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1.  Comprehensive molecular characterization of gastric adenocarcinoma 
Bass, Adam J. | Thorsson, Vesteinn | Shmulevich, Ilya | Reynolds, Sheila M. | Miller, Michael | Bernard, Brady | Hinoue, Toshinori | Laird, Peter W. | Curtis, Christina | Shen, Hui | Weisenberger, Daniel J. | Schultz, Nikolaus | Shen, Ronglai | Weinhold, Nils | Kelsen, David P. | Bowlby, Reanne | Chu, Andy | Kasaian, Katayoon | Mungall, Andrew J. | Robertson, A. Gordon | Sipahimalani, Payal | Cherniack, Andrew | Getz, Gad | Liu, Yingchun | Noble, Michael S. | Pedamallu, Chandra | Sougnez, Carrie | Taylor-Weiner, Amaro | Akbani, Rehan | Lee, Ju-Seog | Liu, Wenbin | Mills, Gordon B. | Yang, Da | Zhang, Wei | Pantazi, Angeliki | Parfenov, Michael | Gulley, Margaret | Piazuelo, M. Blanca | Schneider, Barbara G. | Kim, Jihun | Boussioutas, Alex | Sheth, Margi | Demchok, John A. | Rabkin, Charles S. | Willis, Joseph E. | Ng, Sam | Garman, Katherine | Beer, David G. | Pennathur, Arjun | Raphael, Benjamin J. | Wu, Hsin-Ta | Odze, Robert | Kim, Hark K. | Bowen, Jay | Leraas, Kristen M. | Lichtenberg, Tara M. | Weaver, Stephanie | McLellan, Michael | Wiznerowicz, Maciej | Sakai, Ryo | Getz, Gad | Sougnez, Carrie | Lawrence, Michael S. | Cibulskis, Kristian | Lichtenstein, Lee | Fisher, Sheila | Gabriel, Stacey B. | Lander, Eric S. | Ding, Li | Niu, Beifang | Ally, Adrian | Balasundaram, Miruna | Birol, Inanc | Bowlby, Reanne | Brooks, Denise | Butterfield, Yaron S. N. | Carlsen, Rebecca | Chu, Andy | Chu, Justin | Chuah, Eric | Chun, Hye-Jung E. | Clarke, Amanda | Dhalla, Noreen | Guin, Ranabir | Holt, Robert A. | Jones, Steven J.M. | Kasaian, Katayoon | Lee, Darlene | Li, Haiyan A. | Lim, Emilia | Ma, Yussanne | Marra, Marco A. | Mayo, Michael | Moore, Richard A. | Mungall, Andrew J. | Mungall, Karen L. | Nip, Ka Ming | Robertson, A. Gordon | Schein, Jacqueline E. | Sipahimalani, Payal | Tam, Angela | Thiessen, Nina | Beroukhim, Rameen | Carter, Scott L. | Cherniack, Andrew D. | Cho, Juok | Cibulskis, Kristian | DiCara, Daniel | Frazer, Scott | Fisher, Sheila | Gabriel, Stacey B. | Gehlenborg, Nils | Heiman, David I. | Jung, Joonil | Kim, Jaegil | Lander, Eric S. | Lawrence, Michael S. | Lichtenstein, Lee | Lin, Pei | Meyerson, Matthew | Ojesina, Akinyemi I. | Pedamallu, Chandra Sekhar | Saksena, Gordon | Schumacher, Steven E. | Sougnez, Carrie | Stojanov, Petar | Tabak, Barbara | Taylor-Weiner, Amaro | Voet, Doug | Rosenberg, Mara | Zack, Travis I. | Zhang, Hailei | Zou, Lihua | Protopopov, Alexei | Santoso, Netty | Parfenov, Michael | Lee, Semin | Zhang, Jianhua | Mahadeshwar, Harshad S. | Tang, Jiabin | Ren, Xiaojia | Seth, Sahil | Yang, Lixing | Xu, Andrew W. | Song, Xingzhi | Pantazi, Angeliki | Xi, Ruibin | Bristow, Christopher A. | Hadjipanayis, Angela | Seidman, Jonathan | Chin, Lynda | Park, Peter J. | Kucherlapati, Raju | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Kim, Sang-Bae | Lee, Ju-Seog | Lu, Yiling | Mills, Gordon | Laird, Peter