Comment on: 'ERGC: an efficient referential genome compression algorithm'

Sebastian Deorowicz, Szymon Grabowski, Idoia Ochoa, Mikel Hernaez, Tsachy Weissman

Research output: Contribution to journalArticle

Abstract

Motivation: Data compression is crucial in effective handling of genomic data. Among several recently published algorithms, ERGC seems to be surprisingly good, easily beating all of the competitors. Results: We evaluated ERGC and the previously proposed algorithms GDC and iDoComp, which are the ones used in the original paper for comparison, on a wide data set including 12 assemblies of human genome (instead of only four of them in the original paper). ERGC wins only when one of the genomes (referential or target) contains mixed-cased letters (which is the case for only the two Korean genomes). In all other cases ERGC is on average an order of magnitude worse than GDC and iDoComp.

Original languageEnglish (US)
Pages (from-to)1115-1117
Number of pages3
JournalBioinformatics
Volume32
Issue number7
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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