Jan 31, 2013

CGAL: computing genome assembly likelihoods

Genome Biology
Atif Rahman, Lior Pachter


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


  • References28
  • Citations31


Mentioned in this Paper

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

Related Feeds

Applications of Molecular Barcoding

The concept of molecular barcoding is that each original DNA or RNA molecule is attached to a unique sequence barcode. Sequence reads having different barcodes represent different original molecules, while sequence reads having the same barcode are results of PCR duplication from one original molecule. Discover the latest research on molecular barcoding here.

Related Papers

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Andrew Hooper, Howard A Zebker
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Alexandre CiancioPere Obrador
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Hamid Rahim SheikhAlan Conrad Bovik
© 2020 Meta ULC. All rights reserved