Running kallisto quant for sample W1
[quant] fragment length distribution is truncated gaussian with mean = 200, sd = 30
[index] k-mer length: 31
[index] number of targets: 4,391
[index] number of k-mers: 3,851,033
[index] number of equivalence classes: 4,578
[quant] running in single-end mode
[quant] will process file 1: /dev/fd/63
[quant] finding pseudoalignments for the reads ... done
[quant] processed 14,312,155 reads, 11,014,220 reads pseudoaligned
[ em] quantifying the abundances ... done
[ em] the Expectation-Maximization algorithm ran for 55 rounds
Running kallisto quant for sample W2
[quant] fragment length distribution is truncated gaussian with mean = 200, sd = 30
[index] k-mer length: 31
[index] number of targets: 4,391
[index] number of k-mers: 3,851,033
[index] number of equivalence classes: 4,578
[quant] running in single-end mode
[quant] will process file 1: /dev/fd/63
[quant] finding pseudoalignments for the reads ... done
[quant] processed 16,255,960 reads, 12,922,297 reads pseudoaligned
[ em] quantifying the abundances ... done
[ em] the Expectation-Maximization algorithm ran for 190 rounds
Running kallisto quant for sample W3
[quant] fragment length distribution is truncated gaussian with mean = 200, sd = 30
[index] k-mer length: 31
[index] number of targets: 4,391
[index] number of k-mers: 3,851,033
[index] number of equivalence classes: 4,578
[quant] running in single-end mode
[quant] will process file 1: /dev/fd/63
[quant] finding pseudoalignments for the reads ... done
[quant] processed 15,874,534 reads, 12,802,660 reads pseudoaligned
[ em] quantifying the abundances ... done
[ em] the Expectation-Maximization algorithm ran for 60 rounds