Jan 31, 2013

CGAL: computing genome assembly likelihoods

Genome Biology
Atif Rahman, Lior Pachter

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/.

  • References28
  • Citations31

References

  • References28
  • Citations31

Citations

Mentioned in this Paper

Likelihood Functions
Genome Assembly Sequence
Sequence Determinations, DNA
Genome, Bacterial
Contig Mapping
Genome, Human
Metric

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