MetaGeneAnnotator: Detecting Species-Specific Patterns of Ribosomal Binding Site for Precise Gene Prediction in Anonymous Prokaryotic and Phage Genomes

By H. Noguchi, Takeaki Taniguchi, T. Itoh. DNA Research, 2008

632 citations recorded by the source corpus.

Recent advances in DNA sequencers are accelerating genome sequencing, especially in microbes, and complete and draft genomes from various species have been sequenced in rapid succession. Here, we present a comprehensive gene prediction tool, the MetaGeneAnnotator (MGA), which precisely predicts all kinds of prokaryotic genes from a single or a set of anonymous genomic sequences having a variety of lengths. The MGA integrates statistical models of prophage genes, in addition to those of bacterial and archaeal genes, and also uses a self-training model from input sequences for predictions. As a result, the MGA sensitively detects not only typical genes but also atypical genes, such as horizontally transferred and prophage genes in a prokaryotic genome. In this paper, we also propose a novel approach for analyzing the ribosomal binding site (RBS), which enables us to detect species-specific patterns of the RBSs. The MGA has the ingenious RBS model based on this approach, and precisely predicts translation starts of genes. The MGA also succeeds in improving prediction accuracies for short sequences by using the adapted RBS models (96% sensitivity and 93% specificity for 700 bp fragments). These features of the MGA expedite wide ranges of microbial genome studies, such as genome annotations and metagenome analyses.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4344e78101ef3e9f60b45b6863eeaaae2dcdda3f

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Citations in the corpus

Metagenomics - a guide from sampling to data analysis (2012, methodology): “A number of tools were specifically designed to handle metagenomic prediction of CDS, including FragGeneScan [24], MetaGeneMark [62], MetaGeneAnnotator (MGA)/ Metagene [63] and Orphelia [64,65].”
MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads (2011, positive): “From the assembled scaffolds, protein-coding genes were predicted using the MetaGeneAnnotator [14] software.”
FragGeneScan: predicting genes in short and error-prone reads (2010, methodology): “regions (16,18,23), in a manner similar to conventional gene finding methods such as Glimmer and GeneMark (24–26).”
VIROME: a standard operating procedure for analysis of viral metagenome sequences (2012, methodology): “Sequences within this new file are scanned for the presence of tRNAs using tRNAscan-SE 28] and open reading frames (ORFs) are predicted using MetaGene Annotator [29] (Fig.”
DRAM for distilling microbial metabolism to automate the curation of microbiome function (2020, background): “Specifically, Prokka (30), MetaErg (32) and DRAM use Prodigal (40) to call genes, while DFAST (31) uses MetaGeneAnnotator (54).”
The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST) (2013, methodology): “The phage/plasmid pipeline (invoked automatically for submissions of 100 kb in all contigs) involves ﬁnding the RNAs and close neighbors using the pipeline described earlier in text, but substituting MGA (31) for GLIMMER3 in the initial gene calling step.”
Combining gene prediction methods to improve metagenomic gene annotation (2011, methodology): “However, there are two major limitations that exist in (MG) software program: the lack of ribosomal binding site (RBS) model, and a low sensitivity to atypical genes, whose codon usages are different from those of typical genes [20].”
Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification (2021, methodology): “Existing lightweight command-line annotation tools fail to detect these small proteins through using contemporary de novo gene prediction tools [14, 15] alone.”
Gene and translation initiation site prediction in metagenomic sequences (2012, background): “Many programs have been developed to solve these problems and identify genes in metagenomic fragments, including Metagene (Noguchi et al., 2006), Metagene Annotator (Noguchi et al., 2008), MetaGeneMark (Zhu et al., 2010), Orphelia (Hoff et al., 2008), and FragGeneScan (Rho et al., 2010).”
Orphelia: predicting genes in metagenomic sequencing reads (2009, methodology): “In contrast to the MetaGene and MetaGeneAnnotator web servers, the GeneMark web server was not designed to treat single entries of a multiple fasta file separately, which limits its applicability to metagenomic data.”