VARiD: A variation detection framework for color-space and letter-space platforms
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 posit...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Oxford University Press
2012
|
| Online Access: | http://hdl.handle.net/1721.1/73027 https://orcid.org/0000-0002-8422-0136 |
| _version_ | 1811068736994541568 |
|---|---|
| author | Dalca, Adrian Vasile Rumble, Stephen M. Levy, Samuel Brudno, Michael |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Dalca, Adrian Vasile Rumble, Stephen M. Levy, Samuel Brudno, Michael |
| author_sort | Dalca, Adrian Vasile |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:00:19Z |
| format | Article |
| id | mit-1721.1/73027 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:00:19Z |
| publishDate | 2012 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/730272021-09-09T17:21:02Z VARiD: A variation detection framework for color-space and letter-space platforms Dalca, Adrian Vasile Rumble, Stephen M. Levy, Samuel Brudno, Michael Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Dalca, Adrian Vasile Dalca, Adrian Vasile 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. Natural Sciences and Engineering Research Council of Canada (NSERC) Mathematics of Information Technology and Complex Systems (Network) Life Technologies, Inc. 2012-09-17T20:08:07Z 2012-09-17T20:08:07Z 2010-06 Article http://purl.org/eprint/type/JournalArticle 1460-2059 1367-4803 http://hdl.handle.net/1721.1/73027 Dalca, A. V. et al. “VARiD: A Variation Detection Framework for Color-space and Letter-space Platforms.” Bioinformatics 26.12 (2010): i343–i349. Web. https://orcid.org/0000-0002-8422-0136 en_US http://dx.doi.org/10.1093/bioinformatics/btq184 Bioinformatics Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/2.5 application/pdf Oxford University Press Oxford |
| spellingShingle | Dalca, Adrian Vasile Rumble, Stephen M. Levy, Samuel Brudno, Michael VARiD: A variation detection framework for color-space and letter-space platforms |
| title | VARiD: A variation detection framework for color-space and letter-space platforms |
| title_full | VARiD: A variation detection framework for color-space and letter-space platforms |
| title_fullStr | VARiD: A variation detection framework for color-space and letter-space platforms |
| title_full_unstemmed | VARiD: A variation detection framework for color-space and letter-space platforms |
| title_short | VARiD: A variation detection framework for color-space and letter-space platforms |
| title_sort | varid a variation detection framework for color space and letter space platforms |
| url | http://hdl.handle.net/1721.1/73027 https://orcid.org/0000-0002-8422-0136 |
| work_keys_str_mv | AT dalcaadrianvasile varidavariationdetectionframeworkforcolorspaceandletterspaceplatforms AT rumblestephenm varidavariationdetectionframeworkforcolorspaceandletterspaceplatforms AT levysamuel varidavariationdetectionframeworkforcolorspaceandletterspaceplatforms AT brudnomichael varidavariationdetectionframeworkforcolorspaceandletterspaceplatforms |