Integrated Bioinformatics Pipeline Research Articles

Simultaneous Detection of Gene Fusions and Base Mutations in Cancer Tissue Biopsies by Sequencing Dual Nucleic Acid Templates in Unified Reaction.

Targeted next-generation sequencing is a powerful method to comprehensively identify biomarkers for cancer. Starting material is currently either DNA or RNA for different variations, but splitting to 2 assays is burdensome and sometimes unpractical, causing delay or complete lack of detection of critical events, in particular, potent and targetable fusion events. An assay that analyzes both templates in a streamlined process is eagerly needed. We developed a single-tube, dual-template assay and an integrated bioinformatics pipeline for relevant variant calling. RNA was used for fusion detection, whereas DNA was used for single-nucleotide variations (SNVs) and insertion and deletions (indels). The reaction chemistry featured barcoded adaptor ligation, multiplexed linear amplification, and multiplexed PCR for noise reduction and novel fusion detection. An auxiliary quality control assay was also developed. In a 1000-sample lung tumor cohort, we identified all major SNV/indel hotspots and fusions, as well as MET exon 14 skipping and several novel or rare fusions. The occurrence frequencies were in line with previous reports and were verified by Sanger sequencing. One noteworthy fusion event was HLA-DRB1-MET that constituted the second intergenic MET fusion ever detected in lung cancer. This method should benefit not only a majority of patients carrying core actionable targets but also those with rare variations. Future extension of this assay to RNA expression and DNA copy number profiling of target genes such as programmed death-ligand 1 may provide additional biomarkers for immune checkpoint therapies.

Abstract 2483: An integrated bioinformatics pipeline for profiling cancer-immune interaction from whole exome sequencing of pembrolizumab clinical samples

Abstract Purpose We built an integrated analytic pipeline to robustly and comprehensively profile molecular features of pembrolizumab-treated tumors using whole exome sequencing (WES) data from clinical trials across different indications. Experimental design We implemented a computational framework for analysis of WES data generated by different sequencing vendors for 1467 samples from 8 major tumor types. Reads were first processed by BWA-MEM, Picard, and GATK (v2) to generate analysis-ready BAM files. Quality controls (QC) before downstream analysis included Y/X reads ratio for matched normal sample as patient sex prediction and tumor/normal concordance and contamination estimation by Conpair. Key molecular features were evaluated, including tumor mutation burden (TMB) by MuTect (v1) and VEP; HLA-I typing by OptiType; neoantigen burden by NetMHC (v3.4); mutation signature by deconstructSigs; allele-specific copy number by VarScan2 and Sequenza; clonality by PyClone; presence of oncogenic viruses (eg, EBV, HBV, HPV); and LOH score indicating homologous recombination deficiency (HRD-LOH). Results Concordance rate between predicted and clinical sex was 1446/1464 (98.8%), and 1420/1467 (96.8%) samples passed tumor-normal alignment QC. Highest TMB (median [range]) was detected in melanoma (245 [2-6246]) and urothelial carcinoma (UC) (124 [4-1579]), with lowest TMB in metastatic castration-resistant prostate cancer (52 [1-6143]) and ovarian cancer (OvCa) (49.5 [8-272]). The correlation of median TMB across 7 cancer types in our data and TCGA was Spearman R = 0.957. Within indications, there was no difference in TMB distribution for sequencing data originating from different sequencing vendors and TCGA data, which demonstrated concordance across data sets and robust TMB calling by our integrated pipeline. Neoantigen burden strongly correlated with TMB (N = 1420; Spearman R = 0.890). HPV was detected in 20/129 (15.5%) head and neck squamous cell carcinomas (HNSCC) and 4/6 (66.7%) anal cancers; EBV was detected in 8/129 (6.2%) HNSCC and 18/318 (5.7%) gastric cancers (GC). The dominant mutation signatures by disease included APOBEC for UC (135/236) and HNSCC (22/122), alcohol for HCC (19/35), HRD for OvCa (12/64) and triple-negative breast cancer (51/175), UV exposure for melanoma (145/176), and dMMR for GC (84/287) and CRPC (16/155). Samples with deleterious BRCA mutations showed significantly higher HRD-LOH score (N = 1420; AUROC = 0.61 [95% CI, 0.53-0.69]) and HRD mutational signature (N = 1316; DOR = 6.4 [95% CI, 3.7-11.1]). Conclusion We assembled heterogeneous computational modules into an integrated pipeline to reliably profile diverse molecular features from WES data of nearly 1500 clinical samples across different tumor types. These data serve as a foundation for translational research efforts supporting pembrolizumab development. Citation Format: Xiaoqiao Liu, Xinwei Sher, Hongchao Lu, Jun Zhuang, Weilong Zhao, Andrew Albright, Cinthia Umemoto, Christen Wudarski, Maureen Lane, Mark Ayers, Andrea L. Webber, Sandra C. Souza, Ping Qiu, Diane Levitan, Jennifer Cho, Deepti Aurora-Garg, Matthew Marton, Alexandra Snyder, Michael Morrissey, Andrey Loboda, Ronghua Chen, Razvan Cristescu. An integrated bioinformatics pipeline for profiling cancer-immune interaction from whole exome sequencing of pembrolizumab clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2483.