W. | Hinoue, Toshinori | Weisenberger, Daniel J. | Bootwalla, Moiz S. | Lai, Phillip H. | Shen, Hui | Triche, Timothy | Van Den Berg, David J. | Baylin, Stephen B. | Herman, James G. | Getz, Gad | Chin, Lynda | Liu, Yingchun | Murray, Bradley A. | Noble, Michael S. | Askoy, B. Arman | Ciriello, Giovanni | Dresdner, Gideon | Gao, Jianjiong | Gross, Benjamin | Jacobsen, Anders | Lee, William | Ramirez, Ricardo | Sander, Chris | Schultz, Nikolaus | Senbabaoglu, Yasin | Sinha, Rileen | Sumer, S. Onur | Sun, Yichao | Weinhold, Nils | Thorsson, Vésteinn | Bernard, Brady | Iype, Lisa | Kramer, Roger W. | Kreisberg, Richard | Miller, Michael | Reynolds, Sheila M. | Rovira, Hector | Tasman, Natalie | Shmulevich, Ilya | Ng, Santa Cruz Sam | Haussler, David | Stuart, Josh M. | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Verhaak, Roeland G.W. | Mills, Gordon B. | Leiserson, Mark D. M. | Raphael, Benjamin J. | Wu, Hsin-Ta | Taylor, Barry S. | Black, Aaron D. | Bowen, Jay | Carney, Julie Ann | Gastier-Foster, Julie M. | Helsel, Carmen | Leraas, Kristen M. | Lichtenberg, Tara M. | McAllister, Cynthia | Ramirez, Nilsa C. | Tabler, Teresa R. | Wise, Lisa | Zmuda, Erik | Penny, Robert | Crain, Daniel | Gardner, Johanna | Lau, Kevin | Curely, Erin | Mallery, David | Morris, Scott | Paulauskis, Joseph | Shelton, Troy | Shelton, Candace | Sherman, Mark | Benz, Christopher | Lee, Jae-Hyuk | Fedosenko, Konstantin | Manikhas, Georgy | Potapova, Olga | Voronina, Olga | Belyaev, Smitry | Dolzhansky, Oleg | Rathmell, W. Kimryn | Brzezinski, Jakub | Ibbs, Matthew | Korski, Konstanty | Kycler, Witold | ŁaŸniak, Radoslaw | Leporowska, Ewa | Mackiewicz, Andrzej | Murawa, Dawid | Murawa, Pawel | Spychała, Arkadiusz | Suchorska, Wiktoria M. | Tatka, Honorata | Teresiak, Marek | Wiznerowicz, Maciej | Abdel-Misih, Raafat | Bennett, Joseph | Brown, Jennifer | Iacocca, Mary | Rabeno, Brenda | Kwon, Sun-Young | Penny, Robert | Gardner, Johanna | Kemkes, Ariane | Mallery, David | Morris, Scott | Shelton, Troy | Shelton, Candace | Curley, Erin | Alexopoulou, Iakovina | Engel, Jay | Bartlett, John | Albert, Monique | Park, Do-Youn | Dhir, Rajiv | Luketich, James | Landreneau, Rodney | Janjigian, Yelena Y. | Kelsen, David P. | Cho, Eunjung | Ladanyi, Marc | Tang, Laura | McCall, Shannon J. | Park, Young S. | Cheong, Jae-Ho | Ajani, Jaffer | Camargo, M. Constanza | Alonso, Shelley | Ayala, Brenda | Jensen, Mark A. | Pihl, Todd | Raman, Rohini | Walton, Jessica | Wan, Yunhu | Demchok, John A. | Eley, Greg | Mills Shaw, Kenna R. | Sheth, Margi | Tarnuzzer, Roy | Wang, Zhining | Yang, Liming | Zenklusen, Jean Claude | Davidsen, Tanja | Hutter, Carolyn M. | Sofia, Heidi J. | Burton, Robert | Chudamani, Sudha | Liu, Jia
Nature  2014;513(7517):202-209.
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
doi:10.1038/nature13480
PMCID: PMC4170219  PMID: 25079317
2.  Key nodes of a microRNA network associated with the integrated mesenchymal subtype of high-grade serous ovarian cancer 
Chinese Journal of Cancer  2015;34(1):28-40.
