Primers in Computational Biology

Nature Biotechnology used to regularly feature primers on various topics in computational biology. Here's an incomplete listing based on what looked interesting to me. Some of these are old, but on topics that are fundamental enough not to go out of style. Lot's of these are just mini-tutorials in machine learning.

• What is dynamic programming? Sean R Eddy
• What is Bayesian statistics? Sean R Eddy
• What is a hidden Markov model? Sean R Eddy
• How does gene expression clustering work? Patrik D'haeseleer
• Inference in Bayesian networks Chris J Needham, James R Bradford, Andrew J Bulpitt & David R Westhead
• What are DNA sequence motifs? Patrik D'haeseleer
• How does DNA sequence motif discovery work? Patrik D'haeseleer
• What is a support vector machine? William S Noble
• How do shotgun proteomics algorithms identify proteins? Edward M Marcotte
• What are artificial neural networks? Anders Krogh
• What is principal component analysis? Markus Ringnér
• What is the expectation maximization algorithm? Chuong B Do & Serafim Batzoglou
• What are decision trees? Carl Kingsford & Steven L Salzberg
• Understanding genome browsing Melissa S Cline & W James Kent
• How to map billions of short reads onto genomes Cole Trapnell & Steven L Salzberg
• How to visually interpret biological data using networks Daniele Merico, David Gfeller & Gary D Bader
• How does multiple testing correction work? William S Noble
• What is flux balance analysis? Jeffrey D Orth, Ines Thiele & Bernhard Ø Palsson
• Analyzing 'omics data using hierarchical models Hongkai Ji & X Shirley Liu

...just in case you're in need of some bed-time reading or some mad comp-bio skillz. Sorry if some of these are behind a pay-wall, but there's usually a way around, under or over such walls.