De novo likelihood-based measures for comparing genome assemblies

dc.contributor.authorGhodsi, Mohammadreza
dc.contributor.authorHill, Christopher M
dc.contributor.authorAstrovskaya, Irina
dc.contributor.authorLin, Henry
dc.contributor.authorSommer, Dan D
dc.contributor.authorKoren, Sergey
dc.contributor.authorPop, Mihai
dc.description.abstractThe current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments “read” by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These “gold standards” can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics. We demonstrate that our de novo score can be computed quickly and accurately in a practical setting even for large datasets, by estimating the score from a relatively small sample of the reads. To demonstrate the benefits of our score, we measure the quality of the assemblies generated in the GAGE and Assemblathon 1 assembly “bake-offs” with our metric. Even without knowledge of the true reference sequence, our de novo metric closely matches the reference-based evaluation metrics used in the studies and outperforms other de novo metrics traditionally used to measure assembly quality (such as N50). Finally, we highlight the application of our score to optimize assembly parameters used in genome assemblers, which enables better assemblies to be produced, even without prior knowledge of the genome being assembled. Likelihood-based measures, such as ours proposed here, will become the new standard for de novo assembly evaluation.en_US
dc.identifier.citationGhodsi, M., Hill, C.M., Astrovskaya, I. et al. De novo likelihood-based measures for comparing genome assemblies. BMC Res Notes 6, 334 (2013).en_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Physical Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectSequencing Processen_US
dc.subjectDynamic Programming Algorithmen_US
dc.subjectLikelihood Scoreen_US
dc.subjectAssembly Qualityen_US
dc.subjectSubstitution Erroren_US
dc.titleDe novo likelihood-based measures for comparing genome assembliesen_US


Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
657.34 KB
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
1.57 KB
Item-specific license agreed upon to submission