ArticleBMC bioinformatics2021
A flexible ChIP-sequencing simulation toolkit.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs.Nature genetics · 2026Article
- Chromatin accessibility landscape and its association with heterosis in maize hybrids.Nature communications · 2026Article
- Accurate chromatin marks peak calling with Omnipeak.Nucleic acids research · 2026Article
- Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data.Journal of molecular biology · 2024Article
- Unsupervised contrastive peak caller for ATAC-seq.Genome research · 2023Article
- Unsupervised Contrastive Peak Caller for ATAC-seq.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundA major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.
resultsWe present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips .
conclusionsChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.