A novel substitution matrix fitted to the compositional bias in Mollicutes improves the prediction of homologous relationships

• Published in: BMC bioinformatics Vol. 12; no. 1; p. 457
• Main Authors: Lemaitre, Claire, Barré, Aurélien, Citti, Christine, Tardy, Florence, Thiaucourt, François, Sirand-Pugnet, Pascal, Thébault, Patricia
• Format: Journal Article
• Language: English
• Published: London BioMed Central 24.11.2011; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Amino acids; Bacterial Proteins - genetics; Biochemistry, Molecular Biology; Bioinformatics; Biomedical and Life Sciences; Comparative genomics; Comparative studies; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Science; Evolutionary biology; Genetic aspects; Genomes; Genomics; Genomics - methods; Life Sciences; Microarrays; Mycoplasmatales; Physiological aspects; Probability; Proteins; Quantitative Methods; Research Article; Sequence Homology, Amino Acid; Software; Studies; Tenericutes - classification; Tenericutes - genetics; France; Domain Architecture; Pairwise Alignment; Core Genome; Substitution Matrix; Amino Acid Frequency; mollicutes; mathematical and computational biology; biochemistry and molecular biology; biotechnology and applied microbiology; orthologous predictions; substitution matrix
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-12-457

Background Substitution matrices are key parameters for the alignment of two protein sequences, and consequently for most comparative genomics studies. The composition of biological sequences can vary importantly between species and groups of species, and classical matrices such as those in the BLOSUM series fail to accurately estimate alignment scores and statistical significance with sequences sharing marked compositional biases. Results We present a general and simple methodology to build matrices that are especially fitted to the compositional bias of proteins. Our approach is inspired from the one used to build the BLOSUM matrices and is based on learning substitution and amino acid frequencies on real sequences with the corresponding compositional bias. We applied it to the large scale comparison of Mollicute AT-rich genomes. The new matrix, MOLLI60, was used to predict pairwise orthology relationships, as well as homolog families among 24 Mollicute genomes. We show that this new matrix enables to better discriminate between true and false orthologs and improves the clustering of homologous proteins, with respect to the use of the classical matrix BLOSUM62. Conclusions We show in this paper that well-fitted matrices can improve the predictions of orthologous and homologous relationships among proteins with a similar compositional bias. With the ever-increasing number of sequenced genomes, our approach could prove valuable in numerous comparative studies focusing on atypical genomes.