Comment on:'ERGC: an efficient referential genome compression algorithm'
Bioinformatics, 2016•academic.oup.com
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 …
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 …
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.
Contact: [email protected], [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
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