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Bulk RNAseq experiment aimed at identifying DEG genes across three different tissues taken form GTEX samples; scRNAseq for annotating cell types and identifying corresponding marker genes on murine brain samples taken from PanglaoDB ; Both propedeutic activities were conducted within an academic course centered on the principles of Transcriptomic

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robertoamarie/bulk_and_sc_RNAseq_experiments

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bulk-RNAseq analysis

Project for Transcriptomics Course 2021 (MSc Bioinformatics for Computational Genomics) helded by Prof. Giulio Pavesi at Università degli Studi di Milano.

The vignette can be viewed here GitHub page

Description

The aim of this project is to perform a bioinformatic analysis on bulk RNA-seq samples for finding and characterizing DE genes.

The RNA-seq data are retrieved from Recount2 and the analysis is done on three tissues (three replicates per tissue): colon, heart and liver. Data for each tissue are in the “Ranged Summarized Experiment” format of Recount2.

For calling DE genes edgeR is used to investigated all pairwise comparisons:

  • Colon vs Heart
  • Colon vs Liver
  • Heart vs Liver

For each tissue a list of DE genes is obtained comprising:

  • genes found to be up-(down-)regulated with respect to either one of the other two
  • genes found to be up- (down-) regulated with respect to both the other two

Then, a functional enrichment analysis is performed in order to determine whether the enriched GO annotations are consistent wiht the fact that the genes are up-regulated (or down-regulated) in the specific tissue.

References

Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology, 2017. doi: 10.1038/nbt.3838.

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. doi: 10.1093/bioinformatics/btp616.

sc-RNAseq-brain analysis

Project for Transcriptomics Course 20__ (MSc Bioinformatics for Computational Genomics) helded by Prof. Giulio Pavesi at Università degli Studi di Milano.

The vignette can be viewed here ________

Description

The aim of this project is to perform a single cell RNA-Seq study for finding and characterizing cell subtypes

Single-cell RNA-Seq data are retrieved from PanglaoDB analyzed in this study “Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease” (Kinchen J. et al. 2018) aimed to define how the colonic mesenchyme remodels to fuel inflammation and barrier dysfunction in IBD (Inflammatory Bowel Disease).

The general workflow of the computational analysis here described is based on Seurat and follows the Seurat vignette, to find “well defined” clusters to which assign a cell type based on the DE/marker genes found in each.

References

"Kinchen J, Chen HH, Parikh K, et al. Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease." Cell. 2018;175(2):372-386.e17. doi:10.1016/j.cell.2018.08.067

"ScRNA-Seq Analysis Workflow." (Hillje, R. 2020.) GitHub Repository. https://romanhaa.github.io/projects/scrnaseq_workflow/; GitHub.

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Bulk RNAseq experiment aimed at identifying DEG genes across three different tissues taken form GTEX samples; scRNAseq for annotating cell types and identifying corresponding marker genes on murine brain samples taken from PanglaoDB ; Both propedeutic activities were conducted within an academic course centered on the principles of Transcriptomic

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