Literature DB >> 28717418

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

Mohashin Pathan1, Shivakumar Keerthikumar1, David Chisanga2, Riccardo Alessandro3, Ching-Seng Ang4, Philip Askenase5, Arsen O Batagov6, Alberto Benito-Martin7, Giovanni Camussi8, Aled Clayton9, Federica Collino8,10, Dolores Di Vizio11, Juan Manuel Falcon-Perez12, Pedro Fonseca13, Pamali Fonseka1, Simona Fontana3, Yong Song Gho14, An Hendrix15, Esther Nolte-'t Hoen16, Nunzio Iraci17,18, Kenneth Kastaniegaard19, Thomas Kislinger20, Joanna Kowal21, Igor V Kurochkin6, Tommaso Leonardi18,22, Yaxuan Liang23, Alicia Llorente24,25, Taral R Lunavat26, Sayantan Maji27, Francesca Monteleone3, Anders Øverbye24,25, Theocharis Panaretakis13, Tushar Patel27, Héctor Peinado7,28, Stefano Pluchino18, Simona Principe20, Goran Ronquist29, Felix Royo12, Susmita Sahoo23, Cristiana Spinelli11, Allan Stensballe19, Clotilde Théry21, Martijn J C van Herwijnen16, Marca Wauben16, Joanne L Welton30, Kening Zhao1, Suresh Mathivanan1.   

Abstract

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.

Entities:  

Keywords:  Extracellular vesicles; FunRich; bioinformatics

Year:  2017        PMID: 28717418      PMCID: PMC5505018          DOI: 10.1080/20013078.2017.1321455

Source DB:  PubMed          Journal:  J Extracell Vesicles        ISSN: 2001-3078


Advances in high-throughput techniques including next generation sequencing, RNA sequencing and proteomics have spurred enormous volume of data [1-3]. Currently, it is amenable to characterise the genome, transcriptome, metabolome and proteome of an organism in a robust manner. These technological developments reduced the amount of sample material, shortened the time to collect raw data and substantially decreased the associated costs [4]. Hence, large scale approaches are now accessible by many research laboratories. To harness the true potential of these heterogeneous high-throughput data, software/bioinformatics tools have become indispensable resources for the ensuing analysis [5]. To match the unprecedented growth in data generation, robust software analysis tools are constantly developed by academic and commercial entities [4]. 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, the software tools fail to capture “what the community wants in a tool”. A “community needs software” may overcome these hurdles and aid in the development of a comprehensive data analysis tool. Here, we report a novel community needs software initiative in the context of functional or gene set enrichment analysis. To achieve this, we initially reached out to the scientific community through editorial [6], conference participations, social networking sites and communicated with researchers in the OMICS community via e-mail. FunRich, an open access standalone functional enrichment analysis tool [7], was used as a template for this purpose. By various means of communication, we invited the researchers to request for additional features/changes in FunRich software through the online forum (http://www.funrich.org/forum). We had enthusiastic participation from many researchers who requested for additional features/changes in the existing software. By September 2016, we had 54 unique requests/changes from users worldwide (Supplementary Table 1). The features were prioritised based on the number of users per request and were implemented in FunRich tool over the last 18 months. Remarkably, 90% of the requested features have been implemented in the new version of FunRich (Version 3). The updated version of FunRich is now freely available for download (http://www.funrich.org/download) both for academic and commercial users. To gain biological insights, researchers often rely on functional enrichment analysis of large scale data from high-throughput experiments to identify overrepresented classes. Using FunRich, users can perform functional enrichment analysis with minimal or no support from computational and database experts for more than 13,320 species. The database is integrated from heterogeneous genomic and proteomic resources (>6.8 million annotations). The background database in any analysis tool is critical for the analysis and needs to be constantly updated [8]. However, the currently existing functional enrichment analysis tools do not allow the users to control the databases nor to update them in real time [8]. Using the forum, one of the request from the community pertained to “user controlled databases” and “regular update of background databases”. To address this, FunRich now uniquely allows the users to update the background database for 13,320 species from UniProt, Gene Ontology and Reactome in real time (Figure 1). Additionally, the users can build custom databases with tab delimited files and perform the enrichment analysis irrespective of the organism and the type of dataset (e.g. metabolomics). Hence, these database options allows for longer sustainability of FunRich as a tool to perform functional enrichment analysis.
Figure 1.

