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A novel community driven software for functional enrichment analysis of extracellular vesicles data.

Mohashin PathanShivakumar KeerthikumarDavid ChisangaRiccardo AlessandroChing-Seng AngPhilip AskenaseArsen O BatagovAlberto Benito-MartinGiovanni CamussiAled ClaytonFederica CollinoDolores Di VizioJuan Manuel Falcon-PerezPedro FonsecaPamali FonsekaSimona FontanaYong Song GhoAn HendrixEsther Nolte-'t HoenNunzio IraciKenneth KastaniegaardThomas KislingerJoanna KowalIgor V KurochkinTommaso LeonardiYaxuan LiangAlicia LlorenteTaral R LunavatSayantan MajiFrancesca MonteleoneAnders ØverbyeTheocharis PanaretakisTushar PatelHector PeinadoStefano PluchinoGoran RonquistFelix RoyoSusmita SahooCristiana SpinelliAllan StensballeClotilde ThéryMartijn J C van HerwijnenMarca WaubenJoanne L WeltonKening ZhaoSuresh Mathivanan
Published in: Journal of extracellular vesicles (2017)
Bioinformatics tools are imperative for the in depth analysis of heterogeneous high-throughput data. Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, such software tools may fail to capture "what the community needs in a tool". Here, we describe a novel community-driven approach to build a comprehensive functional enrichment analysis tool. Using the existing FunRich tool as a template, we invited researchers to request additional features and/or changes. Remarkably, with the enthusiastic participation of the community, we were able to implement 90% of the requested features. FunRich enables plugin for extracellular vesicles wherein users can download and analyse data from Vesiclepedia database. By involving researchers early through community needs software development, we believe that comprehensive analysis tools can be developed in various scientific disciplines.
Keyphrases
  • mental health
  • healthcare
  • data analysis
  • high throughput
  • electronic health record
  • big data
  • machine learning
  • mass spectrometry
  • optical coherence tomography
  • drug induced
  • deep learning