If you are a complete beginner, don’t aim to ‘understand’ everything discussed in a course or lecture or book. It’s okay to be partially ignorant but still moving forward. Try to go through 60-70% content of
Complete Beginner
1. Bioinformatics Methods I and II, offered by Toronto University in massive-open-online-course (MOOC) Coursera.org has pretty good materials (video+tutorial).
2. On Shikkhok.com, a MOOC platform in Bengali language, there is a very short course on Bioinformatics, বায়োইনফরমেটিক্স পরিচিতি, offered by Bio-Bio-1 Foundation.
3. Reading books is the best way. I’ve found ‘Essential Bioinformatics’ by Jin Xiong an easy to understand book.
Intermediate
1. Start reading computational biology/bioinformatics-related research papers. It’s
The journal Nature has a series of educational articles where experts describe different concepts in Bioinformatics, Statistics and Data Visualizations. Dr. Xianjun Dong from Harvard University has compiled an index of those papers in a PDF document. I encourage everyone to use this resource as a syllabus.
2. Learn
- Codecademy.com has
great learning environment. - This site contains several slides at its very bottom section ‘Introduction to Programming for Bioinformatics in Python’. I actually
learnt Python from these slides. Just write the commands and try to getsame answers, do the exercise. It’s very easy to understand. - Rosalind.info is a site where one can learn and improve his/her skill
in bioinformatics programming. You can learn python in it’s ‘Python Village’ section. After that, I suggest solving problems in ‘Bioinformatics Stronghold’. The structure of Rosalind.info isvery interesting . Initially, the problems will be easy. But as you startto solve them, the problems will be harder.
Well, have a look into Rosalind Country Ranking, Bangladesh is currently in the 3rd position world-wide!
3. Learn R. More on this later.
Advanced
RNA-Seq
I have used different packages like DESeq2, EdgeR, Limma to do
Video Explaining RNA-Seq Normalization Methods
- A Gentle Introduction to RNA-seq
- A Gentle Introduction to ChIP-seq
- edgeR, part1: Library Normalization
- DESeq2, part1: Library Normalization
- edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
- RNA-seq – The Problem with Technical Replicates
- RPKM, FPKM, and TPM
Pipeline for doing RNA-Seq analysis
Also there are some pipe-line I have followed for doing the analysis. Here’s some link to them:
- From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline
- DESeq2 analysis template in R
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
[Updated: April 8, 2019]
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