Salem, Mohammed H Al-Qahtani, Sudhir Kumar
O16 The supercomputer facility “AZIZ” at KAU: utility and future prospects
Hossam Faheem
O17 New research into the causes of male infertility
Ashok Agarwa
O18 The Klinefelter syndrome: recent progress in pathophysiology and management
Eberhard Nieschlag, Joachim Wistuba, Oliver S. Damm, Mohd A. Beg, Taha A. Abdel-Meguid, Hisham A. Mosli, Osama S. Bajouh, Adel M. Abuzenadah, Mohammed H. Al-Qahtani
O19 A new look to reproductive medicine in the era of genomics
Serdar Coskun
P1 Wnt signalling receptors expression in Saudi breast cancer patients
Muhammad Abu-Elmagd, Abdelbaset Buhmeida, Ashraf Dallol, Jaudah Al-Maghrabi, Sahar Hakamy, Wejdan Al-Qahtani, Asia Al-Harbi, Shireen Hussain, Mourad Assidi, Mohammed Al-Qahtani, Adel Abuzenadah
P2 Analysis of oxidative stress interactome during spermatogenesis: a systems biology approach to reproduction
Burak Ozkosem, Rick DuBois
P3 Interleukin-18 gene variants are strongly associated with idiopathic recurrent pregnancy loss.
Safia S Messaoudi, Maryam T Dandana, Touhami Mahjoub, Wassim Y Almawi
P4 Effect of environmental factors on gene-gene and gene-environment reactions: model and theoretical study applied to environmental interventions using genotype
S. Abdalla, M. Nabil Al-Aama
P5 Genomics and transcriptomic analysis of imatinib resistance in gastrointestinal stromal tumor
Asmaa Elzawahry, Tsuyoshi Takahashi, Sachiyo Mimaki, Eisaku Furukawa, Rie Nakatsuka, Isao Kurosaka, Takahiko Nishigaki, Hiromi Nakamura, Satoshi Serada, Tetsuji Naka, Seiichi Hirota, Tatsuhiro Shibata, Katsuya Tsuchihara, Toshirou Nishida, Mamoru Kato
P6 In-Silico analysis of putative HCV epitopes against Pakistani human leukocyte antigen background: an approach towards development of future vaccines for Pakistani population
Sajid Mehmood, Naeem Mahmood Ashraf, Awais Asif, Muhammad Bilal, Malik Siddique Mehmood, Aadil Hussain
P7 Inhibition of AChE and BuChE with the natural compounds of Bacopa monerri for the treatment of Alzheimer’s disease: a bioinformatics approach
Qazi Mohammad Sajid Jamal, Mughees Uddin Siddiqui, Mohammad A. Alzohairy, Mohammad A. Al Karaawi
P8 Her2 expression in urothelial cell carcinoma of the bladder in Saudi Arabia
Taoufik Nedjadi, Jaudah Al-Maghrabi, Mourad Assidi, Heba Al-Khattabi, Adel Al-Ammari, Ahmed Al-Sayyad, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P9 Association of angiotensinogen single nucleotide polymorphisms with Preeclampsia in patients from North Africa
Hédia Zitouni, Nozha Raguema, Marwa Ben Ali, Wided Malah, Raja Lfalah, Wassim Almawi, Touhami Mahjoub
P10 Systems biology analysis reveals relations between normal skin, benign nevi and malignant melanoma
Mohammed Elanbari, Andrey Ptitsyn
P11 The apoptotic effect of thymoquinone in Jurkat cells
Sana Mahjoub, Rabeb El Ghali, Bechir Achour, Nidhal Ben Amor, Mourad Assidi, Brahim N'siri, Hamid Morjani
P12 Sonic hedgehog contributes in bladder cancer invasion in Saudi Arabia
Taoufik Nedjadi, Adel Al-Ammari, Ahmed Al-Sayyad, Nada Salem, Esam Azhar, Jaudah Al-Maghrabi
P13 Association of Interleukin 18 gene promoter polymorphisms - 607A/C and -137 G/C with colorectal cancer onset in a sample of Tunisian population
Vera Chayeb, Maryam Dendena, Hedia Zitouni, Khedija Zouari-Limayem, Touhami Mahjoub
P14 Pathological expression of interleukin-6, -11, leukemia inhibitory factor and their receptors in tubal gestation with and without tubal cytomegalovirus infection
Bassem Refaat, Ahmed M Ashshi, Sarah A Batwa
P15 Phenotypic and genetic profiling of avian pathogenic and human diarrhegenic Escherichia coli in Egypt
Hazem Ramadan, Amal Awad, Ahmed Ateya
P16 Cancer-targeting dual gene virotherapy as a promising therapeutic strategy for treatment of hepatocellular carcinoma
Adel Galal Ahmed El-Shemi, Ahmad Ashshi, Mohammed Basalamah, Youjin Na, Chae-Ok YUN
P17 Cancer dual gene therapy with oncolytic adenoviruses expressing TRAIL and IL-12 transgenes markedly eradicated human hepatocellular carcinoma both in vitro and in vivo
Adel Galal Ahmed El-Shemi, Ahmad Ashshi, Mohammed Basalamah, Youjin Na, Chae-Ok Yun
P18 Therapy with paricalcitol attenuates tumor growth and augments tumoricidal and anti-oncogenic effects of 5-fluorouracil on animal model of colon cancer
Adel Galal El-Shemi, Bassem Refaat, Osama Kensara, Amr Abdelfattah
P19 The effects of Rubus idaeus extract on normal human lymphocytes and cancer cell line
Batol Imran Dheeb, Mohammed M. F. Al-Halbosiy, Rghad Kadhim Al lihabi, Basim Mohammed Khashman
P20 Etanercept, a TNF-alpha inhibitor, alleviates mechanical hypersensitivity and spontaneous pain in a rat model of chemotherapy-induced neuropathic pain
Djouhri, Laiche, Chaudhary Adeel, Nedjadi, Taoufik
P21 Sleeping beauty mutagenesis system identified genes and neuronal transcription factor network involved in pediatric solid tumour (medulloblastoma)
Hani Al-Afghani, Maria Łastowska, Haya H Al-Balool, Harsh Sheth, Emma Mercer, Jonathan M Coxhead, Chris PF Redfern, Heiko Peters, Alastair D Burt, Mauro Santibanez-Koref, Chris M Bacon, Louis Chesler, Alistair G Rust, David J Adams, Daniel Williamson, Steven C Clifford, Michael S Jackson
P22 Involvement of interleukin-1 in vitiligo pathogenesis
Mala Singh, Mohmmad Shoab Mansuri, Shahnawaz D. Jadeja, Hima Patel, Yogesh S. Marfatia, Rasheedunnisa Begum
P23 Cytogenetics abnormalities in 12,884 referred population for chromosomal analysis and the role of FISH in refining the diagnosis (cytogenetic experience 2004-2013)
Amal M Mohamed, Alaa K Kamel, Nivin A Helmy, Sayda A Hammad, Hesham F Kayed, Marwa I Shehab, Assad El Gerzawy, Maha M. Ead, Ola M Ead, Mona Mekkawy, Innas Mazen, Mona El-Ruby
P24 Analysis of binding properties of angiotensin-converting enzyme 2 through in silico method
S. M. A. Shahid, Qazi Mohammad Sajid Jamal, J. M. Arif, Mohtashim Lohani
