We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads.
- Authors
Pinghao Li; Xiaoqian Jiang; Shuang Wang; Jihoon Kim; Hongkai Xiong; Ohno-Machado, Lucila
- Abstract
Background and objective Short-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data. Methods We developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values. Results The proposed method produced a compression ratio in the range 0.5-0.65, which corresponds to 35-50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/ projects/hierachicaldnac/with a General Public License (GPL) license. Limitation Our method requires having different reference genomes and prolongs the execution time for additional alignments. Conclusions The proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.
- Subjects
GENOMES; SEQUENCE alignment; LICENSES; ALGORITHMS; MEDICAL informatics
- Publication
Journal of the American Medical Informatics Association, 2014, Vol 21, Issue 2, p363
- ISSN
1067-5027
- Publication type
Article
- DOI
10.1136/amiajnl-2013-002147