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)
Hauptverfasser: 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
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container_issue 5
container_start_page
container_title Briefings in bioinformatics
container_volume 25
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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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. 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source MEDLINE; Access via Oxford University Press (Open Access Collection); Oxford University Press Journals All Titles (1996-Current); PubMed Central
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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