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1.  Effective normalization for copy number variation detection from whole genome sequencing 
BMC Genomics  2012;13(Suppl 6):S16.
Whole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.
We apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.
The GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.
Choice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.
PMCID: PMC3481445  PMID: 23134596
2.  Evaluation of the Serum Catalase and Myeloperoxidase Activity in the Chronic Arsenic Exposed Individuals and Concomitant Cytogenetic Damage 
Chronic arsenic exposure through contaminated drinking water is a major environmental health issue. Chronic arsenic exposure is known to exert its toxic effects by a variety of mechanisms, of which generation of reactive oxygen species (ROS) is one of the most important. High level of ROS, in turn, leads to DNA damage that might ultimately culminate in cancer. In order to keep the level of ROS in balance, an array of enzymes is present, of which catalase (CAT) and myeloperoxidase (MPO) are important members. Hence, in this study, we determined the activites of these two enzymes in the sera and chromosomal aberrations (CA) in peripheral blood lymphocytes in individuals exposed and unexposed to arsenic in drinking water. Arsenic in drinking water and in urine was used as a measure of exposure. Our results show that individuals chronically exposed to arsenic have significantly higher CAT and MPO activity and higher incidence of CA. We found moderate positive correlations between CAT and MPO activities, induction of CA and arsenic in urine and water. These results indicate that chronic arsenic exposure causes higher CAT and MPO activity in serum that correlates with induction of genetic damage. We conclude that the serum levels of these enzymes might be used as biomarkers of early arsenic exposure induced disease much before the classical dermatological symptoms of arsenicosis begin to appear.
PMCID: PMC3457024  PMID: 20732340
Arsenic; Catalase; Myeloperoxidase; Reactive oxygen species; Chromosomal aberrations
3.  Arsenic exposure through drinking water increases the risk of liver and cardiovascular diseases in the population of West Bengal, India 
BMC Public Health  2012;12:639.
Arsenic is a natural drinking water contaminant affecting 26 million people in West Bengal, India. Chronic arsenic exposure causes cancer, cardiovascular disease, liver disease, neuropathies and ocular diseases. The aims of the present study were to assess bioindicators of hepatocellular injury as indicated by the levels of liver enzymes, to determine the auto immune status, as indicated by the amounts of anti-nuclear antibodies (ANA) and anti-dsDNA antibodies in their serum, and to predict cardiovascular risk in the arsenic exposed population.
Effect of chronic arsenic exposure on liver was determined by liver function tests. Autoimmune status was measured by measuring ANA and anti-dsDNA in serum. Inflammatory cytokines associated with increased cardiovascular disease risk, IL6, IL8 and MCP-1 were determined.
Our results indicated that serum levels of bilirubin, alanine transaminase, aspartate transaminase, alkaline phosphatase and ANA were increased in the arsenic exposed population. Serum levels of IL6 and IL8 also increased in the arsenic exposed group.
Chronic arsenic exposure causes liver injury, increases the serum levels of autoimmune markers and imparts increased cardiovascular risk.
PMCID: PMC3441389  PMID: 22883023
Arsenic; Antinuclear antibody; Liver function tests; Cytokines
4.  Polymorphisms in the TNF-α and IL10 Gene Promoters and Risk of Arsenic-Induced Skin Lesions and Other Nondermatological Health Effects 
Toxicological Sciences  2011;121(1):132-139.
In West Bengal, India, at present, more than 26 million people are exposed to arsenic through drinking water. Among them, only 15–20% manifest arsenic-induced noncancerous, precancerous, and cancerous skin lesions, indicating that genetic variants play important role in arsenic susceptibility. Chronic arsenic exposure has been associated with impairment of immune systems in the exposed individuals. Because cytokines are important immune mediators, alteration in expression of these gene products may lead to arsenic-specific disease manifestations. The aim of the present work was to investigate the association between the TNF-α−308G>A (rs1800629) and IL10 −3575T>A (rs1800890) polymorphisms and arsenic-induced dermatological and nondermatological health outcomes. A case-control study was conducted in West Bengal, India, involving 207 cases with arsenic-induced skin lesions and 190 controls without skin lesions having similar arsenic exposure. The polymorphisms were determined using conventional PCR-sequencing method. ELISA was done to determine the serum levels of the two cytokines tumor necrosis factor α (TNF-α) and interleukin 10 (IL10). Associations between the polymorphisms studied and nondermatological health effects in the study subjects were determined from our epidemiological survey data. Individuals with GA/AA (−308 TNF-α) and TA/AA (−3575 IL10) genotypes were at higher risk of developing arsenic-induced skin lesions, ocular, and respiratory diseases. Also the −308 TNF A allele corresponded to a higher production of TNF-α, and −3575 IL10 A allele corresponded to a lower production of IL10. Thus, the polymorphisms studied impart significant risk toward development of arsenic-induced dermatological and nondermatological health effects in the chronically exposed population of West Bengal, India.
PMCID: PMC3115675  PMID: 21357384
arsenic; IL10; polymorphisms; skin lesions; susceptibility; TNF-α
5.  Functional Alteration of a Dimeric Insecticidal Lectin to a Monomeric Antifungal Protein Correlated to Its Oligomeric Status 
PLoS ONE  2011;6(4):e18593.
