In this book the author makes an “effort to render both mathematical equations and biology to numbers”. Following this premise, he works out a lot of illustrative examples to make biologists understand the mathematics and computational scientists understand the biology of a wide range of problems in bioinformatics.
In the first chapters the author introduces the mathematics of string-matching algorithms in FASTA and BLAST and explains pairwise and multiple sequence alignments. There are sections about aligning rRNA genes with the constraint of secondary structure and about alignments of nucleotide sequences against amino acid sequences. A whole chapter is devoted to contig assembly algorithms. Several chapters deal with gene and motif predictions. Position weight matrices, perceptrons and hidden Markov models are introduced. The Gibbs sampler is used to identify regulatory sequences in DNA or functional motifs in proteins.
The book also covers the analysis of proteins and proteomes, e.g., calculation of the isoelectric point, protein separation with 2D-PAGE, and mass spectrometry. The reader learns how to calculate expected 2D-PAGE separation patterns and how to use these in-silico gels to identify post-translational protein modifications. After estimating the molecular mass of peptides from MS outputs using charge deconvolutions, the author explains the different steps of peptide mass fingerprinting to identify proteins in spots of 2D-SDS-PAGE gels.
Two chapters introduce the reader to essential biological processes such as genome replication, transcription, and translation within the framework of molecular evolution. A long chapter is devoted to phylogenetic methods. It is an introduction to the construction of branching patterns and shall prepare the readers for more advanced topics in molecular phylogenetics. Other topics covered by the book are the characterization of translation efficiency and clustering algorithms such as UPGMA and self-organizing maps. The reader learns about the EM algorithm for maximum likelihood calculations and about Bayesian inference including Markov chain Monte Carlo algorithms for evaluating posterior probabilities.
The book is addressed to graduate students majoring in sciences and software engineering. Biologists with a sound knowledge of computer programming should be able to implement the presented algorithms in their own programs.