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MAnorm2 for quantitatively comparing groups of ChIP-seq samples.

Shiqi TuMushan LiHaojie ChenFengxiang TanJian XuDavid J WaxmanYijing ZhangZhen Shao
Published in: Genome research (2020)
Eukaryotic gene transcription is regulated by a large cohort of chromatin-associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy for normalization of ChIP-seq data and assesses within-group variability of ChIP-seq signals based on an empirical Bayes framework. In this framework, MAnorm2 allows for abundant differential ChIP-seq signals between groups of samples as well as very different global within-group variability between groups. Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed existing tools for differential ChIP-seq analysis, especially when the groups of samples being compared had distinct global within-group variability.
Keyphrases
  • genome wide
  • single cell
  • high throughput
  • rna seq
  • circulating tumor cells
  • dna methylation
  • electronic health record
  • gene expression
  • copy number
  • machine learning
  • dna damage
  • data analysis
  • artificial intelligence