Identifying and mitigating bias in next-generation sequencing methods for chromatin biology

• Published in: Nature reviews. Genetics Vol. 15; no. 11; pp. 709 - 721
• Main Authors: Meyer, Clifford A., Liu, X. Shirley
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.11.2014; Nature Publishing Group
• Subjects: 631/1647/2210; 631/1647/2210/2211; 631/208/514/2254; Agriculture; Animal Genetics and Genomics; Bias; Binding sites; Biology; Biomedicine; Cancer; Cancer Research; Chromatin; Chromatin - genetics; Chromatin Immunoprecipitation - methods; DNA sequencing; Experiments; Gene Function; Gene loci; Genetic Loci - genetics; Genomes; Genomics; High-Throughput Nucleotide Sequencing - methods; Human Genetics; Methods; Nucleotide sequencing; Physiological aspects; Prevention; Proteins; Research bias; review-article; Sequence Analysis, DNA - methods; Transcription factors; United States > US; Massachusetts
• ISSN: 1471-0056; 1471-0064; 1471-0064
• DOI: 10.1038/nrg3788

Key Points In next-generation sequencing (NGS) chromatin profiling experiments technical artefacts may be introduced at any stage, most importantly in fragmenting DNA, selecting the fragment population of interest, DNA amplification, DNA sequencing itself and read mapping to a reference genome. The effect of technical biases on experimental results will depend, to a large extent, on the genomic scale of the feature being analysed and the scale on which the bias is manifested. Bias will have the greatest effect when the length scale of the bias is similar to the scale of the feature. Genomic experiments should be planned to recognize the potential confounding effects of biases and the limits of the technology. Proper controls to understand and characterize the potential biases in chromatin profiling should be included and sequenced to sufficient depth in such experiments. Nuclease-induced fragmentation is usually biased by DNA sequence in ways that can produce patterns that might seem to have biological importance. Basic principles of statistical analysis should be applied to the analysis of chromatin profiling experiments: variability and bias should be taken into account, and the fit of statistical models to observed data should be characterized. Next-generation sequencing methods can be used to examine features of chromatin biology, although the outputs of these methods can be subject to various potential biases. This Review describes the ways in which biases can be introduced to such experiments and outlines methods to detect and mitigate their effect. Next-generation sequencing (NGS) technologies have been used in diverse ways to investigate various aspects of chromatin biology by identifying genomic loci that are bound by transcription factors, occupied by nucleosomes or accessible to nuclease cleavage, or loci that physically interact with remote genomic loci. However, reaching sound biological conclusions from such NGS enrichment profiles requires many potential biases to be taken into account. In this Review, we discuss common ways in which biases may be introduced into NGS chromatin profiling data, approaches to diagnose these biases and analytical techniques to mitigate their effect.