Introduction to Bioinformatics and Computational Genomics
Eleven weeks from the command line to microbial community analysis, using one bacterium the whole way through.
Christian-Albrechts-Universitat zu Kiel
One organism, all term
Rhizobia and the legume symbiosis
Every exercise uses the same system: nitrogen-fixing bacteria that form root nodules on legumes. A bacterium is small enough to assemble, annotate and compare in a browser, and the analysis steps are the same ones you would run on wheat. The nod, nif and fix genes you meet in week 1 are the ones you annotate in week 5, compare in week 7 and measure the expression of in week 10.
- Week1
Bioinformatics, genomics, and our model organism
What the field is, how genome sequencing got from a 3,569-base phage to a 17-gigabase wheat, and the bacterium we will use for everything that follows.
SlidesCheck - Week2
The command line
The shell is the interface to every tool in this course. Read files, filter them, count things, and chain small tools into one answer.
SlidesLabCheck - Week3
Biological databases and sequence search
Where sequence data lives, why the archives are full of errors, and how BLAST finds a needle in two trillion bases fast enough to be useful.
SlidesExternalCheck - Week4
Sequencing technologies and genome assembly
How reads are made and what they cost you in quality, then the two graph algorithms that put them back together, and the metrics that tell you whether it worked.
SlidesLabCheck - Week5
Genome annotation
An assembly is a string until you say where the genes are. Find open reading frames, model them with hidden Markov models, then interrogate the annotation file.
SlidesLabCheck - Week6
Variant calling and structural variants
Once you have reads aligned to a reference, the question becomes which differences are real. This is the unit where the tools you run are the actual tools the field uses.
SlidesLabCheck - Week7
Gene content and pan-genomes
The E. coli result from week 1, done properly: cluster genes into families across strains, then split the result into core, shell and cloud.
SlidesLabCheck - Week8
Phylogenetic trees
How to read a tree without over-reading it, why you cannot simply try every tree, and what a bootstrap value is actually measuring.
SlidesNotebookCheck - Week9
R
Everything after this point is statistics on tables. R is how the field does that, and it runs in your browser.
SlidesNotebookCheck - Week10
Transcriptomics and differential expression
From a count table to a list of genes that changed, and the three sources of noise that stop you believing the first answer.
SlidesLabNotebookCheck - Week12
Microbiomes and microbial communities
Most bacteria have never been cultured. Sequencing lets you study them anyway, and almost all of the analysis is arithmetic on one table.
SlidesNotebookCheck