ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling

Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents’ potency and experimental no...

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Published inBioinformatics (Oxford, England) Vol. 32; no. 2; pp. 260 - 267
Main Authors Yu, Jiyang, Silva, Jose, Califano, Andrea
Format Journal Article
LanguageEnglish
Published England Oxford University Press 15.01.2016
Subjects
Online AccessGet full text
ISSN1367-4803
1367-4811
1367-4811
DOI10.1093/bioinformatics/btv556

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Abstract Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents’ potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM). Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80–95% of the public datasets. Availability and implementation: R package and source code are available at: https://github.com/jyyu/ScreenBEAM. Contact: ac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.com Supplementary information:  Supplementary data are available at Bioinformatics online.
AbstractList Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library.MOTIVATIONFunctional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library.We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM).METHODWe use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM).Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets.RESULTSResults from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets.R package and source code are available at: https://github.com/jyyu/ScreenBEAM.AVAILABILITY AND IMPLEMENTATIONR package and source code are available at: https://github.com/jyyu/ScreenBEAM.ac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.comCONTACTac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.comSupplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.
Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM). Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets. R package and source code are available at: https://github.com/jyyu/ScreenBEAM. ac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.com Supplementary data are available at Bioinformatics online.
Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents’ potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM). Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80–95% of the public datasets. Availability and implementation: R package and source code are available at: https://github.com/jyyu/ScreenBEAM. Contact: ac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.com Supplementary information:  Supplementary data are available at Bioinformatics online.
Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents’ potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM). Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80–95% of the public datasets. Availability and implementation: R package and source code are available at: https://github.com/jyyu/ScreenBEAM. Contact: ac2248@columbia.edu, jose.silva@mssm.edu, yujiyang@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
Author Yu, Jiyang
Silva, Jose
Califano, Andrea
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Associate Editor: Janet Kelso
Present address: Department of Precision Medicine, Oncology Research Unit, Pfizer Inc., Pearl River, NY 10965, USA
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Snippet Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies...
Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for...
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StartPage 260
SubjectTerms Algorithms
Bayes Theorem
Clustered Regularly Interspaced Short Palindromic Repeats - genetics
Genomics - methods
High-Throughput Nucleotide Sequencing - methods
HL-60 Cells
Humans
Meta-Analysis as Topic
Models, Statistical
Original Papers
RNA Interference
Title ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling
URI https://www.ncbi.nlm.nih.gov/pubmed/26415723
https://www.proquest.com/docview/1760852599
https://pubmed.ncbi.nlm.nih.gov/PMC4907394
https://academic.oup.com/bioinformatics/article-pdf/32/2/260/49016388/bioinformatics_32_2_260.pdf
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