P25 Relationship of genetics markers cis and trans to the β-S globin gene with fetal hemoglobin expression in Tunisian sickle cell patients
Moumni Imen, Chaouch Leila, Ouragini Houyem, Douzi Kais, Chaouachi Dorra Mellouli Fethi, Bejaoui Mohamed, Abbes Salem
P26 Analysis of estrogen receptor alpha gene polymorphisms in breast cancer: link to genetic predisposition in Sudanese women
Areeg Faggad, Amanuel T Gebreslasie, Hani Y Zaki, Badreldin E Abdalla
P27 KCNQI gene polymorphism and its association with CVD and T2DM in the Saudi population
Maha S AlShammari, Rhaya Al-Ali, Nader Al-Balawi , Mansour Al-Enazi, Ali Al-Muraikhi, Fadi Busaleh, Ali Al-Sahwan, Francis Borgio, Abdulazeez Sayyed, Amein Al-Ali, Sadananda Acharya
P28 Clinical, neuroimaging and cytogenetic study of a patient with microcephaly capillary malformation syndrome
Maha S. Zaki, Hala T. El-Bassyouni, Marwa I. Shehab
P29 Altered expression of CD200R1 on dendritic cells of patients with inflammatory bowel diseases: in silico investigations and clinical evaluations
Mohammed F. Elshal, Kaleemuddin M., Alia M. Aldahlawi, Omar Saadah,
J. Philip McCoy
P30 Development of real time PCR diagnostic protocol specific for the Saudi Arabian H1N1 viral strains
Adel E El-Tarras, Nabil S Awad, Abdulla A Alharthi, Mohamed M M Ibrahim
P31 Identification of novel genetic variations affecting Osteoarthritis patients
Haneen S Alsehli, Ashraf Dallol, Abdullah M Gari, Mohammed M Abbas, Roaa A Kadam, Mazen M. Gari, Mohmmed H Alkaff, Adel M Abuzenadah, Mamdooh A Gari
P32 An integrated database of GWAS SNVs and their evolutionary properties
Heba Abusamra, Sajjad Karim, Hend F Nour eldin, Elham M Alhathli, Nada Salem, Sudhir Kumar, Mohammed H Al-Qahtani
P33 Familial hypercholesterolemia in Saudi Arabia: prime time for a national registry and genetic analysis
Fatima A. Moradi, Omran M. Rashidi, Zuhier A. Awan
P34 Comparative genomics and network-based analyses of early hepatocellular carcinoma
Ibrahim Hamza Kaya, Olfat Al-Harazi, Dilek Colak
P35 A TALEN-based oncolytic viral vector approach to knock out ABCB1 gene mediated chemoresistance in cancer stem cells
Nabila A Alkousi, Takis Athanasopoulos
P36 Cartilage differentiation and gene expression of synovial fluid mesenchymal stem cells derived from osteoarthritis patients
Afnan O Bahmaid, Etimad A Alhwait, Mamdooh A Gari, Haneen S Alsehli, Mohammed M Abbas, Mohammed H Alkaf, Roaa Kadam, Ashraf Dallol, Gauthaman Kalamegam
P37 E-GRASP: Adding an evolutionary component to the genome-wide repository of associations (GRASP) resource
Hend F Nour Eldin, Sajjad Karim, Heba Abusamra, Elham Alhathli, Nada Salem, Mohammed H Al-Qahtani, Sudhir Kumar
P38 Screening of AGL gene mutation in Saudi family with glycogen storage disease Type III
Salma N Alsayed, Fawziah H Aljohani, Samaher M Habeeb, Rawan A Almashali, Sulman Basit, Samia M Ahmed
P39 High throughput proteomic data suggest modulation of cAMP dependent protein kinase A and mitochondrial function in infertile patients with varicocele
Rakesh Sharma, Ashok Agarwal, Damayanthi Durairajanayagam, Luna Samanta, Muhammad Abu-Elmagd, Adel M. Abuzenadah, Edmund S. Sabanegh, Mourad Assidi, Mohammed Al-Qahtani
P40 Significant protein profile alterations in men with primary and secondary infertility
Ashok Agarwal, Rakesh Sharma, Luna Samanta, Damayanthi Durairajanayagam, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani, Adel M. Abuzenadah, Edmund S. Sabanegh
P41 Spermatozoa maturation in infertile patients involves compromised expression of heat shock proteins
Luna Samanta, Ashok Agarwal, Rakesh Sharma, Zhihong Cui, Mourad Assidi, Adel M. Abuzenadah, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P42 Array comparative genomic hybridization approach to search genomic answers for spontaneous recurrent abortion in Saudi Arabia
Alaa A Alboogmi, Nuha A Alansari, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Hasan S Jamal, Abdullraheem Rozi, Zeenat Mirza, Adel M Abuzenadah, Sajjad Karim, Mohammed H Al-Qahtani
P43 Global gene expression profiling of Saudi kidney cancer patients
Sajjad Karim, Hans-Juergen Schulten, Ahmad J Al Sayyad, Hasan MA Farsi, Jaudah A Al-Maghrabi, Zeenat Mirza, Reem Alotibi, Alaa Al-Ahmadi, Nuha A Alansari, Alaa A Albogmi, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Mohammed H Al-Qahtani
P44 Downregulated StAR gene and male reproductive dysfunction caused by nifedipine and ethosuximide
Rasha A Ebiya, Samia M Darwish, Metwally M. Montaser
P45 Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes
Heba Abusamra, Vladimir B. Bajic
P46 Prognostic significance of Osteopontin expression profile in colorectal carcinoma
Jaudah Al-Maghrabi, Wafaey Gomaa, Mehenaz Hanbazazh, Mahmoud Al-Ahwal, Asia Al-Harbi, Wejdan Al-Qahtani, Saher Hakamy, Ghali Baba, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P47 High Glypican-3 expression pattern predicts longer disease-specific survival in colorectal carcinoma
Jaudah Al-Maghrabi, Abdullah Al-Harbi, Mahmoud Al-Ahwal, Asia Al-Harbi, Wejdan Al-Qahtani, Sahar Hakamy, Ghalia Baba, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P48 An evolutionary re-assessment of GWAS single nucleotide variants implicated in the Cholesterol traits
Elham M Alhathli, Sajjad Karim, Nada Salem, Hend Nour Eldin, Heba Abusamra, Sudhir Kumar, Mohammed H Al-Qahtani
P49 Derivation and characterization of human Wharton’s jelly stem cells (hWJSCs) in vitro for future therapeutic applications
Aisha A Alyamani, Gauthaman Kalamegam, Etimad A Alhwait, Mamdooh A Gari, Mohammed M Abbas, Mohammed H Alkaf, Haneen S Alsehli, Roaa A Kadam, Mohammed Al-Qahtani
P50 Attitudes of healthcare students toward biomedical research in the post-genomic era
Rawan Gadi, Abdelbaset Buhmeida, Mourad Assidi , Adeel Chaudhary, Leena Merdad
P51 Evaluation of the immunomodulatory effects of thymoquinone on human bone marrow mesenchymal stem cells (BM-MSCs) from osteoarthritic patients
