4.6 Article

Bios2mds: an R package for comparing orthologous protein families by metric multidimensional scaling

Journal

BMC BIOINFORMATICS
Volume 13, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/1471-2105-13-133

Keywords

Metric multidimensional scaling (MDS); Principal coordinate analysis; R program; Supplementary elements; Evolution; Protein family; Phylogeny

Funding

  1. Conseil General de Maine-et-Loire
  2. Centre Hospitalier Universitaire of Angers
  3. CNRS

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Background: The distance matrix computed from multiple alignments of homologous sequences is widely used by distance-based phylogenetic methods to provide information on the evolution of protein families. This matrix can also be visualized in a low dimensional space by metric multidimensional scaling (MDS). Applied to protein families, MDS provides information complementary to the information derived from tree-based methods. Moreover, MDS gives a unique opportunity to compare orthologous sequence sets because it can add supplementary elements to a reference space. Results: The R package bios2mds (from BIOlogical Sequences to MultiDimensional Scaling) has been designed to analyze multiple sequence alignments by MDS. Bios2mds starts with a sequence alignment, builds a matrix of distances between the aligned sequences, and represents this matrix by MDS to visualize a sequence space. This package also offers the possibility of performing K-means clustering in the MDS derived sequence space. Most importantly, bios2mds includes a function that projects supplementary elements (a.k.a. out of sample elements) onto the space defined by reference or active elements. Orthologous sequence sets can thus be compared in a straightforward way. The data analysis and visualization tools have been specifically designed for an easy monitoring of the evolutionary drift of protein sub-families. Conclusions: The bios2mds package provides the tools for a complete integrated pipeline aimed at the MDS analysis of multiple sets of orthologous sequences in the R statistical environment. In addition, as the analysis can be carried out from user provided matrices, the projection function can be widely used on any kind of data.

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