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ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Windows 10 home keygen download. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions.

They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome. Introduction High-throughput sequencing (HTS) has become a standard method in genomics research and has almost completely superseded array-based technologies, owing to the ever-decreasing costs and the variety of different assays that are based on short read sequencing. Most array-based assays have now a counterpart based on HTS, with a generally improved dynamic range in the signal.

Genome sequence (whole genome or exome), DNA-methylation (whole genome bisulfite sequencing), gene expression (RNA-seq, CAGE-seq), chromatin accessibility (DNAse1-seq, ATAC-seq, FAIRE-seq) or chromatin interaction (ChIP-seq) all belong to the standard repertoire of genomic studies, and follow standardized protocols. However, the broad availability of these approaches should not hide the fact that they are still highly complex, requiring a number of experimental steps that can lead to considerable differences in the readout for a same assay performed by different groups [, ]. Large-scale consortia such as ENCODE or Roadmap, Epigenomics, which rely on different sequencing centers for the data collection, have faced the problem of harmonizing the results obtained by different centers, which require systematic bias correction before data integration can be achieved in a meaningful way. Clearly, the more complex the experimental setup is, the more it is subject to biases, which can be introduced in the different steps of the experimental protocol or the downstream analysis [, ]. Among the approaches listed previously, those based on immunoprecipitation are the more complex ones, as the antibody-based precipitation usually represents a critical step, and leads to variations in the precipitation efficiency, the cross-reaction probability, conditioned by the quality of the antibody. Hence, the reproducibility of the assays is often limited, especially in cases with additional constraints, for example low input material.

The amount of noise in the data can be substantial: a standard measure of the signal-to-noise ratio is the FRiP (fraction of reads in peaks), which measures how many sequencing reads are located in enriched regions, compared with the total amount []. In the ENCODE project, this ratio was in the range of a few percent, indicating that the amount of noise is >90%. To detect consistent signal between replicates, special statistical methods have been developed such as the Irreproducible Discovery Rate [], which allow to detect consistent signal between replicates. Detecting differential gene expression between several conditions is one of the most common analysis steps since the advent of genome-wide expression measurements based on microarrays, and a considerable literature has been dedicated to the development of solid statistical procedures. Expression measurements based on HTS has also lead to the development of new tools or the adjustment of existing procedures.

Differential analysis are however not restricted to gene expression but can in principle be extended to any quantitative assay, such as the measurement of DNA methylation levels or the enrichment of ChIP signal. Accordingly, many tools are available either to detect differential gene expression or to delimit differentially methylated regions (DMRs). Similarly, a number of tools and methods have been published to detect differences in ChIP signal between several conditions []. If the underlying question is similar for ChIP-seq experiments (where are the regions showing significant differential signal between two conditions?), there are a number of particularities that make this simple question particularly challenging in the case of ChIP-seq data sets, as compared with RNA-seq or whole-genome bisulfite sequencing.