Design and analysis of ChIP-seq experiments for DNA-binding proteins

By Peter V. Kharchenko, Michael Y. Tolstorukov, P. Park. Nature Biotechnology, 2008

930 citations recorded by the source corpus.

Recent progress in massively parallel sequencing platforms has enabled genome-wide characterization of DNA-associated proteins using the combination of chromatin immunoprecipitation and sequencing (ChIP-seq). Although a variety of methods exist for analysis of the established alternative ChIP microarray (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein-binding positions with high accuracy. Using previously reported data sets for three transcription factors, we illustrate methods for improving tag alignment and correcting for background signals. We compare the sensitivity and spatial precision of three peak detection algorithms with published methods, demonstrating gains in spatial precision when an asymmetric distribution of tags on positive and negative strands is considered. We also analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/e38425de9c943ae6e52f9e2f244378214082710e

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Citations in the corpus

ChIP-Seq: advantages and challenges of a maturing technology (2009, background): “The issue of saturation points has been examined in a recent paper through simulation studie”
csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows (2015, background): “The average fragment length can be estimated using cross-correlation plots (22) or can be manually specified.”
A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. (2014, background): “In case that the fragment length is unknown, it may be estimated from the bimodal pattern [14, 15] or the cross-strand correlation [16].”
Sequence and chromatin determinants of cell-type–specific transcription factor binding (2012, methodology): “TF binding sites were determined by peak calling using the SPP (Kharchenko et al. 2008) R package.”
Inferring direct DNA binding from ChIP-seq (2012, methodology): “The distribution of distances between binding sites predicted by motifs and ChIP-seq peaks has been used previously to compare the quality of ChIP-seq peak-calling algorithms (31).”
ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data (2009, neutral): “The function coverage summarizes [possibly ‘extended’, see Kharchenko et al. (2008)] alignments as vectors tallying the number of reads over each nucleotide in the reference.”
Defining functional DNA elements in the human genome (2014, neutral): “The lower bound estimate that 5% of the human genome has been under evolutionary constraint was based on the excess conservation observed in mammalian alignments (2, 3, 87) relative to a neutral reference (typically ancestral repeats, small introns, or fourfold degenerate codon positions).”
ENCODE Data in the UCSC Genome Browser: year 5 update (2012, methodology): “model-based analysis of ChIP-Seq (MACS) (21), SPP (15) and PeakSeq (16)], signal generators (e.”
Widespread plasticity in CTCF occupancy linked to DNA methylation (2012, neutral): “Briefly, peaks were called using SPP (Kharchenko et al. 2008).”
Integrative annotation of chromatin elements from ENCODE data (2012, neutral): “Supplementary Data are available at NAR Online: Supplementary Tables 1–7, Supplementary Figures 1–23, Supplementary Methods, Supplementary Results and Supplementary References [44–62].”
Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, methodology): “One reason might be that only for this experiment (ENCSR000DLW), peaks have not been called using the uniform ENCODE pipeline including SPP (Kharchenko et al. , 2008), but by another, “unknown” software.”
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (2015, background): “…microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences.”
Distinct Epigenomic Landscapes of Pluripotent and Lineage-committed Human Cells (2010, background): “Most peak-finding programs rely on the assumption of stranded distributions of tags at peaks, and as such cannot detect the broad domains typical of histone modifications (Kharchenko et al., 2008; Zhang et al., 2008).”