Abstract — This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from hundred species of diverse lineages. We defined three different types of fragments: one type from the intra-coding region and the other types are from the gene edges. The general observation was that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show a low annotation error in all of the programs if compared to the genes edges. Keywords- Metagenomic; Orphelia; ...
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be au...
To assess the functional capacities of microbial communities, including those inhabiting the human b...
Background: Computational gene finding algorithms have proven their robustness in identifying genes ...
This manuscript presents the most rigorous benchmark-ing of gene annotation algorithms for metagenom...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
Next-generation sequencing has generated enormous amount of DNA and RNA sequences that potentially c...
Abstract Background Computational approaches, specifically machine-learning techniques, play an impo...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
In an effort to evaluate methods used to analyse metagenomes, we constructed three synthetic metagen...
International audienceBackground: In comparative genomics, orthologs are used to transfer annotation...
Background: Metagenomics is the study of microbial communities by sequencing of genetic material dir...
The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produce...
Abstract Background Gene prediction algorithms (or gene callers) are an essential tool for analyzing...
Motivation: The biases in Open Reading Frame (ORF) prediction tools, which have been based on histor...
International audienceAbstract Background The draft genome assemblies produced by new sequencing tec...
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be au...
To assess the functional capacities of microbial communities, including those inhabiting the human b...
Background: Computational gene finding algorithms have proven their robustness in identifying genes ...
This manuscript presents the most rigorous benchmark-ing of gene annotation algorithms for metagenom...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
Next-generation sequencing has generated enormous amount of DNA and RNA sequences that potentially c...
Abstract Background Computational approaches, specifically machine-learning techniques, play an impo...
Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms f...
In an effort to evaluate methods used to analyse metagenomes, we constructed three synthetic metagen...
International audienceBackground: In comparative genomics, orthologs are used to transfer annotation...
Background: Metagenomics is the study of microbial communities by sequencing of genetic material dir...
The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produce...
Abstract Background Gene prediction algorithms (or gene callers) are an essential tool for analyzing...
Motivation: The biases in Open Reading Frame (ORF) prediction tools, which have been based on histor...
International audienceAbstract Background The draft genome assemblies produced by new sequencing tec...
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be au...
To assess the functional capacities of microbial communities, including those inhabiting the human b...
Background: Computational gene finding algorithms have proven their robustness in identifying genes ...