Metastasis is the main cause of cancer mortality. One of the initiating events of cancer metastasis of epithelial tumors is epithelial-to-mesenchymal transition (EMT), during which cells dedifferentiate from a relatively rigid cell structure/morphology to a flexible and changeable structure/morphology often associated with mesenchymal cells. The presence of EMT in human epithelial tumors is reflected by the increased expression of genes and levels of proteins that are preferentially present in mesenchymal cells. The combined presence of these genes forms the basis of mesenchymal gene signatures, which are the foundation for classifying a mesenchymal subtype of tumors. Indeed, tumor classification schemes that use clustering analysis of large genomic characterizations, like The Cancer Genome Atlas (TCGA), have defined mesenchymal subtype in a number of cancer types, such as high-grade serous ovarian cancer and glioblastoma. However, recent analyses have shown that gene expression-based classifications of mesenchymal subtypes often do not associate with poor survival. This “paradox” can be ameliorated using integrated analysis that combines multiple data types. We recently found that integrating mRNA and microRNA (miRNA) data revealed an integrated mesenchymal subtype that is consistently associated with poor survival in multiple cohorts of patients with serous ovarian cancer. This network consists of 8 major miRNAs and 214 mRNAs. Among the 8 miRNAs, 4 are known to be regulators of EMT. This review provides a summary of these 8 miRNAs, which were associated with the integrated mesenchymal subtype of serous ovarian cancer.
doi:10.5732/cjc.014.10284
PMCID: PMC4302087  PMID: 25556616
MicroRNA (miRNA); epithelial-to-mesenchymal transition (EMT); cancer; ovary; miR-506; miR-101
3.  Multiscale Representation of Genomic Signals 
Nature methods  2014;11(6):689-694.
Genomic information is encoded on a wide range of distance scales, ranging from tens of base pairs to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as GC content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations, including genes, nuclear lamina associated domains, and repeat elements. By integrating the information across all scales, as compared to using any single scale, we demonstrate improved prediction of gene expression from Polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements and we observed that gene expression differences in colorectal cancer are not most strongly related to gene body methylation, but rather to methylation patterns that extend beyond the single-gene scale.
doi:10.1038/nmeth.2924
PMCID: PMC4040162  PMID: 24727652
4.  Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers 
Nucleic Acids Research  2014;42(21):12973-12983.
Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein–DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.
doi:10.1093/nar/gku1031
PMCID: PMC4245936  PMID: 25378323
5.  The Somatic Genomic Landscape of Glioblastoma 
Cell  2013;155(2):462-477.
We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.
doi:10.1016/j.cell.2013.09.034
PMCID: PMC3910500  PMID: 24120142
6.  Association between BRCA2 but not BRCA1 Mutations and Beneficial Survival, Chemotherapy Sensitivity, and Gene Mutator Phenotype in Patients with Ovarian Cancer 
Context
Attempts to determine the clinical significance of BRCA1/2 mutations in ovarian cancer (OvCa) have produced conflicting results.
Objective
To determine the relationships between BRCA1/2 deficiency (i.e., mutation and promoter hypermethylation) and overall survival (OS), progression-free survival (PFS), chemotherapy response, and whole exome mutation rate in OvCa.
Design, Setting, and Patients
Observational study of multidimensional genomics and clinical data on 316 high-grade serous OvCa cases that were made public between 2009 and 2010 via The Cancer Genome Atlas project.
Main Outcome Measures
OS and PFS rates (primary outcomes) and chemotherapy response (secondary outcome).
Results
BRCA2 mutations (29 cases) were associated with significantly better OS (adjusted hazard ratio [HR], 0.33; 95% CI, 0.16–0.69, P=0.003; 5-year OS: 61% for BRCA2 mutated vs. 25% for BRCA wild-type [wt] cases) and PFS (adjusted HR, 0.40; 95% CI, 0.22–0.74, P=0.004; 3-year PFS: 44% for BRCA2 mutated vs. 16% for BRCA wt cases), whereas neither BRCA1 mutations (37 cases) nor BRCA1 methylation (33 cases) were associated with prognosis. Moreover, BRCA2 mutations were associated with a significantly higher primary chemotherapy sensitivity rate (100% for BRCA2 mutated vs. 82% [P=0.02] and 80% [P=0.05] for BRCA wt and BRCA1 mutated cases, respectively) and longer platinum-free duration (median platinum-free duration: 18.0 months for BRCA2 mutated vs. 11.7 [P=0.02] and 12.5 [P=0.04] months for BRCA wt and BRCA1 mutated cases, respectively). Further investigation revealed that BRCA2 mutated, but not BRCA1 mutated cases, exhibited a “mutator phenotype” by containing significantly more mutations than BRCA wt cases across the whole exome (median mutation number per sample: 84 for BRCA2 mutated vs. 52 for BRCA wt cases, false-discovery rate <0.1).
