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  Letting the Data Speak for Themselves: a Fully Bayesian Approach to Transcriptome Assembly

Schulz, M. H. (2014). Letting the Data Speak for Themselves: a Fully Bayesian Approach to Transcriptome Assembly. Genome Biology, 15(10): 498. doi:10.1186/s13059-014-0498-8.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-934A-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0026-A2DF-C
Genre: Journal Article
Latex : Letting the Data Speak for Themselves: a Fully {B}ayesian Approach to Transcriptome Assembly

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 Creators:
Schulz, Marcel H.1, Author           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              

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 Abstract: 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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Language(s): eng - English
 Dates: 2014-10-312014
 Publication Status: Issued
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s13059-014-0498-8
BibTex Citekey: s13059-014-0498-8
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Title: Genome Biology
Source Genre: Journal
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Publ. Info: London : BioMed Central Ltd.
Pages: - Volume / Issue: 15 (10) Sequence Number: 498 Start / End Page: - Identifier: ISSN: 1465-6906
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000224390_1