PeakSeq: Systematic Scoring of ChIP-Seq Experiments Relative to Controls

By Joel S. Rozowsky, G. Euskirchen, Raymond K. Auerbach, Zhengdong D. Zhang, Theodore Gibson, R. Bjornson, N. Carriero, Michael Snyder, Mark Gerstein. Nature Biotechnology, 2009

589 citations recorded by the source corpus.

Chromatin immunoprecipitation (ChIP) followed by tag sequencing (ChIP-seq) using high-throughput next-generation instrumentation is fast, replacing chromatin immunoprecipitation followed by genome tiling array analysis (ChIP-chip) as the preferred approach for mapping of sites of transcription-factor binding and chromatin modification. Using two deeply sequenced data sets for human RNA polymerase II and STAT1, each with matching input-DNA controls, we describe a general scoring approach to address unique challenges in ChIP-seq data analysis. Our approach is based on the observation that sites of potential binding are strongly correlated with signal peaks in the control, likely revealing features of open chromatin. We develop a two-pass strategy called PeakSeq to compensate for this. A two-pass strategy compensates for signal caused by open chromatin, as revealed by inclusion of the controls. The first pass identifies putative binding sites and compensates for genomic variation in the 'mappability' of sequences. The second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. Our scoring procedure enables us to optimize experimental design by estimating the depth of sequencing required for a desired level of coverage and demonstrating that more than two replicates provides only a marginal gain in information.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a041be5af6fd2301a029c2d2d5223f4c9385c47b

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Citations in the corpus

Computation for ChIP-seq and RNA-seq studies (2009, background): “They can be conceptually subdivided into following fundamental components: (1) a signal profile definition for a ChIP region, (2) a background model, (3) peak call criteria, (4) post call filtering of artifactual peaks, and (5) significance ranking of called peaks.”
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia (2012, methodology): “This result is expected, as studies of numerous factors by ENCODE and by other groups have repeatedly found a continuum of ChIP signal strength, rather than a sharply bounded and discrete set of positive sites (Rozowsky et al. 2009; Myers et al. 2011).”
ChIP-Seq: advantages and challenges of a maturing technology (2009, background): “For example, only 48% of the human genome is non-repetitive, but 80% is mappable with 30 bp reads and 89% is mappable with 70 bp read”
AlleleSeq: analysis of allele-specific expression and binding in a network framework (2011, methodology): “We used the GENCODE 3c set of gene annotation and binding sites were determined using PeakSeq (Rozowsky et al , 2009) with default parameters. that heterozygous sites showing apparent allele-speciﬁc behavior can be caused by regions in the genome that have been duplicated.”
Comprehensive Genome-wide Protein-DNA Interactions Detected at Single Nucleotide Resolution (2011, background): “Because unbound DNA contaminates the immunoprecipitate, ChIP only provides a set of statistically enriched high-occupancy binding regions, rather than a complete and precise set of bound locations (Peng et al., 2007; Rozowsky et al., 2009; Tuteja et al., 2009).”
A User's Guide to the Encyclopedia of DNA Elements (ENCODE) (2011, background): “For instance, a different RNA Pol2 antibody that recognizes unphosphorylated, non-elongating RNA Pol2 bound only at promoters requires fewer reads to reach saturation [105].”
Annotating non-coding regions of the genome (2010, methodology): “To correctly construct local thresholds, it is important to model or simulate an appropriate null process for the backgroun”
Fast Computation and Applications of Genome Mappability (2012, methodology): “unless the proper normalization factor is known [6].”
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection (2010, methodology): “Different programs have implemented models of varying complexity, such as Poisson [14,27], local Poisson [13], tdistribution [23], conditional binomial [15,21,28], and hidden Markov [29,30] models.”
A clustering approach for identification of enriched domains from histone modification ChIP-Seq data (2009, unknown): “We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions.”