Nevertheless, one recurrent conclusion is that the performance of different peak callers depends on the particular data set examined ( Laajala et al. However, only a small number of data sets were used in these studies. 2009 Wilbanks and Facciotti 2010 Kim et al. Numerous peak calling algorithms have been systematically compared in many studies ( Laajala et al. Most commonly, read distribution is modeled by a Poisson or negative binomial distribution ( Pepke et al. Finally, a variety of statistical tests are applied to identify peaks as regions with significantly increased read density. Regions for hypothesis testing are chosen with a sliding window, or alternatively, some programs generate a continuous coverage and specify a minimum height criterion in order to report peaks. Following fragment-length estimation, in order to better represent the original DNA fragment rather than just the 5′ sequence read, most peak calling algorithms either shift the read in the 3′ direction toward the peak center or computationally extend tags to the estimated length of the original fragments. Therefore, an initial step in many algorithms is the estimation of the actual fragment-length distribution. Around true binding sites of the target protein, this results in a characteristic bimodal distribution of reads on the forward and reverse strands, which depends on the distribution of fragment lengths in the library and can be exploited for signal detection and evaluation. The sequence reads represent only the 5′ ends of the coprecipitated DNA fragments, which are generally 100- to 500-bp in length. Numerous peak calling algorithms have been presented, most of which address the same basic analytical tasks with methods to estimate the mean DNA fragment length from the data, to shift or extend the reads toward the center of the binding peak, to identify candidate peak regions, and to evaluate the statistical significance of the read depth of the candidate peaks. 2009).Ī crucial challenge in the computational analysis of ChIP-seq data pertains to finding peaks in ChIP-seq data that correspond to protein–DNA binding sites. The ChIP-seq procedure involves formaldehyde-mediated crosslinking of chromatin followed by fragmentation of protein–DNA complexes into short fragments, which are then subjected to immunoprecipitation using an antibody directed against a protein of interest (e.g., a transcription factor or a modified histone), thereby enriching genomic segments that are bound by the protein of interest prior to sequencing ( Laajala et al. ChIP-seq can identify both sharp peaks typically associated with sequence-specific transcription factors, as well as broad histone-modification signals ( Park 2009 Peng and Zhao 2011), and has become a central technology for the investigation of gene regulation. The method is implemented in C+l+ and is freely available under an open source license.Ĭhromatin immunoprecipitation (ChIP) followed by massively parallel sequencing (ChIP-seq) is designed to detect genome-wide protein–DNA interaction. We show that Q has superior performance in the delineation of double RNAPII and H3K4me3 peaks surrounding transcription start sites related to a better ability to resolve individual peaks. We show that our method not only is substantially faster than several competing methods but also demonstrates statistically significant advantages with respect to reproducibility of results and in its ability to identify peaks with reproducible binding site motifs. In this work, we introduce an algorithm, Q, that uses an assessment of the quadratic enrichment of reads to center candidate peaks followed by statistical analysis of saturation of candidate peaks by 5′ ends of reads. Computational ChIP-seq peak calling infers the location of protein–DNA interactions based on various measures of enrichment of sequence reads. Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is a powerful technology to identify the genome-wide locations of transcription factors and other DNA binding proteins.
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