Page Comparison

IF YOU HAVE YOUR OWN DATA:

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1. RAW DATA:  Start with 6 raw fastq files from the following dataset:
   Bottomley et Al mouse dataset SRA026846.1 (http://dx.doi.org/10.1371/journal.pone.0017820)
   Single end RNA-Seq data generated on Illumina GAIIx for 2 strains of mice (B6 and D2) to detect differential striatal gene expression between the two nbred mouse strains. 
   We have provided three B6 and three D2 fastq files for you to work with as well as the MM10 reference files.

   Code Block
   title Get the data
   cds cd my_rnaseq_course cp -r /work2/projects/BioITeam/projects/courses/rnaseq_course/day_4_bottomley_raw_data . &

   
   

2. MAPPED FILES: Start with 6 bam files (mapped to the mouse MM9 genome) from the following dataset:
   Bottomley et Al mouse dataset SRA026846.1 (http://dx.doi.org/10.1371/journal.pone.0017820)
   Single end RNA-Seq data generated on Illumina GAIIx for 2 strains of mice (B6 and D2) to detect differential striatal gene expression between the two nbred mouse strains. 
   We have provided three B6 and three D2 fastq files for you to work with.
   
   

   Code Block
   title Get the data
   cds cd my_rnaseq_course cp -r /work2/projects/BioITeam/projects/courses/rnaseq_course/day_4_bottomley_mapped_data . &

   
   

3. GENE COUNT DATA: Start with a table providing per-gene read counts  for 3 treated and 4 untreatead Drosophila samples. From the following dataset:
   Brooks et al, 2011 dataset GSE18508 (PMID: 20921232)
   The experiment studied the effects of RNAi knockdown of Pasilla, the Drosophila melanogaster ortholog of mammalian NOVA1 and NOVA2, on the transcriptome. Treated samples have been RNAi depleted of mRNAs encoding RNA binding proteins and untreated samples have not.

   Code Block
   title Get the data
   cds cd my_rnaseq_course cp -r /work2/projects/BioITeam/projects/courses/rnaseq_course/day_4_brooks_gene_count_data . &

   
   

4. Want to find a different RNA-Seq dataset of your own?
   1. Search GEO for gene expression matrixes (Also download the series matrix file for metadata): https://www.ncbi.nlm.nih.gov/geo/
   2. COVID RNA-Seq datasets I've vetted: 
      1. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157859

      RNA-Seq of PBMC samples from 18 patients with mild, moderate or severe COVID-19 symptoms during the treatment, convalescence and rehabilitation stages.  Both mRNA and non-coding RNA were sequenced.

            ii. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171110

      Whole blood RNA-Seq of 44 severe covid-19 patients and 10 healthy donors used to identify predictors for disease severity.

            iii. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157103

      Large RNA-Seq dataset consisting of128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity (ICU or non-ICU).

           iv. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152418

      RNAseq of PBMCs in a group of 17 COVID-19 subjects and 17 healthy controls.

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