Allium sativum leaf agglutinin (ASAL) is a 25-kDa homodimeric, insecticidal, mannose binding lectin whose subunits are assembled by the C-terminal exchange process. An attempt was made to convert dimeric ASAL into a monomeric form to correlate the relevance of quaternary association of subunits and their functional specificity. Using SWISS-MODEL program a stable monomer was designed by altering five amino acid residues near the C-terminus of ASAL.
Methodology/Principal Findings
By introduction of 5 site-specific mutations (-DNSNN-), a β turn was incorporated between the 11th and 12th β strands of subunits of ASAL, resulting in a stable monomeric mutant ASAL (mASAL). mASAL was cloned and subsequently purified from a pMAL-c2X system. CD spectroscopic analysis confirmed the conservation of secondary structure in mASAL. Mannose binding assay confirmed that molecular mannose binds efficiently to both mASAL and ASAL. In contrast to ASAL, the hemagglutination activity of purified mASAL against rabbit erythrocytes was lost. An artificial diet bioassay of Lipaphis erysimi with mASAL displayed an insignificant level of insecticidal activity compared to ASAL. Fascinatingly, mASAL exhibited strong antifungal activity against the pathogenic fungi Fusarium oxysporum, Rhizoctonia solani and Alternaria brassicicola in a disc diffusion assay. A propidium iodide uptake assay suggested that the inhibitory activity of mASAL might be associated with the alteration of the membrane permeability of the fungus. Furthermore, a ligand blot assay of the membrane subproteome of R. solani with mASAL detected a glycoprotein receptor having interaction with mASAL.
Conversion of ASAL into a stable monomer resulted in antifungal activity. From an evolutionary aspect, these data implied that variable quaternary organization of lectins might be the outcome of defense-related adaptations to diverse situations in plants. Incorporation of mASAL into agronomically-important crops could be an alternative method to protect them from dramatic yield losses from pathogenic fungi in an effective manner.
PMCID: PMC3072408  PMID: 21490929
6.  PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment 
BMC Bioinformatics  2009;10(Suppl 9):S7.
The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions.
We present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our Physician Accessible Preclinical Analytics Application (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action.
PAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.
PMCID: PMC2745694  PMID: 19761577
7.  Identifying combinatorial regulation of transcription factors and binding motifs 
Genome Biology  2004;5(8):R56.
A novel method that integrates chromatin immunoprecipitation data with microarray expression data and combinatorial TF-motif analysis was used to systematically identify combinations of transcription factors and of motifs and to reconstruct a new combinatorial regulatory map of the yeast cell cycle.
Combinatorial interaction of transcription factors (TFs) is important for gene regulation. Although various genomic datasets are relevant to this issue, each dataset provides relatively weak evidence on its own. Developing methods that can integrate different sequence, expression and localization data have become important.
Here we use a novel method that integrates chromatin immunoprecipitation (ChIP) data with microarray expression data and with combinatorial TF-motif analysis. We systematically identify combinations of transcription factors and of motifs. The various combinations of TFs involved multiple binding mechanisms. We reconstruct a new combinatorial regulatory map of the yeast cell cycle in which cell-cycle regulation can be drawn as a chain of extended TF modules. We find that the pairwise combination of a TF for an early cell-cycle phase and a TF for a later phase is often used to control gene expression at intermediate times. Thus the number of distinct times of gene expression is greater than the number of transcription factors. We also see that some TF modules control branch points (cell-cycle entry and exit), and in the presence of appropriate signals they can allow progress along alternative pathways.
Combining different data sources can increase statistical power as demonstrated by detecting TF interactions and composite TF-binding motifs. The original picture of a chain of simple cell-cycle regulators can be extended to a chain of composite regulatory modules: different modules may share a common TF component in the same pathway or a TF component cross-talking to other pathways.
PMCID: PMC507881  PMID: 15287978
8.  Identifying cooperativity among transcription factors controlling the cell cycle in yeast 
Nucleic Acids Research  2003;31(23):7024-7031.
Transcription regulation in eukaryotes is known to occur through the coordinated action of multiple transcription factors (TFs). Recently, a few genome-wide transcription studies have begun to explore the combinatorial nature of TF interactions. We propose a novel approach that reveals how multiple TFs cooperate to regulate transcription in the yeast cell cycle. Our method integrates genome-wide gene expression data and chromatin immunoprecipitation (ChIP-chip) data to discover more biologically relevant synergistic interactions between different TFs and their target genes than previous studies. Given any pair of TFs A and B, we define a novel measure of cooperativity between the two TFs based on the expression patterns of sets of target genes of only A, only B, and both A and B. If the cooperativity measure is significant then there is reason to postulate that the presence of both TFs is needed to influence gene expression. Our results indicate that many cooperative TFs that were previously characterized experimentally indeed have high values of cooperativity measures in our analysis. In addition, we propose several novel, experimentally testable predictions of cooperative TFs that play a role in the cell cycle and other biological processes. Many of them hold interesting clues for cross talk between the cell cycle and other processes including metabolism, stress response and pseudohyphal differentiation. Finally, we have created a web tool where researchers can explore the exhaustive list of cooperative TFs and survey the graphical representation of the target genes’ expression profiles. The interface includes a tool to dynamically draw a TF cooperativity network of 113 TFs with user-defined significance levels. This study is an example of how systematic combination of diverse data types along with new functional genomic approaches can provide a rigorous platform to map TF interactions more efficiently.
PMCID: PMC290262  PMID: 14627835

Results 1-8 (8)