Evolution of Protein Molecules

The quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step. Here is presented a new software, named BMGE (Block Mapping and Gathering with Entropy), that is designed to select regions in a multiple sequence alignment that are suited for phylogenetic inference. For each character, BMGE computes a score closely related to an entropy value. Calculation of these entropy-like scores is weighted with BLOSUM or PAM similarity matrices in order to distinguish among biologically expected and unexpected variability for each aligned character. Sets of contiguous characters with a score above a given threshold are considered as not suited for phylogenetic inference and then removed. Simulation analyses show that the character trimming performed by BMGE produces datasets leading to accurate trees, especially with alignments including distantly-related sequences. BMGE also implements trimming and recoding methods aimed at minimizing phylogeny reconstruction artefacts due to compositional heterogeneity. BMGE is able to perform biologically relevant trimming on a multiple alignment of DNA, codon or amino acid sequences. Java source code and executable are freely available at ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/
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BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

A number of methods for recognizing protein coding genes in DNA sequence have been published over the last 13 years, and new, more comprehensive algorithms, drawing on the repertoire of existing techniques, continue to be developed. To optimize continued development, it is valuable to systematically review and evaluate published techniques. At the core of most gene recognition algorithms is one or more coding measures--functions which produce, given any sample window of sequence, a number or vector intended to measure the degree to which a sample sequence resembles a window of 'typical' exonic DNA. In this paper we review and synthesize the underlying coding measures from published algorithms. A standardized benchmark is described, and each of the measures is evaluated according to this benchmark. Our main conclusion is that a very simple and obvious measure--counting oligomers--is more effective than any of the more sophisticated measures. Different measures contain different information. However there is a great deal of redundancy in the current suite of measures. We show that in future development of gene recognition algorithms, attention can probably be limited to six of the twenty or so measures proposed to date.

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Assessment of protein coding measures

Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.

Genome-scale phylogeny and the detection of systematic biases

Universal data compression algorithms fail to compress genetic sequences. It is due to the specificity of this particular kind of “text.” We analyze in some detail the properties of the sequences, which cause the failure of classical algorithms. We then present a lossless algorithm, biocompress-2, to compress the information contained in DNA and RNA sequences, based on the detection of regularities, such as the presence of palindromes. The algorithm combines substitutional and statistical methods, and to the best of our knowledge, leads to the highest compression of DNA. The results, although not satisfactory, give insight to the necessary correlation between compression and comprehension of genetic sequences.

/pdf/a-new-challenge-for-compression-algorithms-genetic-sequences-16h1avr38u.pdf

A new challenge for compression algorithms: genetic sequences

A generalization of substitution evolution models of nucleotides to genetic motifs

A stochastic model of gene evolution with time dependent pseudochaotic mutations.

Research Article: Plant microRNA detection using the circular code information

With the three-letter alphabet {R,Y,N} (R = purine, Y = pyrimidine, N= R or Y), there are 26 codons (NNN being excluded): RNN, . ,NNY (six codons at two unspecified bases N), RRN,. ,NYY (12 codons at one unspecified base N), RRR ,.._, YYY (eight specified codons). A statistical methodology that uses the codon frequency and a reduced centered variable leads to similar results for a codon occurrence study, regardless of gene function- and regardless of a particular protein coding gene taxonomic population. Therefore, this variable can be considered a new codon usage index, whose use removes certain nonsignificant results found with the frequency statistic. This methodology identifies the common and rare codons (i.e., the codons having the highest and lowest occurrence) and leads to a model of codon evolution at three successive states: RNN, then RNY, and finally RYY. Some biological relations between this model and the YRY(N),YRY preferential occurrence are also presented.

/pdf/a-study-of-the-purine-pyrimidine-codon-occurrence-with-a-noz0f0lut5.pdf

A study of the purine/pyrimidine codon occurrence with a reduced centered variable and an evaluation compared to the frequency statistic.

In 1996, a trinucleotide circular code which is maximum, self-complementary, and , called , was identified statistically on a large gene population of eukaryotes and prokaryotes (Arques and Michel (1996)). Transition and transversions I and II are classical molecular evolution processes. A comprehensive computer analysis of these three evolution processes in the code shows some new results; in particular (i) transversion I on the 2nd position of any subset of trinucleotides of generates trinucleotide circular codes which are always and (ii) transversion II on the three positions of any subset of trinucleotides of yields no trinucleotide circular codes. These new results extend our theory of circular code in genes to its evolution under transition and transversion.

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Christian J. Michel

Papers

A generalization of substitution evolution models of nucleotides to genetic motifs

A stochastic model of gene evolution with time dependent pseudochaotic mutations.

Research Article: Plant microRNA detection using the circular code information

A study of the purine/pyrimidine codon occurrence with a reduced centered variable and an evaluation compared to the frequency statistic.

Transition and Transversion on the Common Trinucleotide Circular Code