Conclusions
BRCA2 mutation, but not BRCA1 deficiency, is associated with improved survival, chemotherapy response, and genome instability compared with BRCA wild-type.
doi:10.1001/jama.2011.1456
PMCID: PMC4159096  PMID: 21990299
BRCA1; BRCA2; mutations; survival; platinum-based drug response
7.  Gene pair signatures in cell type transcriptomes reveal lineage control 
Nature methods  2013;10(6):577-583.
The distinct cell types of multicellular organisms arise due to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We compiled a resource of curated human expression data comprising 166 cell types and 2,602 transcription regulating genes and developed a data driven method built around the concept of expression reversal defined at the level of gene pairs, such as those participating in toggle switch circuits. This approach allows us to organize the cell types into their ontogenetic lineage-relationships and to reflect regulatory relationships among genes that explain their ability to function as determinants of cell fate. We show that this method identifies genes belonging to regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, thus offering a novel large-scale perspective on lineage specification.
doi:10.1038/nmeth.2445
PMCID: PMC4131748  PMID: 23603899
8.  Large-scale molecular characterization and analysis of gastric cancer 
Chinese Journal of Cancer  2014;33(8):369-370.
doi:10.5732/cjc.014.10116
PMCID: PMC4135364  PMID: 25313412
9.  Quantitative analysis of colony morphology in yeast 
BioTechniques  2014;56(1):18-27.
Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.
doi:10.2144/000114123
PMCID: PMC3996921  PMID: 24447135
colony morphology; image analysis; software; yeast; phenotype; time-lapse
10.  Post-transcriptional regulatory network of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions 
Epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), play important roles in embryogenesis, stem cell biology, and cancer progression. EMT can be regulated by many signaling pathways and regulatory transcriptional networks. Furthermore, post-transcriptional regulatory networks regulate EMT; these networks include the long non-coding RNA (lncRNA) and microRNA (miRNA) families. Specifically, the miR-200 family, miR-101, miR-506, and several lncRNAs have been found to regulate EMT. Recent studies have illustrated that several lncRNAs are overexpressed in various cancers and that they can promote tumor metastasis by inducing EMT. MiRNA controls EMT by regulating EMT transcription factors or other EMT regulators, suggesting that lncRNAs and miRNA are novel therapeutic targets for the treatment of cancer. Further efforts have shown that non-coding-mediated EMT regulation is closely associated with epigenetic regulation through promoter methylation (e.g., miR-200 or miR-506) and protein regulation (e.g., SET8 via miR-502). The formation of gene fusions has also been found to promote EMT in prostate cancer. In this review, we discuss the post-transcriptional regulatory network that is involved in EMT and MET and how targeting EMT and MET may provide effective therapeutics for human disease.
doi:10.1186/1756-8722-7-19
PMCID: PMC3973872  PMID: 24598126
Long non-coding RNA (lncRNA); microRNA (miRNA); Epithelial-to-mesenchymal transition (EMT); Mesenchymal-to-epithelial transition (MET)
11.  Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer 
Cancer cell  2013;23(2):186-199.
Summary
Integrated genomic analyses revealed a miRNA-regulatory network, which further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141 and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition (EMT) by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.
doi:10.1016/j.ccr.2012.12.020
PMCID: PMC3603369  PMID: 23410973
12.  The Cancer Genome Atlas Pan-Cancer Analysis Project 
Nature genetics  2013;45(10):1113-1120.
Cancer can take hundreds of different forms depending on the location, cell of origin and spectrum of genomic alterations that promote oncogenesis and affect therapeutic response. Although many genomic events with direct phenotypic impact have been identified, much of the complex molecular landscape remains incompletely charted for most cancer lineages. For that reason, The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumours to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences, and emergent themes across tumour lineages. The Pan-Cancer initiative compares the first twelve tumour types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumour types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
doi:10.1038/ng.2764
PMCID: PMC3919969  PMID: 24071849
13.  Transcriptome and Small RNA Deep Sequencing Reveals Deregulation of miRNA Biogenesis in Human Glioma 
The Journal of pathology  2013;229(3):10.1002/path.4109.
