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

Author:

Shamsaei Behrouz1,Chojnacki Szymon1,Pilarczyk Marcin1,Najafabadi Mehdi1,Niu Wen1,Chen Chuming2ORCID,Ross Karen3,Matlock Andrea4,Muhlich Jeremy5,Chutipongtanate Somchai67,Zheng Jie8,Turner John9,Vidović Dušica9,Jaffe Jake10,MacCoss Michael11,Wu Cathy23,Pillai Ajay12ORCID,Ma’ayan Avi13ORCID,Schürer Stephan9ORCID,Kouril Michal14,Medvedovic Mario115ORCID,Meller Jarek11416ORCID

Affiliation:

1. Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA

2. Center for Bioinformatics & Computational Biology; University of Delaware, USA

3. Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, USA

4. Department of Biomedical Sciences, Cedars-Sinai Medical Center, USA

5. Department of Systems Biology, Harvard Medical School, USA

6. Department of Cancer Biology, University of Cincinnati College of Medicine, USA

7. Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand

8. Department of Genetics, University of Pennsylvania Perelman School of Medicine, USA

9. Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA

10. Broad Institute of MIT and Harvard & Inzen Therapeutics, USA

11. Department of Genome Sciences, University of Washington, USA

12. Human Genome Research Institute, National Institutes of Health, Bethesda, USA

13. Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, USA

14. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, USA

15. Department of Biomedical Informatics, University of Cincinnati College of Medicine, USA

16. Department of Electrical Engineering and Computer Science, University of Cincinnati, USA

Abstract

Abstract 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.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Genetics

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