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 (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.
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The authors thank members of X.S.L and M. Brown's laboratories for their discussions. This work is supported by the US National Institutes of Health grant R01GM099409.
The authors declare no competing financial interests.
(Chromatin immunoprecipitation followed by next-generation DNA sequencing). A method to identify DNA-associated protein-binding sites.
A method in which micrococcal nuclease (MNase) digestion of chromatin is followed by next-generation sequencing to identify loci of high nucleosome occupancy.
(Formaldehyde-assisted isolation of regulatory elements followed by sequencing). A method to determine regulatory regions of the genome.
A method in which DNase I digestion of chromatin is combined with next-generation sequencing to identify regulatory regions of the genome, including enhancers and promoters.
An extension of chromosome conformation capture that uses next-generation sequencing to observe long-range interaction frequencies between different regions of the genome.
(Chromatin interaction analysis by paired-end tag sequencing). A method that combines chromatin immunoprecipitation-based enrichment and chromatin proximity ligation with paired-end next-generation sequencing to determine genome-wide chromatin interactions.
(Assay for transposase-accessible chromatin using sequencing). A method that combines next-generation sequencing with in vitro transposition of sequencing adapters into native chromatin.
- Random barcoding
A technique that ligates a diverse assortment of short random DNA sequences to an unamplified DNA sample, which can be used to distinguish duplicates produced by PCR from those originating from the unamplified DNA.
Controls that are known quantities of readily identifiable nucleic acids, which are added to a sample prior to critical steps in an experimental protocol. Such controls may be used for bias assessment and calibration purposes.
Flexible smooth nonlinear functions that are defined piecewise by polynomials for fitting nonlinear trends.
- Locally estimated scatterplot smoothing
(LOESS). A simple yet robust method for fitting nonlinear trends.
- Quantile regression
A statistical regression method that estimates the median or other quantile of the response variables and that is robust against outliers.
- Surrogate variable analysis
A statistical analysis to identify and model variables that are not explicitly annotated but that have measureable effects.
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Meyer, C., Liu, X. Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat Rev Genet 15, 709–721 (2014). https://doi.org/10.1038/nrg3788
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