An integrative bioinformatics pipeline to demonstrate the alteration of the interaction between the ALDH2*2 allele with NAD+ and Disulfiram.

Alcohol use disorder (AUD) is a multifactorial psychiatric behavior disorder. Disulfiram is the first approved drug by the Food and Drug Administration for alcohol-dependent patients, which targets the ALDH2 enzyme. Several genes are known to be involved in alcohol metabolism; mutations in any of these genes are known to be associated with AUD. The E504K mutation in the ALDH2 of the precursor protein or the E487K of the mature protein (E504K/E487K; ALDH2*2 allele) is carried by approximately 8% of the world population. In this study, we aimed to test the known inactive allele ALDH2*2, to validate the use of our extensive computational pipeline (in silico tools, molecular modeling, and molecular docking) for testing the interaction between the ALDH2*2 allele, NAD+, and Disulfiram. In silico predictions showed that the E504K variant of ALDH2 to be pathogenic and destabilizing with the maximum number of prediction in silico tools. Consequently, we studied the effect of this mutation mainly on the interaction between NAD+ -E504K and Disulfiram-E504K complexes using molecular docking technique, and molecular dynamics (MD) analysis. From the molecular docking analysis with NAD+ , we observed that the interaction affinity of the NAD+ decreases with the impact of E504K variant. On the other hand, the drug Disulfiram showed similar interaction in both the native and mutant ALDH2 proteins. Further, the comprehensive MD analysis predicted that the E504K destabilizes the protein and influences the NAD+ and Disulfiram interactions. Our findings reveal that the interaction of NAD+ to the protein is disturbed by the E504K/E487K variant whereas the drug Disulfiram has a similar effect as both native ALDH2 and ALDH2 bearing E504K/E487K variant. This study provides a platform to understand the effect of E504K/E487K on the molecular interaction with NAD+ and Disulfiram.

A Platform of Synthetic Lethal Gene Interaction Networks Reveals that the GNAQ Uveal Melanoma Oncogene Controls the Hippo Pathway through FAK

Activating mutations in GNAQ/GNA11, encoding Gαq G proteins, are initiating oncogenic events in uveal melanoma (UM). However, there are no effective therapies for UM. Using an integrated bioinformatics pipeline, we found that PTK2, encoding focal adhesion kinase (FAK), represents a candidate synthetic lethal gene with GNAQ activation. We show that Gαq activates FAK through TRIO-RhoA non-canonical Gαq-signaling, and genetic ablation or pharmacological inhibition of FAK inhibits UM growth. Analysis of the FAK-regulated transcriptome demonstrated that GNAQ stimulates YAP through FAK. Dissection of the underlying mechanism revealed that FAK regulates YAP by tyrosine phosphorylation of MOB1, inhibiting core Hippo signaling. Our findings establish FAK as a potential therapeutic target for UM and other Gαq-driven pathophysiologies that involve unrestrained YAP function.

