2021
DOI: 10.1002/essoar.10507053.2
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PYOMPA version 0.3: Technical Note

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Cited by 8 publications
(10 citation statements)
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“…(2019) and the python package pyompa, described in Shrikumar et al. (2022). The water masses considered are upper and lower members for Pacific Subpolar Upper Water (PSUW) and Eastern North Pacific Central Water (ENPCW), and water masses of equatorial region, namely 13˚C Water (13CW) and Northern Equatorial Pacific Intermediate Water (NEPIW).…”
Section: Methodsmentioning
confidence: 99%
“…(2019) and the python package pyompa, described in Shrikumar et al. (2022). The water masses considered are upper and lower members for Pacific Subpolar Upper Water (PSUW) and Eastern North Pacific Central Water (ENPCW), and water masses of equatorial region, namely 13˚C Water (13CW) and Northern Equatorial Pacific Intermediate Water (NEPIW).…”
Section: Methodsmentioning
confidence: 99%
“…We computed importance scores for the counts output using the DeepSHAP 25 implementation of DeepLIFT algorithm 26 . We next ran the TF-MoDISco algorithm 27 to perform de novo motif discovery in each cell type. We mapped the discovered motifs to position weight matrices (PWMs) from HOCOMOCO 28 using TomTom 29 (Extended Data Fig.…”
Section: Methodsmentioning
confidence: 99%
“…To identify classes of informative motifs, we used TF-MoDISco [50,51] to cluster common DNA subsequences contributing to either profile or quantity. The majority of DNA sequence motifs important for predicting the quantity of transcription initiation resembled the known consensus motifs of strong transcriptional activators (Fig.…”
Section: Distinct Dna Sequence Architecture Controls Initiation Quant...mentioning
confidence: 99%
“…DeepSHAP profile and quantity scores were computed on the set of high confidence PRO-cap peaks described above. The lite implementation [50] (version 2.2.0) of TF-MoDISco [51] was used to cluster high importance subsequences (seqlets) into summary motifs (seqlets per metacluster = 100, 000), which were then matched against the JASPAR database [89] (2022, non-redundant vertebrate) using the TOMTOM algorithm [90]. We identified 116,351 quantity seqlets (115,602 positive, 749 negative) and profile seqlets 132,121 (115,989 positive, 16,132 negative), which then clustered into 62 quantity motifs (51 positive, 11 negative) and 100 profile motifs (60 positive and 40 negative).…”
Section: Motif Discovery and Frequency Analysismentioning
confidence: 99%