June 2023 Version

This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github:

Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments

Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.

Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.

Slides.zip contains all the slides used in the workshop.

Raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.

Vampirium setup This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github: Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline. Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology. Slides.zip contains all the slides used in the workshop. raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.

This archive contians datasets, exercises and slides used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course material can be found on Github.

Data.zip contains fastqc and multiqc examples of bulk RNAseq experiments, plus count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv).

Slides.zip contains all the slides used in the workshop.

In order to identify the transcriptional changes that correlate with kidney regeneration or fibrosis development, we performed a time-course bulk RNA-seq from whole-kidneys at 3, 7, 14, 28 and 42 days after the initial ischemic kidney from two distinct murine models.

BackgroundAlthough an increase in neutrophil count has been observed in patients with coronavirus disease 2019 (COVID-19), the relationship between the systemic neutrophil transcriptome and clinical course of COVID-19 remains unclear. Hence, we examined the relationship between the clinical course and RNA sequencing analysis results in COVID-19 patients.MethodsPeripheral blood samples were obtained from 28 patients with COVID-19-associated ARDS and 16 healthy controls. Bulk RNA sequencing was performed, and clustering analysis was used to explore relationships between gene expression and clinical characteristics. In a separate cohort, neutrophils were isolated from the peripheral blood of five COVID-19 patients with ARDS for single-cell RNA sequencing to further characterize the neutrophil subpopulations.ResultsIn bulk RNA sequencing analysis, COVID-19 patients with ARDS had elevated gene expression associated with neutrophils compared with healthy controls.Clustering analysis revealed no differences in the clinical characteristics of COVID-19 patients with ARDS. In the single-cell RNA sequencing analysis, clustering analysis showed that the patients were divided into two groups: those who could be weaned from the ventilator within 28 days and those who could not be weaned.ConclusionThese findings indicate that differences in neutrophil gene expression may have important clinical implications. This study may support the exploratory identification of genomic factors, such as neutrophil gene expression, that are relevant to clinical parameters.

This gtf has been generated based on https://doi.org/10.5281/zenodo.7510406 and extends 3' of genes using RefSeq and bulk RNA-seq from GSE106225, GSE113885 and time-course samples from GSE205781. All command lines can be found at https://github.com/lldelisle/extendMouseGTFUsingGastruloidData.