title: Comment on: 'ERGC: an efficient referential genome compression algorithm'.
abstract: 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.
  
  
  
  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.
  
  
  
  sebastian.deorowicz@polsl.pl, iochoa@stanford.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100 Poland.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Deorowicz
authors.affiliation: Institute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 Łódź, Poland and.
authors.forename: Szymon
authors.initials: S
authors.lastname: Grabowski
authors.affiliation: Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.
authors.forename: Idoia
authors.initials: I
authors.lastname: Ochoa
authors.affiliation: Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.
authors.forename: Mikel
authors.initials: M
authors.lastname: Hernaez
authors.affiliation: Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.
authors.forename: Tsachy
authors.initials: T
authors.lastname: Weissman
mesh_terms: D000465:Algorithms; D044962:Data Compression; D016678:Genome; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D017422:Sequence Analysis, DNA