Y. L. Ramachandra

Bio: Y. L. Ramachandra is an academic researcher from Kuvempu University. The author has contributed to research in topics: Terminalia chebula & Gene expression profiling. The author has an hindex of 14, co-authored 32 publications receiving 4118 citations.

Human Protein Reference Database—2009 update

Abstract: Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

NetPath: a public resource of curated signal transduction pathways

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.

An evaluation of human protein-protein interaction data in the public domain

Abstract: Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations. We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and <1,000 in the remaining databases. The number of literature citations for the PPIs included in the databases was 43,634 in HPRD, 11,480 in MINT, 10,331 in IntAct, 8,020 in BIND and <2,100 in the remaining databases. Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.

Human Proteinpedia enables sharing of human protein data.

Abstract: Proteomic technologies, such as yeast two-hybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories.

Human Proteinpedia: a unified discovery resource for proteomics research

Abstract: Sharing proteomic data with the biomedical community through a unified proteomic resource, especially in the context of individual proteins, is a challenging prospect. We have developed a community portal, designated as Human Proteinpedia (http://www.humanproteinpedia.org/), for sharing both unpublished and published human proteomic data through the use of a distributed annotation system designed specifically for this purpose. This system allows laboratories to contribute and maintain protein annotations, which are also mapped to the corresponding proteins through the Human Protein Reference Database (HPRD; http://www.hprd.org/). Thus, it is possible to visualize data pertaining to experimentally validated posttranslational modifications (PTMs), protein isoforms, protein–protein interactions (PPIs), tissue expression, expression in cell lines, subcellular localization and enzyme substrates in the context of individual proteins. With enthusiastic participation of the proteomics community, the past 15 months have witnessed data contributions from more than 75 labs around the world including 2710 distinct experiments, >1.9 million peptides, >4.8 million MS/MS spectra, 150 368 protein expression annotations, 17 410 PTMs, 34 624 PPIs and 2906 subcellular localization annotations. Human Proteinpedia should serve as an integrated platform to store, integrate and disseminate such proteomic data and is inching towards evolving into a unified human proteomics resource.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrated genomic analyses of ovarian carcinoma

Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

Integrated genomic analyses of ovarian carcinoma

Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients’ lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool

Abstract: System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr .

Network Medicine: A Network-Based Approach to Human Disease

Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.