Saadiah M Alfakeeh, Etimad A Alhwait, Mamdooh A Gari, Mohammed M Abbas, Mohammed H Alkaf, Haneen S Alsehli, Roaa Kadam, Gauthaman Kalamegam
P52 Implication of IL-10 and IL-28 polymorphism with successful anti-HCV therapy and viral clearance
Rubi Ghazala, Shilu Mathew, M.Haroon Hamed, Mourad Assidi, Mohammed Al-Qahtani, Ishtiaq Qadri
P53 Selection of flavonoids against obesity protein (FTO) using in silico and in vitro approaches
Shilu Mathew, Lobna Mira, Manal Shaabad, Shireen Hussain, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P54 Computational selection and in vitro validation of flavonoids as new antidepressant agents
Shilu Mathew, Manal Shaabad, Lobna Mira, Shireen Hussain, Mourad Assidi, Muhammad Abu-Elmagd, Mohammed Al-Qahtani
P55 In Silico prediction and prioritization of aging candidate genes associated with
progressive telomere shortening
Ahmed Rebai, Mourad Assidi, Abdelbaset Buhmeida, Muhammad Abu-Elmagd, Ashraf Dallol, Jerry W Shay
P56 Identification of new cancer testis antigen genes in diverse types of malignant human tumour cells
Mikhlid H Almutairi
P57 More comprehensive forensic genetic marker analyses for accurate human remains identification using massively parallel sequencing (MPS)
Angie Ambers, Jennifer Churchill, Jonathan King, Monika Stoljarova, Harrell Gill-King, Mourad Assidi, Muhammad Abu-Elmagd, Abdelbaset Buhmeida, Muhammad Al-Qatani, Bruce Budowle
P58 Flow cytometry approach towards treatment men infertility in Saudi Arabia
Muhammad Abu-Elmagd, Farid Ahmed, Ashraf Dallol, Mourad Assidi, Taha Abo Almagd, Sahar Hakamy, Ashok Agarwal, Muhammad Al-Qahtani, Adel Abuzenadah
P59 Tissue microarray based validation of CyclinD1 expression in renal cell carcinoma of Saudi kidney patients
Sajjad Karim, Hans-Juergen Schulten, Ahmad J Al Sayyad, Hasan MA Farsi, Jaudah A Al-Maghrabi, Abdelbaset Buhmaida, Zeenat Mirza, Reem Alotibi, Alaa Al-Ahmadi, Nuha A Alansari, Alaa A Albogmi, Maha M Al-Quaiti, Fai T Ashgan, Afnan Bandah, Mohammed H Al-Qahtani
P60 Assessment of gold nanoparticles in molecular diagnostics and DNA damage studies
Rukhsana Satar, Mahmood Rasool, Waseem Ahmad, Nazia Nazam, Mohamad I Lone, Muhammad I Naseer, Mohammad S Jamal, Syed K Zaidi, Peter N Pushparaj, Mohammad A Jafri, Shakeel A Ansari, Mohammed H Alqahtani
P61 Surfing the biospecimen management and processing workflow at CEGMR Biobank
Hanan Bashier, Abrar Al Qahtani, Shilu Mathew, Amal M. Nour, Heba Alkhatabi, Adel M. Abu Zenadah, Abdelbaset Buhmeida, Mourad Assidi, Muhammed Al Qahtani
P62 Autism Spectrum Disorder: knowledge, attitude and awareness in Jeddah, Kingdom of Saudi Arabia
Muhammad Faheem, Shilu Mathew, Shiny Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P63 Simultaneous genetic screening of the coagulation pathway genes using the Thromboscan targeted sequencing panel
Hani A. Alhadrami, Ashraf Dallol, Adel Abuzenadah
P64 Genome wide array comparative genomic hybridization analysis in patients with syndromic congenital heart defects
Ibtessam R. Hussein, Adeel G. Chaudhary, Rima S Bader, Randa Bassiouni, Maha Alquaiti, Fai Ashgan, Hans Schulten, Mohamed Nabil Alama, Mohammad H. Al Qahtani
P65 Toxocogenetic evaluation of 1, 2-Dichloroethane in bone marrow, blood and cells of immune system using conventional, molecular and flowcytometric approaches
Mohammad I Lone, Nazia Nizam, Waseem Ahmad, Mohammad A Jafri, Mahmood Rasool, Shakeel A Ansari, Muhammed H Al-Qahtani
P66 Molecular cytogenetic diagnosis of sexual development disorders in newborn: A case of ambiguous genitalia
Eradah Alshihri, Muhammad Abu-Elmagd, Lina Alharbi, Mourad Assidi, Mohammed Al-Qahtani
P67 Identification of disease specific gene expression clusters and pathways in hepatocellular carcinoma using In Silico methodologies
Shilu Mathew, Peter Pushparaj Natesan, Muhammed Al Qahtani
P68 Human Wharton’s Jelly stem cell conditioned medium inhibits primary ovarian cancer cells in vitro: Identification of probable targets and mechanisms using systems biology
Gauthaman Kalamegam, Peter Natesan Pushparaj, Fazal Khan, Roaa Kadam, Farid Ahmed, Mourad Assidi, Khalid Hussain Wali Sait, Nisreen Anfinan, Mohammed Al Qahtani
P69 Mutation spectrum of ASPM (Abnormal Spindle-like, Microcephaly-associated) gene in Saudi Arabian population
Muhammad I Naseer, Adeel G Chaudhary, Mohammad S Jamal, Shilu Mathew, Lobna S Mira, Peter N Pushparaj, Shakeel A Ansari, Mahmood Rasool, Mohammed H AlQahtani
P70 Identification and characterization of novel genes and mutations of primary microcephaly in Saudi Arabian population
Muhammad I Naseer, Adeel G Chaudhary, Shilu Mathew, Lobna S Mira, Mohammad S Jamal, Sameera Sogaty, Randa I Bassiouni, Mahmood Rasool, Mohammed H AlQahtani
P71 Molecular genetic analysis of hereditary nonpolyposis colorectal cancer (Lynch Syndrome) in Saudi Arabian population
Mahmood Rasool, Shakeel A Ansari, Mohammad S Jamal, Peter N Pushparaj, Abdulrahman MS Sibiani, Waseem Ahmad, Abdelbaset Buhmeida, Mohammad A Jafri, Mohiuddin K Warsi, Muhammad I Naseer, Mohammed H Al-Qahtani
P72 Function predication of hypothetical proteins from genome database of chlamydia trachomatis
Rubi, Kundan Kumar, Ahmad AT Naqvi, Faizan Ahmad, Md I Hassan, Mohammad S Jamal, Mahmood Rasool, Mohammed H AlQahtani
P73 Transcription factors as novel molecular targets for skin cancer
Ashraf Ali, Jummanah Jarullah, Mahmood Rasool, Abdelbasit Buhmeida, Shahida Khan, Ghufrana Abdussami, Maryam Mahfooz, Mohammad A Kamal, Ghazi A Damanhouri, Mohammad S Jamal
P74 An In Silico analysis of Plumbagin binding to apoptosis executioner: Caspase-3 and Caspase-7
Bushra Jarullah, Jummanah Jarullah, Mohammad SS Jarullah, Ashraf Ali, Mahmood Rasool, Mohammad S Jamal
P75 Single cell genomics applications for preimplantation genetic screening optimization: Comparative analysis of whole genome amplification technologies
