2019
DOI: 10.1093/bioinformatics/btz810
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OLOGRAM: determining significance of total overlap length between genomic regions sets

Abstract: Motivation Various bioinformatics analyses provide sets of genomic coordinates of interest. Whether two such sets possess a functional relation is a frequent question. This is often determined by interpreting the statistical significance of their overlaps. However, only few existing methods consider the lengths of the overlap, and they do not provide a resolutive P-value. Results Here, we introduce OLOGRAM, which performs ove… Show more

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Cited by 24 publications
(38 citation statements)
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“…To identify overlapping peaks between samples or other genomic interval or features the bedtools intersect tool was used with settings dependent on the prospected outcome of the processing. To test if sets of genomic coordinates representing various features show statistically significant overlaps across the genome, Monte Carlo simulations have been performed using the python pipeline OLOGRAM, part of the gtftk package (Ferré et al, 2019). p -values are calculated based on the occurrence of intersections between intervals and overall length of overlap (in bp) across the genome.…”
Section: Star Methodsmentioning
confidence: 99%
“…To identify overlapping peaks between samples or other genomic interval or features the bedtools intersect tool was used with settings dependent on the prospected outcome of the processing. To test if sets of genomic coordinates representing various features show statistically significant overlaps across the genome, Monte Carlo simulations have been performed using the python pipeline OLOGRAM, part of the gtftk package (Ferré et al, 2019). p -values are calculated based on the occurrence of intersections between intervals and overall length of overlap (in bp) across the genome.…”
Section: Star Methodsmentioning
confidence: 99%
“…To do that, we randomly selected 10%, 30%, 60%, and 90% of the control and case samples (repeated for 10 permutations) and then repeated all the analyses to identify statistically significant regions. As the significant regions identified in each permutation may have different start and stops, we used the OLOGRAM tool (Ferré et al, 2019) to identify whether the regions identified in the permutations significantly overlapped with one of the original 47 significant regions. The results demonstrate that the number of significant regions is reduced commensurate to a reduction in the cohort size, as expected.…”
Section: Effect Of Down-samplingmentioning
confidence: 99%
“…Binding motifs for the same transcription factor were merged. Clustered distal ATAC peaks were queried for enrichment against the merged set of motifs using the OLOGRAM tool (Ferré et al, 2019) from the Pygtftk package .…”
Section: Motif Enrichmentmentioning
confidence: 99%