2018
DOI: 10.1093/bioinformatics/bty486
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ClusterScan: simple and generalistic identification of genomic clusters

Abstract: Supplementary data are available at Bioinformatics online.

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Cited by 12 publications
(9 citation statements)
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“…We used the clusterdist function in ClusterScan v.0.2.2 [127] to calculate the number of genes that form clusters in the genome for the conserved group with the option dist = 500. We counted the number of conserved CN genes that were defined inside the clusters and used to calculate their proportion in the conserved CN group.…”
Section: Clustering Analysis and The Bootstrap Methodsmentioning
confidence: 99%
“…We used the clusterdist function in ClusterScan v.0.2.2 [127] to calculate the number of genes that form clusters in the genome for the conserved group with the option dist = 500. We counted the number of conserved CN genes that were defined inside the clusters and used to calculate their proportion in the conserved CN group.…”
Section: Clustering Analysis and The Bootstrap Methodsmentioning
confidence: 99%
“…Middle lane: genes. Lower three lanes: Origin clusters determined using the clusterdist function of clusterscan (34), set at 30 kb (see Materials & Methods). H. Number of clusters found in each ini-seq 2 origin efficiency class.…”
Section: Resultsmentioning
confidence: 99%
“…Ini-domains were generated using the merge function at a maximum distance of 100kb, only domains containing at least 6 origins were kept. Cluster analysis of high, medium and low efficiency origins was performed using the option of (34) with a distance for determining a cluster of 30 kb. Feature coverage around origins was plotted using (35).…”
Section: Methodsmentioning
confidence: 99%
“…Having defined the final set of promoters (i.e., consensus clusters), the promoter density has been calculated as follows. First, by using the clusterdist algorithm of the ClusterScan tool (-d 10,000 parameter) (Volpe et al , 2018), the zebrafish genome has been scanned grouping together all the consecutive promoters closer than 10 kb. Then, for each group of promoters (clusters), the promoter density has been calculated by dividing the number of promoters by the cluster length in kilobases.…”
Section: Methodsmentioning
confidence: 99%