Mourad Assidi, Muhammad Abu-Elmagd, Osama Bajouh, Peter Natesan Pushparaj, Mohammed Al-Qahtani, Adel Abuzenadah
P76 ZFP36 regulates miRs-34a in anti-IgM triggered immature B cells
Mohammad S Jamal, Jummanah Jarullah, Abdulah EA Mathkoor, Hashim MA Alsalmi, Anas MM Oun, Ghazi A Damanhauri, Mahmood Rasool, Mohammed H AlQahtani
P77 Identification of a novel mutation in the STAMBP gene in a family with microcephaly-capillary malformation syndrome
Muhammad I. Naseer, Mahmood Rasool, Sameera Sogaty, Adeel G. Chudhary, Yousif A. Abutalib, Daniele Merico, Susan Walker, Christian R. Marshall, Mehdi Zarrei, Stephen W. Scherer, Mohammad H. Al-Qahtani
P78 Copy number variations in Saudi patients with intellectual disability and epilepsy
Muhammad I. Naseer, Muhammad Faheem, Adeel G. Chaudhary, Mahmood Rasool, Gauthaman Kalamegam, Fai Talal Ashgan, Mourad Assidi, Farid Ahmed, Syed Kashif Zaidi, Mohammed M. Jan, Mohammad H. Al-Qahtani
P79 Prognostic significance of CD44 expression profile in colorectal carcinoma
Maryam Al-Zahrani, Sahira Lary, Sahar Hakamy, Ashraf Dallol, Mahmoud Al-Ahwal, Jaudah Al-Maghrabi, Emmanuel Dermitzakis, Adel Abuzenadah, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P80 Association of the endothelial nitric oxide synthase (eNOS) gene G894T polymorphism with hypertension risk and complications
Abeer A Al-refai, Mona Saleh, Rehab I Yassien, Mahmmoud Kamel, Rabab M Habeb
P81 SNPs array to screen genetic variation among diabetic patients
Najlaa Filimban, Ashraf Dallol, Nadia Ghannam, Mohammed Al-Qahtani, Adel Mohammed Abuzenadah
P82 Detection and genotyping of Helicobacter pylori among gastric cancer patients from Saudi Arabian population
Fehmida Bibi, Sana Akhtar, Esam I. Azhar, Muhammad Yasir, Muhammad I. Nasser, Asif A. Jiman-Fatani, Ali Sawan
P83 Antimicrobial drug resistance and molecular detection of susceptibility to Fluoroquinolones among clinical isolates of Salmonella species from Jeddah-Saudi Arabia
Ruaa A Lahzah, Asho Ali
P84 Identification of the toxic and virulence nature of MAP1138c protein of Mycobacterium avium subsp. paratuberculosis
Syed A Hassan, Seyed E Hasnain, Iftikhar A Tayubi, Hamza A Abujabal, Alaa O Magrabi
P85 In vitro and in silico evaluation of miR137 in human breast cancer
Fazal Khan, Gauthaman Kalamegam, Peter Natesan Pushparaj, Adel Abuzenada, Taha Abduallah Kumosani, Elie Barbour, Mohammed Al-Qahtani
P86 Auruka gene is over-expressed in Saudi breast cancer
Manal Shabaad, Shilu Mathew, Ashraf Dallol, Adnan Merdad, Abdelbaset Buhmeida, Mohammed Al-Qahtani
P87 The potential of immunogenomics in personalized healthcare
Mourad Assidi, Muhammad Abu-Elmagd, Kalamegam Gauthaman, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P88 In Silico physiochemical and structural characterization of a putative ORF MAP0591 and its implication in the pathogenesis of Mycobacterium paratuberculosis in ruminants and humans
Syed A Hassan, Iftikhar A Tayubi, Hani MA Aljahdali
P89 Effects of heat shock on human bone marrow mesenchymal stem cells (BM-MSCs): Implications in regenerative medicine
Reham Al Nono, Mamdooh Gari, Haneen Alsehli, Farid Ahmed, Mohammed Abbas, Gauthaman Kalamegam, Mohammed Al-Qahtani
P90 In Silico analyses of the molecular targets of Resveratrol unravels its importance in mast cell mediated allergic responses
Shilu Mathew, Fazal Khan, Mahmood Rasool, Mohammed Sarwar Jamal, Muhammad Imran Naseer, Zeenat Mirza, Sajjad Karim, Shakeel Ansari, Mourad Assidi, Gauthaman Kalamegam, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P91 Effects of environmental particulate matter on bone-marrow mesenchymal stem cells
Muhammad Abu-Elmagd, Gauthaman Kalamegam, Roaa Kadam, Mansour A Alghamdi, Magdy Shamy, Max Costa, Mamdouh I Khoder, Mourad Assidi, Peter Natesan Pushparaj, Mamdooh Gari, Mohammed Al-Qahtani
P92 Distinctive charge clusters in human virus proteomes
Najla Kharrat, Sabrine Belmabrouk, Rania Abdelhedi, Riadh Benmarzoug, Mourad Assidi, Mohammed H. Al Qahtani, Ahmed Rebai
P93 In vitro experimental model and approach in identification of new biomarkers of inflammatory forms of arthritis
Ghazi Dhamanhouri, Peter Natesan Pushparaj, Abdelwahab Noorwali, Mohammad Khalid Alwasiyah, Afnan Bahamaid, Saadiah Alfakeeh, Aisha Alyamani, Haneen Alsehli, Mohammed Abbas, Mamdooh Gari, Ali Mobasheri, Gauthaman Kalamegam, Mohammed Al-Qahtani
P94 Molecular docking of GABAA receptor subunit γ-2 with novel anti-epileptic compounds
Muhammad Faheem, Shilu Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P95 Breast cancer knowledge, awareness, and practices among Saudi females residing in Jeddah
Shilu Mathew, Muhammad Faheem, Shiny Mathew, Peter Natesan Pushparaj, Mohammad H. Al-Qahtani
P96 Anti-inflammatory role of Sesamin by Attenuation of Iba1/TNF-α/ICAM-1/iNOS signaling in Diabetic Retinopathy
Mohammad Sarwar Jamal, Syed Kashif Zaidi, Raziuddin Khan, Kanchan Bhatia, Mohammed H. Al-Qahtani, Saif Ahmad
P97 Identification of drug lead molecule against vp35 protein of Ebola virus: An In-Silico approach
Iftikhar AslamTayubi, Manish Tripathi, Syed Asif Hassan, Rahul Shrivastava
P98 An approach to personalized medicine from SNP-calling through disease analysis using whole exome-sequencing of three sub-continental populations
Iftikhar A Tayubi, Syed Hassan, Hamza A.S Abujabal
P99 Low versus high frequency of Glucose –6 – Phosphate Dehydrogenase (G6PD) deficiency in urban against tribal population of Gujarat – A signal to natural selection
Ishani Shah, Bushra Jarullah, Mohammad S Jamal, Jummanah Jarullah
P100 Spontaneous preterm birth and single nucleotide gene polymorphisms: a recent update
Ishfaq A Sheikh, Ejaz Ahmad, Mohammad S Jamal, Mohd Rehan, Muhammad Abu-Elmagd, Iftikhar A Tayubi, Samera F AlBasri, Osama S Bajouh, Rola F Turki, Adel M Abuzenadah, Ghazi A Damanhouri, Mohd A Beg, Mohammed Al-Qahtani
