Using average nucleotide identity to improve taxonomic assignments in prokaryotic genomes at the NCBI.
Ontology highlight
ABSTRACT:
SUBMITTER: Ciufo S
PROVIDER: S-EPMC6978984 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The average nucleotide identity (ANI) determines if two genomes belong to the same species. Using ANI, we detected mislabeled genomes and recommend verifying with ANI and multilocus phylogenetic analysis the species affiliations of the announced genomes. The slightly different results obtained with different ANI calculation software can potentially mislead taxonomic inferences.
| S-EPMC4256179 | biostudies-literature
Project description:Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.
| S-EPMC5001611 | biostudies-literature
Project description:Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well as rare and closely related species. The RGDv2 contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp., while the TGDv1 contains 454 enteric bacterial genomes representing 42 different species. The analysis showed that, when a standard minimum of 70% genome bases alignment existed, the ANI threshold values determined for these species were ≥95 for Escherichia/Shigella and Vibrio species, ≥93% for Salmonella species, and ≥92% for Campylobacter and Listeria species. Using these metrics, the RGDv2 accurately classified all validation strains in TGDv1 at the species level, which is consistent with the classification based on previous gold standard methods.
| S-EPMC10752928 | biostudies-literature
Project description:Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.
| S-EPMC11353866 | biostudies-literature
Project description:Recent technological innovations have ignited an explosion in virus genome sequencing that promises to fundamentally alter our understanding of viral biology and profoundly impact public health policy. Yet, any potential benefits from the billowing cloud of next generation sequence data hinge upon well implemented reference resources that facilitate the identification of sequences, aid in the assembly of sequence reads and provide reference annotation sources. The NCBI Viral Genomes Resource is a reference resource designed to bring order to this sequence shockwave and improve usability of viral sequence data. The resource can be accessed at http://www.ncbi.nlm.nih.gov/genome/viruses/ and catalogs all publicly available virus genome sequences and curates reference genome sequences. As the number of genome sequences has grown, so too have the difficulties in annotating and maintaining reference sequences. The rapid expansion of the viral sequence universe has forced a recalibration of the data model to better provide extant sequence representation and enhanced reference sequence products to serve the needs of the various viral communities. This, in turn, has placed increased emphasis on leveraging the knowledge of individual scientific communities to identify important viral sequences and develop well annotated reference virus genome sets.
| S-EPMC4383986 | biostudies-literature
Project description:Taxonomic assignment of sequence reads is a challenging task in metagenomic data analysis, for which the present methods mainly use either composition- or homology-based approaches. Though the homology-based methods are more sensitive and accurate, they suffer primarily due to the time needed to generate the Blast alignments. We developed the MetaBin program and web server for better homology-based taxonomic assignments using an ORF-based approach. By implementing Blat as the faster alignment method in place of Blastx, the analysis time has been reduced by severalfold. It is benchmarked using both simulated and real metagenomic datasets, and can be used for both single and paired-end sequence reads of varying lengths (≥45 bp). To our knowledge, MetaBin is the only available program that can be used for the taxonomic binning of short reads (<100 bp) with high accuracy and high sensitivity using a homology-based approach. The MetaBin web server can be used to carry out the taxonomic analysis, by either submitting reads or Blastx output. It provides several options including construction of taxonomic trees, creation of a composition chart, functional analysis using COGs, and comparative analysis of multiple metagenomic datasets. MetaBin web server and a standalone version for high-throughput analysis are available freely at http://metabin.riken.jp/.
| S-EPMC3319535 | biostudies-literature
Project description:SummaryWe present NCBI-taxonomist - a command-line tool written in Python that collects and manages taxonomic data from the National Center for Biotechnology Information (NCBI). NCBI-taxonomist does not depend on a pre-downloaded taxonomic database but can store data locally. NCBI-taxonomist has six commands to map, collect, extract, resolve, import and group taxonomic data that can be linked together to create powerful analytical pipelines. Because many life science databases use the same taxonomic information, the data managed by NCBI-taxonomist is not limited to NCBI and can be used to find data linked to taxonomic information present in other scientific databases.Availability and implementationNCBI-taxonomist is implemented in Python 3 (≥3.8) and available at https://gitlab.com/janpb/ncbi-taxonomist and via PyPi (https://pypi.org/project/ncbi-taxonomist/), as a Docker container (https://gitlab.com/janpb/ncbi-taxonomist/container_registry/) and Singularity (v3.5.3) image (https://cloud.sylabs.io/library/jpb/ncbi-taxonomist). NCBI-taxonomist is licensed under the GPLv3.Supplementary informationhttps://ncbi-taxonomist.readthedocs.io/en/latest/.
