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Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly
A novel method for transcriptome assembly, Bayesembler, provides greater accuracy without sacrifice of computational speed, and particular advantages for alternative transcripts expressed at low levels.
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2014
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318165/ https://www.ncbi.nlm.nih.gov/pubmed/25830215 http://dx.doi.org/10.1186/s13059-014-0498-8 |
| _version_ | 1782355813713575936 |
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| author | Schulz, Marcel H |
| author_facet | Schulz, Marcel H |
| author_sort | Schulz, Marcel H |
| collection | PubMed |
| description | A novel method for transcriptome assembly, Bayesembler, provides greater accuracy without sacrifice of computational speed, and particular advantages for alternative transcripts expressed at low levels. |
| format | Online Article Text |
| id | pubmed-4318165 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2014 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-43181652015-02-06 Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly Schulz, Marcel H Genome Biol Research Highlight A novel method for transcriptome assembly, Bayesembler, provides greater accuracy without sacrifice of computational speed, and particular advantages for alternative transcripts expressed at low levels. BioMed Central 2014-10-31 2014 /pmc/articles/PMC4318165/ /pubmed/25830215 http://dx.doi.org/10.1186/s13059-014-0498-8 Text en © Schulz; licensee BioMed Central Ltd. 2014 The licensee has exclusive rights to distribute this article, in any medium, for 12 months following its publication. After this time, the article is available under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| spellingShingle | Research Highlight Schulz, Marcel H Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title | Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title_full | Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title_fullStr | Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title_full_unstemmed | Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title_short | Letting the data speak for themselves: a fully Bayesian approach to transcriptome assembly |
| title_sort | letting the data speak for themselves: a fully bayesian approach to transcriptome assembly |
| topic | Research Highlight |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318165/ https://www.ncbi.nlm.nih.gov/pubmed/25830215 http://dx.doi.org/10.1186/s13059-014-0498-8 |
| work_keys_str_mv | AT schulzmarcelh lettingthedataspeakforthemselvesafullybayesianapproachtotranscriptomeassembly |