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PePr specifications


Unique identifier OMICS_04058
Name PePr
Alternative name Peak Prioritization pipeline
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License GNU General Public License version 2.0
Computer skills Advanced
Stability Stable
Maintained Yes


No version available



  • person_outline Yanxiao Zhang

Publication for Peak Prioritization pipeline

PePr citations


Epigenetic impacts of stress priming of the neuroinflammatory response to sarin surrogate in mice: a model of Gulf War illness

PMCID: 5857314
PMID: 29549885
DOI: 10.1186/s12974-018-1113-9

[…] Hiseq 2500 platform. The average read per sample was 131,727,189 with 98.9% average mapped reads. There were four samples per group, with each sample having immunoprecipitated and input DNA sequenced.PePr (Python) and diffReps (Perl) packages were used for ChIP-seq analysis, as a recent paper by Steinhauser et al. [] suggested that both are good tools when biological replicates are available. The […]


RASSF1A uncouples Wnt from Hippo signalling and promotes YAP mediated differentiation via p73

Nat Commun
PMCID: 5789973
PMID: 29382819
DOI: 10.1038/s41467-017-02786-5

[…] Bowtie aligner using default parameters. We used Picard tools to remove sequencing PCR duplicates. The ChIP seq peaks were identified independently by MACS2 ( and PePr packages. The peaks called by both methods were subjected to further analyses. Both packages were run in narrow peaks detection mode and FDR threshold was 0.01. RNA seq reads were pre-processed t […]


Ritornello: high fidelity control free chromatin immunoprecipitation peak calling

Nucleic Acids Res
PMCID: 5716106
PMID: 28981893
DOI: 10.1093/nar/gkx799

[…] (i) it does not cover all artifactual regions, (ii) it is not generalizable to different cell types and (iii) it is only available for human and mouse. We note that a few peak calling tools, such as PePr (), optionally remove artifacts in regions where the local read coverage in the ChIP is similar to that in the matched control. However, these methods still require a matched control. Ritornello […]


Genomic binding of PAX8 PPARG fusion protein regulates cancer related pathways and alters the immune landscape of thyroid cancer

PMCID: 5351587
PMID: 28008156
DOI: 10.18632/oncotarget.14050
call_split See protocol

[…] The quality of the reads was assessed using FastQC []. ChIP-seq and input reads were aligned to the mouse reference genome (mm9) using BWA (version 0.5.9-r16) with default options. PePr (v1.0.9) [] was used to identify consistent PPFP peaks across the replicates using q-value < 1e-12 as the cutoff. Peaks that intersected with ENCODE blacklisted regions were removed []. The media […]


Recent advances in ChIP seq analysis: from quality management to whole genome annotation

Brief Bioinform
PMCID: 5444249
PMID: 26979602
DOI: 10.1093/bib/bbw023

[…] ] predict protein-binding events using an EM algorithm. SISSRs [], Peakzilla [] and Q [] focus on the equivalence between the read numbers of positive and negative strands to improve peak resolution. PePr [] and JAMM [] integrate information from multiple replicates to identify consistent or differential binding sites. The multiple hypothesis correction is performed to calculate false-discovery ra […]


A comprehensive comparison of tools for differential ChIP seq analysis

Brief Bioinform
PMCID: 5142015
PMID: 26764273
DOI: 10.1093/bib/bbv110

[…] of DR between the two conditions is not consistent either for both histone data sets, with some tools predicting more DR in one direction while other predict the opposite (, Supplementary Figure S1). PePr, for example, predicts a 12-fold higher number of peaks enriched in the NTKO condition, while diffBind predicts an 8-fold enrichment in the other direction.Unlike the FoxA1 data set, in the H3K36 […]


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PePr institution(s)
Department of Computational Medicine and Bioinformatics, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
PePr funding source(s)
Funding for this work was provided by a Career Development Award from National Institutes of Health (NIH) (SPORE P50 CA097248/CA/NCI), National Cancer Institute (NCI)/National Institute of Dental and Craniofacial Research (NIDCR) (R01CA158286-01) and National Institute of Environmental Health Sciences (NIEHS) (P30 ES017885-01).

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