Generating a reportΒΆ

[1]:
from itpseq import DataSet

data = DataSet('tetracenomycin/', keys=['sample'])
# export the report as PDF
# this takes a long time for the first execution
data.report(output='Tetracenomycin_X.pdf')

[2]:
# directly output the report in the notebook
data.report()
[2]:
Inverse toe-printing report for tcx

iTP-seq dataset

Statistics of the iTP reads

  total_sequences noadaptor contaminant lowqual toolong extra0 extra1 extra2 MAX_LEN
noa.1 9 036 255 799 955 1 219 700 374 4 903 682 2 434 092 2 709 762 3 092 446 32
noa.2 8 154 560 407 750 1 318 680 813 5 562 060 2 329 190 2 582 052 2 835 568 32
noa.3 7 725 561 623 037 1 065 353 401 4 904 268 2 216 460 2 402 957 2 483 107 32
tcx.1 8 384 889 714 414 1 192 685 017 4 973 412 2 308 291 2 528 638 2 833 546 32
tcx.2 9 120 203 498 202 1 659 513 308 7 129 339 2 673 062 2 972 850 2 976 089 32
tcx.3 8 490 958 1 043 590 1 328 409 697 5 328 959 2 243 720 2 555 783 2 647 865 32

Virtual inverse toeprint gel

2025-02-21T17:01:48.810369 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Distribution of iTP lengths

2025-02-21T17:01:51.366473 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Distribution of iTP lengths per sample

noa

2025-02-21T17:01:51.513972 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

tcx

2025-02-21T17:01:51.655038 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Amino-acid enrichment per position

tcx vs noa

2025-02-21T17:01:51.858692 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Amino-acid enrichment per combination of positions

tcx vs noa

2025-02-21T17:01:56.485857 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Motif: -2:A

tcx vs noa

p-value

adjusted p-value

Motif: E:A

tcx vs noa

p-value

adjusted p-value

Motif: E:P

tcx vs noa

p-value

adjusted p-value