Altered expression of oncogenic and tumor-suppressing microRNAs (miRNAs) is widely associated with tumorigenesis. However, the regulatory mechanisms underlying these alterations are poorly understood. We sought to shed light on the deregulation of miRNA biogenesis promoting the aberrant miRNA expression profiles identified in these tumors. Using sequencing technology to perform both whole-transcriptome and small RNA sequencing of glioma patient samples, we examined precursor and mature miRNAs to directly evaluate the miRNA maturation process, and interrogated expression profiles for genes involved in the major steps of miRNA biogenesis. We found that ratios of mature to precursor forms of a large number of miRNAs increased with the progression from normal brain to low-grade and then to high-grade gliomas. The expression levels of genes involved in each of the three major steps of miRNA biogenesis (nuclear processing, nucleo-cytoplasmic transport, and cytoplasmic processing) were systematically altered in glioma tissues. Survival analysis of an independent data set demonstrated that the alteration of genes involved in miRNA maturation correlates with survival in glioma patients. Direct quantification of miRNA maturation with deep sequencing demonstrated that deregulation of the miRNA biogenesis pathway is a hallmark for glioma genesis and progression.
doi:10.1002/path.4109
PMCID: PMC3857031  PMID: 23007860
microRNA; biogenesis; glioma
14.  POMO - Plotting Omics analysis results for Multiple Organisms 
BMC Genomics  2013;14:918.
Background
Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited.
Results
We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly, yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input.
Conclusions
The POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration.
doi:10.1186/1471-2164-14-918
PMCID: PMC3880012  PMID: 24365393
Omics; Association; Visualization; Ortholog; Phenolog; Genome-wide; Network; Model organism
15.  Differing clinical impact of BRCA1 and BRCA2 mutations in serous ovarian cancer 
Pharmacogenomics  2012;13(13):1523-1535.
A key function of BRCA1 and BRCA2 is the participation in dsDNAbreak repair via homologous recombination. BRCA1 and BRCA2 mutations, which occur in most hereditary ovarian cancers (OCs) and approximately 10% of all OC cases, are associated with defects in homologous recombination and genomic instability, a phenotype termed ‘BRCAness’. The clinical effects of BRCA1 and BRCA2 mutations have commonly been analyzed together; however, it is becoming increasingly apparent that these mutations do not have the same effects in OC. Recently, three major reports highlighted the unequal clinical characteristics of OCs with BRCA1 and BRCA2 mutations. All studies demonstrated that BRCA2-mutated patients are associated with better survival and therapeutic response than BRCA1-mutated and wild-type patients with serous OC. The differing prognostic effects of the BRCA2 and BRCA1 mutations is likely due to differing roles of BRCA1 and BRCA2 in homologous recombination repair and a stronger association between the BRCA2 mutation and a hypermutator phenotype. These new findings have potentially important implications for clinical management of patients with serous OC.
doi:10.2217/pgs.12.137
PMCID: PMC3603383  PMID: 23057551
BRCA mutation; drug response; homologous recombination; ovarian cancer; PARP inhibitor; survival
16.  On the Limitations of Biological Knowledge 
Current Genomics  2012;13(7):574-587.
Scientific knowledge is grounded in a particular epistemology and, owing to the requirements of that epistemology, possesses limitations. Some limitations are intrinsic, in the sense that they depend inherently on the nature of scientific knowledge; others are contingent, depending on the present state of knowledge, including technology. Understanding limitations facilitates scientific research because one can then recognize when one is confronted by a limitation, as opposed to simply being unable to solve a problem within the existing bounds of possibility. In the hope that the role of limiting factors can be brought more clearly into focus and discussed, we consider several sources of limitation as they apply to biological knowledge: mathematical complexity, experimental constraints, validation, knowledge discovery, and human intellectual capacity.
doi:10.2174/138920212803251445
PMCID: PMC3468890  PMID: 23633917
Complexity; Gene regulatory networks; Epistemology; Experimental design; Genomics; Knowledge discovery; Modeling; Validation.
17.  Information-Theoretic Analysis of the Dynamics of an Executable Biological Model 
PLoS ONE  2013;8(3):e59303.
To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.
doi:10.1371/journal.pone.0059303
PMCID: PMC3602105  PMID: 23527156
18.  Fastbreak: a tool for analysis and visualization of structural variations in genomic data 
Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.
doi:10.1186/1687-4153-2012-15
PMCID: PMC3605143  PMID: 23046488
Cancer genomics; Structural variation; Translocation
19.  Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology 
PLoS ONE  2012;7(8):e42779.