A merged lung cancer transcriptome dataset for clinical predictive modeling

The Gene Expression Omnibus (GEO) database is an excellent public source of whole transcriptomic profiles of multiple cancers. The main challenge is the limited accessibility of such large-scale genomic data to people without a background in bioinformatics or computer science. This presents difficulties in data analysis, sharing and visualization. Here, we present an integrated bioinformatics pipeline and a normalized dataset that has been preprocessed using a robust statistical methodology; allowing others to perform large-scale meta-analysis, without having to conduct time-consuming data mining and statistical correction. Comprising 1,118 patient-derived samples, the normalized dataset includes primary non-small cell lung cancer (NSCLC) tumors and paired normal lung tissues from ten independent GEO datasets, facilitating differential expression analysis. The data has been merged, normalized, batch effect-corrected and filtered for genes with low variance via multiple open source R packages integrated into our workflow. Overall this dataset (with associated clinical metadata) better represents the diseased population and serves as a powerful tool for early predictive biomarker discovery.

McClintock: An Integrated Pipeline for Detecting Transposable Element Insertions in Whole-Genome Shotgun Sequencing Data.

Transposable element (TE) insertions are among the most challenging types of variants to detect in genomic data because of their repetitive nature and complex mechanisms of replication . Nevertheless, the recent availability of large resequencing data sets has spurred the development of many new methods to detect TE insertions in whole-genome shotgun sequences. Here we report an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae. We find substantial variation among McClintock component methods in their ability to detect nonreference TEs in the yeast genome, but show that nonreference TEs at nearly all biologically realistic locations can be detected in simulated data by combining multiple methods that use split-read and read-pair evidence. In general, our results reveal that split-read methods detect fewer nonreference TE insertions than read-pair methods, but generally have much higher positional accuracy. Analysis of a large sample of real yeast genomes reveals that most McClintock component methods can recover known aspects of TE biology in yeast such as the transpositional activity status of families, target preferences, and target site duplication structure, albeit with varying levels of accuracy. Our work provides a general framework for integrating and analyzing results from multiple TE detection methods, as well as useful guidance for researchers studying TEs in yeast resequencing data.

Abstract A20: A systematic patient-derived xenograft based solution for pre-clinical biomarker discovery

Abstract There is an acute need for biomarkers at every phases of drug development from selecting preclinical models in pharmacogenomic studies to enrollment of patients in clinical trials. However, their identification remains extremely challenging due to the limited availability of clinical samples. In contrast, standard tumor models such as cell lines are available but are genetically relatively far from patient tumors. Use patient-derived xenografts (PDX) for anticancer agent-testing is of increasing interest due to their closer similarity to patient tumors compared to cell lines. Over the last 30 years, we have established a collection of 400 PDX covering more than 30 different cancer types. PDX models have been extensively characterized using the microarray or next-generation sequencing technologies for gene expression, copy number variations and whole-exome mutations. Biomarker research is now possible using these data in combination with drug response data from in vivo or in vitro 2D or 3D assays routinely performed on-site with large panels of 100-200 PDX. We present here a fully integrated bioinformatics pipeline dedicated to biomarker discovery in which the complete molecular profiles of our PDX have been systematically tested for association with drug sensitivity. To identify the biomarkers associated with drug response, several statistical tests have been performed. Drug response data were treated either as continuous variables using the Spearman or Wilcoxon tests, or as categorical variables (with two groups of responders and non-responders) using the LIMMA, t-test or Fisher exact test. Given that high throughput data frequently leads to large biomarker lists, we used specific filters to narrow down the list of candidates by defining thresholds based on corrected p-values, by intersecting results from different tests, or by integrating the tumor type into the statistical tests. Since sensitivity to anticancer agents is often multi-factorial, we also used integrative approaches that combined gene mutations, copy number loss and lack of gene expression for association with drug response. Finally, significant biomarkers were visualized using clustering heatmaps and enrichment GO/pathway approaches to get more insight in their biological function. Using a selection of several datasets of PDX drug responses to chemotherapeutics and targeted therapies (targeting RTK/RAS/RAF and PI3K/MTOR pathways and using specific compounds such as Vemurafenib, Erlotinib or Cetuximab), we demonstrate the efficacy of our approach to retrieve biomarkers of known clinical utility. Using these datasets we also could address the questions of model panel sizes, molecular data type and tumor subtype representation, and show how more accurate biomarkers can be validated using an independent dataset of samples. The development of strategies for testing anticancer agents using PDX in mouse clinical trials, or high throughput in vitro 2D, 3D screening approaches coupled to a more systematic biomarker research should significantly contribute to early biomarker identification and facilitate drug development. Citation Format: Bruno Zeitouni, Anne-Lise Peille, Zakia Amalou, Thomas Metz, Heinz-Herbert Fiebig, Vincent Vuaroqueaux. A systematic patient-derived xenograft based solution for pre-clinical biomarker discovery. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A20.

