Ontology highlight
ABSTRACT:
PROVIDER: PRJNA339968 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR4053800.fastq.gz | Fastqsanger.gz | |||
| SRR4053810.fastq.gz | Fastqsanger.gz | |||
| SRR4053820.fastq.gz | Fastqsanger.gz | |||
| SRR4053801.fastq.gz | Fastqsanger.gz | |||
| SRR4053811.fastq.gz | Fastqsanger.gz |
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Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
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Project description:In this study, we aimed to comprehensively characterize in 113 myeloproliferative neoplasms (MPN) patients the mutational landscape of the granulocyte transcriptome using RNA sequencing data. We implemented workflows for fusion and variant calling, and differential splicing analysis for patients with hotspot SF3B1 mutations. Finally, we characterized the neoantigen landscape on individualized level taking each patient's HLA genotype (derived from the same RNA-seq dataset) into account.
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Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository
Project description:'Background: Large-scale sequencing of cDNA (RNA-seq) has been a boon to the quantitative analysis of transcriptomes. A notable application of significant biomedical relevance is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to represent RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the `percent spliced in'' measure or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, as well as to suggest improvements to commonly used workflows. Results: We assess the performance of representative workflows using synthetic data, and explore the effect of using non-standard counting bin definitions as input to a state-of-the-art inference engine (DEXSeq). Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects, and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene, and that isoform-level pre-filtering can considerably improve the false discovery rate (FDR) control. Conclusion: Count-based methods generally perform well in detection of differential transcript usage. Controlling the FDR at the imposed threshold is difficult, mainly in complex organisms, but can be improved by pre-filtering of the annotation catalog.'
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