Evidence map›Paper›PMID 27342318›Full record

ArticleBMC bioinformatics2016

Non-Markovian effects on protein sequence evolution due to site dependent substitution rates.

Francesca Rizzato, Alex Rodriguez, Alessandro Laio

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Francesca RizzatoInternational School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy.
Alex RodriguezInternational School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy.
Alessandro LaioInternational School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy. laio@sissa.it.
Scuola Internazionale Superiore di Studi Avanzati · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany models of protein sequence evolution, in particular those based on Point Accepted Mutation (PAM) matrices, assume that its dynamics is Markovian. Nevertheless, it has been observed that evolution seems to proceed differently at different time scales, questioning this assumption. In 2011 Kosiol and Goldman proved that, if evolution is Markovian at the codon level, it can not be Markovian at the amino acid level. However, it remains unclear up to which point the Markov assumption is verified at the codon level.

resultsHere we show how also the among-site variability of substitution rates makes the process of full protein sequence evolution effectively not Markovian even at the codon level. This may be the theoretical explanation behind the well known systematic underestimation of evolutionary distances observed when omitting rate variability. If the substitution rate variability is neglected the average amino acid and codon replacement probabilities are affected by systematic errors and those with the largest mismatches are the substitutions involving more than one nucleotide at a time. On the other hand, the instantaneous substitution matrices estimated from alignments with the Markov assumption tend to overestimate double and triple substitutions, even when learned from alignments at high sequence identity.

conclusionsThese results discourage the use of simple Markov models to describe full protein sequence evolution and encourage to employ, whenever possible, models that account for rate variability by construction (such as hidden Markov models or mixture models) or substitution models of the type of Le and Gascuel (2008) that account for it explicitly.

Indexed as

Evolution, MolecularModels, GeneticAmino Acid SequenceCodonMarkov ChainsProteinsCodonProteinsAmino acid substitution matricesEvolutionary distancesNon-Markovian evolutionProtein sequence evolutionSubstitution rate variability

Identifiers

PMID27342318
PMCPMC4921000
OpenAlexW2464891412

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.