4,784 research outputs found
Indexing large genome collections on a PC
Motivation: The availability of thousands of invidual genomes of one species
should boost rapid progress in personalized medicine or understanding of the
interaction between genotype and phenotype, to name a few applications. A key
operation useful in such analyses is aligning sequencing reads against a
collection of genomes, which is costly with the use of existing algorithms due
to their large memory requirements.
Results: We present MuGI, Multiple Genome Index, which reports all
occurrences of a given pattern, in exact and approximate matching model,
against a collection of thousand(s) genomes. Its unique feature is the small
index size fitting in a standard computer with 16--32\,GB, or even 8\,GB, of
RAM, for the 1000GP collection of 1092 diploid human genomes. The solution is
also fast. For example, the exact matching queries are handled in average time
of 39\,s and with up to 3 mismatches in 373\,s on the test PC with
the index size of 13.4\,GB. For a smaller index, occupying 7.4\,GB in memory,
the respective times grow to 76\,s and 917\,s.
Availability: Software and Suuplementary material:
\url{http://sun.aei.polsl.pl/mugi}
Reference Based Genome Compression
DNA sequencing technology has advanced to a point where storage is becoming
the central bottleneck in the acquisition and mining of more data. Large
amounts of data are vital for genomics research, and generic compression tools,
while viable, cannot offer the same savings as approaches tuned to inherent
biological properties. We propose an algorithm to compress a target genome
given a known reference genome. The proposed algorithm first generates a
mapping from the reference to the target genome, and then compresses this
mapping with an entropy coder. As an illustration of the performance: applying
our algorithm to James Watson's genome with hg18 as a reference, we are able to
reduce the 2991 megabyte (MB) genome down to 6.99 MB, while Gzip compresses it
to 834.8 MB.Comment: 5 pages; Submitted to the IEEE Information Theory Workshop (ITW) 201
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