P101 Prevalence of congenital heart diseases among Down syndrome cases in Saudi Arabia: role of molecular genetics in the pathogenesis
Sahar AF Hammoudah, Khalid M AlHarbi, Lama M El-Attar, Ahmed MZ Darwish
P102 Combinatorial efficacy of specific pathway inhibitors in breast cancer cells
Sara M Ibrahim, Ashraf Dallol, Hani Choudhry, Adel Abuzenadah, Jalaludden Awlia, Adeel Chaudhary, Farid Ahmed, Mohammed Al-Qahtani
P103 MiR-143 and miR-145 cluster as potential replacement medicine for the treatment of cancer
Mohammad A Jafri, Muhammad Abu-Elmagd, Mourad Assidi, Mohammed Al-Qahtani
P104 Metagenomic profile of gut microbiota during pregnancy in Saudi population
Imran khan, Muhammad Yasir, Esam I. Azhar, Sameera Al-basri, Elie Barbour, Taha Kumosani
P105 Exploration of anticancer targets of selected metabolites of Phoenix dactylifera L. using systems biological approaches
Fazal Khan, Gauthaman Kalamegam, Peter Natesan Pushparaj, Adel Abuzenada, Taha Abduallah Kumosani, Elie Barbour
P106 CD226 and CD40 gene polymorphism in susceptibility to Juvenile rheumatoid arthritis in Egyptian patients
Heba M. EL Sayed, Eman A. Hafez
P107 Paediatric exome sequencing in autism spectrum disorder ascertained in Saudi families
Hans-Juergen Schulten, Aisha Hassan Elaimi, Ibtessam R Hussein, Randa Ibrahim Bassiouni, Mohammad Khalid Alwasiyah, Richard F Wintle, Adeel Chaudhary, Stephen W Scherer, Mohammed Al-Qahtani
P108 Crystal structure of the complex formed between Phospholipase A2 and the central core hydrophobic fragment of Alzheimer’s β- amyloid peptide: a reductionist approach
Zeenat Mirza, Vikram Gopalakrishna Pillai, Sajjad Karim, Sujata Sharma, Punit Kaur, Alagiri Srinivasan, Tej P Singh, Mohammed Al-Qahtani
P109 Differential expression profiling between meningiomas from female and male patients
Reem Alotibi, Alaa Al-Ahmadi, Fatima Al-Adwani, Deema Hussein, Sajjad Karim, Mona Al-Sharif, Awatif Jamal, Fahad Al-Ghamdi, Jaudah Al-Maghrabi, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Hans-Juergen Schulten, Mohammed Al-Qahtani
P110 Neurospheres as models of early brain development and therapeutics
Muhammad Faheem, Peter Natesan Pushparaj, Shilu Mathew, Taha Abdullah Kumosani, Gauthaman Kalamegam, Mohammed Al-Qahtani
P111 Identification of a recurrent causative missense mutation p.(W577C) at the LDLR exon 12 in familial hypercholesterolemia affected Saudi families
Faisal A Al-Allaf, Zainularifeen Abduljaleel, Abdullah Alashwal, Mohiuddin M. Taher, Abdellatif Bouazzaoui, Halah Abalkhail, Faisal A. Ba-Hammam, Mohammad Athar
P112 Epithelial ovarian carcinoma (EOC): Systems oncological approach to identify diagnostic, prognostic and therapeutic biomarkers
Gauthaman Kalamegam, Peter Natesan Pushparaj, Muhammad Abu-Elmagd, Farid Ahmed Khalid HussainWali Sait, Nisreen Anfinan, Mamdooh Gari, Adeel Chaudhary, Adel Abuzenadah, Mourad Assidi, Mohammed Al-Qahtani
P113 Crohn’s disease phenotype in northern Tunisian population
Naira Ben Mami, Yosr Z Haffani, Mouna Medhioub, Lamine Hamzaoui, Ameur Cherif, Msadok Azouz
P114 Establishment of In Silico approaches to decipher the potential toxicity and mechanism of action of drug candidates and environmental agents
Gauthaman Kalamegam, Fazal Khan, Shilu Mathew, Mohammed Imran Nasser, Mahmood Rasool, Farid Ahmed, Peter Natesan Pushparaj, Mohammed Al-Qahtani
P115 1q Gain predicts poor prognosis marker for young breast cancer patients
Shereen A Turkistany, Lina M Al-harbi, Ashraf Dallol, Jamal Sabir, Adeel Chaudhary, Adel Abuzenadah
P116 Disorders of sex chromosomes in a diagnostic genomic medicine unit in Saudi Arabia: Prevalence, diagnosis and future guidelines
Basmah Al-Madoudi, Bayan Al-Aslani, Khulud Al-Harbi, Rwan Al-Jahdali, Hanadi Qudaih, Emad Al Hamzy, Mourad Assidi, Mohammed Al Qahtani
P117 Combination of WYE354 and Sunitinib demonstrate synergistic inhibition of acute myeloid leukemia in vitro
Asad M Ilyas, Youssri Ahmed, Mamdooh Gari, Farid Ahmed, Mohammed Alqahtani
P118 Integrated use of evolutionary information in GWAS reveals important SNPs in Asthma
Nada Salem, Sajjad Karim, Elham M Alhathli, Heba Abusamra, Hend F Nour Eldin, Mohammed H Al-Qahtani, Sudhir Kumar
P119 Assessment of BRAF, IDH1, IDH2, and EGFR mutations in a series of primary brain tumors
Fatima Al-Adwani, Deema Hussein, Mona Al-Sharif, Awatif Jamal, Fahad Al-Ghamdi, Jaudah Al-Maghrabi, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Mohammed Al-Qahtani, Hans-Juergen Schulten
P120 Expression profiles distinguish oligodendrogliomas from glioblastoma multiformes with or without oligodendroglioma component
Alaa Alamandi, Reem Alotibi, Deema Hussein, Sajjad Karim, Jaudah Al-Maghrabi, Fahad Al-Ghamdi, Awatif Jamal, Saleh S Baeesa, Mohammed Bangash, Adeel Chaudhary, Hans-Juergen Schulten, Mohammed Al-Qahtani
P121 Hierarchical clustering in thyroid goiters and hyperplastic lesions
Ohoud Subhi, Nadia Bagatian, Sajjad Karim, Adel Al-Johari, Osman Abdel Al-Hamour, Hosam Al-Aradati, Abdulmonem Al-Mutawa, Faisal Al-Mashat, Jaudah Al-Maghrabi, Hans-Juergen Schulten, Mohammad Al-Qahtani
P122 Differential expression analysis in thyroiditis and papillary thyroid carcinomas with or without coexisting thyroiditis
Nadia Bagatian, Ohoud Subhi, Sajjad Karim, Adel Al-Johari, Osman Abdel Al-Hamour, Abdulmonem Al-Mutawa, Hosam Al-Aradati, Faisal Al-Mashat, Mohammad Al-Qahtani, Hans-Juergen Schulten, Jaudah Al-Maghrabi
P123 Metagenomic analysis of waste water microbiome in Sausdi Arabia
Muhammad W shah, Muhammad Yasir, Esam I Azhar, Saad Al-Masoodi
P124 Molecular characterization of Helicobacter pylori from faecal samples of Tunisian patients with gastric cancer
Yosr Z Haffani, Msadok Azouz, Emna Khamla, Chaima Jlassi, Ahmed S. Masmoudi, Ameur Cherif, Lassaad Belbahri
P125 Diagnostic application of the oncoscan© panel for the identification of hereditary cancer syndrome
