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Detecting differential peaks in ChIP-seq signals with ODIN

ABSTRACT: Motivation: Detection of changes in DNA-protein interactions from ChIP-seq data is a crucial step in unraveling the regulatory networks behind biological processes. The simplest variation of this problem is the differential peak calling problem. Here one has to find genomic regions with ChIP-seq signal changes between two cellular conditions in the interaction of a protein with DNA. The great majority of peak calling methods can only analyse one ChIP-seq signal at a time and are unable to perform differential peak calling. Recently, a few approaches based on the combination of these peak callers with statistical tests for detecting differential digital expression have been proposed. However, these methods fail to detect detailed changes of protein-DNA interactions. Results: We propose ODIN; an HMM-based approach to detect and analyse differential peaks in pairs of ChIP-seq data. ODIN performs genomic signal processing, peak calling and p-value calculation in an integrated framework. We also propose an evaluation methodology to compare ODIN with competing methods. The evaluation method is based on the association of differential peaks with expression changes in the same cellular conditions. Our empirical study based on several ChIP-seq experiments from transcription factors, histone modifications and simulated data shows that ODIN outperforms considered competing methods in most scenarios.

ORGANISM(S): Mus musculus

PROVIDER: GSE57563 | GEO | 2015/01/07

SECONDARY ACCESSION(S): PRJNA246724

REPOSITORIES: GEO

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Detecting differential peaks in ChIP-seq signals with ODIN

Project description:Motivation: Detection of changes in DNA-protein interactions from ChIP-seq data is a crucial step in unraveling the regulatory networks behind biological processes. The simplest variation of this problem is the differential peak calling problem. Here one has to find genomic regions with ChIP-seq signal changes between two cellular conditions in the interaction of a protein with DNA. The great majority of peak calling methods can only analyse one ChIP-seq signal at a time and are unable to perform differential peak calling. Recently, a few approaches based on the combination of these peak callers with statistical tests for detecting differential digital expression have been proposed. However, these methods fail to detect detailed changes of protein-DNA interactions. Results: We propose ODIN; an HMM-based approach to detect and analyse differential peaks in pairs of ChIP-seq data. ODIN performs genomic signal processing, peak calling and p-value calculation in an integrated framework. We also propose an evaluation methodology to compare ODIN with competing methods. The evaluation method is based on the association of differential peaks with expression changes in the same cellular conditions. Our empirical study based on several ChIP-seq experiments from transcription factors, histone modifications and simulated data shows that ODIN outperforms considered competing methods in most scenarios. H3K4me1 and PU.1 occupancy in MPP, CDP, cDC and pDC

2015-01-07 | E-GEOD-57563 | biostudies-arrayexpress

Mus musculus

Project description:Differential Peak Calling of ChIP-Seq Signals with Replicates with THOR

| PRJNA296058 | ENA

ChIP-AP - An Integrated ChIP-Seq Analysis Pipeline

Project description:ChIP-Seq is a technique used to analyse protein-DNA interactions. The protein-DNA complex is pulled down using a protein antibody, after which sequencing and analysis of the bound DNA fragments is performed. A key bioinformatics analysis step is “peak” calling - identifying regions of enrichment. Benchmarking studies have consistently shown that no optimal peak caller exists. Peak callers have distinct selectivity and specificity characteristics which are often not additive and seldom completely overlap in many scenarios. In the absence of a universal peak caller, we rationalized one ought to utilize multiple peak-callers to 1) gauge peak confidence as determined through detection by multiple algorithms, and 2) more thoroughly survey the protein-bound landscape by capturing peaks not detected by individual peak callers owing to algorithmic limitations and biases. We therefore developed an integrated ChIP-Seq Analysis Pipeline (ChIP AP) which performs all analysis steps from raw fastq files to final result, and utilizes four commonly used peak callers to more thoroughly and comprehensively analyse datasets. Results are integrated and presented in a single file enabling users to apply selectivity and sensitivity thresholds to select the consensus peak set, the union peak set, or any sub-set in-between to more confidently and comprehensively explore the protein bound landscape. (https://github.com/JSuryatenggara/ChIP-AP).

2022-01-03 | GSE172355 | GEO

peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.

2018-05-14 | GSE105177 | GEO

Optimization of transcription factor binding map accuracy by utilizing their knockout-mouse models

Project description:Genome-wide assessment of protein-DNA interactions binding by ChIP-seq is a key technology to study transcription factor (TF) localization and regulation of gene-expression. In ChIP-seq, signal-to-noise-ratio as well as signal specificity depend on many variables including antibody quality, and efforts to improve ChIP-seq data thus far focused mostly on generating better reagents. Here we introduce KOIN (KO implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout-mice as a critical control for ChIP-seq. We tested our new peak calling strategy (KO implemented normalization = KOIN) on different ChIP-seq datasets to increase signal specificity and reduce noise.

2014-07-17 | E-GEOD-55317 | biostudies-arrayexpress

Ibf contributes to CP190 recruitment to chromatin [ChIP-seq]

Project description:ChIP-Seq peak calling of CP190 in wild-type and Ibf2 mutant Drosophila melanogaster third instar larvae

2014-04-18 | GSE52240 | GEO

MeRIP-seq of decitabine-treated WT HL-60 cells

Project description:We performed data preprocessing, m6A peak calling, motif analysis, meta-gene plotting, and differential RNA methylation analysis using multiple tools. Scripts are publically avalible https://github.com/abearab/imRIP.

2023-01-13 | GSE222824 | GEO

Regional centromeres in Candida lusitaniae lack pericentromeric heterochromatin (ChIP-seq)

Project description:The goal of this study is to identifiy centromere locations of Candida lusitaniae through ChIP-Seq analysis. We applied ChIP-Seq on two centromere proteins: Cse4 and Mif2. After peak calling, we identified one peak in each of the 8 supercontigs of Candida lusitaniae which may represent the potential centromere location.

2015-09-14 | GSE71659 | GEO

Ibf contributes to CP190 recruitment to chromatin [ChIP-seq]

Project description:ChIP-Seq peak calling of CP190 in wild-type and Ibf2 mutant Drosophila melanogaster third instar larvae Two wild-type and two Ibf2 mutant Drosophila melanogaster third instar larvae were sequenced.

2014-04-18 | E-GEOD-52240 | biostudies-arrayexpress

Quantifying Biopolymer Sequence Recognition using Biophysically Informed Machine Learning

Project description:Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene expression regulation and signal transduction. Given their importance, next-generation sequencing (NGS) based assays that characterize such recognition with high-throughput are increasingly being used to profile a range of protein classes and interactions. However, these methods do not measure the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to quantify sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from \textit{in vivo} data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it quantifies the absolute affinity of protein-ligand interactions. Its applicability extends beyond thermodynamic equilibrium binding, to the kinetics of kinase-substrate interactions. Altogether, ProBound provides a versatile algorithmic framework for understanding sequence recognition in a wide variety of biological contexts.

2021-06-02 | GSE175942 | GEO

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