Ab initio gene identification in metagenomic sequences

By Wenhan Zhu, Alexandre Lomsadze, Mark Borodovsky. Nucleic Acids Research, 2010

1,616 citations recorded by the source corpus.

We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3cee40494377c0e7d9c7c23a3811b481e55bce39

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Citations in the corpus

Structure and Function of the Bacterial Root Microbiota in Wild and Domesticated Barley (2015, methodology): “Complete and partial coding sequences (CDSs) for protein encoding genes were predicted with MetaGeneMark (Zhu et al., 2010).”
Combining gene prediction methods to improve metagenomic gene annotation (2011, methodology): “In order to build a mixture dual model, they are further divided into two sets: (1) 38 Archaea species and 319 Bacteria species; (2) 316 Mesophilic species and 41 Thermophilic species [22].”
Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing (2015, methodology): “Analysis of unique genes: The assembled contigs from the combined reads (v2-t otal, v3-total, v2+v3total, v2+v3+HiSeq-total), were used to predict put ative genes using the MetaGeneMark [25] tool (Fig.”
Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis (2013, methodology): “ORFs were then called on the resulting contigs using MetaGeneMark (Zhu et al., 2010).”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, positive): “Shotgun Metagenomics Taxonomic classification MetaPhlAn4 [158], Kaiju [159], Kraken [160] Assembly of metagenomic reads metaSPAdes [86], metaIDBA [87] Protein databases for taxonomic classification NCBI non-redundant protein database [83] Gene annotation Prokka [88], MetaGeneMark [89] Databases for ”
Strengths and Limitations of 16S rRNA Gene Amplicon Sequencing in Revealing Temporal Microbial Community Dynamics (2014, methodology): “The resulting contigs were annotated using MeteGeneMark [36].”
Identification of protein coding regions in RNA transcripts (2014, methodology): “We used the functions that reflect dependence of oligonucleotide composition of protein coding regions on G + C content of the sequence; the functions were derived for a set of complete prokaryotic genomes (15).”