Shadi Al-Khayyat, Roba Attas, Atlal Abu-Sanad, Mohammed Abuzinadah, Adnan MerdadAshraf Dallol, Adeel Chaudhary, Mohammed Al-Qahtani, Adel Abuzenadah
P126 Characterization of clinical and neurocognitive features in a family with a novel OGT gene missense mutation c. 1193G > A/ (p. Ala319Thr)
Habib Bouazzi, Carlos Trujillo, Mohammad Khalid Alwasiyah, Mohammed Al-Qahtani
P127 Case report: a rare homozygous deletion mutation of TMEM70 gene associated with 3-Methylglutaconic Aciduria and cataract in a Saudi patient
Maha Alotaibi, Rami Nassir
P128 Isolation and purification of antimicrobial milk proteins
Ishfaq A Sheikh, Mohammad A Kamal, Essam H Jiffri, Ghulam M Ashraf, Mohd A Beg
P129 Integrated analysis reveals association of ATP8B1 gene with colorectal cancer
Mohammad A Aziz, Rizwan Ali, Mahmood Rasool, Mohammad S Jamal, Nusaibah samman, Ghufrana Abdussami, Sathish Periyasamy, Mohiuddin K Warsi, Mohammed Aldress, Majed Al Otaibi, Zeyad Al Yousef, Mohamed Boudjelal, Abdelbasit Buhmeida, Mohammed H Al-Qahtani, Ibrahim AlAbdulkarim
P130 Implication of IL-10 and IL-28 polymorphism with successful anti-HCV therapy and viral clearance
Rubi Ghazala, Shilu Mathew, M. Haroon Hamed, Mourad Assidi, Mohammed Al-Qahtani, Ishtiaq Qadri
P131 Interactions of endocrine disruptor di-(2-ethylhexyl) phthalate (DEHP) and its metabolite mono-2-ethylhexyl phthalate (MEHP) with progesterone receptor
Ishfaq A Sheikh, Muhammad Abu-Elmagd, Rola F Turki, Ghazi A Damanhouri, Mohd A. Beg
P132 Association of HCV nucleotide polymorphism in the development of hepatocellular carcinoma
Mohd Suhail, Abid Qureshi, Adil Jamal, Peter Natesan Pushparaj, Mohammad Al-Qahtani, Ishtiaq Qadri
P133 Gene expression profiling by DNA microarrays in colon cancer treated with chelidonine alkaloid
Mahmoud Z El-Readi, Safaa Y Eid, Michael Wink
P134 Successful in vitro fertilization after eight failed trials
Ahmed M. Isa, Lulu Alnuaim, Johara Almutawa, Basim Abu-Rafae, Saleh Alasiri, Saleh Binsaleh
P135 Genetic sensitivity analysis using SCGE, cell cycle and mitochondrial membrane potential in OPs stressed leukocytes in Rattus norvegicus through flow cytometric input
Nazia Nazam, Mohamad I Lone, Waseem Ahmad, Shakeel A Ansari, Mohamed H Alqahtani
doi:10.1186/s12864-016-2858-0
PMCID: PMC4959372  PMID: 27454254
17.  Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach 
PLoS ONE  2010;5(4):e9969.
Background
Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome.
Methodology/Principal Findings
We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/.
Conclusions
We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.
doi:10.1371/journal.pone.0009969
PMCID: PMC2848617  PMID: 20376314
18.  Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure 
BMC Systems Biology  2016;10:25.
Background
Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of “omics” data to shed light on Alzheimer’s and Parkinson’s diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences.
Results
In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine).
Conclusions
This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-016-0270-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-016-0270-7
PMCID: PMC4776441  PMID: 26935435
Systems biology; Network analysis; Graphs; Alzheimer’s diseases; Parkinson’s disease; Communities; Clustering; Network comparison
19.  Contig-Layout-Authenticator (CLA): A Combinatorial Approach to Ordering and Scaffolding of Bacterial Contigs for Comparative Genomics and Molecular Epidemiology 
PLoS ONE  2016;11(6):e0155459.
A wide variety of genome sequencing platforms have emerged in the recent past. High-throughput platforms like Illumina and 454 are essentially adaptations of the shotgun approach generating millions of fragmented single or paired sequencing reads. To reconstruct whole genomes, the reads have to be assembled into contigs, which often require further downstream processing. The contigs can be directly ordered according to a reference, scaffolded based on paired read information, or assembled using a combination of the two approaches. While the reference-based approach appears to mask strain-specific information, scaffolding based on paired-end information suffers when repetitive elements longer than the size of the sequencing reads are present in the genome. Sequencing technologies that produce long reads can solve the problems associated with repetitive elements but are not necessarily easily available to researchers. The most common high-throughput technology currently used is the Illumina short read platform. To improve upon the shortcomings associated with the construction of draft genomes with Illumina paired-end sequencing, we developed Contig-Layout-Authenticator (CLA). The CLA pipeline can scaffold reference-sorted contigs based on paired reads, resulting in better assembled genomes. Moreover, CLA also hints at probable misassemblies and contaminations, for the users to cross-check before constructing the consensus draft. The CLA pipeline was designed and trained extensively on various bacterial genome datasets for the ordering and scaffolding of large repetitive contigs. The tool has been validated and compared favorably with other widely-used scaffolding and ordering tools using both simulated and real sequence datasets. CLA is a user friendly tool that requires a single command line input to generate ordered scaffolds.
doi:10.1371/journal.pone.0155459
PMCID: PMC4889084  PMID: 27248146
20.  QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards 
BMC Research Notes  2011;4:57.
Background
High-throughput biological experiments can produce a large amount of data showing little overlap with current knowledge. This may be a problem when evaluating alternative scoring mechanisms for such data according to a gold standard dataset because standard statistical tests may not be appropriate.
