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.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
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Project description:Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
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Project description:Systematic assessment of RNA-Seq experimental workflows for plasma cell-free transcriptome profiling
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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.
| EGAS00001003486 | EGA
Project description:Primary outcome(s): 1. Evaluation of genome abnormality and gene expression by omics analysis of tumor etc. 2. TCR repertoire analysis and RNA expression analysis etc. of T cells in tumor tissue and peripheral blood. 3. Prediction and identification of tumor neo-antigen and evaluation of immunogenicity etc. 4. Analyze ctDNA(16S rRNA PCR) and feces of patients with advanced solid malignancies over time to profile and monitor cancer-related genomic alterations 5. Assessment of the relationship between the analysis above and clinical pathological features or therapeutic efficacy etc.
| 2648665 | ecrin-mdr-crc
Project description:The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers is hindered by high variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in library amplification steps and sequencing depth variation. Our analytical contributions are the description of the Linear Quadratic (LQ) relation between the mean and variance of the sequence counts in an RNA-seq experiment and the derivation of the Poisson truncated mixture as the underlying probability distribution for RNA-seq data. Using a large number of sequencing datasets, we demonstrate here how one can use this modeling framework to calculate empirical correction factors for ligase bias, while accounting for random variation in sequence counts. Bias correction may remove the majority of bias in the absence of differential expression and more than 40% of the bias in the presence of variable expression of miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition.
2017-03-15 | GSE93399 | GEO
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.'
2015-07-23 | E-MTAB-3766 | biostudies-arrayexpress
Project description:Purpose: To understand differences of gene expression profiles between colorectal cell lines and their ENU mutants. Methods: RNA profiles were generated by deep sequencing using Illumina Next-seq. Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (GRCm38 release-95). Conclusions: Our study represents the detailed differential transcriptomic analysis using ENU mutants of colorectal tumors, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2024-06-07 | GSE176264 | GEO
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