Genome-wide variant analysis of simplex autism families with an integrative clinical-bioinformatics pipeline.

Autism spectrum disorders (ASDs) are a group of developmental disabilities that affect social interaction and communication and are characterized by repetitive behaviors. There is now a large body of evidence that suggests a complex role of genetics in ASDs, in which many different loci are involved. Although many current population-scale genomic studies have been demonstrably fruitful, these studies generally focus on analyzing a limited part of the genome or use a limited set of bioinformatics tools. These limitations preclude the analysis of genome-wide perturbations that may contribute to the development and severity of ASD-related phenotypes. To overcome these limitations, we have developed and utilized an integrative clinical and bioinformatics pipeline for generating a more complete and reliable set of genomic variants for downstream analyses. Our study focuses on the analysis of three simplex autism families consisting of one affected child, unaffected parents, and one unaffected sibling. All members were clinically evaluated and widely phenotyped. Genotyping arrays and whole-genome sequencing were performed on each member, and the resulting sequencing data were analyzed using a variety of available bioinformatics tools. We searched for rare variants of putative functional impact that were found to be segregating according to de novo, autosomal recessive, X-linked, mitochondrial, and compound heterozygote transmission models. The resulting candidate variants included three small heterozygous copy-number variations (CNVs), a rare heterozygous de novo nonsense mutation in MYBBP1A located within exon 1, and a novel de novo missense variant in LAMB3. Our work demonstrates how more comprehensive analyses that include rich clinical data and whole-genome sequencing data can generate reliable results for use in downstream investigations.

An integrative approach for a network based meta-analysis of viral RNAi screens

BackgroundBig data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization.ResultsTo address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules.ConclusionsWe apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users.

An Integrative Analysis of Meningioma Tumors Reveals the Determinant Genes and Pathways of Malignant Transformation

Meningiomas are frequent central nervous system neoplasms, which despite their predominant benignity, show sporadically malignant behavior. Type 2 neurofibromatosis and polymorphisms in several genes have been associated with meningioma risk and are probably involved in its pathogenesis. Although GWAS studies have found loci related to meningioma risk, little is known about the factors determining malignant transformation. Thus, this study is aimed to identify the genomic and transcriptomic factors influencing evolution from benignity toward aggressive phenotypes. By applying an integrative bioinformatics pipeline combining public information on a wealth of biological layers of complexity (from genetic polymorphisms to protein interactions), this study identified a module of co-expressed genes highly correlated with tumor stage and statistically linked to several genomic regions (module Quantitative Trait Loci, mQTLs). Ontology analysis of the transcription hub genes identified microtubule-associated cell-cycle processes as key drivers of such network. mQTLs and single nucleotide polymorphisms associated with meningioma stage were replicated in an alternative meningioma cohort, and integration of these results with up-to-date scientific literature and several databases retrieved a list of genes and pathways with a potentially important role in meningioma malignancy. As a result, cytoskeleton and cell–cell adhesion pathways, calcium-channels and glutamate receptors, as well as oxidoreductase and endoplasmic reticulum-associated degradation pathways were found to be the most important and redundant findings associated to meningioma progression. This study presents an integrated view of the pathways involved in meningioma malignant conversion and paves the way for the development of new research lines that will improve our understanding of meningioma biology.

Sebnif: An Integrated Bioinformatics Pipeline for the Identification of Novel Large Intergenic Noncoding RNAs (lincRNAs) - Application in Human Skeletal Muscle Cells

Ab initio assembly of transcriptome sequencing data has been widely used to identify large intergenic non-coding RNAs (lincRNAs), a novel class of gene regulators involved in many biological processes. To differentiate real lincRNA transcripts from thousands of assembly artifacts, a series of filtering steps such as filters of transcript length, expression level and coding potential, need to be applied. However, an easy-to-use and publicly available bioinformatics pipeline that integrates these filters is not yet available. Hence, we implemented sebnif, an integrative bioinformatics pipeline to facilitate the discovery of bona fide novel lincRNAs that are suitable for further functional characterization. Specifically, sebnif is the only pipeline that implements an algorithm for identifying high-quality single-exonic lincRNAs that were often omitted in many studies. To demonstrate the usage of sebnif, we applied it on a real biological RNA-seq dataset from Human Skeletal Muscle Cells (HSkMC) and built a novel lincRNA catalog containing 917 highly reliable lincRNAs. Sebnif is available at http://sunlab.lihs.cuhk.edu.hk/sebnif/.