Features available in FunRich. FunRich is a free standalone functional enrichment analysis tool. Users can obtain customisable graphs, charts, interaction networks and heatmaps. All features of these images including color and font is editable and thus allows for quick representation of analysis. In spite of its ease of use, the images produced are of publication quality and can be directly imported into manuscripts. In addition, users have multiple background database options for more than 13,320 species. One of the features requested by the community is the option to update the background databases in real time. FunRich now allows the users to download data from UniProt, Reactome and Gene Ontology databases in real time. Furthermore, users can perform analyses in the context of biological pathways, gene ontology categories, protein domains, site of expression, cancer signatures, transcription factors, clinical phenotypes, extracellular vesicles, miRNA enrichment, protein interaction network and cross database accession conversion. The custom database option allows users to use any data type for any species thereby allowing for flexibility.

Features available in FunRich. FunRich is a free standalone functional enrichment analysis tool. Users can obtain customisable graphs, charts, interaction networks and heatmaps. All features of these images including color and font is editable and thus allows for quick representation of analysis. In spite of its ease of use, the images produced are of publication quality and can be directly imported into manuscripts. In addition, users have multiple background database options for more than 13,320 species. One of the features requested by the community is the option to update the background databases in real time. FunRich now allows the users to download data from UniProt, Reactome and Gene Ontology databases in real time. Furthermore, users can perform analyses in the context of biological pathways, gene ontology categories, protein domains, site of expression, cancer signatures, transcription factors, clinical phenotypes, extracellular vesicles, miRNA enrichment, protein interaction network and cross database accession conversion. The custom database option allows users to use any data type for any species thereby allowing for flexibility. Other popular requests that have been implemented in FunRich include miRNA enrichment analysis (requested by most users – Supplementary Table 1), customisable heat maps, plugin to analyse extracellular vesicle datasets, comparison of oncogenes using COSMIC database and customisable colour for all the publication quality graphs. In miRNA enrichment analysis, users can submit a list of miRNA and identify biological pathways that may be perturbed. Gene set enrichment analysis is normally performed with the number of input genes/proteins and the quantitative data is often ignored. In FunRich, users can upload quantitative data and perform enrichment analysis for gene/protein expression values. For instance, total mRNA/protein abundance of genes involved in Wnt signalling pathway is compared between datasets in addition to number of genes. The quantitative data can also be utilised to generate customisable heat maps. Furthermore, users have complete control on all the graphs where the text and colour can be customised. Based on popular requests, FunRich now allows users to automatically download data from Vesiclepedia, an online compendium that hosts RNA and protein data pertaining to extracellular vesicles including exosomes [9]. The input datasets can be compared with filtered Vesiclepedia data either through enrichment analysis or through Venn diagrams. In addition users can customise the data that can be downloaded from Vesiclepedia by using filters based on extracellular vesicles subtype, sample type, isolation method, cargo type and identification method. Overall, with the involvement of researchers in the early phase of software development, we have developed a comprehensive tool for functional enrichment analysis. As databases are not regularly updated in most of the functional enrichment analysis tools [8], the community requested features pertaining to automatic database update and custom database feature will allow for the continuous use of FunRich. Though we have completed 90% of the requests from researchers, we are constantly implementing the newer requests. We envision the development of a web-based version of FunRich and implementation of metabolomic analysis in the near future. With the advent of large volumes of data, it is critical to build such comprehensive software tools for data analysis. Based on this fruitful experience, we strongly encourage community driven software development for research purposes so as to build comprehensive software tools and to curtail software duplications. Click here for additional data file.
  9 in total

