MetaGene: prokaryotic gene finding from environmental genome shotgun sequences

By Hideki Noguchi, Jungho Park, Toshihisa Takagi. Nucleic Acids Research, 2006

451 citations recorded by the source corpus.

Exhaustive gene identification is a fundamental goal in all metagenomics projects. However, most metagenomic sequences are unassembled anonymous fragments, and conventional gene-finding methods cannot be applied. We have developed a prokaryotic gene-finding program, MetaGene, which utilizes di-codon frequencies estimated by the GC content of a given sequence with other various measures. MetaGene can predict a whole range of prokaryotic genes based on the anonymous genomic sequences of a few hundred bases, with a sensitivity of 95% and a specificity of 90% for artificial shotgun sequences (700 bp fragments from 12 species). MetaGene has two sets of codon frequency interpolations, one for bacteria and one for archaea, and automatically selects the proper set for a given sequence using the domain classification method we propose. The domain classification works properly, correctly assigning domain information to more than 90% of the artificial shotgun sequences. Applied to the Sargasso Sea dataset, MetaGene predicted almost all of the annotated genes and a notable number of novel genes. MetaGene can be applied to wide variety of metagenomic projects and expands the utility of metagenomics.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6e4f024da771be793178b754aeebe553a98a7f6

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Citations in the corpus

Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. (2008, unknown): “Genes were predicted from individual sequencing reads and contigs using MetaGene ( Noguchi et al., 2006 ).”
A human gut microbial gene catalog established by metagenomic sequencing (2010, methodology): “To establish a non-redundant human gut microbiome gene set we first used the MetaGene 20 program to predict ORFs in our contigs and found 14,048,045 ORFs longer than 100 bp (Supplementary Table 6).”
A Bioinformatician's Guide to Metagenomics (2008, unknown): “For example, MetaGene (95), a gene prediction program developed specifically for metagenomic”
Gut microbiome based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease (2017, unknown): “, 2002) ORFs were generated from assembled contigs and unassembled singleton reads using MetaGene (Noguchi et al., 2006).”
Bioinformatics challenges of new sequencing technology. (2008, unknown): “The MetaGene system of Noguchiet al. [ 41 ] is designed specifically for metagenomic data from short reads.”
Machine learning for metagenomics: methods and tools (2015, background): “In addition to the novel computational methods presented in this review a number of integrated pipelines has been developed such as the EBI Metagenomics Webserver (Hunter et al., 2014) based on FragGeneScan, RAMMCAP (Li, 2009) based on MetaGene and SmashCommunity based on GeneMark and MetaGene (Arum”
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).”
The genomic basis of trophic strategy in marine bacteria (2009, methodology): “The inclusion of the metagenomic samples into the matrix was accomplished by predicting ORF boundaries from all unassembled trimmed reads using MetaGene (36), conceptual translation of the ORFs and calculation of the overall percentages in COG composition, and protein localization distribution as de”
Ultrafast clustering algorithms for metagenomic sequence analysis (2012, unknown): “Compared with GOS ORFs, which contain more than half spurious ORFs due to the six reading frame translation, the MetaHIT genes predicted using Metagene [77] have far fewer false ORFs.”
FragGeneScan: predicting genes in short and error-prone reads (2010, unknown): “The nine complete genomic sequences of various GC contents (Table 1) we used are also widely used for testing gene predictors in previous studies (16,18).”
Ab initio gene identification in metagenomic sequences (2010, methodology): “Particularly, the authors of MetaGene (14) saw a significant potential in the ‘heuristic model’ method (9); they have extended the method to use of di-codon frequencies.”