2017
DOI: 10.1093/bioinformatics/btx413
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pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R

Abstract: Supplementary data are available at Bioinformatics online.

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Cited by 148 publications
(125 citation statements)
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“…The motifs were found to be enriched in regulatory regions, especially promoters, first introns, and telomeres . Subsequent studies have led to the broadening of the G4‐motif definition and to the ongoing refinement of G4‐mining algorithms . However, major G4‐prone genomic fragments, including telomeres and oncogene promoters, are well established and the bioinformatics predictions are generally consistent with those from the G4‐sequencing data .…”
Section: Introductionmentioning
confidence: 94%
See 1 more Smart Citation
“…The motifs were found to be enriched in regulatory regions, especially promoters, first introns, and telomeres . Subsequent studies have led to the broadening of the G4‐motif definition and to the ongoing refinement of G4‐mining algorithms . However, major G4‐prone genomic fragments, including telomeres and oncogene promoters, are well established and the bioinformatics predictions are generally consistent with those from the G4‐sequencing data .…”
Section: Introductionmentioning
confidence: 94%
“…[18,19] Subsequent studies have led to the broadening of the G4-motif definition [20][21][22] and to the ongoing refinement of G4-mining algorithms. [23][24][25][26] However, major G4-prone genomic fragments, including telomeres and oncogene promoters, are well established and the bioinformatics predictions are generally consistent with those from the G4-sequencing data. [27] In living human cells, G4 foci have been found both inside and outside of telomeres using G4-specific antibodies.…”
Section: Introductionmentioning
confidence: 96%
“…We implemented the regular expression in python, returning a boolean expression dependent on the presence of a match in the presented sequence. The remaining three methods that were developed for the scoring of G4 forming potential are G4Hunter (Bedrat, Lacroix, and Mergny 2016) , Quadron (Sahakyan et al 2017) and Pqsfinder (Hon et al 2017) . We re-implemented G4Hunter in python as the available code did not appear functional (code available at our repository).…”
Section: Evaluation Schemementioning
confidence: 99%
“…A second wave of computational methods attempted to predict G4 locations after the publication of this dataset. Among the most accurate and still functional methods are G4Hunter (Bedrat, Lacroix, and Mergny 2016) , which expands the regular expression methods by scoring on G enrichment, Pqsfinder, which focuses on allowing customization for non-canonical G4s and was trained on the G4-Seq dataset (Hon et al 2017) , as well as Quadron (Sahakyan et al 2017) , a Machine Learning method trained on the G4-Seq dataset, utilizing the tree based Gradient Boosting Machine approach.…”
Section: Introductionmentioning
confidence: 99%
“…For our prediction, we used G-tracts -3 or 4 and loop length 1-20 nucleotide [63]. The results were further cross verified by using QGRS Mapper [64] and PGSFinder [65] tools.…”
Section: G {T1} [X] {L1} G {T1} [X] {L2} G {T1} [X] {L3} G {T1}mentioning
confidence: 99%