1.  Genomic analyses identify molecular subtypes of pancreatic cancer.

Authors:  Peter Bailey; David K Chang; Katia Nones; Amber L Johns; Ann-Marie Patch; Marie-Claude Gingras; David K Miller; Angelika N Christ; Tim J C Bruxner; Michael C Quinn; Craig Nourse; L Charles Murtaugh; Ivon Harliwong; Senel Idrisoglu; Suzanne Manning; Ehsan Nourbakhsh; Shivangi Wani; Lynn Fink; Oliver Holmes; Venessa Chin; Matthew J Anderson; Stephen Kazakoff; Conrad Leonard; Felicity Newell; Nick Waddell; Scott Wood; Qinying Xu; Peter J Wilson; Nicole Cloonan; Karin S Kassahn; Darrin Taylor; Kelly Quek; Alan Robertson; Lorena Pantano; Laura Mincarelli; Luis N Sanchez; Lisa Evers; Jianmin Wu; Mark Pinese; Mark J Cowley; Marc D Jones; Emily K Colvin; Adnan M Nagrial; Emily S Humphrey; Lorraine A Chantrill; Amanda Mawson; Jeremy Humphris; Angela Chou; Marina Pajic; Christopher J Scarlett; Andreia V Pinho; Marc Giry-Laterriere; Ilse Rooman; Jaswinder S Samra; James G Kench; Jessica A Lovell; Neil D Merrett; Christopher W Toon; Krishna Epari; Nam Q Nguyen; Andrew Barbour; Nikolajs Zeps; Kim Moran-Jones; Nigel B Jamieson; Janet S Graham; Fraser Duthie; Karin Oien; Jane Hair; Robert Grützmann; Anirban Maitra; Christine A Iacobuzio-Donahue; Christopher L Wolfgang; Richard A Morgan; Rita T Lawlor; Vincenzo Corbo; Claudio Bassi; Borislav Rusev; Paola Capelli; Roberto Salvia; Giampaolo Tortora; Debabrata Mukhopadhyay; Gloria M Petersen; Donna M Munzy; William E Fisher; Saadia A Karim; James R Eshleman; Ralph H Hruban; Christian Pilarsky; Jennifer P Morton; Owen J Sansom; Aldo Scarpa; Elizabeth A Musgrove; Ulla-Maja Hagbo Bailey; Oliver Hofmann; Robert L Sutherland; David A Wheeler; Anthony J Gill; Richard A Gibbs; John V Pearson; Nicola Waddell; Andrew V Biankin; Sean M Grimmond
Journal:  Nature       Date:  2016-02-24       Impact factor: 49.962

2.  FunRich: An open access standalone functional enrichment and interaction network analysis tool.

Authors:  Mohashin Pathan; Shivakumar Keerthikumar; Ching-Seng Ang; Lahiru Gangoda; Camelia Y J Quek; Nicholas A Williamson; Dmitri Mouradov; Oliver M Sieber; Richard J Simpson; Agus Salim; Antony Bacic; Andrew F Hill; David A Stroud; Michael T Ryan; Johnson I Agbinya; John M Mariadason; Antony W Burgess; Suresh Mathivanan
Journal:  Proteomics       Date:  2015-06-17       Impact factor: 3.984

3.  FunRich proteomics software analysis, let the fun begin!

Authors:  Alberto Benito-Martin; Héctor Peinado
Journal:  Proteomics       Date:  2015-08       Impact factor: 3.984

4.  Impact of outdated gene annotations on pathway enrichment analysis.

Authors:  Lina Wadi; Mona Meyer; Joel Weiser; Lincoln D Stein; Jüri Reimand
Journal:  Nat Methods       Date:  2016-08-30       Impact factor: 28.547

5.  Proteogenomic characterization of human colon and rectal cancer.

Authors:  Bing Zhang; Jing Wang; Xiaojing Wang; Jing Zhu; Qi Liu; Zhiao Shi; Matthew C Chambers; Lisa J Zimmerman; Kent F Shaddox; Sangtae Kim; Sherri R Davies; Sean Wang; Pei Wang; Christopher R Kinsinger; Robert C Rivers; Henry Rodriguez; R Reid Townsend; Matthew J C Ellis; Steven A Carr; David L Tabb; Robert J Coffey; Robbert J C Slebos; Daniel C Liebler
Journal:  Nature       Date:  2014-07-20       Impact factor: 49.962

6.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

7.  Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation.

