Universal Count Correction for High-Throughput Sequencing
We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base seque...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2014
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| Online Access: | http://hdl.handle.net/1721.1/86363 https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 |
| _version_ | 1811074073225068544 |
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| author | Hashimoto, Tatsunori Benjamin Edwards, Matthew Douglas Gifford, David K. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hashimoto, Tatsunori Benjamin Edwards, Matthew Douglas Gifford, David K. |
| author_sort | Hashimoto, Tatsunori Benjamin |
| collection | MIT |
| description | We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives. |
| first_indexed | 2024-09-23T09:42:44Z |
| format | Article |
| id | mit-1721.1/86363 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:42:44Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/863632022-09-30T16:20:53Z Universal Count Correction for High-Throughput Sequencing Hashimoto, Tatsunori Benjamin Edwards, Matthew Douglas Gifford, David K. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hashimoto, Tatsunori Benjamin Edwards, Matthew Douglas Gifford, David K. We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives. National Institutes of Health (U.S.) (NIH grant no. 5-U01-HG007037) National Science Foundation (U.S.) (NSF grant no. 0645960) Qatar Computing Research Institute 2014-05-02T14:50:09Z 2014-05-02T14:50:09Z 2014-03 Article http://purl.org/eprint/type/JournalArticle 1553-7358 http://hdl.handle.net/1721.1/86363 Hashimoto, Tatsunori B., Matthew D. Edwards, and David K. Gifford. “Universal Count Correction for High-Throughput Sequencing.” Edited by Alice Carolyn McHardy. PLoS Comput Biol 10, no. 3 (March 6, 2014): e1003494. https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 en_US http://dx.doi.org/10.1371/journal.pcbi.1003494 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLoS |
| spellingShingle | Hashimoto, Tatsunori Benjamin Edwards, Matthew Douglas Gifford, David K. Universal Count Correction for High-Throughput Sequencing |
| title | Universal Count Correction for High-Throughput Sequencing |
| title_full | Universal Count Correction for High-Throughput Sequencing |
| title_fullStr | Universal Count Correction for High-Throughput Sequencing |
| title_full_unstemmed | Universal Count Correction for High-Throughput Sequencing |
| title_short | Universal Count Correction for High-Throughput Sequencing |
| title_sort | universal count correction for high throughput sequencing |
| url | http://hdl.handle.net/1721.1/86363 https://orcid.org/0000-0003-0521-5855 https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 |
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