Author
Hariprasad Padhukasahasram
Bio: Hariprasad Padhukasahasram is an academic researcher. The author has contributed to research in topics: WikiPathways : Pathways for the people. The author has an hindex of 1, co-authored 1 publications receiving 420 citations.
Papers
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Johns Hopkins University1, Kuvempu University2, National Institutes of Health3, University of Iowa4, Johns Hopkins University School of Medicine5, Ludwig Institute for Cancer Research6, Osaka University7, Tokyo University of Science8, Wayne State University9, Memorial Sloan Kettering Cancer Center10, University of Toronto11
TL;DR: NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles.
Abstract: We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches.
467 citations
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Kyriaki Michailidou1, Kyriaki Michailidou2, Sara Lindström3, Sara Lindström4 +393 more•Institutions (127)
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.
1,014 citations
TL;DR: This protocol describes pathway enrichment analysis of gene lists from RNA-seq and other genomics experiments using g:Profiler, GSEA, Cytoscape and EnrichmentMap software, and describes innovative visualization techniques.
Abstract: Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.
958 citations
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.
806 citations
Memorial Sloan Kettering Cancer Center1, Bilkent University2, SRI International3, Université libre de Bruxelles4, Ontario Institute for Cancer Research5, New York University6, National Institutes of Health7, National Autonomous University of Mexico8, Boston University9, Cold Spring Harbor Laboratory10, Johns Hopkins University11, University of Toronto12, Rothamsted Research13, University of Rennes14, Cell Signaling Technology15, Broad Institute16, Food and Drug Administration17, Virginia Tech18, Oregon Health & Science University19, United States Environmental Protection Agency20, Argonne National Laboratory21, University of Connecticut22, Harvard University23, National Institute of Standards and Technology24, University of Cambridge25, National University of Ireland, Galway26, Konrad Lorenz Institute for Evolution and Cognition Research27, Maastricht University28, University of Auckland29, Syngenta30, Stanford University31, Yale University32, Loyola Marymount University33, St. John's University34, Columbia University35, SRA International36, Novartis37, University of Ottawa38, Vertex Pharmaceuticals39, Medical College of Wisconsin40, Gladstone Institutes41, Cornell University42, Takeda Pharmaceutical Company43, University of Chicago44, Total S.A.45, Kyoto University46, California Institute of Technology47
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Abstract: Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
673 citations
TL;DR: iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species.
Abstract: RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. Combining comprehensive analytic functionalities with massive annotation databases, iDEP (
http://ge-lab.org/idep/
) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.
618 citations