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Algorithmic considerations when analysing capture Hi-C data.

Linden Disney-HoggBen KinnersleyRichard S Houlston
Published in: Wellcome open research (2020)
Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promotor capture Hi-C data generated on 28 different cell lines as a case study.
Keyphrases
  • electronic health record
  • big data
  • quality improvement
  • machine learning
  • gene expression
  • copy number
  • deep learning
  • data analysis
  • genome wide
  • drug delivery
  • neural network