VARiD: A variation detection framework for color-space and letter-space platforms
Name
Dalca-2010-VARiD.pdf
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1.43 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Dalca, Adrian Vasile
Rumble, Stephen M.
Levy, Samuel
Brudno, Michael
Date Issued
June 2010
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Dalca, A. V. et al. “VARiD: A Variation Detection Framework for Color-space and Letter-space Platforms.” Bioinformatics 26.12 (2010): i343–i349. Web.
Version
Final published version
Abstract
Motivation: High-throughput sequencing (HTS) technologies are transforming the study of genomic variation. The various HTS technologies have different sequencing biases and error rates, and while most HTS technologies sequence the residues of the genome directly, generating base calls for each position, the Applied Biosystem's SOLiD platform generates dibase-coded (color space) sequences. While combining data from the various platforms should increase the accuracy of variation detection, to date there are only a few tools that can identify variants from color space data, and none that can analyze color space and regular (letter space) data together.
Results: We present VARiD—a probabilistic method for variation detection from both letter- and color-space reads simultaneously. VARiD is based on a hidden Markov model and uses the forward-backward algorithm to accurately identify heterozygous, homozygous and tri-allelic SNPs, as well as micro-indels. Our analysis shows that VARiD performs better than the AB SOLiD toolset at detecting variants from color-space data alone, and improves the calls dramatically when letter- and color-space reads are combined.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution Non-Commercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btq184
