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Table 2 Post-processing comparative results of ebwt2snp (i.e. building clusters from the eBWT and performing SNP calling) and DiscoSnp++ (i.e. running KisSNP2 and kissreads2 using the pre-computed de Bruijn graph)

From: SNPs detection by eBWT positional clustering

Tool Param. Wall clock RAM (MB) TP FP FN SEN (%) PREC (%) Non-isol. SNP
Individual HG00096 vs reference (chromosome 22, 50818468bp), coverage 29× per sample
 DiscoSnp++ b = 0 5:07 101 32,773 3719 13,274 71.17 89.81 4707/8658
b = 1 16:39 124 37,155 10,599 8892 80.69 77.80 5770/8658
b = 2 20:42 551 40,177 58,227 5870 87.25 40.83 6325/8658
 ebwt2snp \(\hbox {cov}=4\) 35:56 314 42,309 1487 3738 91.88 96.60 7233/8658
\(\hbox {cov}=6\) 22:19 300 40,741 357 5306 88.47 99.13 6884/8658
Individual HG00100 vs reference (chromosome 16, 90338345bp), coverage 22× per sample
 DiscoSnp++ b=0 6:20 200 48,119 10,226 18,001 72.78 82.47 6625/11,055
b=1 31:57 208 53,456 24,696 12,664 80.85 68.40 7637/11,055
b=2 51:45 1256 57,767 124,429 8353 87.37 31.71 8307/11,055
 ebwt2snp \(\hbox {cov}=4\) 33:24 418 59,668 898 6452 90.24 98.51 9287/11,055
\(\hbox {cov}=6\) 44:53 337 53,749 190 12,371 81.29 99.64 8169/11,055
  1. Wall clock (mm:ss) 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. We recall that DiscoSnp++ makes use of multiple cores while ebwt2snp is currently designed to use one core only, thus explaining the difference in speed