Findings
To address this problem we have implemented the QiSampler tool that uses a repetitive sampling strategy to evaluate several scoring schemes or experimental parameters for any type of high-throughput data given a gold standard. We provide two example applications of the tool: selection of the best scoring scheme for a high-throughput protein-protein interaction dataset by comparison to a dataset derived from the literature, and evaluation of functional enrichment in a set of tumour-related differentially expressed genes from a thyroid microarray dataset.
Conclusions
QiSampler is implemented as an open source R script and a web server, which can be accessed at http://cbdm.mdc-berlin.de/tools/sampler/.
doi:10.1186/1756-0500-4-57
PMCID: PMC3060832  PMID: 21388526
21.  MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets 
PLoS Computational Biology  2011;7(7):e1002119.
Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology (‘MCAM’) employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.
Author Summary
Proteomic measurements, especially modification measurements, are greatly expanding the current knowledge of the state of proteins under various conditions. Harnessing these measurements to understand how these modifications are enzymatically regulated and their subsequent function in cellular signaling and physiology is a challenging new problem. Clustering has been very useful in reducing the dimensionality of many types of high-throughput biological data, as well inferring function of poorly understood molecular species. However, its implementation requires a great deal of technical expertise since there are a large number of parameters one must decide on in clustering, including data transforms, distance metrics, and algorithms. Previous knowledge of useful parameters does not exist for measurements of a new type. In this work we address two issues. First, we develop a framework that incorporates any number of possible parameters of clustering to produce a suite of clustering solutions. These solutions are then judged on their ability to infer biological information through statistical enrichment of existing biological annotations. Second, we apply this framework to dynamic phosphorylation measurements of the ERBB network, constructing the first extensive analysis of clustering of phosphoproteomic data and generating insight into novel components and novel functions of known components of the ERBB network.
doi:10.1371/journal.pcbi.1002119
PMCID: PMC3140961  PMID: 21799663
22.  Multi-tissue Analysis of Co-expression Networks by Higher-Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules 
PLoS Genetics  2014;10(1):e1004006.
Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.
Author Summary
Complex biological interactions and processes can be modelled as networks, for instance metabolic pathways or protein-protein interactions. The growing availability of large high-throughput data in several experimental conditions now permits the full-scale analysis of biological interactions and processes. However, no reliable and computationally efficient methods for simultaneous analysis of multiple large-scale interaction datasets (networks) have been developed to date. To overcome this shortcoming, we have developed a new computational framework that is parameter-free, computationally efficient and highly reliable. We showed how these distinctive properties make it a useful tool for real genomic data exploration and analyses. Indeed, in extensive simulation studies and real-data analyses we have demonstrated that our method outperformed existing approaches in terms of efficiency and, most importantly, reproducibility of the results. Beyond the computational advantages, we illustrated how our method can be effectively applied to leverage the vast stream of genome-scale transcriptional data that has risen exponentially over the last years. In contrast with existing approaches, using our method we were able to identify and replicate multi-tissue gene co-expression networks that were associated with specific functional processes relevant to phenotypic variation and disease in rats and humans.
doi:10.1371/journal.pgen.1004006
PMCID: PMC3879165  PMID: 24391511
23.  CTF: a CRF-based transcription factor binding sites finding system 
BMC Genomics  2012;13(Suppl 8):S18.
Background
Identifying the location of transcription factor bindings is crucial to understand transcriptional regulation. Currently, Chromatin Immunoprecipitation followed with high-throughput Sequencing (ChIP-seq) is able to locate the transcription factor binding sites (TFBSs) accurately in high throughput and it has become the gold-standard method for TFBS finding experimentally. However, due to its high cost, it is impractical to apply the method in a very large scale. Considering the large number of transcription factors, numerous cell types and various conditions, computational methods are still very valuable to accurate TFBS identification.
Results
In this paper, we proposed a novel integrated TFBS prediction system, CTF, based on Conditional Random Fields (CRFs). Integrating information from different sources, CTF was able to capture patterns of TFBSs contained in different features (sequence, chromatin and etc) and predicted the TFBS locations with a high accuracy. We compared CTF with several existing tools as well as the PWM baseline method on a dataset generated by ChIP-seq experiments (TFBSs of 13 transcription factors in mouse genome). Results showed that CTF performed significantly better than existing methods tested.
Conclusions
CTF is a powerful tool to predict TFBSs by integrating high throughput data and different features. It can be a useful complement to ChIP-seq and other experimental methods for TFBS identification and thus improve our ability to investigate functional elements in post-genomic era.
Availability: CTF is freely available to academic users at: http://cbb.sjtu.edu.cn/~ccwei/pub/software/CTF/CTF.php
doi:10.1186/1471-2164-13-S8-S18
PMCID: PMC3535700  PMID: 23282203
24.  Ultra-Sensitive Detection of Plasmodium falciparum by Amplification of Multi-Copy Subtelomeric Targets 
PLoS Medicine  2015;12(3):e1001788.
Background
Planning and evaluating malaria control strategies relies on accurate definition of parasite prevalence in the population. A large proportion of asymptomatic parasite infections can only be identified by surveillance with molecular methods, yet these infections also contribute to onward transmission to mosquitoes. The sensitivity of molecular detection by PCR is limited by the abundance of the target sequence in a DNA sample; thus, detection becomes imperfect at low densities. We aimed to increase PCR diagnostic sensitivity by targeting multi-copy genomic sequences for reliable detection of low-density infections, and investigated the impact of these PCR assays on community prevalence data.
Methods and Findings
Two quantitative PCR (qPCR) assays were developed for ultra-sensitive detection of Plasmodium falciparum, targeting the high-copy telomere-associated repetitive element 2 (TARE-2, ∼250 copies/genome) and the var gene acidic terminal sequence (varATS, 59 copies/genome). Our assays reached a limit of detection of 0.03 to 0.15 parasites/μl blood and were 10× more sensitive than standard 18S rRNA qPCR. In a population cross-sectional study in Tanzania, 295/498 samples tested positive using ultra-sensitive assays. Light microscopy missed 169 infections (57%). 18S rRNA qPCR failed to identify 48 infections (16%), of which 40% carried gametocytes detected by pfs25 quantitative reverse-transcription PCR. To judge the suitability of the TARE-2 and varATS assays for high-throughput screens, their performance was tested on sample pools. Both ultra-sensitive assays correctly detected all pools containing one low-density P. falciparum–positive sample, which went undetected by 18S rRNA qPCR, among nine negatives. TARE-2 and varATS qPCRs improve estimates of prevalence rates, yet other infections might still remain undetected when absent in the limited blood volume sampled.
Conclusions
Measured malaria prevalence in communities is largely determined by the sensitivity of the diagnostic tool used. Even when applying standard molecular diagnostics, prevalence in our study population was underestimated by 8% compared to the new assays. Our findings highlight the need for highly sensitive tools such as TARE-2 and varATS qPCR in community surveillance and for monitoring interventions to better describe malaria epidemiology and inform malaria elimination efforts.
Ingrid Felger and colleagues developed an assay that targets multi-copy genomic sequences and can detect low-density infections with falciparum malaria parasites.
