RESUMO
Small non-coding RNAs (sRNAs) are regulatory RNA molecules that have been identified in a multitude of bacterial species and shown to control numerous cellular processes through various regulatory mechanisms. In the last decade, next generation RNA sequencing (RNA-seq) has been used for the genome-wide detection of bacterial sRNAs. Here we describe sRNA-Detect, a novel approach to identify expressed small transcripts from prokaryotic RNA-seq data. Using RNA-seq data from three bacterial species and two sequencing platforms, we performed a comparative assessment of five computational approaches for the detection of small transcripts. We demonstrate that sRNA-Detect improves upon current standalone computational approaches for identifying novel small transcripts in bacteria.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/estatística & dados numéricos , RNA Bacteriano/genética , Pequeno RNA não Traduzido/genética , Análise de Sequência de RNA/estatística & dados numéricos , Algoritmos , Sequência de Bases , Biologia Computacional/métodos , Biologia Computacional/estatística & dados numéricos , Bases de Dados de Ácidos Nucleicos/estatística & dados numéricos , Deinococcus/genética , Erwinia amylovora/genética , Cadeias de Markov , Rhodobacter capsulatus/genética , Software , Design de SoftwareRESUMO
The models of nucleotide substitution used by most maximum likelihood-based methods assume that the evolutionary process is stationary, reversible, and homogeneous. We present an extension of the Barry and Hartigan model, which can be used to estimate parameters by maximum likelihood (ML) when the data contain invariant sites and there are violations of the assumptions of stationarity, reversibility, and homogeneity. Unlike most ML methods for estimating invariant sites, we estimate the nucleotide composition of invariant sites separately from that of variable sites. We analyze a bacterial data set where problems due to lack of stationarity and homogeneity have been previously well noted and use the parametric bootstrap to show that the data are consistent with our general Markov model. We also show that estimates of invariant sites obtained using our method are fairly accurate when applied to data simulated under the general Markov model.
Assuntos
Bactérias/genética , Evolução Molecular , Modelos Genéticos , Filogenia , Bacillus subtilis/classificação , Bacillus subtilis/genética , Bactérias/classificação , Sequência de Bases , Deinococcus/classificação , Deinococcus/genética , Funções Verossimilhança , Cadeias de Markov , RNA Ribossômico 16S/química , Análise de Sequência de DNA , Thermotoga maritima/classificação , Thermotoga maritima/genética , Thermus thermophilus/classificação , Thermus thermophilus/genéticaRESUMO
BACKGROUND: Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. RESULTS: This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. CONCLUSIONS: While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.