Transcription factor-DNA interactions, central to cellular regulation and control, are commonly described by position weight matrices (PWMs). These matrices are frequently used to predict transcription factor binding sites in regulatory regions of DNA to complement and guide further experimental investigation. The DNA sequence preferences of transcription factors, encoded in PWMs, are dictated primarily by select residues within the DNA binding domain(s) that interact directly with DNA. Therefore, the DNA binding properties of homologous transcription factors with identical DNA binding domains may be characterized by PWMs derived from different species. Accordingly, we have implemented a fully automated domain-level homology searching method for identical DNA binding sequences.
By applying the domain-level homology search to transcription factors with existing PWMs in the JASPAR and TRANSFAC databases, we were able to significantly increase coverage in terms of the total number of PWMs associated with a given species, assign PWMs to transcription factors that did not previously have any associations, and increase the number of represented species with PWMs over an order of magnitude. Additionally, using protein binding microarray (PBM) data, we have validated the domain-level method by demonstrating that transcription factor pairs with matching DNA binding domains exhibit comparable DNA binding specificity predictions to transcription factor pairs with completely identical sequences.
The increased coverage achieved herein demonstrates the potential for more thorough species-associated investigation of protein-DNA interactions using existing resources. The PWM scanning results highlight the challenging nature of transcription factors that contain multiple DNA binding domains, as well as the impact of motif discovery on the ability to predict DNA binding properties. The method is additionally suitable for identifying domain-level homology mappings to enable utilization of additional information sources in the study of transcription factors. The domain-level homology search method, resulting PWM mappings, web-based user interface, and web API are publicly available at http://dodoma.systemsbiology.netdodoma.systemsbiology.net.
doi:10.1371/journal.pone.0042779
PMCID: PMC3428306  PMID: 22952610
20.  Integrated Analysis of Gene Expression and Tumor Nuclear Image Profiles Associated with Chemotherapy Response in Serous Ovarian Carcinoma 
PLoS ONE  2012;7(5):e36383.
Background
Small sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.
Methodology/Principal Findings
A gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.
Conclusions
A gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.
doi:10.1371/journal.pone.0036383
PMCID: PMC3348145  PMID: 22590536
21.  DETERMINISTIC AND STOCHASTIC MODELS OF GENETIC REGULATORY NETWORKS 
Methods in enzymology  2009;467:335-356.
Traditionally molecular biology research has tended to reduce biological pathways to composite units studied as isolated parts of the cellular system. With the advent of high throughput methodologies that can capture thousands of data points, and powerful computational approaches, the reality of studying cellular processes at a systems level is upon us. As these approaches yield massive datasets, systems level analyses have drawn upon other fields such as engineering and mathematics, adapting computational and statistical approaches to decipher relationships between molecules. Guided by high quality datasets and analyses, one can begin the process of predictive modeling. The findings from such approaches are often surprising and beyond normal intuition. We discuss four classes of dynamical systems used to model genetic regulatory networks. The discussion is divided into continuous and discrete models, as well as deterministic and stochastic model classes. For each combination of these categories, a model is presented and discussed in the context of the yeast cell cycle, illustrating how different types of questions can be addressed by different model classes.
doi:10.1016/S0076-6879(09)67013-0
PMCID: PMC3230268  PMID: 19897099
22.  EPEPT: A web service for enhanced P-value estimation in permutation tests 
BMC Bioinformatics  2011;12:411.
Background
In computational biology, permutation tests have become a widely used tool to assess the statistical significance of an event under investigation. However, the common way of computing the P-value, which expresses the statistical significance, requires a very large number of permutations when small (and thus interesting) P-values are to be accurately estimated. This is computationally expensive and often infeasible. Recently, we proposed an alternative estimator, which requires far fewer permutations compared to the standard empirical approach while still reliably estimating small P-values [1].
Results
The proposed P-value estimator has been enriched with additional functionalities and is made available to the general community through a public website and web service, called EPEPT. This means that the EPEPT routines can be accessed not only via a website, but also programmatically using any programming language that can interact with the web. Examples of web service clients in multiple programming languages can be downloaded. Additionally, EPEPT accepts data of various common experiment types used in computational biology. For these experiment types EPEPT first computes the permutation values and then performs the P-value estimation. Finally, the source code of EPEPT can be downloaded.
Conclusions
Different types of users, such as biologists, bioinformaticians and software engineers, can use the method in an appropriate and simple way.
Availability
http://informatics.systemsbiology.net/EPEPT/
doi:10.1186/1471-2105-12-411
PMCID: PMC3277916  PMID: 22024252
23.  A regression model approach to enable cell morphology correction in high-throughput flow cytometry 
Large variations in cell size and shape can undermine traditional gating methods for analyzing flow cytometry data. Correcting for these effects enables analysis of high-throughput data sets, including >5000 yeast samples with diverse cell morphologies.
