Notes from Jean-Yves Sgro
FYI: I mentioned the dynamic small print web blog server based on MarkDown called “Jekyll”
I installed it and tried it on my Mac last year but eventually it was best to make my material available on the
Biochem web site.
Info about Jekyll: e.g. Building Static Sites with Jekyll https://code.tutsplus.com/articles/building-static-sites-with-jekyll–net-22211
– My BIOCHEM Tutorials are here: https://biochem.wisc.edu/bcrf/tutorials NOte: SCROLL DOWN!
– BIOTECH adverts for all day workshops – materials is not public.
– Also mentioned was KALLISTO: here is a nice web site summary: kallisto paper summary: Near-optimal RNA-seq quantification http://nextgenseek.com/2015/05/kallisto-paper-summary-near-optimal-rna-seq-quantification/
– Next month Oleg will talk about single cell RNA-seq
– Next time we may discuss RNA-Seq from info below:
Here are the links to the documentations I mentioned for an “ADVANCED RNA-SEQ” class I am thinking of offering.
Some remarks are specific to BRC computer as I send this to someone at Biotech before.
The links are in BOX (uwmadison.box.com) or on the web:
1) This is the first one that “inspired” me to offer the advanced version:
Introduction to differential gene expression analysis using RNA-seq
FYI I downloaded all the FASTQ (600+) into the workshop directory
/mnt/software/workshop/data/ RNASEQ/RNASEQ_R/FASTQ_ALL/
as fastq.gz files . It took over 1 day to download all of them
and they are 51Gb in total.
This is derived from a Course at COrnell Univ found here:
http://chagall.med.cornell. edu/RNASEQcourse/
The BAM and BAM.bai files listed on the course are saved in
/mnt/software/workshop/data/ RNASEQ/RNASEQ_R/
2) The tutorial suggested by Derek: Statistical analysis of RNA-Seq data
I FWD below the remarks I made to him about it’s content e.g the data
is with RNAi from an R package.
There are some nice, interesting statistical remarks, but I would not
be able to explain the derivation of the formulaes.
3) RNA-Seq workflow: a workflow using R: (web link in HTML – click on the “Get PDF” button for PDF)
RNA-Seq workflow: gene-level exploratory analysis and differential expression
On page 31 of my PDF there is an entry that is interesting: “Removing hidden batch effects” that could be appropriate and useful to an advanced class.
4) Guidelines for RNA-Seq data analysis – Full PDF
<h2>Guidelines for RNA-Seq data analysis</h2>
<h2>Guidelines for RNA-Seq data analysis</h2>
<h2>Guidelines for RNASeq data analysis</h2>
5) A survey of best practices for RNA-seq data analysis
6) DESEQ (PDF) and DESEQ2 (PDF)-
7) The “pasilla” dataset:
– Data preprocessing and creation of the data objects auxiliary for the DEXSeq package
– Generation of transcript counts from pasilla dataset with kallisto
It is an endless job to find methods online… I think this is a good set for the purpose.
The first step is to decide on an genome and dataset. then follow with some of the methods proposed.
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This topic was modified 7 years, 5 months ago by Yury Bukhman.
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This topic was modified 7 years, 5 months ago by Yury Bukhman.