A Novel Algorithm for Prediction of Protein Coding DNA from Non-coding DNA in Microbial Genomes Using Genomic Composition and Dinucleotide Compositional Skew

AbstractLow-cost, high-throughput sequencing has led to an enormous increase in the number of sequenced microbial genomes, with well over 100,000 genomes in public archives today. Automatic genome annotation tools are integral to understanding these organisms, yet older gene finding methods must be retrained on each new genome. We have developed a universal model of prokaryotic genes by fitting a temporal convolutional network to amino-acid sequences from a large, diverse set of microbial genomes. We incorporated the new model into a gene finding system, Balrog (Bacterial Annotation by Learned Representation Of Genes), which does not require genome-specific training and which matches or outperforms other state-of-the-art gene finding tools. Balrog is freely available under the MIT license at https://github.com/salzberg-lab/Balrog.Author summaryAnnotating the protein-coding genes in a newly sequenced prokaryotic genome is a critical part of describing their biological function. Relative to eukaryotic genomes, prokaryotic genomes are small and structurally simple, with 90% of their DNA typically devoted to protein-coding genes. Current computational gene finding tools are therefore able to achieve close to 99% sensitivity to known genes using species-specific gene models.Though highly sensitive at finding known genes, all current prokaryotic gene finders also predict large numbers of additional genes, which are labelled as “hypothetical protein” in GenBank and other annotation databases. Many hypothetical gene predictions likely represent true protein-coding sequence, but it is not known how many of them represent false positives. Additionally, all current gene finding tools must be trained specifically for each genome as a preliminary step in order to achieve high sensitivity. This requirement limits their ability to detect genes in fragmented sequences commonly seen in metagenomic samples.We took a data-driven approach to prokaryotic gene finding, relying on the large and diverse collection of already-sequenced genomes. By training a single, universal model of bacterial genes on protein sequences from many different species, we were able to match the sensitivity of current gene finders while reducing the overall number of gene predictions. Our model does not need to be refit on any new genome. Balrog (Bacterial Annotation by Learned Representation of Genes) represents a fundamentally different yet effective method for prokaryotic gene finding.

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A method for achieving complete microbial genomes and improving bins from metagenomics data

10.1101/2020.03.05.979740 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lauren M. Lui ◽

Torben N. Nielsen ◽

Adam P. Arkin

Keyword(s):

Ribosomal Rna ◽

16S Rrna Gene Sequencing ◽

Rrna Gene ◽

Genomic Context ◽

Protein Coding ◽

Microbial Genomes ◽

A Genome ◽

Sequencing Studies ◽

Metagenomics Data ◽

Rrna Gene Sequencing

AbstractMetagenomics facilitates the study of the genetic information from uncultured microbes and complex microbial communities. Assembling complete microbial genomes (i.e., circular with no misassemblies) from metagenomics data is difficult because most samples have high organismal complexity and strain diversity. Less than 100 circularized bacterial and archaeal genomes have been assembled from metagenomics data despite the thousands of datasets that are available. Circularized genomes are important for (1) building a reference collection as scaffolds for future assemblies, (2) providing complete gene content of a genome, (3) confirming little or no contamination of a genome, (4) studying the genomic context and synteny of genes, and (5) linking protein coding genes to ribosomal RNA genes to aid metabolic inference in 16S rRNA gene sequencing studies. We developed a method to achieve circularized genomes using iterative assembly, binning, and read mapping. In addition, this method exposes potential misassemblies from k-mer based assemblies. We chose species of the Candidate Phyla Radiation (CPR) to focus our initial efforts because they have small genomes and are only known to have one ribosomal RNA operon. We present 34 circular CPR genomes, one circular Margulisbacteria genome, and two circular megaphage genomes from 19 public and published datasets. We demonstrate findings that would likely be difficult without circularizing genomes, including that ribosomal genes are likely not operonic in the majority of CPR, and that some CPR harbor diverged forms of RNase P RNA. Code and a tutorial for this method is available at https://github.com/lmlui/Jorg.

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Accurate annotation of protein coding sequences with IDTAXA

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab080 ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Nicholas P Cooley ◽

Erik S Wright

Keyword(s):

Antibiotic Resistance ◽

R Package ◽

Protein Coding ◽

Common Error ◽

New Approach ◽

Coding Sequences ◽

Microbial Genomes ◽

Prokaryotic Genomes ◽

Multi Level ◽

Novel Associations

Abstract The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA’s utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

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