S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

Guanjue Xiang, Belinda M. Giardine, Shaun Mahony, Yu Zhang, Ross C. Hardison

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Summary: Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic datasets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic datasets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these datasets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics datasets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis.

Original languageEnglish (US)
Pages (from-to)3011-3013
Number of pages3
JournalBioinformatics
Volume37
Issue number18
DOIs
StatePublished - Sep 15 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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