1959/01/01 by Pritam Chanda, Eran Elhaik, Joel S. Bader +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Social Sciences · #Advanced Data Storage Technologies #Algorithms and Data Compression #Genetic Associations and Epidemiology #Genomics and Phylogenetic Studies #Language and cultural evolution
paper · pdf · doi:10.1093/nar/gks709
openalex publication_date 1959/01/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01
The rapidly growing amount of genomic sequence data being generated and made publicly available necessitate the development of new data storage and archiving methods. The vast amount of data being shared and manipulated also create new challenges for network resources. Thus, developing advanced data compression techniques is becoming an integral part of data production and analysis. The HapMap project is one of the largest public resources of human single-nucleotide polymorphisms (SNPs), characterizing over 3 million SNPs genotyped in over 1000 individuals. The standard format and biological properties of HapMap data suggest that a dedicated genetic compression method can outperform generic compression tools. We propose a compression methodology for genetic data by introducing HapZipper, a lossless compression tool tailored to compress HapMap data beyond benchmarks defined by generic tools such as gzip, bzip2 and lzma. We demonstrate the usefulness of HapZipper by compressing HapMap 3 populations to <5% of their original sizes. HapZipper is freely downloadable from https://bitbucket.org/pchanda/hapzipper/downloads/HapZipper.tar.bz2.