Genome Assembly
Genome assembly
Summary ...
Introductory reading
| Pop et al. (2004) Comparative genome assembly. Brief Bioinformatics 5:237-48. (pmid: 15383210) |
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[ PubMed ] [ DOI ] One of the most complex and computationally intensive tasks of genome sequence analysis is genome assembly. Even today, few centres have the resources, in both software and hardware, to assemble a genome from the thousands or millions of individual sequences generated in a whole-genome shotgun sequencing project. With the rapid growth in the number of sequenced genomes has come an increase in the number of organisms for which two or more closely related species have been sequenced. This has created the possibility of building a comparative genome assembly algorithm, which can assemble a newly sequenced genome by mapping it onto a reference genome. We describe here a novel algorithm for comparative genome assembly that can accurately assemble a typical bacterial genome in less than four minutes on a standard desktop computer. The software is available as part of the open-source AMOS project. |
| Medvedev & Brudno (2009) Maximum likelihood genome assembly. J Comput Biol 16:1101-16. (pmid: 19645596) |
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[ PubMed ] [ DOI ] Whole genome shotgun assembly is the process of taking many short sequenced segments (reads) and reconstructing the genome from which they originated. We demonstrate how the technique of bidirected network flow can be used to explicitly model the double-stranded nature of DNA for genome assembly. By combining an algorithm for the Chinese Postman Problem on bidirected graphs with the construction of a bidirected de Bruijn graph, we are able to find the shortest double-stranded DNA sequence that contains a given set of k-long DNA molecules. This is the first exact polynomial time algorithm for the assembly of a double-stranded genome. Furthermore, we propose a maximum likelihood framework for assembling the genome that is the most likely source of the reads, in lieu of the standard maximum parsimony approach (which finds the shortest genome subject to some constraints). In this setting, we give a bidirected network flow-based algorithm that, by taking advantage of high coverage, accurately estimates the copy counts of repeats in a genome. Our second algorithm combines these predicted copy counts with matepair data in order to assemble the reads into contigs. We run our algorithms on simulated read data from Escherichia coli and predict copy counts with extremely high accuracy, while assembling long contigs. |
Contents
Genome Assembly - Presentation by J.Christian Somody, BCB410 - 2011
Exercises
Exercises - by J.Christian Somody, BCB410 - 2011
Further reading and resources
