Exact and approximation algorithms for DNA sequence reconstruction
Exact and approximation algorithms for DNA sequence reconstruction
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
The fragment assembly string graph
Bioinformatics
Correction of sequencing errors in a mixed set of reads
Bioinformatics
Computability of models for sequence assembly
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
Localized genome assembly from reads to scaffolds: practical traversal of the paired string graph
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Pathset graphs: a novel approach for comprehensive utilization of paired reads in genome assembly
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
SpliceGrapherXT: From Splice Graphs to Transcripts Using RNA-Seq
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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As whole genome sequencing has become a routine biological experiment, algorithms for assembly of whole genome shotgun data has become a topic of extensive research, with a plethora of off-the-shelf methods that can reconstruct the genomes of many organisms. Simultaneously, several recently sequenced genomes exhibit very high polymorphism rates. For these organisms genome assembly remains a challenge as most assemblers are unable to handle highly divergent haplotypes in a single individual. In this paper we describe Hapsembler, an assembler for highly polymorphic genomes, which makes use of paired reads. Our experiments show that Hapsembler produces accurate and contiguous assemblies of highly polymorphic genomes, while performing on par with the leading tools on haploid genomes. Hapsembler is available for download at http://compbio.cs.toronto.edu/hapsembler.