Add to ORKGMotivation: Identification of genes coding for ribosomal RNA (rRNA) is considered an important goal in the analysis of data from metagenomics projects. Here, we report the development of a software program designed for the identification of rRNA genes from metagenomic fragments based on hidden Markov models (HMMs). This program provides rRNA gene predictions with high sensitivity and specificity on artificially fragmented genomic DNAs. Availability: Supplementary files, scripts and sample data are available a
Motivation:Mitochondrial genomes encode their own transfer RNAs (tRNAs). These are often degenerate ...
We have developed a hidden Markov model (HMM) to detect the protein coding regions within one megaba...
International audienceThe number of available genomic sequences is growing very fast, due to the dev...
The publication of a complete genome sequence is usually accompanied by annotations of its genes. In...
The topic of this thesis is the analysis of large data sets of DNA sequence data produced from moder...
Motivation Technological advances in meta-transcriptomics have enabled a deeper understanding of the...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
models in biological sequence analysis The vast increase of data in biology has meant that many aspe...
Microorganisms are organised in complex communities and are ubiquitous in all ecosystems, including ...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
Abstract Background Computational approaches, specifically machine-learning techniques, play an impo...
The term “metagenomics” represents a combination of molecular and bioinformatic tools used to assess...
We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides th...
Hidden Markov models (HMMs) are well developed statistical models to capture hidden information from...
International audienceMetatranscriptomic data contributes another piece of the puzzle to understandi...
Motivation:Mitochondrial genomes encode their own transfer RNAs (tRNAs). These are often degenerate ...
We have developed a hidden Markov model (HMM) to detect the protein coding regions within one megaba...
International audienceThe number of available genomic sequences is growing very fast, due to the dev...
The publication of a complete genome sequence is usually accompanied by annotations of its genes. In...
The topic of this thesis is the analysis of large data sets of DNA sequence data produced from moder...
Motivation Technological advances in meta-transcriptomics have enabled a deeper understanding of the...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
models in biological sequence analysis The vast increase of data in biology has meant that many aspe...
Microorganisms are organised in complex communities and are ubiquitous in all ecosystems, including ...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
Abstract Background Computational approaches, specifically machine-learning techniques, play an impo...
The term “metagenomics” represents a combination of molecular and bioinformatic tools used to assess...
We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides th...
Hidden Markov models (HMMs) are well developed statistical models to capture hidden information from...
International audienceMetatranscriptomic data contributes another piece of the puzzle to understandi...
Motivation:Mitochondrial genomes encode their own transfer RNAs (tRNAs). These are often degenerate ...
We have developed a hidden Markov model (HMM) to detect the protein coding regions within one megaba...
International audienceThe number of available genomic sequences is growing very fast, due to the dev...