SlimCodeML: An Optimized Version of CodeML for the Branch-Site Model

Schabauer, Hannes; Valle, M.; Pacher, Christoph; Stockinger, Heinz; Stamatakis, Alexandros; Robinson‐Rechavi, Marc; Yang, Ziheng; Salamin, Nicolas

doi:10.1109/ipdpsw.2012.88

Cited by 20 publications

(19 citation statements)

References 35 publications

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“…Selec on was analysed with the use of FEL, MEME and RELAX tools of the HyPhy Datamonkey server (Weaver et al 2018) (see SM8 for output files). Branch-site analysis was done in slimcodeml (Schabauer et al 2012) (see SM8 for input and output files).…”

Section: Fig 8 Amentioning

confidence: 99%

Reconstructing the evolutionary history of a functionally diverse gene family reveals complexity at the genetic origins of novelty

Koludarov

Suranse³

et al. 2019

Preprint

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Gene duplica on is associated with the evolu on of many novel biological func ons at the molecular level. The dominant view, o en referred to as "neofunc onaliza on", is that duplica ons precede many novel gene func ons by crea ng func onally redundant copies which are less constrained than singletons. Numerous alterna ve models have been formulated, however, including several (such as "subfunc onaliza on" and "escape from adap ve constraints") in which novel func ons emerge prior to duplica on. Unfortunately, few studies have reconstructed the evolu onary history of a func onally diverse gene family sufficiently well to differen ate between these models. In order to understand how gene families evolve and to what extent they fit par cular evolu onary models, here we examined the evolu on of the g2 family of phospholipase A2 in 92 genomes from all major lineages of Vertebrata. This family is evolu onarily important and has been co-opted for a diverse range of func ons, including innate immunity and venom. The genomic region in which this family is located is remarkably syntenic. This allowed us to reconstruct all duplica on events over hundreds of millions of years of evolu onary history using a novel method to annotate gene clusters, which overcomes many limita ons of automa c annota on. Surprisingly, we found that even at this level of resolu on our data could not be unambiguously fit to exis ng models of gene family evolu on. This suggests that each model may describe a part-truth that doesn't capture the full complexity of gene family evolu on. Graphical abstract Introduc onPerhaps the most important goal in evolu onary biology remains the explana on of the origins of novelty -how do new func ons, traits, and ul mately organisms arise? Gene duplica on is widely considered one of the most important mechanisms facilita ng the evolu on of novel func ons (Ohno 1970;Innan and Kondrashov 2010) . However, duplica on itself is o en treated as a "black box" -a form of "random" muta on -and numerous apparently contradictory models have been ar culated to explain the fates of duplicate genes (Conant and Wolfe 2008;Innan and Kondrashov 2010) . Although discussion of duplica on and redundancy arguably goes back to Darwin (who had no knowledge of genes and spoke of redundant "organs") and has a rich history in the 20th Century (Taylor and Raes 2004) , the "neofunc onaliza on" model of Susumu Ohno has loomed large in the field of molecular evolu on since the publica on of the seminal text "Evolu on by Gene Duplica on" in 1970 (Ohno 1970) . Briefly, this model describes gene duplica on (a neutral process) facilita ng the genesis of novelty by crea ng func onally redundant gene copies which, no longer constrained by the func onal role of the molecule encoded by the "parent" gene, enjoy a period of relaxed selec on in which neutral muta ons may accumulate. Any poten ally beneficial muta ons acquired during this period of neutral change may then be fixed by posi ve selec on.

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Section: Fig 8 Amentioning

confidence: 99%

Reconstructing the evolutionary history of a functionally diverse gene family reveals complexity at the genetic origins of novelty

Koludarov

Suranse³

et al. 2019

Preprint

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“…The codeml program of the PAML 4.7 (Yang, 2007) package and a specifically optimized version of codeml, slimcodeml (Schabauer et al, 2012), were used to estimate dN:dS. codeml and slimcodeml were used with the branch and branch-site models, respectively.…”

Section: Dn:ds Estimationmentioning

confidence: 99%

Evolutionary Dynamics of the Cellulose Synthase Gene Superfamily in Grasses

et al. 2015

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“…The most time consuming stages of the likelihood computation are matrix exponentiation and tree pruning. FastCodeML uses highly optimized algorithms to do matrix exponentiation (Schabauer et al, 2012) and state aggregation improves the time to perform the tree pruning steps of the likelihood calculations ( Fig. S1 A, B).…”

Section: Discussionmentioning

confidence: 99%

“…In this case the reuse of the eigenvectors and eigenvalues for a set of branches can improve computational performance (Schabauer et al, 2012;Valle et al, 2014). Other optimization techniques that involve, for example, transforming the problem of exponentiating an asymmetric matrix into a symmetric problem, or performing matrix-matrix multiplication rather than matrix-vectors for the estimation of conditional vectors, have also been shown to speedup the calculations of the likelihood (Schabauer et al, 2012). There has also been some progress on Bayesian computation, e.g.…”

Section: Introductionmentioning

confidence: 99%

State aggregation for fast likelihood computations in molecular evolution

Davydov

Robinson‐Rechavi²,

Salamin³

2015

Preprint

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Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large dimensionality of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the dimensionality of codon models and, thus, improve the computational performance of likelihood estimation on these models. We show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analysed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuoustime Markov process-based model with large dimensionality, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.

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SlimCodeML: An Optimized Version of CodeML for the Branch-Site Model

Cited by 20 publications

References 35 publications

Reconstructing the evolutionary history of a functionally diverse gene family reveals complexity at the genetic origins of novelty

Reconstructing the evolutionary history of a functionally diverse gene family reveals complexity at the genetic origins of novelty

Evolutionary Dynamics of the Cellulose Synthase Gene Superfamily in Grasses

State aggregation for fast likelihood computations in molecular evolution

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