Identification of Cytotoxic T Lymphocyte Epitopes on Swine Viruses: Multi-Epitope Design for Universal T Cell Vaccine

Classical swine fever (CSF), foot-and-mouth disease (FMD) and porcine reproductive and respiratory syndrome (PRRS) are the primary diseases affecting the pig industry globally. Vaccine induced CD8+ T cell-mediated immune response might be long-lived and cross-serotype and thus deserve further attention. Although large panels of synthetic overlapping peptides spanning the entire length of the polyproteins of a virus facilitate the detection of cytotoxic T lymphocyte (CTL) epitopes, it is an exceedingly costly and cumbersome approach. Alternatively, computational predictions have been proven to be of satisfactory accuracy and are easily performed. Such a method enables the systematic identification of genome-wide CTL epitopes by incorporating epitope prediction tools in analyzing large numbers of viral sequences. In this study, we have implemented an integrated bioinformatics pipeline for the identification of CTL epitopes of swine viruses including the CSF virus (CSFV), FMD virus (FMDV) and PRRS virus (PRRSV) and assembled these epitopes on a web resource to facilitate vaccine design. Identification of epitopes for cross protections to different subtypes of virus are also reported in this study and may be useful for the development of a universal vaccine against such viral infections among the swine population. The CTL epitopes identified in this study have been evaluated in silico and possibly provide more and wider protection in compared to traditional single-reference vaccine design. The web resource is free and open to all users through http://sb.nhri.org.tw/ICES.

An integrative bioinformatics pipeline for the genomewide identification of novel porcine microRNA genes

MicroRNA (miRNA) is a pivotal type of noncoding RNA gene in posttranscriptional gene regulation (David 2004). The majority of miRNAs in pig (Sus scrofa), an important domestic animal, remain unknown. From this perspective, we attempted the genomewide identification of novel porcine miRNAs. Here, we propose a novel integrative bioinformatics pipeline to identify conservative and nonconservative novel miRNAs. We used methods such as homology searching against known metazoan miRNAs, conservation filtering, and ab initio approaches. As a result, 222 new porcine miRNAs were identified and 14 more porcine miRNA gene families were discovered. miRNA:target pairs, 78,060, between 224 miRNAs and 4384 mRNAs have been predicted, and these miRNA targets were involved in a wide spectrum of regulatory functions and metabolic biological processes. An overall analysis of genome-scale gene locations and sequence characteristics was also conducted, and a detailed user manual of the pipeline is provided. Through this study, the number of identified porcine miRNAs increased, and an effective alternative strategy for genomewide miRNA identification was developed.

Gene expression profiling of peripheral blood mononuclear cells in the setting of peripheral arterial disease

BackgroundPeripheral arterial disease (PAD) is a relatively common manifestation of systemic atherosclerosis that leads to progressive narrowing of the lumen of leg arteries. Circulating monocytes are in contact with the arterial wall and can serve as reporters of vascular pathology in the setting of PAD. We performed gene expression analysis of peripheral blood mononuclear cells (PBMC) in patients with PAD and controls without PAD to identify differentially regulated genes.MethodsPAD was defined as an ankle brachial index (ABI) ≤0.9 (n = 19) while age and gender matched controls had an ABI > 1.0 (n = 18). Microarray analysis was performed using Affymetrix HG-U133 plus 2.0 gene chips and analyzed using GeneSpring GX 11.0. Gene expression data was normalized using Robust Multichip Analysis (RMA) normalization method, differential expression was defined as a fold change ≥1.5, followed by unpaired Mann-Whitney test (P < 0.05) and correction for multiple testing by Benjamini and Hochberg False Discovery Rate. Meta-analysis of differentially expressed genes was performed using an integrated bioinformatics pipeline with tools for enrichment analysis using Gene Ontology (GO) terms, pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular event enrichment using Reactome annotations and network analysis using Ingenuity Pathway Analysis suite. Extensive biocuration was also performed to understand the functional context of genes.ResultsWe identified 87 genes differentially expressed in the setting of PAD; 40 genes were upregulated and 47 genes were downregulated. We employed an integrated bioinformatics pipeline coupled with literature curation to characterize the functional coherence of differentially regulated genes.ConclusionNotably, upregulated genes mediate immune response, inflammation, apoptosis, stress response, phosphorylation, hemostasis, platelet activation and platelet aggregation. Downregulated genes included several genes from the zinc finger family that are involved in transcriptional regulation. These results provide insights into molecular mechanisms relevant to the pathophysiology of PAD.

