GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes
Abstract High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Her...
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Veröffentlicht in: | Briefings in bioinformatics 2024-07, Vol.25 (5) |
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creator | Yousefi, Meisam See, Wayne Ren Aw-Yong, Kam Leng Lee, Wai Suet Yong, Cythia Lingli Fanusi, Felic Smith, Gavin J D Ooi, Eng Eong Li, Shang Ghosh, Sujoy Ooi, Yaw Shin |
description | Abstract
High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN ‘Rank Aggregation’, we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN’s ‘Rank Correlation’, where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN’s ‘Rank Uniqueness’, we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN. |
doi_str_mv | 10.1093/bib/bbae452 |
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High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN ‘Rank Aggregation’, we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN’s ‘Rank Correlation’, where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN’s ‘Rank Uniqueness’, we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.</description><identifier>ISSN: 1467-5463</identifier><identifier>ISSN: 1477-4054</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbae452</identifier><identifier>PMID: 39293806</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Cancer ; Computational Biology - methods ; Coronaviruses ; Correlation ; COVID-19 ; COVID-19 - genetics ; COVID-19 - virology ; CRISPR ; Datasets ; Encephalitis ; Genes ; Genetic screening ; Humans ; Meta-analysis ; Neoplasms - genetics ; Problem Solving Protocol ; Redundancy ; SARS-CoV-2 - genetics ; Severe acute respiratory syndrome coronavirus 2 ; Software ; Statistical analysis ; Uniqueness ; Vector-borne diseases ; Viral diseases</subject><ispartof>Briefings in bioinformatics, 2024-07, Vol.25 (5)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c329t-1f8f0005b28c1c509a1befa9a2315fc4783becaffa51a8ff90be77d48badbbf03</cites><orcidid>0000-0002-7346-803X ; 0000-0001-9014-1365</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11410378/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11410378/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,887,1586,1606,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39293806$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yousefi, Meisam</creatorcontrib><creatorcontrib>See, Wayne Ren</creatorcontrib><creatorcontrib>Aw-Yong, Kam Leng</creatorcontrib><creatorcontrib>Lee, Wai Suet</creatorcontrib><creatorcontrib>Yong, Cythia Lingli</creatorcontrib><creatorcontrib>Fanusi, Felic</creatorcontrib><creatorcontrib>Smith, Gavin J D</creatorcontrib><creatorcontrib>Ooi, Eng Eong</creatorcontrib><creatorcontrib>Li, Shang</creatorcontrib><creatorcontrib>Ghosh, Sujoy</creatorcontrib><creatorcontrib>Ooi, Yaw Shin</creatorcontrib><title>GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN ‘Rank Aggregation’, we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN’s ‘Rank Correlation’, where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN’s ‘Rank Uniqueness’, we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.</description><subject>Cancer</subject><subject>Computational Biology - methods</subject><subject>Coronaviruses</subject><subject>Correlation</subject><subject>COVID-19</subject><subject>COVID-19 - genetics</subject><subject>COVID-19 - virology</subject><subject>CRISPR</subject><subject>Datasets</subject><subject>Encephalitis</subject><subject>Genes</subject><subject>Genetic screening</subject><subject>Humans</subject><subject>Meta-analysis</subject><subject>Neoplasms - genetics</subject><subject>Problem Solving Protocol</subject><subject>Redundancy</subject><subject>SARS-CoV-2 - genetics</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Uniqueness</subject><subject>Vector-borne diseases</subject><subject>Viral