CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.01 - Jan.-Feb.
Issue No.01 - Jan.-Feb. (2013 vol.10)
Mark Howison , Center for Comput. & Visualization, Brown Univ., Providence, RI, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.160
Compression has become a critical step in storing next-generation sequencing (NGS) data sets because of both the increasing size and decreasing costs of such data. Recent research into efficiently compressing sequence data has focused largely on improving compression ratios. Yet, the throughputs of current methods now lag far behind the I/O bandwidths of modern storage systems. As biologists move their analyses to high-performance systems with greater I/O bandwidth, low-throughput compression becomes a limiting factor. To address this gap, we present a new storage model called SeqDB, which offers high-throughput compression of sequence data with minimal sacrifice in compression ratio. It achieves this by combining the existing multithreaded Blosc compressor with a new data-parallel byte-packing scheme, called SeqPack, which interleaves sequence data and quality scores.
Throughput, Arrays, Bandwidth, Libraries, Bioinformatics, Instruction sets, Genomics,FASTQ, Compression, data storage, next-generation sequencing
Mark Howison, "High-Throughput Compression of FASTQ Data with SeqDB", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 1, pp. 213-218, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.160