Monday, September 19, 2016

Primers in computational biology

I recently stumbled across this collection of computational biology primers in Nature Biotechnology. Many of these are old, but they're still great resources to get a fundamental understanding of the topic. Here they are in no particular order.


How does multiple testing correction work?

What is principal component analysis?

SNP imputation in association studies

How does gene expression clustering work?

What is a hidden Markov model?

What is a support vector machine?

What is the expectation maximization algorithm?

Where did the BLOSUM62 alignment score matrix come from?

What are DNA sequence motifs?

How does DNA sequence motif discovery work?

What are decision trees?

What is dynamic programming?

What is Bayesian statistics?

What are artificial neural networks?

How does eukaryotic gene prediction work?

How to map billions of short reads onto genomes

How to apply de Bruijn graphs to genome assembly

No comments:

Post a Comment

Creative Commons License
Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.