diseases</subject><issn>1467-5463</issn><issn>1477-4054</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kU2LFDEQhoMo7jp68i4BQQS33aTTnyeRRVdhVRA9h0q6MmZNJ71JWpg_4W82w4yLevBUBfXkoSovIY85e8nZKM6VVedKATZtfYec8qbvq4a1zd193_VV23TihDxI6ZqxmvUDv09OxFiPYmDdKfl5iR4_wwf8SNGDcpio9Rm3EbIN_ozqMC8Qbdr34Cc6Y4YKPLhdsokGQ-fVZbs4pBH8d5zotviosyknmgO1E_psza54fEKf1nRGV29vVjzodIgRHeTjw_SQ3DPgEj461g35-vbNl4t31dWny_cXr68qLeoxV9wMhjHWqnrQXLdsBK7QwAi14K3RTT8IhRqMgZbDYMzIFPb91AwKJqUMExvy6uBdVjXjpMuWEZxcop0h7mQAK_-eePtNbsMPyXnDmSj-DXl-NMRQzklZzjZpdA48hjVJwVnXCz4OXUGf_oNehzWWP9xTXDRdXfO6UC8OlI4hpYjmdhvO5D5oWYKWx6AL_eTPA27Z38kW4NkBCOvyX9Mvizi1-A</recordid><startdate>20240725</startdate><enddate>20240725</enddate><creator>Yousefi, Meisam</creator><creator>See, Wayne Ren</creator><creator>Aw-Yong, Kam Leng</creator><creator>Lee, Wai Suet</creator><creator>Yong, Cythia Lingli</creator><creator>Fanusi, Felic</creator><creator>Smith, Gavin J D</creator><creator>Ooi, Eng Eong</creator><creator>Li, Shang</creator><creator>Ghosh, Sujoy</creator><creator>Ooi, Yaw Shin</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7346-803X</orcidid><orcidid>https://orcid.org/0000-0001-9014-1365</orcidid></search><sort><creationdate>20240725</creationdate><title>GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes</title><author>Yousefi, Meisam ; See, Wayne Ren ; Aw-Yong, Kam Leng ; Lee, Wai Suet ; Yong, Cythia Lingli ; Fanusi, Felic ; Smith, Gavin J D ; Ooi, Eng Eong ; Li, Shang ; Ghosh, Sujoy ; Ooi, Yaw Shin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c329t-1f8f0005b28c1c509a1befa9a2315fc4783becaffa51a8ff90be77d48badbbf03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cancer</topic><topic>Computational Biology - methods</topic><topic>Coronaviruses</topic><topic>Correlation</topic><topic>COVID-19</topic><topic>COVID-19 - genetics</topic><topic>COVID-19 - virology</topic><topic>CRISPR</topic><topic>Datasets</topic><topic>Encephalitis</topic><topic>Genes</topic><topic>Genetic screening</topic><topic>Humans</topic><topic>Meta-analysis</topic><topic>Neoplasms - genetics</topic><topic>Problem Solving Protocol</topic><topic>Redundancy</topic><topic>SARS-CoV-2 - genetics</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Uniqueness</topic><topic>Vector-borne diseases</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yousefi, Meisam</creatorcontrib><creatorcontrib>See, Wayne Ren</creatorcontrib><creatorcontrib>Aw-Yong, Kam Leng</creatorcontrib><creatorcontrib>Lee, Wai Suet</creatorcontrib><creatorcontrib>Yong, Cythia Lingli</creatorcontrib><creatorcontrib>Fanusi, Felic</creatorcontrib><creatorcontrib>Smith, Gavin J D</creatorcontrib><creatorcontrib>Ooi, Eng Eong</creatorcontrib><creatorcontrib>Li, Shang</creatorcontrib><creatorcontrib>Ghosh, Sujoy</creatorcontrib><creatorcontrib>Ooi, Yaw Shin</creatorcontrib><collection>Access via Oxford University Press (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yousefi, Meisam</au><au>See, Wayne Ren</au><au>Aw-Yong, Kam Leng</au><au>Lee, Wai Suet</au><au>Yong, Cythia Lingli</au><au>Fanusi, Felic</au><au>Smith, Gavin J D</au><au>Ooi, Eng Eong</au><au>Li, Shang</au><au>Ghosh, Sujoy</au><au>Ooi, Yaw Shin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2024-07-25</date><risdate>2024</risdate><volume>25</volume><issue>5</issue><issn>1467-5463</issn><issn>1477-4054</issn><eissn>1477-4054</eissn><abstract>Abstract
High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN ‘Rank Aggregation’, we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN’s ‘Rank Correlation’, where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN’s ‘Rank Uniqueness’, we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>39293806</pmid><doi>10.1093/bib/bbae452</doi><orcidid>https://orcid.org/0000-0002-7346-803X</orcidid><orcidid>https://orcid.org/0000-0001-9014-1365</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cancer Computational Biology - methods Coronaviruses Correlation COVID-19 COVID-19 - genetics COVID-19 - virology CRISPR Datasets Encephalitis Genes Genetic screening Humans Meta-analysis Neoplasms - genetics Problem Solving Protocol Redundancy SARS-CoV-2 - genetics Severe acute respiratory syndrome coronavirus 2 Software Statistical analysis Uniqueness Vector-borne diseases Viral diseases |
title | GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes |
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