Adaptive local false discovery rate procedures for highly spiky data and their application to RNA sequencing data of yeast SET4 deletion mutants
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ABSTRACT:
PROVIDER: PRJNA726542 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14370193_1.fastq.gz | Fastqsanger.gz | |||
| SRR14370193_2.fastq.gz | Fastqsanger.gz | |||
| SRR14370194_1.fastq.gz | Fastqsanger.gz | |||
| SRR14370194_2.fastq.gz | Fastqsanger.gz | |||
| SRR14370195_1.fastq.gz | Fastqsanger.gz |
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Project description:Genome-wide gene expression data were genertated for wildtype and set4 mutant yeast to determine the contribution of Set4 to gene expression under standard growth conditions. Illumina-based sequencing was performed on strand-specific libraries prepared from mRNA purified from yeast under standard (YPD-rich medium, no stress) growth conditions. Reads were procesed for qualiy control, mapped to the genome, and differential gene expression was determined using based on log fold-change difference between set4 mutants and wildtype. The distribution of the data was highly spiky and an adaptive local false discovery rate analysis was applied to analyze these data.
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Project description:Chromatin dynamics are central to the regulation of gene expression and genome stability. In order to improve understanding of the factors regulating chromatin dynamics, the genes encoding these factors are deleted and the differential gene expression profiles are determined using approaches such as RNA sequencing. Here, we analyzed a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). The main theme of this paper focuses on identifying the highly differentially expressed genes in cells deleted for Set4 (referred to as Set4 Δ mutant dataset) compared to the wild-type yeast cells. The Set4 Δ mutant data produce a spiky distribution on the log-fold changes of their expressions, and it is reasonably assumed that genes which are not highly differentially expressed come from a mixture of two normal distributions. We propose an adaptive local false discovery rate (FDR) procedure, which estimates the null distribution of the log-fold changes empirically. We numerically show that, unlike existing approaches, our proposed method controls FDR at the aimed level (0.05) and also has competitive power in finding differentially expressed genes. Finally, we apply our procedure to analyzing the Set4 Δ mutant dataset.
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Project description:Genome-wide gene expression data were generated for wildtype and set4 mutant yeast under aerobic and hypoxic conditions to determine the contribution of Set4 to gene expression. Illumina-based sequencing was performed on strand-specific libraries prepared from mRNA purified from yeast grown in rich medium aerobically or under hypoxia. Reads were processed for qualiy control, mapped to the genome, and differential gene expression was determined based on log fold-change differences between set4 mutants and wildtype.
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Project description:Purpose: The goal of this study was to globally characterize the transcript levels of genes in Saccharomyces cerevisiae WT and set4∆ strains during hypoxia. Using transcriptome profiling of isogenic WT and set4∆ strains grown under aerobic or 8 hours of hypoxia. We analyzed the changes in gene expression that occur during aerobic and hypoxic conditions and identified sets of upregulated and downregulated gene expression.
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Project description:The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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