The regression model approach corrects for the effects of cell morphology on fluorescence, as well as an extremely small and restrictive gate, but without removing any of the cells.In contrast to traditional gating, this approach enables the quantitative analysis of high-throughput flow cytometry experiments, since the regression model can compare between biological samples that show no or little overlap in terms of the morphology of the cells.The analysis of a high-throughput yeast flow cytometry data set consisting of >5000 biological samples identified key proteins that affect the time and intensity of the bifurcation event that happens after the carbon source transition from glucose to fatty acids. Here, some yeast cells undergo major structural changes, while others do not.
Flow cytometry is a widely used technique that enables the measurement of different optical properties of individual cells within large populations of cells in a fast and automated manner. For example, by targeting cell-specific markers with fluorescent probes, flow cytometry is used to identify (and isolate) cell types within complex mixtures of cells. In addition, fluorescence reporters can be used in conjunction with flow cytometry to measure protein, RNA or DNA concentration within single cells of a population.
One of the biggest advantages of this technique is that it provides information of how each cell behaves instead of just measuring the population average. This can be essential when analyzing complex samples that consist of diverse cell types or when measuring cellular responses to stimuli. For example, there is an important difference between a 50% expression increase of all cells in a population after stimulation and a 100% increase in only half of the cells, while the other half remains unresponsive. Another important advantage of flow cytometry is automation, which enables high-throughput studies with thousands of samples and conditions. However, current methods are confounded by populations of cells that are non-uniform in terms of size and granularity. Such variability affects the emitted fluorescence of the cell and adds undesired variability when estimating population fluorescence. This effect also frustrates a sensible comparison between conditions, where not only fluorescence but also cell size and granularity may be affected.
Traditionally, this problem has been addressed by using ‘gates' that restrict the analysis to cells with similar morphological properties (i.e. cell size and cell granularity). Because cells inside the gate are morphologically similar to one another, they will show a smaller variability in their response within the population. Moreover, applying the same gate in all samples assures that observed differences between these samples are not due to differential cell morphologies.
Gating, however, comes with costs. First, since only a subgroup of cells is selected, the final number of cells analyzed can be significantly reduced. This means that in order to have sufficient statistical power, more cells have to be acquired, which, if even possible in the first place, increases the time and cost of the experiment. Second, finding a good gate for all samples and conditions can be challenging if not impossible, especially in cases where cellular morphology changes dramatically between conditions. Finally, gating is a very user-dependent process, where both the size and shape of the gate are determined by the researcher and will affect the outcome, introducing subjectivity in the analysis that complicates reproducibility.
In this paper, we present an alternative method to gating that addresses the issues stated above. The method is based on a regression model containing linear and non-linear terms that estimates and corrects for the effect of cell size and granularity on the observed fluorescence of each cell in a sample. The corrected fluorescence thus becomes ‘free' of the morphological effects.
Because the model uses all cells in the sample, it assures that the corrected fluorescence is an accurate representation of the sample. In addition, the regression model can predict the expected fluorescence of a sample in areas where there are no cells. This makes it possible to compare between samples that have little overlap with good confidence. Furthermore, because the regression model is automated, it is fully reproducible between labs and conditions. Finally, it allows for a rapid analysis of big data sets containing thousands of samples.
To probe the validity of the model, we performed several experiments. We show how the regression model is able to remove the morphological-associated variability as well as an extremely small and restrictive gate, but without the caveat of removing cells. We test the method in different organisms (yeast and human) and applications (protein level detection, separation of mixed subpopulations). We then apply this method to unveil new biological insights in the mechanistic processes involved in transcriptional noise.
Gene transcription is a process subjected to the randomness intrinsic to any molecular event. Although such randomness may seem to be undesirable for the cell, since it prevents consistent behavior, there are situations where some degree of randomness is beneficial (e.g. bet hedging). For this reason, each gene is tuned to exhibit different levels of randomness or noise depending on its functions. For core and essential genes, the cell has developed mechanisms to lower the level of noise, while for genes involved in the response to stress, the variability is greater.
This gene transcription tuning can be determined at many levels, from the architecture of the transcriptional network, to epigenetic regulation. In our study, we analyze the latter using the response of yeast to the presence of fatty acid in the environment. Fatty acid can be used as energy by yeast, but it requires major structural changes and commitments. We have observed that at the population level, there is a bifurcation event whereby some cells undergo these changes and others do not. We have analyzed this bifurcation event in mutants for all the non-essential epigenetic regulators in yeast and identified key proteins that affect the time and intensity of this bifurcation. Even though fatty acid triggers major morphological changes in the cell, the regression model still makes it possible to analyze the over 5000 flow cytometry samples in this data set in an automated manner, whereas a traditional gating approach would be impossible.
