Analysis of Information Content for Biological Sequences

Abstract

Decomposing a biological sequence into modular domains is a basic prerequisite to identifY functional units in biological molecules. The commonly used segmentation procedures usually have two steps: First) collect and align a set of sequences which arc homologous to the target sequence; then parse this multiple alignment into several blocks and identify the functionally important ones by using a semi~ automatic method; which combines manual analysis and expert knowledge. In this paper) we present a novel exploratory approach to parsing and analYlling the above multiple alignment. It is based on an flJlaly-sis~of-variance (ANOYA) type decomposition of the sequence information content. Unlike the traditional change--point method; our approach takes into account not only the composition biases but also the over dispersion effects among the blocks. More generally; our approach provides a better way for judbring some important residues in a protein. Our approach tested on the families of ribosomal proteins has a promising performance. Some subsets of residues critical to these proteins are found.

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

“…Computationally, the optimal segmentation can be difficult to obtain. We use a heuristic algorithm similar to that by Zhang in [11] and described in more detail by Yan in [10] .…”

“…This paper presents an outline of the segmentation algorithm (see Yan [10]) for multipattern consensus regions in aligned protein sequences, similar to Zhang [11], but applied to statistical variation patterns rather than the original amino acids. The segmentation algorithm analyzes the sequences after identifying the initial label of the statistical variation patterns for each aligned site.…”

“…The segmentation algorithm analyzes the sequences after identifying the initial label of the statistical variation patterns for each aligned site. The optimization of the segmentation algorithm can be computationally explosive, see Zhang [11]. We use a heuristic segmentation algorithm and adopt a split-and-merge strategy to divide the aligned sequences into multipattern consensus regions.…”

Monophyletic clustering and characterization of protein families

“…The protein sequences in each family bear certain degrees of diversity and homology, showing the traces of their evolution. See [16,17]. We aimed at giving a characterization of the group-wide evolutionary relationship among these protein families.…”