Journal
GENOME BIOLOGY
Volume 14, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/gb-2013-14-1-r8
Keywords
Genome assembly; evaluation; likelihood; sequencing
Funding
- NIH [R21 HG006583]
- Fulbright Science & Technology Fellowship [15093630]
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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/.
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