Open squigzzz opened 5 months ago
Hi, thanks for your query. We used liftover to create a GRCh38 version of the coordinates.
[PARAMS]
stage1_motif_string=TTAGGGTTAGGGTTAGGG
;stage1_motif_string=TTAGGGTTAGGGTTAGGGTTAGGG
;stage2_motif_string=TTAGGG
stage2_motif_regex=(...GGG){2,}|(CCC...){2,}
;;stage2_motif_regex=((TTA|TCA|TTC|GTA|TGA|TTG|TAA|ATA|CTA|TTT|TTAA)GGG){2,}|(CCC(TAA|TGA|GAA|TAC|TCA|CAA|TTA|TAT|TAG|AAA|TTAA)){2,}
stage1_string_rev_comp=true
window_size=10000
includes_only=false
[INCLUDES]
;; name, regions (sequence:start-stop)
chr1p chr1:10001-12464
chr1q chr1:248943708-248946421
chr2p chr2:10001-12592
chr2q chr2:242146750-242148749
chr2xA chr2:242181358-242183529
chr3p chr3:18323-20322
chr3q chr3:198233559-198235558
chr3xB chr3:198170705-198176526
chr4p chr4:10001-12193
chr4q chr4:190120458-190123120
chr5p chr5:10001-13806
chr5q chr5:181476259-181478258
chr6p chr6:60001-62000
chr6q chr6:170743979-170745978
chr7p chr7:10001-12238
chr7q chr7:159333868-159335972
chr8p chr8:60001-62000
chr8q chr8:145076636-145078635
chr9p chr9:10001-12359
chr9q chr9:138260981-138262980
chr10p chr10:14061-16061
chr10q chr10:133785144-133787421
chr11p chr11:60001-62000
chr11q chr11:135074564-135076621
chr12p chr12:43740-45739
chr12q chr12:133262872-133265308
chr12xC chr12:10001-12582
chr13p chr13:18445861-18447860
chr13q chr13:114342403-114344402
chr14p chr14:18243524-18245523
chr14q chr14:106879333-106881349
chr15p chr15:19794748-19796747
chr15q chr15:101978766-101981188
chr16p chr16:10001-12033
chr16q chr16:90226345-90228344
chr17p chr17:150208-152207
chr17q chr17:83245442-83247441
chr18p chr18:10001-12621
chr18q chr18:80256343-80259271
chr19p chr19:60001-62000
chr19q chr19:58605455-58607615
chr20p chr20:79360-81359
chr20q chr20:64332167-64334166
chr21p chr21:8522361-8524360
chr21q chr21:46697876-46699982
chr22p chr22:15926017-15927980
chr22q chr22:50804138-50806137
chrXp chrX:10001-12033
chrXq chrX:156028068-156030894
chrYp chrY:10001-12033
chrYq chrY:57214588-57217414
;..
[EXCLUDES]
; regions (sequence:start-stop)
;chr1:143274114-143274336
;..
great thank you, it is quite unclear in both the publication and all of the documentation how you go from the XML output to telomere length in kb ? how is this done ?
We don’t normalise directly to genome coverage. Rather, we simply scale to a nominal read count of 1B reads to allow for simple comparisons between BAMs with different numbers of reads. So if your BAM has 0.5B reads, all of the scaled scores will be double the raw counts and if your BAM has 2B reads, the scaled scores would be half of the raw numbers. We don’t take any account of unmapped reads, secondary alignments etc when scaling, we just count every read. We take this simple approach because when you are talking about tumours, the correct approach is non-obvious - for example, if we have 3 chromosomes with whole-arm amplifications, how should we take account of that? Clever/correct scaling is left as an exercise for the user as they know their data best. With all of those caveats, qMotif scaled scores correlate very well with wet-lab techniques as we showed in the qMotif paper so we think the simple scaling approach probably works well enough in the majority of cases.
Hello-
I am interested in deploying qmotif to quantify telomeres however I have HG38 aligned bams, is it possible to just liftover the coordinates you provided in the configureration file to hg38 using ucsc or a similiar tool or can you all make the appropriate hg38 coordinates available for this purpose ?