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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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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. |
format | Online Article Text |
id | pubmed-5006419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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