Accounting for gene rate heterogeneity in phylogenetic inference

被引:11
作者
Bevan, Rachel B.
Bryant, David
Lang, B. Franz
机构
[1] McGill Univ, McGill Ctr Bioinformat, Montreal, PQ H3A 2B4, Canada
[2] Univ Montreal, Program Evolutionary Biol, Canadian Inst Adv Res Ctr Robert Cedergren, Dept Biochim, Montreal, PQ H3T 1J4, Canada
[3] Univ Auckland, Dept Math, Auckland 1, New Zealand
基金
加拿大自然科学与工程研究理事会; 加拿大健康研究院;
关键词
AIC; gene rates; phylogenetic integration; phylogenomics; rate heterogeneity;
D O I
10.1080/10635150701291804
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Traditionally, phylogenetic analyses over many genes combine data into a contiguous block. Under this concatenated model, all genes are assumed to evolve at the same rate. However, it is clear that genes evolve at very different rates and that accounting for this rate heterogeneity is important if we are to accurately infer phylogenies from heterogeneous multigene data sets. There remain open questions regarding how best to incorporate gene rate parameters into phylogenetic models and which properties of real data correlate with improved fit over the concatenated model. In this study, two methods of accounting for gene rate heterogeneity are compared: the n-parameter method, which allows for each of the n gene partitions to have a gene rate parameter, and the a-parameter method, which fits a distribution to the gene rates. Results demonstrate that the n-parameter method is both computationally faster and in general provides a better fit over the concatenated model than the a-parameter method. Furthermore, improved model fit over the concatenated model is highly correlated with the presence of a gene with a slow relative rate of evolution. [AIC; gene rates; phylogenetic integration; phylogenomics; rate heterogeneity].
引用
收藏
页码:194 / 205
页数:12
相关论文
共 31 条
[1]   NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].
AKAIKE, H .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723
[2]   The analysis of 100 genes supports the grouping of three highly divergent amoebae:: Dictyostelium, Entamoeba, and Mastigamoeba [J].
Bapteste, E ;
Brinkmann, H ;
Lee, JA ;
Moore, DV ;
Sensen, CW ;
Gordon, P ;
Duruflé, L ;
Gaasterland, T ;
Lopez, P ;
Müller, M ;
Philippe, H .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2002, 99 (03) :1414-1419
[3]   Calculating the evolutionary rates of different genes: A fast, accurate estimator with applications to maximum likelihood phylogenetic analysis [J].
Bevan, RB ;
Lang, BF ;
Bryant, D .
SYSTEMATIC BIOLOGY, 2005, 54 (06) :900-915
[4]   An empirical assessment of long-branch attraction artefacts in deep eukaryotic phylogenomics [J].
Brinkmann, H ;
Van der Giezen, M ;
Zhou, Y ;
De Raucourt, GP ;
Philippe, H .
SYSTEMATIC BIOLOGY, 2005, 54 (05) :743-757
[5]   Archaea sister group of bacteria? Indications from tree reconstruction artifacts in ancient phylogenies [J].
Brinkmann, H ;
Philippe, H .
MOLECULAR BIOLOGY AND EVOLUTION, 1999, 16 (06) :817-825
[6]   PARTITIONING AND COMBINING DATA IN PHYLOGENETIC ANALYSIS [J].
BULL, JJ ;
HUELSENBECK, JP ;
CUNNINGHAM, CW ;
SWOFFORD, DL ;
WADDELL, PJ .
SYSTEMATIC BIOLOGY, 1993, 42 (03) :384-397
[7]  
BURNHAM K.P., 2002, MODEL SELECTION MULT, P352, DOI DOI 10.1007/B97636
[8]   Choosing the best genes for the job: The case for stationary genes in genome-scale phylogenetics [J].
Collins, TM ;
Fedrigo, O ;
Naylor, GJP .
SYSTEMATIC BIOLOGY, 2005, 54 (03) :493-500
[9]   Closing the gap between rocks and clocks [J].
Cranston, K ;
Rannala, B .
HEREDITY, 2005, 94 (05) :461-462
[10]   Analyses of RNA polymerase II genes from free-living protists: Phylogeny, long branch attraction, and the eukaryotic big bang [J].
Dacks, JB ;
Marinets, A ;
Doolittle, WF ;
Cavalier-Smith, T ;
Logsdon, JM .
MOLECULAR BIOLOGY AND EVOLUTION, 2002, 19 (06) :830-840