Sciweavers

Share
BMCBI
2010

Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction

10 years 2 months ago
Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction
Background: With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and apply it to de novo sequencing of new organisms. As an example, this paper demonstrates how such extra information can be used to improve de novo assemblies by augmenting the overlapping step. Finding all pairs of overlapping reads is a key task in many genome assemblers, and to this end, highly efficient algorithms have been developed to find alignments in large collections of sequences. It is well known that due to repeated sequences, many aligned pairs of reads nevertheless do not overlap. But no overlapping algorithm to date takes a rigorous approach to separating aligned but non-overlapping read pairs from true overlaps. Results: We present an approach that extends the Minimus assembler by a data driven step to classify overlaps as true or false prior to contig construction. We trained several different clas...
Lance E. Palmer, Mathäus Dejori, Randall A. B
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Lance E. Palmer, Mathäus Dejori, Randall A. Bolanos, Daniel P. Fasulo
Comments (0)
books