Simple and accurate estimation of ancestral protein sequences

Hall, Barry G.

doi:10.1073/pnas.0508991103

Cited by 52 publications

(45 citation statements)

References 24 publications

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“…Several studies have shown that ASR works reasonably well (e.g., Koshi and Goldstein 1996;Zhang and Nei 1997;Cai et al 2004;Hall 2006;Williams et al 2006). Importantly, a common assumption of ASR methods is that all the positions in the alignment have evolved under the same phylogeny, and therefore that there is a unique, single most recent common ancestor (MRCA) for all the sequences in the sample.…”

Section: A Ncestral Sequence Reconstruction (Asr) Is Onementioning

confidence: 99%

The Effect of Recombination on the Reconstruction of Ancestral Sequences

Arenas

Posada

2010

Genetics

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While a variety of methods exist to reconstruct ancestral sequences, all of them assume that a single phylogeny underlies all the positions in the alignment and therefore that recombination has not taken place. Using computer simulations we show that recombination can severely bias ancestral sequence reconstruction (ASR), and quantify this effect. If recombination is ignored, the ancestral sequences recovered can be quite distinct from the grand most recent common ancestor (GMRCA) of the sample and better resemble the concatenate of partial most recent common ancestors (MRCAs) at each recombination fragment. When independent phylogenetic trees are assumed for the different recombinant segments, the estimation of the fragment MRCAs improves significantly. Importantly, we show that recombination can change the biological predictions derived from ASRs carried out with real data. Given that recombination is widespread on nuclear genes and in particular in RNA viruses and some bacteria, the reconstruction of ancestral sequences in these cases should consider the potential impact of recombination and ideally be carried out using approaches that accommodate recombination.

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Section: A Ncestral Sequence Reconstruction (Asr) Is Onementioning

confidence: 99%

The Effect of Recombination on the Reconstruction of Ancestral Sequences

Arenas

Posada

2010

Genetics

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“…One approach to understanding ancestral states follows a present-day-backwards strategy, whereby genomic sequences from extant (modern) organisms are incorporated into evolutionary models that estimate the extinct (ancient) character states of genes no longer present on Earth (Fitch 1971;Shih et al 1993;Benner 1995;Koshi and Goldstein 1996;Schultz et al 1996;Cunningham 1999;Omland 1999;Pagel 1999;Schultz and Churchill 1999;Chang and Donoghue 2000;Thornton 2004;Hall 2006;Liberles 2007). These inferred ancestral gene sequences act as hypotheses that can be tested in the laboratory through the resurrection of the ancestral proteins themselves.…”

Section: Ancestral Sequence Reconstructionmentioning

confidence: 99%

Deep Phylogeny--How a Tree Can Help Characterize Early Life on Earth

Gaucher

Kratzer²,

Randall³

2009

Cold Spring Harbor Perspectives in Biology

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“…To test whether this type of phylogenetic information will aid in identifying homologous ncRNAs, we used an empirical Bayesian approach (Huelsenbeck and Bollback 2001) to stochastically sample ancestral sequences (ASR) (details of the sampling can be found in the Supplemental Methods). The Bayesian approach has been proven (in the case of proteins) to be the most accurate method (Hall 2006). The phylogenetic tree and model parameters were estimated using MrBayes v3 (Huelsenbeck and Ronquist 2001).…”

Section: Phylogenetic Informationmentioning

confidence: 99%

Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

Freyhult¹,

Bollback²,

Gardner³

2006

Genome Res.

121

100

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Homology search is one of the most ubiquitous bioinformatic tasks, yet it is unknown how effective the currently available tools are for identifying noncoding RNAs (ncRNAs). In this work, we use reliable ncRNA data sets to assess the effectiveness of methods such as BLAST, FASTA, HMMer, and Infernal. Surprisingly, the most popular homology search methods are often the least accurate. As a result, many studies have used inappropriate tools for their analyses. On the basis of our results, we suggest homology search strategies using the currently available tools and some directions for future development.[Supplemental material is available online at www.genome.org and http://www.binf.ku.dk/ ∼ pgardner/bralibase/ bralibase3/.]Compared with the relatively trivial task of protein homology search, ncRNA homology search is more challenging because of the fact that intra-and intermolecular base pairs are, in evolutionary terms, preserved to a higher degree than the sequence. The wobble GU and other noncanonical base pairs allow RNA sequences to evolve seemingly unrelated sequences along nearly neutral paths through structure space (e.g., A · U ↔ G · U ↔ G · C). Thus, specialized homology search techniques, such as nucleotide specific scoring schemes (States et al. 1991), profile hidden Markov model (profile HMMs) (Haussler et al. 1993;Krogh et al. 1994), and covariance models (CMs) (Eddy and Durbin 1994), are necessary for accurate ncRNA homology search.The goal of this study is to identify programs that balance sensitivity (true predictions) and specificity (false predictions) for practical ncRNA homology search situations. We use large highquality ncRNA data sets and randomized control data sets to test the 12 homology search programs summarized in Table 1. Briefly, sequences are sampled from each ncRNA data set and then used as input sequences for each algorithm against the original (true homologs) and randomized data sets. Our test data sets are composed of a subclass of ncRNAs that tend to be highly structured, and therefore there is more information for homology detection than for unconstrained ncRNAs. The algorithms that do not perform well on these data sets are not likely to perform better on more challenging classes of ncRNAs. To ensure our results reflect practical scenarios, we have used both predicted alignments and secondary structures to generate input data for the alignment and structure-based methods.Homology search programs fall into one of three classes: sequence based methods, profile HMM methods, and structure enhanced methods (Fig. 1). In addition to evaluating homology search programs, we extend the use of ancestral sequence reconstructions (ASR) and introduce the novel phylogeny-based predictive sequence reconstruction (PSR) method for use in homology searches (Collins et al. 2003;McCormack 2003;Qian and Goldstein 2003;Cai et al. 2004) to the RNA homology search problem (see Supplemental Fig. 1). Briefly, we discuss each of these in turn.The most popular homology search methods are sequence based....

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Simple and accurate estimation of ancestral protein sequences

Cited by 52 publications

References 24 publications

The Effect of Recombination on the Reconstruction of Ancestral Sequences

The Effect of Recombination on the Reconstruction of Ancestral Sequences

Deep Phylogeny--How a Tree Can Help Characterize Early Life on Earth

Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

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