Functional repertoire, molecular pathways and diseases associated with 3D domain swapping in the human proteome

Background3D domain swapping is a novel structural phenomenon observed in diverse set of protein structures in oligomeric conformations. A distinct structural feature, where structural segments in a protein dimer or higher oligomer were shared between two or more chains of a protein structure, characterizes 3D domain swapping. 3D domain swapping was observed as a key mediator of numerous functional mechanisms and play pathogenic role in various diseases including conformational diseases like amyloidosis, Alzheimer's disease, Parkinson's disease and prion diseases. We report the first study with a focus on identifying functional classes, pathways and diseases mediated by 3D domain swapping in the human proteome.MethodsWe used a panel of four enrichment tools with two different ontologies and two annotations database to derive biological and clinical relevant information associated with 3D domain swapping. Protein domain enrichment analysis followed by Gene Ontology (GO) term enrichment analysis revealed the functional repertoire of proteins involved in swapping. Pathway analysis using KEGG annotations revealed diverse pathway associations of human proteins involved in 3D domain swapping. Disease Ontology was used to find statistically significant associations with proteins in swapped conformation and various disease categories (P-value < 0.05).ResultsWe report meta-analysis results of a literature-curated dataset of human gene products involved in 3D domain swapping and discuss new insights about the functional repertoire, pathway associations and disease implications of proteins involved in 3D domain swapping.ConclusionsOur integrated bioinformatics pipeline comprising of four different enrichment tools, two ontologies and two annotations revealed new insights into the functional and disease correlations with 3D domain swapping. GO term enrichment were used to infer terms associated with three different GO categories. Protein domain enrichment was used to identify conserved domains enriched in swapped proteins. Pathway enrichment analysis using KEGG annotations revealed that proteins with swapped conformations are present in all six classes of KEGG BRITE hierarchy and significantly enriched KEGG pathways were observed in five classes. Five major classes of disease were found to be associated with 3D domain swapping using functional disease ontology based enrichment analysis. Five classes of human diseases: cancer, diseases of the respiratory or pulmonary system, degenerative diseases of the central nervous system, vascular disease and encephalitis were found to be significant. In conclusion, our study shows that bioinformatics based analytical approaches using curated data can enhance the understanding of functional and disease implications of 3D domain swapping.

Integrated Transcriptome and Binding Sites Analysis Implicates E2F in the Regulation of Self-Renewal in Human Pluripotent Stem Cells

Rapid cellular growth and multiplication, limited replicative senescence, calibrated sensitivity to apoptosis, and a capacity to differentiate into almost any cell type are major properties that underline the self-renewal capabilities of human pluripotent stem cells (hPSCs). We developed an integrated bioinformatics pipeline to understand the gene regulation and functions involved in maintaining such self-renewal properties of hPSCs compared to matched fibroblasts. An initial genome-wide screening of transcription factor activity using in silico binding-site and gene expression microarray data newly identified E2F as one of major candidate factors, revealing their significant regulation of the transcriptome. This is underscored by an elevated level of its transcription factor activity and expression in all tested pluripotent stem cell lines. Subsequent analysis of functional gene groups demonstrated the importance of the TFs to self-renewal in the pluripotency-coupled context; E2F directly targets the global signaling (e.g. self-renewal associated WNT and FGF pathways) and metabolic network (e.g. energy generation pathways, molecular transports and fatty acid metabolism) to promote its canonical functions that are driving the self-renewal of hPSCs. In addition, we proposed a core self-renewal module of regulatory interplay between E2F and, WNT and FGF pathways in these cells. Thus, we conclude that E2F plays a significant role in influencing the self-renewal capabilities of hPSCs.