Unknown

Dataset Information

Sequence Factorization with Multiple References.


ABSTRACT: The success of high-throughput sequencing has lead to an increasing number of projects which sequence large populations of a species. Storage and analysis of sequence data is a key challenge in these projects, because of the sheer size of the datasets. Compression is one simple technology to deal with this challenge. Referential factorization and compression schemes, which store only the differences between input sequence and a reference sequence, gained lots of interest in this field. Highly-similar sequences, e.g., Human genomes, can be compressed with a compression ratio of 1,000:1 and more, up to two orders of magnitude better than with standard compression techniques. Recently, it was shown that the compression against multiple references from the same species can boost the compressio

SUBMITTER: Wandelt S 

PROVIDER: S-EPMC4589410 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature