Email updates

Keep up to date with the latest news and content from Genome Biology and BioMed Central.

Open Access Highly Accessed Method

CGAL: computing genome assembly likelihoods

Atif Rahman1 and Lior Pachter12*

Author affiliations

1 Department of Electrical Engineering and Computer Sciences, 387 Soda Hall, UC Berkeley, Berkeley, CA 94720, USA

2 Departments of Mathematics and Molecular & Cell Biology, 970 Evans Hall, UC Berkeley, Berkeley, CA 94720, USA

For all author emails, please log on.

Citation and License

Genome Biology 2013, 14:R8  doi:10.1186/gb-2013-14-1-r8

Published: 29 January 2013

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/ webcite.

Keywords:
Genome assembly; evaluation; likelihood; sequencing.