Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA296058 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2435580.fastq.gz | Fastqsanger.gz | |||
| SRR2435590_1.fastq.gz | Fastqsanger.gz | |||
| SRR2435590_2.fastq.gz | Fastqsanger.gz | |||
| SRR2435581.fastq.gz | Fastqsanger.gz | |||
| SRR2435582.fastq.gz | Fastqsanger.gz |
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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
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.
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Project description:Detecting differential peaks in ChIP-seq signals with ODIN
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Project description:We examined the viral epitranscriptome in EBV transformed lymphoblastoid cell lines (LcLs) and EBV-positive Burkitt's lymphoma, Akata cells, using methylated RNA immunoprecipitation followed by sequencing (MeRIP-seq). Biological replicates of ribo-RNA deleted mRNA of each cell type were prepared for MeRIP-seq followed by peak calling using the exome Peak package with settings for stringent peak calling on both strands of the genome.
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Project description:We report the in vivo androgen receptor (AR) binding sites in caput epididymis of intact SPARKI and wild-type mice using ChIP-sequencing. SPARKI (specificity affecting androgen receptor knock-in) mouse line has the second zinc finger of AR replaced by that of glucocorticoid receptors. In vivo analysis of SPARKI and wild-type AR genome-wide binding sites identified cis-element with less stringent sequence requirements that specify selective AR-binding sites. The ChIP experiments have been performed using antibody specific to AR and IgG non-specific antibody as a negative control. Examination of AR binding sites in epididymis of wild-type and SPARKI mice using ChIP-seq. Two biological replicates from intact wild-type and SPARKI mice and two control IgG samples were sequenced and used in peak calling. To increase the depth of the analysis, replicate ChIP-seq samples were merged and the concatanated samples were used in peak calling.
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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
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.
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