A biologically informed method for detecting rare variant associations

Carrie Colleen Buchanan Moore, Anna Okula Basile, John Robert Wallace, Alex Thomas Frase, Marylyn Deriggi Ritchie

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number27
JournalBioData Mining
Volume9
Issue number1
DOIs
StatePublished - Aug 30 2016

Fingerprint

Binning
Software Package
Software packages
Software
Bioinformatics
Computational Biology
Nucleic Acid Regulatory Sequences
Type I error
Streamlines
Open Source
Genomics
Low Frequency
Pathway
Simulation
Infrastructure
Pipelines
Genes
Transform
Gene
Proteins

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Moore, C. C. B., Basile, A. O., Wallace, J. R., Frase, A. T., & Ritchie, M. D. (2016). A biologically informed method for detecting rare variant associations. BioData Mining, 9(1), [27]. https://doi.org/10.1186/s13040-016-0107-3
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. In: BioData Mining. 2016 ; Vol. 9, No. 1.
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Moore, CCB, Basile, AO, Wallace, JR, Frase, AT & Ritchie, MD 2016, 'A biologically informed method for detecting rare variant associations', BioData Mining, vol. 9, no. 1, 27. https://doi.org/10.1186/s13040-016-0107-3

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.

In: BioData Mining, Vol. 9, No. 1, 27, 30.08.2016.

Research output: Contribution to journalArticle

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