title: Improving genome-wide scans of positive selection by using protein isoforms of similar length.
abstract: Large-scale evolutionary studies often require the automated construction of alignments of a large number of homologous gene families. The majority of eukaryotic genes can produce different transcripts due to alternative splicing or transcription initiation, and many such transcripts encode different protein isoforms. As analyses tend to be gene centered, one single-protein isoform per gene is selected for the alignment, with the de facto approach being to use the longest protein isoform per gene (Longest), presumably to avoid including partial sequences and to maximize sequence information. Here, we show that this approach is problematic because it increases the number of indels in the alignments due to the inclusion of nonhomologous regions, such as those derived from species-specific exons, increasing the number of misaligned positions. With the aim of ameliorating this problem, we have developed a novel heuristic, Protein ALignment Optimizer (PALO), which, for each gene family, selects the combination of protein isoforms that are most similar in length. We examine several evolutionary parameters inferred from alignments in which the only difference is the method used to select the protein isoform combination: Longest, PALO, the combination that results in the highest sequence conservation, and a randomly selected combination. We observe that Longest tends to overestimate both nonsynonymous and synonymous substitution rates when compared with PALO, which is most likely due to an excess of misaligned positions. The estimation of the fraction of genes that have experienced positive selection by maximum likelihood is very sensitive to the method of isoform selection employed, both when alignments are constructed with MAFFT and with Prank(+F). Longest performs better than a random combination but still estimates up to 3 times more positively selected genes than the combination showing the highest conservation, indicating the presence of many false positives. We show that PALO can eliminate the majority of such false positives and thus that it is a more appropriate approach for large-scale analyses than Longest. A web server has been set up to facilitate the use of PALO given a user-defined set of gene families; it is available at http://evolutionarygenomics.imim.es/palo.
authors: Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.
authors: José Luis
authors: JL
authors: Villanueva-Cañas
authors: Steve
authors: S
authors: Laurie
authors: M Mar
authors: MM
authors: Albà
mesh_terms: D000465:Algorithms; D017124:Conserved Sequence / Q000235:genetics; D019143:Evolution, Molecular; D016678:Genome; D020407:Internet; D010802:Phylogeny; D020033:Protein Isoforms / Q000235:genetics; D012641:Selection, Genetic / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013045:Species Specificity
chemical_list: D020033:Protein Isoforms
references: Mol Biol Evol. 2012 Jan;29(1):1-5
references: 21772063
references: Mol Biol Evol. 2006 Feb;23(2):327-37
references: 16237209
references: Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W609-12
references: 16845082
references: Science. 2007 Apr 13;316(5822):222-34
references: 17431167
references: BMC Genomics. 2010 Oct 01;11:534
references: 20920313
references: Mol Biol Evol. 2007 May;24(5):1219-28
references: 17339634
references: Proc Natl Acad Sci U S A. 2007 May 1;104(18):7489-94
references: 17449636
references: PLoS Comput Biol. 2006 Apr;2(4):e38
references: 16683019
references: Genome Res. 2011 Jun;21(6):863-74
references: 21393387
references: Brief Bioinform. 2008 Jul;9(4):286-98
references: 18372315
references: Mol Biol Evol. 2000 Jan;17(1):68-74
references: 10666707
references: Genome Res. 2007 Dec;17(12):1797-808
references: 17984227
references: Science. 2008 Jun 20;320(5883):1632-5
references: 18566285
references: Nucleic Acids Res. 2002 Jul 15;30(14):3059-66
references: 12136088
references: Genome Biol Evol. 2009 Jun 05;1:114-8
references: 20333182
references: Mol Biol Evol. 2010 Oct;27(10):2257-67
references: 20447933
references: Mol Biol Evol. 2011 Sep;28(9):2513-20
references: 21398349
references: Mol Biol Evol. 2005 Mar;22(3):598-606
references: 15537804
references: Genome Res. 2009 Feb;19(2):327-35
references: 19029536
references: Nature. 2012 Sep 6;489(7414):101-8
references: 22955620
references: Trends Ecol Evol. 2006 May;21(5):230-2
references: 16697908
references: Mol Biol Evol. 2007 Aug;24(8):1586-91
references: 17483113
references: Mol Biol Evol. 2012 Jul;29(7):1837-49
references: 22319161
references: Science. 2008 Jan 25;319(5862):473-6
references: 18218900
references: Genome Res. 2009 May;19(5):922-33
references: 19411606
references: PLoS Genet. 2008 Aug 01;4(8):e1000144
references: 18670650
references: Genome Res. 2012 Mar;22(3):478-85
references: 22128134
references: Genetics. 2001 Jun;158(2):927-31
references: 11430355
references: Mol Biol Evol. 2005 Dec;22(12):2472-9
references: 16107592
references: Mol Biol Evol. 2004 Feb;21(2):236-9
references: 14595094
references: Mol Biol Evol. 2012 Apr;29(4):1125-39
references: 22049066
references: Proc Natl Acad Sci U S A. 2005 Jul 26;102(30):10557-62
references: 16000407
references: Mol Biol Evol. 2011 Jan;28(1):383-98
references: 20688808
references: Nucleic Acids Res. 2012 Jan;40(Database issue):D84-90
references: 22086963
references: Nat Rev Genet. 2011 Sep 07;12(10):671-82
references: 21897427
references: Science. 2003 Dec 12;302(5652):1960-3
references: 14671302
references: Nature. 2002 Dec 5;420(6915):520-62
references: 12466850