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Lakshmi Dhevi N. Selvan

Bio: Lakshmi Dhevi N. Selvan is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Genome project & Genome. The author has an hindex of 13, co-authored 20 publications receiving 2372 citations. Previous affiliations of Lakshmi Dhevi N. Selvan include National Institute of Mental Health and Neurosciences.

Papers
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Journal ArticleDOI
29 May 2014-Nature
TL;DR: A draft map of the human proteome is presented using high-resolution Fourier-transform mass spectrometry to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-c coding RNAs and upstream open reading frames.
Abstract: The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

1,965 citations

Journal ArticleDOI
TL;DR: A compendium of potential biomarkers that can be systematically validated by the pancreatic cancer community is described.
Abstract: Akhilesh Pandey and colleagues describe a compendium of potential biomarkers that can be systematically validated by the pancreatic cancer community.

281 citations

Journal ArticleDOI
TL;DR: In this paper, a SILAC-based quantitative proteomic strategy was employed to systematically identify potential targets of miR-143, which is a miRNA involved in tumorigenesis in multiple types of cancer, smooth muscle cell fate and adipocyte differentiation.
Abstract: Although the targets of most miRNAs have not been experimentally identified, microRNAs (miRNAs) have begun to be extensively characterized in physiological, developmental and disease-related contexts in recent years. Thus far, mainly computational approaches have been employed to predict potential targets for the large majority of miRNAs. Although miRNAs exert a major influence on the efficiency of translation of their targets in animals, most studies describing experimental identification of miRNA target genes are based on detection of altered mRNA levels. miR-143 is a miRNA involved in tumorigenesis in multiple types of cancer, smooth muscle cell fate and adipocyte differentiation. Only a few miR-143 targets are experimentally verified, so we employed a SILAC-based quantitative proteomic strategy to systematically identify potential targets of miR-143. In total, we identified >1200 proteins from MiaPaCa2 pancreatic cancer cells, of which 93 proteins were downregulated >2-fold in miR-143 mimic transfected cells as compared to controls. Validation of 34 of these candidate targets in luciferase assays showed that 10 of them were likely direct targets of miR-143. Importantly, we also carried out gene expression profiling of the same cells and observed that the majority of the candidate targets identified by proteomics did not show a concomitant decrease in mRNA levels confirming that miRNAs affect the expression of most targets through translational inhibition. Our study clearly demonstrates that quantitative proteomic approaches are important and necessary for identifying miRNA targets.

66 citations

Journal ArticleDOI
TL;DR: The main objective of this database is to provide detailed information pertaining to genes and proteins involved in primary immunodeficiency diseases along with other relevant information about protein–protein interactions, mouse studies and microarray gene-expression profiles in various organs and cells of the immune system.
Abstract: Availability of a freely accessible, dynamic and integrated database for primary immunodeficiency diseases (PID) is important both for researchers as well as clinicians. To build a PID informational platform and also as a part of action to initiate a network of PID research in Asia, we have constructed a web-based compendium of molecular alterations in PID, named Resource of Asian Primary Immunodeficiency Diseases ( RAPID), which is available as a worldwide web resource at http://rapid.rcai.riken.jp/. It hosts information on sequence variations and expression at the mRNA and protein levels of all genes reported to be involved in PID patients. The main objective of this database is to provide detailed information pertaining to genes and proteins involved in primary immunodeficiency diseases along with other relevant information about protein-protein interactions, mouse studies and microarray gene-expression profiles in various organs and cells of the immune system. RAPID also hosts a tool, mutation viewer, to predict deleterious and novel mutations and also to obtain mutation-based 3D structures for PID genes. Thus, information contained in this database should help physicians and other biomedical investigators to further investigate the role of these molecules in PID.

44 citations


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Journal ArticleDOI
23 Jan 2015-Science
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

9,745 citations

Journal ArticleDOI
TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

6,201 citations

Journal ArticleDOI
TL;DR: The developments in PRIDE resources and related tools are summarized and a brief update on the resources under development 'PRIDE Cluster' and 'PRide Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available inPRIDE Archive are given.
Abstract: The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data Since the beginning of 2014, PRIDE Archive (http://wwwebiacuk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013 PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month) We outline some statistics on the current PRIDE Archive data contents We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive

3,375 citations

Journal ArticleDOI
TL;DR: The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.
Abstract: Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.

2,209 citations