Publication:
piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Issued Date

2020-07-02

Resource Type

Article

ISSN

13624962

DOI

10.1093/nar/gkaa436

Other identifier(s)

2-s2.0-85087321323

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

Nucleic acids research. Vol.48, No.W1 (2020), W85-W93

Suggested Citation

Behrouz Shamsaei, Szymon Chojnacki, Marcin Pilarczyk, Mehdi Najafabadi, Wen Niu, Chuming Chen, Karen Ross, Andrea Matlock, Jeremy Muhlich, Somchai Chutipongtanate, Jie Zheng, John Turner, Dušica Vidović, Jake Jaffe, Michael MacCoss, Cathy Wu, Ajay Pillai, Avi Ma'ayan, Stephan Schürer, Michal Kouril, Mario Medvedovic, Jarek Meller piNET: a versatile web platform for downstream analysis and visualization of proteomics data. Nucleic acids research. Vol.48, No.W1 (2020), W85-W93. doi:10.1093/nar/gkaa436 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/57703

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Title

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Author(s)

Behrouz Shamsaei
 Szymon Chojnacki
 Marcin Pilarczyk
 Mehdi Najafabadi
 Wen Niu
 Chuming Chen
 Karen Ross
 Andrea Matlock
 Jeremy Muhlich
 Somchai Chutipongtanate
 Jie Zheng
 John Turner
 Dušica Vidović
 Jake Jaffe
 Michael MacCoss
 Cathy Wu
 Ajay Pillai
 Avi Ma'ayan
 Stephan Schürer
 Michal Kouril
 Mario Medvedovic
 Jarek Meller

Other Contributor(s)

Cincinnati Children's Hospital Medical Center
 University of Cincinnati
 University of Delaware
 Georgetown University Medical Center
 University of Cincinnati College of Medicine
 Cedars-Sinai Medical Center
 Icahn School of Medicine at Mount Sinai
 Mahidol University
 Sylvester Comprehensive Cancer Center
 National Institutes of Health (NIH)
 University of Pennsylvania Perelman School of Medicine
 Harvard Medical School
 Broad Institute
 University of Washington

Abstract

© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Keyword(s)

Biochemistry, Genetics and Molecular Biology

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/57703

Collections

Scopus 2020