ArticleNAR genomics and bioinformatics2021
SpikChIP: a novel computational methodology to compare multiple ChIP-seq using spike-in chromatin.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- High-Efficiency Genomic Mapping of Chromatin-Associated Targets with CUT&RUN.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Highly quantitative measurement of differential protein-genome binding with PerCell chromatin sequencing.Cell reports methods · 2025Article
- Multiplexed chromatin immunoprecipitation sequencing for quantitative study of histone modifications and chromatin factors.Nature protocols · 2025Review
- EZH2 inhibition enhances T cell immunotherapies by inducing lymphoma immunogenicity and improving T cell function.Cancer cell · 2025Article
- The Wild West of spike-in normalization.Nature biotechnology · 2024Article
- SpikeFlow: automated and flexible analysis of ChIP-Seq data with spike-in control.NAR genomics and bioinformatics · 2024Article
- Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experiments with spikChIP.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Productive visualization of high-throughput sequencing data using the SeqCode open portable platform.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to evaluate cell- and disease-specific changes in the interacting strength of chromatin targets, ChIP-seq signal across multiple conditions must undergo robust normalization. However, this is not possible using the standard ChIP-seq scheme, which lacks a reference for the control of biological and experimental variabilities. While several studies have recently proposed different solutions to circumvent this problem, substantial analytical differences among methodologies could hamper the experimental reproducibility and quantitative accuracy. Here, we propose a computational method to accurately compare ChIP-seq experiments, with exogenous spike-in chromatin, across samples in a genome-wide manner by using a local regression strategy (spikChIP). In contrast to the previous methodologies, spikChIP reduces the influence of sequencing noise of spike-in material during ChIP-seq normalization, while minimizes the overcorrection of non-occupied genomic regions in the experimental ChIP-seq. We demonstrate the utility of spikChIP with both histone and non-histone chromatin protein, allowing us to monitor for experimental reproducibility and the accurate ChIP-seq comparison of distinct experimental schemes. spikChIP software is available on GitHub (https://github.com/eblancoga/spikChIP).
Identifiers
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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 OpenQuestion graph.