Editors' Summary
Background
Nearly half the world's population is at risk of malaria, and more than 600,000 people die from this mosquito-borne parasitic infection every year. Most of these deaths are caused by Plasmodium falciparum, which is transmitted to people by night-flying Anopheles mosquitoes. These insects inject “sporozoites” into people, a parasitic form that replicates inside human liver cells. After a few days, the liver cells release “merozoites,” which invade red blood cells, where they replicate rapidly before bursting out and infecting more red blood cells. This increase in parasitic burden causes malaria's characteristic fever, which needs to be treated promptly to prevent anemia and organ damage. Infected red blood cells also release “gametocytes,” which infect mosquitoes when they take a blood meal. In the mosquito, the gametocytes multiply and develop into sporozoites, thus completing the parasite's life cycle. Malaria can be prevented by controlling the mosquitoes that spread the parasite and by avoiding mosquito bites. Effective treatment with antimalarial drugs also helps to reduce malaria transmission and is a key component of global efforts to control and eliminate malaria.
Why Was This Study Done?
Planning and evaluating malaria control and elimination efforts relies on having accurate and sensitive methods to measure parasite prevalence—the proportion of a population infected with parasites. It is particularly important to know how many people are carrying low-density infections because although these individuals have no symptoms, they contribute to malaria transmission. In the past, malaria was usually diagnosed by looking for parasites in blood using light microscopy, but molecular tests based on “quantitative polymerase chain reactions” (qPCRs) are now available that detect much lower parasite densities in blood (submicroscopic infections). qPCRs detect parasite-specific DNA sequences in patient blood samples, but reliable detection of low-density infections remains imperfect because the abundance of target sequences in patient samples limits the sensitivity of current qPCR methods. Here, the researchers investigate whether the sensitivity of P. falciparum detection using qPCR can be improved by targeting multi-copy genomic sequences—DNA sequences that are repeated many times in the parasite's genetic blueprint.
What Did the Researchers Do and Find?
The researchers developed two new qPCRs for P. falciparum by using the telomere-associated repetitive element 2 (TARE-2; 250 copies/genome) and the var gene acidic terminal sequence (varATS; 59 copies/genome) as target sequences. Direct comparison of these qPCRs with the standard 18S rRNA qPCR for P. falciparum, which targets a gene present at 5–8 copies/genome, indicated that the new assays were ten times more sensitive than the standard assay and could detect as few as 0.03–0.15 parasites/μl blood. Next, the researchers used light microscopy, 18S rRNA qPCR, and the two new qPCRs to look for P. falciparum parasites in 498 samples randomly selected from a malaria survey undertaken in Tanzania. Parasite prevalences were 25% by light microscopy, 50% by 18S rRNA qPCR, and 58% by TARE-2 or varATS qPCR. Compared to TARE-2 or varATS qPCR, 18S rRNA qPCR failed to identify 48 infections (16% of infections). Moreover, 40% of the positive samples missed by 18S rRNA qPCR contained gametocytes (detected by a different PCR-based assay) and therefore came from individuals capable of transmitting malaria parasites to mosquitoes. Finally, to test the suitability of the new ultra-sensitive assays for use in high-throughput screens, the researchers tested performance of the assays on sample pools. Both tests correctly identified all pools containing one low-density P. falciparum–positive sample among nine negative samples, whereas 18S rRNA qPCR identified none of these pools.
What Do These Findings Mean?
These findings provide evidence of low-density malaria infections in individuals previously thought to be parasite-free, even after testing with a molecular diagnostic. Notably, in the population considered in this study, the standard 18S rRNA qPCR underestimated parasite prevalence by nearly 10%. The assays developed in this study have some important limitations, however. First, they detect only P. falciparum, and malaria control programs ideally need assays that detect all the Plasmodium species that cause malaria. Second, because the TARE-2 and varATS qPCRs require advanced laboratory infrastructure, they cannot be used in remote field settings. Nevertheless, because low-density infections are likely to become increasingly common as countries improve malaria control, these findings highlight the need for ultra-sensitive tools such as the TARE-2 and varATS qPCRs for community surveillance and for monitoring the progress of malaria control and elimination programs.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001788.
Information is available from the World Health Organization on malaria (in several languages), including information on malaria diagnosis; the World Malaria Report 2014 provides details of the current global malaria situation
The US Centers for Disease Control and Prevention also provides information on all aspects of malaria; its website provides a selection of personal stories about malaria
Information is available from the Roll Back Malaria Partnership on the global control of malaria and on the Global Malaria Action Plan (in English and French)
MedlinePlus provides links to additional information on malaria (in English and Spanish)
doi:10.1371/journal.pmed.1001788
PMCID: PMC4348198  PMID: 25734259
25.  Integrating Sequencing Technologies in Personal Genomics: Optimal Low Cost Reconstruction of Structural Variants 
PLoS Computational Biology  2009;5(7):e1000432.
The goal of human genome re-sequencing is obtaining an accurate assembly of an individual's genome. Recently, there has been great excitement in the development of many technologies for this (e.g. medium and short read sequencing from companies such as 454 and SOLiD, and high-density oligo-arrays from Affymetrix and NimbelGen), with even more expected to appear. The costs and sensitivities of these technologies differ considerably from each other. As an important goal of personal genomics is to reduce the cost of re-sequencing to an affordable point, it is worthwhile to consider optimally integrating technologies. Here, we build a simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs). SV reconstruction is considered the most challenging step in human genome re-sequencing. (It is sometimes even harder than de novo assembly of small genomes because of the duplications and repetitive sequences in the human genome.) To this end, we formulate canonical problems that are representative of issues in reconstruction and are of small enough scale to be computationally tractable and simulatable. Using semi-realistic simulations, we show how we can combine different technologies to optimally solve the assembly at low cost. With mapability maps, our simulations efficiently handle the inhomogeneous repeat-containing structure of the human genome and the computational complexity of practical assembly algorithms. They quantitatively show how combining different read lengths is more cost-effective than using one length, how an optimal mixed sequencing strategy for reconstructing large novel SVs usually also gives accurate detection of SNPs/indels, how paired-end reads can improve reconstruction efficiency, and how adding in arrays is more efficient than just sequencing for disentangling some complex SVs. Our strategy should facilitate the sequencing of human genomes at maximum accuracy and low cost.
Author Summary
In recent years, the development of high throughput sequencing and array technologies has enabled the accurate re-sequencing of individual genomes, especially in identifying and reconstructing the variants in an individual's genome compared to a “reference”. The costs and sensitivities of these technologies differ considerably from each other, and even more technologies are expected to appear in the near future. To both reduce the total cost of re-sequencing to an affordable point and be adaptive to these constantly evolving bio-technologies, we propose to build a computationally efficient simulation framework that can help us optimize the combination of different technologies to perform low cost comparative genome re-sequencing, especially in reconstructing large structural variants, which is considered in many respects the most challenging step in genome re-sequencing. Our simulation results quantitatively show how much improvement one can gain in reconstructing large structural variants by integrating different technologies in optimal ways. We envision that in the future, more experimental technologies will be incorporated into this simulation framework and its results can provide informative guidelines for the actual experimental design to achieve optimal genome re-sequencing output at low costs.
doi:10.1371/journal.pcbi.1000432
PMCID: PMC2700963  PMID: 19593373

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