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Citations per year

Number of citations per year for the bioinformatics software tool CLIPper
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Tool usage distribution map

This map represents all the scientific publications referring to CLIPper per scientific context
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Protocols

CLIPper specifications

Information


Unique identifier OMICS_14764
Name CLIPper
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Gabriel Pratt

Publication for CLIPper

CLIPper citations

 (6)
library_books

CLIP related methodologies and their application to retrovirology

2018
Retrovirology
PMCID: 5930818
PMID: 29716635
DOI: 10.1186/s12977-018-0417-2

[…] a binding event is merely a result of transcript abundance or a more specific interaction between the RBP and its target RNA. Several peak calling programs have been developed and include Piranha [], CLIPper [], PIPE-CLIP [], Pyicos [] that work with all CLIP variants, and PARalyzer [] and wavClusteR that are specifically developed for PAR-CLIP analysis. For more details on the statistical models […]

library_books

Identification of high confidence RNA regulatory elements by combinatorial classification of RNA–protein binding sites

2017
Genome Biol
PMCID: 5591525
PMID: 28886744
DOI: 10.1186/s13059-017-1298-8

[…] ]; the remaining data are from high throughput RNA sequencing and crosslinking (HITS-CLIP) [] (Additional file : Table S1).For ENCODE’s eCLIP data, we directly downloaded the binding peaks defined by CLIPper [], with options –s hg19 –o –bonferroni –superlocal–threshold-method binomial–save-pickle, considering read 2 only (the read that is enriched for termination at the crosslink site) [].For PAR- […]

library_books

NEAT1 Scaffolds RNA Binding Proteins and the Microprocessor to Globally Enhance Pri miRNA Processing

2017
PMCID: 5766049
PMID: 28846091
DOI: 10.1038/nsmb.3455

[…] computed to identify differentially expressed miRNAs as summarized in .CLIP-seq for NONO and PSF as well as associated data analysis were as previously described,, and the peak calling was done with CLIPper. The distribution of binding was computed with program DeepTools2 (ref). The sequencing statistics was listed in . […]

call_split

Global identification of hnRNP A1 binding sites for SSO based splicing modulation

2016
BMC Biol
PMCID: 4932749
PMID: 27380775
DOI: 10.1186/s12915-016-0279-9
call_split See protocol

[…] sites, we extended these crosslinking sites by 10 bp to either side, or in reads with deletions it was defined as ±10 bases from the deletion site. Peak detection was performed on these regions using CLIPper []. We used the “superlocal” algorithm to account for local sequencing bias in 1-kb flanking regions, and the “random” algorithm to estimate p values within these windows. We set a threshold a […]

library_books

Musashi 2 Attenuates AHR Signaling to Expand Human Hematopoietic Stem Cells

2016
Nature
PMCID: 4880456
PMID: 27121842
DOI: 10.1038/nature17665

[…] CLIP-seq library using a custom script of the same method as, otherwise reads were kept at each nucleotide position when more than one read's 5' end was mapped. Clusters were then assigned using the CLIPper software with parameters --bonferroni --superlocal --threshold-. The ranked list of significant targets was calculated assuming a Poisson distribution, where the observed value is the number o […]

library_books

Robust transcriptome wide discovery of RNA binding protein binding sites with enhanced CLIP (eCLIP)

2016
Nat Methods
PMCID: 4887338
PMID: 27018577
DOI: 10.1038/nmeth.3810

[…] ion, and random-mer sequence to leave “Usable” reads. eCLIP datasets with multiple inline barcodes were merged at the usable read stage, and cluster identification was performed on usable reads using CLIPper (available at https://github.com/YeoLab/clipper/releases/tag/1.0) with options –s hg19 –o –bonferroni –superlocal --threshold-method binomial --save-pickle, considering read 2 only (the read t […]


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CLIPper institution(s)
Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi, India; Department of Bioinformatics, Banasthali Vidyapith, Banasthali, India; Kusuma School of Biological Sciences, IIT Delhi, India; Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
CLIPper funding source(s)
This work was supported by grants from the National Institute of Health (U54 HG007005, R01 HG004659, R01 GM084317, R01 NS075449, HL045182, DK094699, CA112970 and CA126551). This work was also supported by the Director, Office of Science, and Office of Biological & Environmental Research of the US Department of Energy under Contract No. DE-AC02-05CH1123, by US National Institutes of Health grant RO1 GM49662.

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