[Yury Bukhman]

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.

 

• This topic was modified 7 years, 6 months ago by Yury Bukhman.
• This topic was modified 7 years, 6 months ago by Yury Bukhman.

[Yury Bukhman]

Note from Yury Bukhman

The paper on using Docker and Continuous Integration to set up reproducible data analysis workflows that I have mentioned is here: http://www.nature.com.ezproxy.library.wisc.edu/nbt/journal/v35/n4/full/nbt.3780.html

 

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