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A biologically informed method for detecting rare variant associations

BACKGROUND: BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary...

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Autores principales: Moore, Carrie Colleen Buchanan, Basile, Anna Okula, Wallace, John Robert, Frase, Alex Thomas, Ritchie, Marylyn DeRiggi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006419/
https://www.ncbi.nlm.nih.gov/pubmed/27582876
http://dx.doi.org/10.1186/s13040-016-0107-3
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author Moore, Carrie Colleen Buchanan
Basile, Anna Okula
Wallace, John Robert
Frase, Alex Thomas
Ritchie, Marylyn DeRiggi
author_facet Moore, Carrie Colleen Buchanan
Basile, Anna Okula
Wallace, John Robert
Frase, Alex Thomas
Ritchie, Marylyn DeRiggi
author_sort Moore, Carrie Colleen Buchanan
collection PubMed
description BACKGROUND: BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting. PURPOSE OF THE STUDY: In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool. RESULTS: Simulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data. CONCLUSIONS AND POTENTIAL IMPLICATIONS: The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0107-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-50064192016-09-01 A biologically informed method for detecting rare variant associations Moore, Carrie Colleen Buchanan Basile, Anna Okula Wallace, John Robert Frase, Alex Thomas Ritchie, Marylyn DeRiggi BioData Min Software Article BACKGROUND: BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting. PURPOSE OF THE STUDY: In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool. RESULTS: Simulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data. CONCLUSIONS AND POTENTIAL IMPLICATIONS: The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0107-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-30 /pmc/articles/PMC5006419/ /pubmed/27582876 http://dx.doi.org/10.1186/s13040-016-0107-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software Article
Moore, Carrie Colleen Buchanan
Basile, Anna Okula
Wallace, John Robert
Frase, Alex Thomas
Ritchie, Marylyn DeRiggi
A biologically informed method for detecting rare variant associations
title A biologically informed method for detecting rare variant associations
title_full A biologically informed method for detecting rare variant associations
title_fullStr A biologically informed method for detecting rare variant associations
title_full_unstemmed A biologically informed method for detecting rare variant associations
title_short A biologically informed method for detecting rare variant associations
title_sort biologically informed method for detecting rare variant associations
topic Software Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006419/
https://www.ncbi.nlm.nih.gov/pubmed/27582876
http://dx.doi.org/10.1186/s13040-016-0107-3
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