Open AccessGathering Computational Genomics and Proteomics to Unravel Adaptive Evolution
Author(s) -
Agostinho Antunes,
Maria João Ramos
Publication year - 2007
Publication title -
evolutionary bioinformatics online
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.502
H-Index - 32
ISSN - 1176-9343
DOI - 10.1177/117693430700300004
Subject(s) - selection (genetic algorithm) , genomics , adaptation (eye) , data science , computational biology , proteomics , computer science , bioinformatics , biology , genome , genetics , gene , artificial intelligence , neuroscience
A recent editorial in PLoS Biology by MacCallum and Hill (2006) pointed out the inappropriateness of studies evaluating signatures of positive selection based solely in single-site analyses. Therefore the rising number of articles claiming positive selection that have been recently published urges the question of how to improve the bioinformatics standards for reliably unravel positive selection? Deeper integrative efforts using state-of-the-art methodologies at the gene-level and protein-level are improving positive selection studies. Here we provide some computational guidelines to thoroughly document molecular adaptation.