| S-EPMC8016462 | biostudies-literature
Project description:Infections due to Clostridioides difficile (previously known as Clostridium difficile) are a major problem in hospitals, where cases can be caused by community-acquired strains as well as by nosocomial spread. Whole genome sequences from clinical samples contain a lot of information but that needs to be analyzed and compared in such a way that the outcome is useful for clinicians or epidemiologists. Here, we compare 663 public available complete genome sequences of C. difficile using average amino acid identity (AAI) scores. This analysis revealed that most of these genomes (640, 96.5%) clearly belong to the same species, while the remaining 23 genomes produce four distinct clusters within the Clostridioides genus. The main C. difficile cluster can be further divided into sub-clusters, depending on the chosen cutoff. We demonstrate that MLST, either based on partial or full gene-length, results in biased estimates of genetic differences and does not capture the true degree of similarity or differences of complete genomes. Presence of genes coding for C. difficile toxins A and B (ToxA/B), as well as the binary C. difficile toxin (CDT), was deduced from their unique PfamA domain architectures. Out of the 663 C. difficile genomes, 535 (80.7%) contained at least one copy of ToxA or ToxB, while these genes were missing from 128 genomes. Although some clusters were enriched for toxin presence, these genes are variably present in a given genetic background. The CDT genes were found in 191 genomes, which were restricted to a few clusters only, and only one cluster lacked the toxin A/B genes consistently. A total of 310 genomes contained ToxA/B without CDT (47%). Further, published metagenomic data from stools were used to assess the presence of C. difficile sequences in blinded cases of C. difficile infection (CDI) and controls, to test if metagenomic analysis is sensitive enough to detect the pathogen, and to establish strain relationships between cases from the same hospital. We conclude that metagenomics can contribute to the identification of CDI and can assist in characterization of the most probable causative strain in CDI patients.
| S-EPMC6132499 | biostudies-literature
Project description:Taxonomy identification is fundamental to all microbiology studies. Particularly in metagenomics, which identify the composition of microorganisms using thousands of sequences, its importance is even greater. Identification is inevitably affected by the choice of database. This study was conducted to evaluate the accuracy of three widely used 16S databases, Greengenes, Silva, and EzBioCloud, and to suggest basic guidelines for selecting reference databases. Using public mock community data, each database was used to assign taxonomy and to test its accuracy. We showed that EzBioCloud performs well compared to other existing databases.
| S-EPMC6440677 | biostudies-literature
Project description:Whole-genome comparisons based on average nucleotide identities (ANI) and the genome-to-genome distance calculator have risen to prominence in rapidly classifying prokaryotic taxa using whole-genome sequences. Some implementations have even been proposed as a new standard in species classification and have become a common technique for papers describing newly sequenced genomes. However, attempts to apply whole-genome divergence data to the delineation of higher taxonomic units and to phylogenetic inference have had difficulty matching those produced by more complex phylogenetic methods. We present a novel method for generating statistically supported phylogenies of archaeal and bacterial groups using a combined ANI and alignment fraction-based metric. For the test cases to which we applied the developed approach, we obtained results comparable with other methodologies up to at least the family level. The developed method uses nonparametric bootstrapping to gauge support for inferred groups. This method offers the opportunity to make use of whole-genome comparison data, that is already being generated, to quickly produce phylogenies including support for inferred groups. Additionally, the developed ANI methodology can assist the classification of higher taxonomic groups.[Average nucleotide identity (ANI); genome evolution; prokaryotic species delineation; taxonomy.].
| S-EPMC8830074 | biostudies-literature