Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA79561 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR035678.fastq.gz | Fastqsanger.gz | |||
| SRR037439.fastq.gz | Fastqsanger.gz | |||
| SRR037440.fastq.gz | Fastqsanger.gz | |||
| SRR037441.fastq.gz | Fastqsanger.gz | |||
| SRR037442.fastq.gz | Fastqsanger.gz |
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Project description:The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomic data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filter on peptide and sample level, followed by an evaluation of several popular normalization methods and visualization of missing value. The user then selects an adequate normalization method and one of several imputation methods used for the subsequent comparison of different differential abundance/expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results is demonstrated on a Tandem Mass Tag mass spectrometry example data set, where the proteome of three different breast cancer cell lines was profiled with and without hydroxyurea treatment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for a differential abundance/expression analysis.
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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
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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.
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