Authors:  Hina Kalra; Richard J Simpson; Hong Ji; Elena Aikawa; Peter Altevogt; Philip Askenase; Vincent C Bond; Francesc E Borràs; Xandra Breakefield; Vivian Budnik; Edit Buzas; Giovanni Camussi; Aled Clayton; Emanuele Cocucci; Juan M Falcon-Perez; Susanne Gabrielsson; Yong Song Gho; Dwijendra Gupta; H C Harsha; An Hendrix; Andrew F Hill; Jameel M Inal; Guido Jenster; Eva-Maria Krämer-Albers; Sai Kiang Lim; Alicia Llorente; Jan Lötvall; Antonio Marcilla; Lucia Mincheva-Nilsson; Irina Nazarenko; Rienk Nieuwland; Esther N M Nolte-'t Hoen; Akhilesh Pandey; Tushar Patel; Melissa G Piper; Stefano Pluchino; T S Keshava Prasad; Lawrence Rajendran; Graca Raposo; Michel Record; Gavin E Reid; Francisco Sánchez-Madrid; Raymond M Schiffelers; Pia Siljander; Allan Stensballe; Willem Stoorvogel; Douglas Taylor; Clotilde Thery; Hadi Valadi; Bas W M van Balkom; Jesús Vázquez; Michel Vidal; Marca H M Wauben; María Yáñez-Mó; Margot Zoeller; Suresh Mathivanan
Journal:  PLoS Biol       Date:  2012-12-18       Impact factor: 8.029

8.  Comparative analysis of the transcriptome across distant species.

Authors:  Mark B Gerstein; Joel Rozowsky; Koon-Kiu Yan; Daifeng Wang; Chao Cheng; James B Brown; Carrie A Davis; LaDeana Hillier; Cristina Sisu; Jingyi Jessica Li; Baikang Pei; Arif O Harmanci; Michael O Duff; Sarah Djebali; Roger P Alexander; Burak H Alver; Raymond Auerbach; Kimberly Bell; Peter J Bickel; Max E Boeck; Nathan P Boley; Benjamin W Booth; Lucy Cherbas; Peter Cherbas; Chao Di; Alex Dobin; Jorg Drenkow; Brent Ewing; Gang Fang; Megan Fastuca; Elise A Feingold; Adam Frankish; Guanjun Gao; Peter J Good; Roderic Guigó; Ann Hammonds; Jen Harrow; Roger A Hoskins; Cédric Howald; Long Hu; Haiyan Huang; Tim J P Hubbard; Chau Huynh; Sonali Jha; Dionna Kasper; Masaomi Kato; Thomas C Kaufman; Robert R Kitchen; Erik Ladewig; Julien Lagarde; Eric Lai; Jing Leng; Zhi Lu; Michael MacCoss; Gemma May; Rebecca McWhirter; Gennifer Merrihew; David M Miller; Ali Mortazavi; Rabi Murad; Brian Oliver; Sara Olson; Peter J Park; Michael J Pazin; Norbert Perrimon; Dmitri Pervouchine; Valerie Reinke; Alexandre Reymond; Garrett Robinson; Anastasia Samsonova; Gary I Saunders; Felix Schlesinger; Anurag Sethi; Frank J Slack; William C Spencer; Marcus H Stoiber; Pnina Strasbourger; Andrea Tanzer; Owen A Thompson; Kenneth H Wan; Guilin Wang; Huaien Wang; Kathie L Watkins; Jiayu Wen; Kejia Wen; Chenghai Xue; Li Yang; Kevin Yip; Chris Zaleski; Yan Zhang; Henry Zheng; Steven E Brenner; Brenton R Graveley; Susan E Celniker; Thomas R Gingeras; Robert Waterston
Journal:  Nature       Date:  2014-08-28       Impact factor: 49.962

9.  The real cost of sequencing: scaling computation to keep pace with data generation.

Authors:  Paul Muir; Shantao Li; Shaoke Lou; Daifeng Wang; Daniel J Spakowicz; Leonidas Salichos; Jing Zhang; George M Weinstock; Farren Isaacs; Joel Rozowsky; Mark Gerstein
Journal:  Genome Biol       Date:  2016-03-23       Impact factor: 13.583

  9 in total
  120 in total

1.  A comprehensive proteomics analysis of JC virus Agnoprotein-interacting proteins: Agnoprotein primarily targets the host proteins with coiled-coil motifs.