Cells exposed to stimuli exhibit a wide range of responses ensuring phenotypic variability across the population. Such single cell behavior is often examined by flow cytometry; however, gating procedures typically employed to select a small subpopulation of cells with similar morphological characteristics make it difficult, even impossible, to quantitatively compare cells across a large variety of experimental conditions because these conditions can lead to profound morphological variations. To overcome these limitations, we developed a regression approach to correct for variability in fluorescence intensity due to differences in cell size and granularity without discarding any of the cells, which gating ipso facto does. This approach enables quantitative studies of cellular heterogeneity and transcriptional noise in high-throughput experiments involving thousands of samples. We used this approach to analyze a library of yeast knockout strains and reveal genes required for the population to establish a bimodal response to oleic acid induction. We identify a group of epigenetic regulators and nucleoporins that, by maintaining an ‘unresponsive population,' may provide the population with the advantage of diversified bet hedging.
doi:10.1038/msb.2011.64
PMCID: PMC3202802  PMID: 21952134
flow cytometry; high-throughput experiments; statistical regression model; transcriptional noise
24.  Trade-off between Responsiveness and Noise Suppression in Biomolecular System Responses to Environmental Cues 
PLoS Computational Biology  2011;7(6):e1002091.
When living systems detect changes in their external environment their response must be measured to balance the need to react appropriately with the need to remain stable, ignoring insignificant signals. Because this is a fundamental challenge of all biological systems that execute programs in response to stimuli, we developed a generalized time-frequency analysis (TFA) framework to systematically explore the dynamical properties of biomolecular networks. Using TFA, we focused on two well-characterized yeast gene regulatory networks responsive to carbon-source shifts and a mammalian innate immune regulatory network responsive to lipopolysaccharides (LPS). The networks are comprised of two different basic architectures. Dual positive and negative feedback loops make up the yeast galactose network; whereas overlapping positive and negative feed-forward loops are common to the yeast fatty-acid response network and the LPS-induced network of macrophages. TFA revealed remarkably distinct network behaviors in terms of trade-offs in responsiveness and noise suppression that are appropriately tuned to each biological response. The wild type galactose network was found to be highly responsive while the oleate network has greater noise suppression ability. The LPS network appeared more balanced, exhibiting less bias toward noise suppression or responsiveness. Exploration of the network parameter space exposed dramatic differences in system behaviors for each network. These studies highlight fundamental structural and dynamical principles that underlie each network, reveal constrained parameters of positive and negative feedback and feed-forward strengths that tune the networks appropriately for their respective biological roles, and demonstrate the general utility of the TFA approach for systems and synthetic biology.
Author Summary
Biological systems constantly balance noise suppression with responsiveness. In a fluctuating environment, some changes are insignificant to living cells while others represent cues to which they must respond. These stimuli are interpreted by molecular circuits that enable the cell to strike an appropriate balance between responsiveness and noise suppression. This trade-off is governed by the structure and kinetic parameters of molecular networks, which have been tuned by evolutionary selection for different stimuli and responses. We consider three regulatory circuits (two from yeast and one from mammalian cells), which respond to different environments and involve very different physiological processes. To investigate the responses to a time varying signal, we developed a generalized time-frequency analysis framework for studying such trade-offs using mathematical models of regulatory circuits and explore how the structure and parameters of the circuit affect the trade-offs between noise suppression and responsiveness. The generalized TFA approach represents an effective tool for exploring and analyzing different systems-level dynamical properties. Making use of such properties can facilitate prediction and network control for systems- and synthetic biology applications.
doi:10.1371/journal.pcbi.1002091
PMCID: PMC3127798  PMID: 21738459
25.  Taming Data 
Cell host & microbe  2008;4(4):312-313.
A challenge in systems-level investigations of the immune response is the principled integration of disparate data sets for constructing predictive models. InnateDB (Lynn et al., 2008; http://www.innatedb.ca), a publicly available, manually curated database of experimentally verified molecular interactions and pathways involved in innate immunity, is a powerful new resource that facilitates such integrative systems-level analyses.
doi:10.1016/j.chom.2008.09.011
PMCID: PMC3074406  PMID: 18854235

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