Yang Chen1, Shue Chen1, Elissa P Lei1. 1. Nuclear Organization and Gene Expression Section, Laboratory of Biochemistry and Genetics, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, MD, USA.
Abstract
MOTIVATION: ChIP-seq detects protein-DNA interactions within chromatin, such as that of chromatin structural components and transcription machinery. ChIP-seq profiles are often noisy and variable across replicates, posing a challenge to the development of effective algorithms to accurately detect differential peaks. Methods have recently been designed for this purpose but sometimes yield conflicting results that are inconsistent with the underlying biology. Most existing algorithms perform well on limited datasets. To improve differential analysis of ChIP-seq, we present a novel Differential analysis method for ChIP-seq based on limma (DiffChIPL). RESULTS: DiffChIPL is adaptive to asymmetrical or symmetrical data and can accurately report global differences. We used simulated and real datasets for transcription factor (TF) and histone modification marks to validate and benchmark our algorithm. DiffChIPL shows superior performance in sensitivity and false positive rate (FPR) in different simulations and control datasets. DiffChIPL also performs well on real ChIP-seq, CUT&RUN, CUT&Tag, and ATAC-seq datasets. DiffChIPL is an accurate and robust method, exhibiting better performance in differential analysis for a variety of applications including TF binding, histone modifications, and chromatin accessibility. AVAILABILITY: https://github.com/yancychy/DiffChIPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2022.
MOTIVATION: ChIP-seq detects protein-DNA interactions within chromatin, such as that of chromatin structural components and transcription machinery. ChIP-seq profiles are often noisy and variable across replicates, posing a challenge to the development of effective algorithms to accurately detect differential peaks. Methods have recently been designed for this purpose but sometimes yield conflicting results that are inconsistent with the underlying biology. Most existing algorithms perform well on limited datasets. To improve differential analysis of ChIP-seq, we present a novel Differential analysis method for ChIP-seq based on limma (DiffChIPL). RESULTS: DiffChIPL is adaptive to asymmetrical or symmetrical data and can accurately report global differences. We used simulated and real datasets for transcription factor (TF) and histone modification marks to validate and benchmark our algorithm. DiffChIPL shows superior performance in sensitivity and false positive rate (FPR) in different simulations and control datasets. DiffChIPL also performs well on real ChIP-seq, CUT&RUN, CUT&Tag, and ATAC-seq datasets. DiffChIPL is an accurate and robust method, exhibiting better performance in differential analysis for a variety of applications including TF binding, histone modifications, and chromatin accessibility. AVAILABILITY: https://github.com/yancychy/DiffChIPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2022.
Authors: Caryn S Ross-Innes; Rory Stark; Andrew E Teschendorff; Kelly A Holmes; H Raza Ali; Mark J Dunning; Gordon D Brown; Ondrej Gojis; Ian O Ellis; Andrew R Green; Simak Ali; Suet-Feung Chin; Carlo Palmieri; Carlos Caldas; Jason S Carroll Journal: Nature Date: 2012-01-04 Impact factor: 49.962
Authors: Maureen A Sartor; Craig R Tomlinson; Scott C Wesselkamper; Siva Sivaganesan; George D Leikauf; Mario Medvedovic Journal: BMC Bioinformatics Date: 2006-12-19 Impact factor: 3.169