NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets

Methods Mol Biol. 2016;1418:243-65. doi: 10.1007/978-1-4939-3578-9_13.

Abstract

The combination of massive parallel sequencing with a variety of modern DNA/RNA enrichment technologies provides means for interrogating functional protein-genome interactions (ChIP-seq), genome-wide transcriptional activity (RNA-seq; GRO-seq), chromatin accessibility (DNase-seq, FAIRE-seq, MNase-seq), and more recently the three-dimensional organization of chromatin (Hi-C, ChIA-PET). In systems biology-based approaches several of these readouts are generally cumulated with the aim of describing living systems through a reconstitution of the genome-regulatory functions. However, an issue that is often underestimated is that conclusions drawn from such multidimensional analyses of NGS-derived datasets critically depend on the quality of the compared datasets. To address this problem, we have developed the NGS-QC Generator, a quality control system that infers quality descriptors for any kind of ChIP-sequencing and related datasets. In this chapter we provide a detailed protocol for (1) assessing quality descriptors with the NGS-QC Generator; (2) to interpret the generated reports; and (3) to explore the database of QC indicators (www.ngs-qc.org) for >21,000 publicly available datasets.

Keywords: ChIP-sequencing; Database; Galaxy; Massive parallel sequencing; Next-generation sequencing; Quality control.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatin Immunoprecipitation* / methods
  • Computational Biology / methods*
  • Computational Biology / standards
  • Databases, Genetic
  • Genomics / methods*
  • Genomics / standards
  • High-Throughput Nucleotide Sequencing* / methods
  • Humans
  • Quality Control*
  • Sequence Analysis, DNA* / methods
  • Software*
  • Web Browser