Codon Usage in Prokaryotes

Bachvarov, Boris I.; Kirilov, Kiril; Ivanov, Iván

doi:10.1080/13102818.2008.10817533

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…It is now much easier to generate immense sets of raw data than to establish relations and provide their biological interpretation [1-3]. Considering cases of supervised statistical learning, huge sets of measured/collected variables are typically used as explanatory variables, all with a potential impact on some response variable, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…With the tremendous increase in data collection techniques in modern biology, it has become possible to sample observations on a huge number of genetic, phenotypic and ecological variables simultaneously. It is now much easier to generate immense sets of raw data than to establish relations and provide their biological interpretation [ 1 - 3 ]. Considering cases of supervised statistical learning, huge sets of measured/collected variables are typically used as explanatory variables, all with a potential impact on some response variable, e.g.…”

Section: Introductionmentioning

confidence: 99%

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A Partial Least Squares based algorithm for parsimonious variable selection

Mehmood

Martens

Sæbø

et al. 2011

Algorithms Mol Biol

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BackgroundIn genomics, a commonly encountered problem is to extract a subset of variables out of a large set of explanatory variables associated with one or several quantitative or qualitative response variables. An example is to identify associations between codon-usage and phylogeny based definitions of taxonomic groups at different taxonomic levels. Maximum understandability with the smallest number of selected variables, consistency of the selected variables, as well as variation of model performance on test data, are issues to be addressed for such problems.ResultsWe present an algorithm balancing the parsimony and the predictive performance of a model. The algorithm is based on variable selection using reduced-rank Partial Least Squares with a regularized elimination. Allowing a marginal decrease in model performance results in a substantial decrease in the number of selected variables. This significantly improves the understandability of the model. Within the approach we have tested and compared three different criteria commonly used in the Partial Least Square modeling paradigm for variable selection; loading weights, regression coefficients and variable importance on projections. The algorithm is applied to a problem of identifying codon variations discriminating different bacterial taxa, which is of particular interest in classifying metagenomics samples. The results are compared with a classical forward selection algorithm, the much used Lasso algorithm as well as Soft-threshold Partial Least Squares variable selection.ConclusionsA regularized elimination algorithm based on Partial Least Squares produces results that increase understandability and consistency and reduces the classification error on test data compared to standard approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Partial Least Squares based algorithm for parsimonious variable selection

Mehmood

Martens

Sæbø

et al. 2011

Algorithms Mol Biol

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“…The annotated open reading frame (ORF) of BmaA starts with a GTG codon. It has been reported that the GTG (Val) and TTG (Leu) codons, and to a minor extent CTG (Val), are usually recognized as the initiation site for gene translation in prokaryotes, as alternative codons to the canonical ATG (Met) ( Bachvarov et al., 2008 ). BmaA shows the characteristic C-terminal translocator domain of classical autotransporters, consisting of 12 antiparallel β strands (Jpred4).…”

Section: Resultsmentioning

confidence: 99%

Adhesive Functions or Pseudogenization of Type Va Autotransporters in Brucella Species

Bialer

Ferrero

Delpino

et al. 2021

Front. Cell. Infect. Microbiol.

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Adhesion to host cells is a key step for successful infection of many bacterial pathogens and may define tropism to different host tissues. To do so, bacteria display adhesins on their surfaces. Brucella is an intracellular pathogen capable of proliferating in a wide variety of cell types. It has been described that BmaC, a large protein that belongs to the classical (type Va) autotransporter family, is required for efficient adhesion of Brucella suis strain 1330 to epithelial cells and fibronectin. Here we show that B. suis 1330 harbors two other type Va autotransporters (BmaA and BmaB), which, although much smaller, share significant sequence similarities with BmaC and contain the essential domains to mediate proper protein translocation to the bacterial surface. Gain and loss of function studies indicated that BmaA, BmaB, and BmaC contribute, to a greater or lesser degree, to adhesion of B. suis 1330 to different cells such as synovial fibroblasts, osteoblasts, trophoblasts, and polarized epithelial cells as well as to extracellular matrix components. It was previously shown that BmaC localizes to a single bacterial pole. Interestingly, we observed here that, similar to BmaC, the BmaB adhesin is localized mostly at a single cell pole, reinforcing the hypothesis that Brucella displays an adhesive pole. Although Brucella species have strikingly similar genomes, they clearly differ in their host preferences. Mainly, the differences identified between species appear to be at loci encoding surface proteins. A careful in silico analysis of the putative type Va autotransporter orthologues from several Brucella strains showed that the bmaB locus from Brucella abortus and both, the bmaA and bmaC loci from Brucella melitensis are pseudogenes in all strains analyzed. Results reported here evidence that all three autotransporters play a role in the adhesion properties of B. suis 1330. However, Brucella spp. exhibit extensive variations in the repertoire of functional adhesins of the classical autotransporter family that can be displayed on the bacterial surface, making them an interesting target for future studies on host preference and tropism.

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“…Tremendous advances in technology has made it possible to sample observations based on a huge number of genetic and ecological variables. It is much easier to generate gigantic sets of raw data, establish relations and provide a biological understanding (Bachvarov et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data

Mehmood

Rasheed

2015

CSAM

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The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

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Codon Usage in Prokaryotes

Cited by 10 publications

References 9 publications

A Partial Least Squares based algorithm for parsimonious variable selection

A Partial Least Squares based algorithm for parsimonious variable selection

Adhesive Functions or Pseudogenization of Type Va Autotransporters in Brucella Species

Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data

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