CGAL: computing genome assembly likelihoods
- Creators
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Rahman, Atif
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Pachter, Lior
Abstract
Assembly algorithms have been extensively benchmarked using simulated data so that results can be compared to ground truth. However, in de novo assembly, only crude metrics such as contig number and size are typically used to evaluate assembly quality. We present CGAL, a novel likelihood-based approach to assembly assessment in the absence of a ground truth. We show that likelihood is more accurate than other metrics currently used for evaluating assemblies, and describe its application to the optimization and comparison of assembly algorithms. Our methods are implemented in software that is freely available at http://bio.math.berkeley.edu/cgal/.
Additional Information
© 2013 Rahman and Pachter, licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 23 August 2012. Accepted: 29 January 2013. Published: 29 January 2013. We thank Michael Eisen, Aaron Kleinman, Harold Pimentel and Adam Roberts for helpful conversations in the development of the likelihood-based approach for assembly evaluation. LP was funded in part by NIH R21 HG006583. AR was funded in part by Fulbright Science & Technology Fellowship 15093630. Authors' contributions: AR and LP conceived the project and developed the methodology. AR implemented the method in the CGAL software and obtained the results of the paper. AR and LP wrote the manuscript. All authors read and approved the final manuscript. The authors have no competing interests.Attached Files
Published - art_3A10.1186_2Fgb-2013-14-1-r8.pdf
Supplemental Material - 13059_2012_3037_MOESM10_ESM.eps
Supplemental Material - 13059_2012_3037_MOESM11_ESM.eps
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Supplemental Material - 13059_2012_3037_MOESM9_ESM.eps
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Additional details
- PMCID
- PMC3663106
- Eprint ID
- 74740
- Resolver ID
- CaltechAUTHORS:20170303-155431520
- R21 HG006583
- NIH
- 15093630
- Fulbright Foundation
- Created
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2017-03-06Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field