Computational and statistical approaches to analyzing variants identified by exome sequencing
New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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BioMed Central Ltd.
2012
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| Online Access: | http://hdl.handle.net/1721.1/70548 |
| _version_ | 1826210469339201536 |
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| author | Stitziel, Nathan O. Kiezun, Adam Sunyaev, Shamil R. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Stitziel, Nathan O. Kiezun, Adam Sunyaev, Shamil R. |
| author_sort | Stitziel, Nathan O. |
| collection | MIT |
| description | New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged. |
| first_indexed | 2024-09-23T14:50:23Z |
| format | Article |
| id | mit-1721.1/70548 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:50:23Z |
| publishDate | 2012 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/705482022-10-01T22:49:10Z Computational and statistical approaches to analyzing variants identified by exome sequencing Stitziel, Nathan O. Kiezun, Adam Sunyaev, Shamil R. Harvard University--MIT Division of Health Sciences and Technology Sunyaev, Shamil R. Sunyaev, Shamil R. New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged. National Institutes of Health (U.S.) (Grant R01-MH084676) National Institutes of Health (U.S.) (Grant R01-GM078598) National Institutes of Health (U.S.) (Training grant T32-HL07604-25) Brigham and Women's Hospital (Division of Cardiovascular Medicine) 2012-05-09T19:19:29Z 2012-05-09T19:19:29Z 2011-09 Article http://purl.org/eprint/type/JournalArticle 1465-6906 1474-7596 http://hdl.handle.net/1721.1/70548 Stitziel, Nathan O, Adam Kiezun, and Shamil Sunyaev. “Computational and Statistical Approaches to Analyzing Variants Identified by Exome Sequencing.” Genome Biology 12.9 (2011): 227. Web. en_US http://dx.doi.org/10.1186/gb-2011-12-9-227 Genome Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 application/pdf BioMed Central Ltd. BioMed Central |
| spellingShingle | Stitziel, Nathan O. Kiezun, Adam Sunyaev, Shamil R. Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title | Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title_full | Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title_fullStr | Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title_full_unstemmed | Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title_short | Computational and statistical approaches to analyzing variants identified by exome sequencing |
| title_sort | computational and statistical approaches to analyzing variants identified by exome sequencing |
| url | http://hdl.handle.net/1721.1/70548 |
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