CLIPper statistics

info info

Citations per year


Popular tool citations

chevron_left Peak calling chevron_right

Tool usage distribution map

Tool usage distribution map
info info

Associated diseases

Associated diseases
Want to access the full stats & trends on this tool?


CLIPper specifications


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




No version available



  • person_outline Gabriel Pratt <>

Publication for CLIPper

CLIPper citations


CLIP related methodologies and their application to retrovirology

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

[…] 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 […]


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

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

[…] by piranha [] and paralyzer []; the binding peaks of hits-clip data were defined by piranha and cims [] (see detail in “methods”). we also downloaded the binding peaks of eclip data, defined by clipper [], from the encode data portal ( first, we can see that different rbps display a very different number of binding peaks, ranging broadly from several […]


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

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

[…] 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 ., sgrnas (listed in ) were designed using the crispr tool […]


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

PMCID: 4932749
PMID: 27380775
DOI: 10.1186/s12915-016-0279-9

[…] 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 […]


Musashi 2 Attenuates AHR Signaling to Expand Human Hematopoietic Stem Cells

PMCID: 4880456
PMID: 27121842
DOI: 10.1038/nature17665
call_split See protocol

[…] 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 […]


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

PMCID: 4887338
PMID: 27018577
DOI: 10.1038/nmeth.3810

[…] improves library complexity for eclip experiments., examination of individual binding sites revealed comparable read density between iclip and eclip for rbfox2 binding sites (). using the clipper peak-calling algorithm, we observed that peaks from both iclip and eclip showed enrichment in proximal and distal introns and were significantly enriched for the rbfox2 motif (), in agreement […]

Want to access the full list of citations?
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.

CLIPper reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review CLIPper