By Paul G. Higgs
Within the present period of whole genome sequencing, Bioinformatics and Molecular Evolution offers an up to date and entire advent to bioinformatics within the context of evolutionary biology.
This obtainable textual content:
- provides a radical exam of series research, organic databases, development popularity, and functions to genomics, microarrays, and proteomics
- emphasizes the theoretical and statistical tools utilized in bioinformatics courses in a manner that's obtainable to organic technological know-how students
- places bioinformatics within the context of evolutionary biology, together with inhabitants genetics, molecular evolution, molecular phylogenetics, and their applications
- features end-of-chapter difficulties and self-tests to assist scholars synthesize the fabrics and observe their understanding
- is followed by way of a devoted site - www.blackwellpublishing.com/higgs - containing downloadable sequences, hyperlinks to net assets, solutions to self-test questions, and all paintings in downloadable layout (artwork additionally on hand to teachers on CD-ROM).
This very important textbook will equip readers with a radical figuring out of the quantitative equipment utilized in the research of molecular evolution, and should be crucial interpreting for complicated undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics classes.
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Additional resources for Bioinformatics and Molecular Evolution
A. A. 00 Nucleic acids, proteins, and amino acids l 29 In fact, there is a huge number of different clustering methods. This testifies to the fact that there are a lot of different people from a lot of different disciplines who find clustering useful for describing the patterns in their data. Unfortunately, it also means that there is not one single clustering method that everyone agrees is best. Different methods will give different answers when applied to the same data; therefore, there has to be some degree of subjectivity in deciding which method to use for any particular data set.
In well-aligned parts of protein sequences, we often find that all the residues in a column have the same color. The coloring scheme can thus help with constructing alignments and spotting important conserved motifs. When we look at protein sequence evolution (Chapter 4) it turns out that substitutions are more frequent between amino acids with similar properties. So, clustering according to properties is also relevant for evolution. In the broader context, however, clustering algorithms are very general and can be used for almost any type of data.
First, the decision to stop at six clusters is subjective. If we use the same method (cos θ and group average) and stop at seven, the difference is that the acids are separated from the amides. If we stop at five, cysteine is joined with the hydrophobic cluster. A second point to consider is the rule for similarity between clusters. In hierarchical clustering methods, we could in principle plot the similarity of the pair of clusters that we connect at each step of the process as a function of the number of steps made.
Bioinformatics and Molecular Evolution by Paul G. Higgs