Authors:  A Sami Saribas; Prasun K Datta; Mahmut Safak
Journal:  Virology       Date:  2019-10-20       Impact factor: 3.616

2.  Senescence cell-associated extracellular vesicles serve as osteoarthritis disease and therapeutic markers.

Authors:  Ok Hee Jeon; David R Wilson; Cristina C Clement; Sona Rathod; Christopher Cherry; Bonita Powell; Zhenghong Lee; Ahmad M Khalil; Jordan J Green; Judith Campisi; Laura Santambrogio; Kenneth W Witwer; Jennifer H Elisseeff
Journal:  JCI Insight       Date:  2019-04-04

3.  Proteomic profiling of peritoneal dialysis effluent-derived extracellular vesicles: a longitudinal study.

Authors:  Laura Carreras-Planella; Jordi Soler-Majoral; Cristina Rubio-Esteve; Miriam Morón-Font; Marcella Franquesa; Jordi Bonal; Maria Isabel Troya-Saborido; Francesc E Borràs
Journal:  J Nephrol       Date:  2019-10-15       Impact factor: 3.902

4.  Reactivation of oncogenes involved in G1/S transcription and apoptosis pathways by low dose decitabine promotes HT29 human colon cancer cell growth in vitro.

Authors:  Xiaojie Wang; Pan Chi
Journal:  Am J Transl Res       Date:  2020-12-15       Impact factor: 4.060

5.  Cadmium exposure upregulates SNAIL through miR-30 repression in human lung epithelial cells.

Authors:  Vinay Singh Tanwar; Xiaoru Zhang; Lakshmanan Jagannathan; Cynthia C Jose; Suresh Cuddapah
Journal:  Toxicol Appl Pharmacol       Date:  2019-04-16       Impact factor: 4.219

6.  Transplantation of Cardiac Mesenchymal Stem Cell-Derived Exosomes for Angiogenesis.

Authors:  Chengwei Ju; Youngjun Li; Yan Shen; Yutao Liu; Jingwen Cai; Naifeng Liu; Gengshan Ma; Yaoliang Tang
Journal:  J Cardiovasc Transl Res       Date:  2018-10-01       Impact factor: 4.132

Review 7.  Exosomes and Their MicroRNA Cargo: New Players in Peripheral Nerve Regeneration.

Authors:  Liming Qing; Huanwen Chen; Juyu Tang; Xiaofeng Jia
Journal:  Neurorehabil Neural Repair       Date:  2018-09       Impact factor: 3.919

8.  A novel method of high-purity extracellular vesicle enrichment from microliter-scale human serum for proteomic analysis.

Authors:  Xiaohui Ji; Sisi Huang; Jie Zhang; Terri F Bruce; Zhijing Tan; Donglin Wang; Jianhui Zhu; R Kenneth Marcus; David M Lubman
Journal:  Electrophoresis       Date:  2021-02       Impact factor: 3.535

Review 9.  The role and potential application of extracellular vesicles in liver cancer.

Authors:  Xuewei Qi; Shuzhen Chen; Huisi He; Wen Wen; Hongyang Wang
Journal:  Sci China Life Sci       Date:  2021-04-09       Impact factor: 6.038

10.  Nuclear miR-30b-5p suppresses TFEB-mediated lysosomal biogenesis and autophagy.

Authors:  Huijie Guo; Mei Pu; Yusi Tai; Yuxiang Chen; Henglei Lu; Junwen Qiao; Guanghui Wang; Jing Chen; Xinming Qi; Ruimin Huang; Zhouteng Tao; Jin Ren
Journal:  Cell Death Differ       Date:  2020-08-06       Impact factor: 15.828

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