Osho Rawal, Berk Turhan, Irene Font Peradejordi, Shreya Chandrasekar, Selim Kalayci, Jeffrey Johnson, Mehdi Bouhaddou, Zeynep H. Gumus
{"title":"PhosNetVis: a web-based tool for kinase enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data","authors":"Osho Rawal, Berk Turhan, Irene Font Peradejordi, Shreya Chandrasekar, Selim Kalayci, Jeffrey Johnson, Mehdi Bouhaddou, Zeynep H. Gumus","doi":"arxiv-2402.05016","DOIUrl":null,"url":null,"abstract":"Protein phosphorylation is a vital process in cellular signaling that\ninvolves the reversible modification of a protein (substrate) residue by\nanother protein (kinase). Advances in liquid chromatography-mass spectrometry\nhave enabled the rapid generation of massive protein phosphorylation datasets\nacross multiple conditions by many research groups. Researchers are then tasked\nwith inferring kinases responsible for changes in phosphorylation sites of each\nsubstrate. Despite the recent explosion of tools to infer kinase-substrate\ninteractions (KSIs) from such datasets, these are not optimized for the\ninteractive exploration of the resulting large and complex KSI networks\ntogether with significant phosphorylation sites and states. There are also no\ndedicated tools that streamline kinase inferences together with interactive\nvisualizations of the resulting networks. There is thus an unmet need for a\ntool that facilitates uster-intuitive analysis, interactive exploration,\nvisualization, and communication of datasets from phosphoproteomics\nexperiments. Here, we present PhosNetVis, a freely available web-based tool for\nresearchers of all computational skill levels to easily infer, generate and\ninteractively explore KSI networks in 2D or 3D by streamlining multiple\nphosphoproteomics data analysis steps within one single tool. PhostNetVis\nsignificantly lowers the barriers for researchers in rapidly generating\nhigh-quality visualizations to translate their rich phosphoproteomics datasets\ninto biological and clinical insights.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"96 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.05016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Protein phosphorylation is a vital process in cellular signaling that
involves the reversible modification of a protein (substrate) residue by
another protein (kinase). Advances in liquid chromatography-mass spectrometry
have enabled the rapid generation of massive protein phosphorylation datasets
across multiple conditions by many research groups. Researchers are then tasked
with inferring kinases responsible for changes in phosphorylation sites of each
substrate. Despite the recent explosion of tools to infer kinase-substrate
interactions (KSIs) from such datasets, these are not optimized for the
interactive exploration of the resulting large and complex KSI networks
together with significant phosphorylation sites and states. There are also no
dedicated tools that streamline kinase inferences together with interactive
visualizations of the resulting networks. There is thus an unmet need for a
tool that facilitates uster-intuitive analysis, interactive exploration,
visualization, and communication of datasets from phosphoproteomics
experiments. Here, we present PhosNetVis, a freely available web-based tool for
researchers of all computational skill levels to easily infer, generate and
interactively explore KSI networks in 2D or 3D by streamlining multiple
phosphoproteomics data analysis steps within one single tool. PhostNetVis
significantly lowers the barriers for researchers in rapidly generating
high-quality visualizations to translate their rich phosphoproteomics datasets
into biological and clinical insights.