DynMap: mapping short reads to multiple related genomes

Authors:
Tomáš Flouri;Costas S. Iliopoulos;Solon P. Pissis
Affiliations:
Czech Technical University in Prague;King's College London, London, England, United Kingdom;King's College London, London, England, United Kingdom
Venue:
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2011

Citing 6
Cited 0

Algorithms on strings, trees, and sequences: computer science and computational biology

Algorithms on strings, trees, and sequences: computer science and computational biology
A guided tour to approximate string matching

ACM Computing Surveys (CSUR)
Fast and accurate short read alignment with Burrows–Wheeler transform

Bioinformatics
SOAP2

Bioinformatics
Fast and SNP-tolerant detection of complex variants and splicing in short reads

Bioinformatics
REAL: an efficient REad ALigner for next generation sequencing reads

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The constant advances in sequencing technology have redefined the way genome sequencing is performed. They are able to produce millions of short sequences (reads) during a single experiment, and with a much lower cost than previously possible. Due to the dramatic increase in the amount of data generated, efficient algorithms for aligning (mapping) these reads to reference genomes are in great demand, and recently, there has been ample work for publishing such algorithms. In this paper, we study a different version of this problem; mapping these reads to multiple related genomes (e.g. individuals of the same species). We present DynMap, a new practical algorithm, which employs a suitable data structure that takes into account potential inherent genomic variability (replacements, insertions, deletions) between related genomes. Therefore, if a small number of differences occurs within a reference sequence, the already mapped reads can be altered dynamically. The presented experimental results demonstrate that DynMap can match or even outperform the most popular tools in terms of sensitivity, accuracy, and speed.