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Table 1 Pre-processing comparative results of ebwt2snp (i.e. building the eBWT using either eGSA or BCR) and DiscoSnp++ (i.e. building the de Bruijn graph)

From: SNPs detection by eBWT positional clustering

Dataset Coverage per sample #reads Preprocessing Wall clocka
HG00096 (ch. 22) 29× 15,000,000 gsacak 0:53:34 100,607
eGSA 1:41:37 30,720
BCR 4:18:00 1,970
DiscoSnp++ 0:01:09 5,170
HG00100 (ch. 16) 22× 20,000,000 gsacak 1:13:04 112,641
eGSA 3:39:04 30,720
BCR 6:10:28 3,262
DiscoSnp++ 0:02:01 6,111
HG00419+NA19017 (ch. 1) 43×–47× 93,657,983 BCR 105:28:30 73,977
DiscoSnp++ 0:32:37 621
  1. Wall clock is the elapsed time from start to completion of the instance, while RAM is the peak Resident Set Size (RSS). Both values were taken with /usr/bin/time command. Note that for the last collection we have used a variant of BCR that keeps the \({ \textsf {ebwt}} (\mathcal {S})\) in internal memory. eGSA and gsacak have not been tested on the last dataset since they required too much disk space and RAM, respectively
  2. aWe recall that DiscoSnp++ makes use of multiple cores while ebwt2snp is currently designed to use one core only, thus explaining the difference in speed