Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Metagenomics is a rapidly expanding field that uses next-generation sequencing technology to analyze the genetic makeup of environmental samples. However, accurately identifying the organisms in a metagenomic sample can be complex, and traditional reference-based methods may need to be more effective in some instances. In this study, we present a novel approach for metagenomic identification, using data compressors as a feature for taxonomic classification. By evaluating a comprehensive set of compressors, including both general-purpose and genomic-specific, we demonstrate the effectiveness of this method in accurately identifying organisms in metagenomic samples. The results indicate that using features from multiple compressors can help identify taxonomy. An overall accuracy of 95% was achieved using this method using an imbalanced dataset with classes with limited samples. The study also showed that the correlation between compression and classification is insignificant, highlighting the need for a multi-faceted approach to metagenomic identification. This approach offers a significant advancement in the field of metagenomics, providing a reference-less method for taxonomic identification that is both effective and efficient while revealing insights into the statistical and algorithmic nature of genomic data. The code to validate this study is publicly available at https://github.com/ieeta-pt/xgTaxonomy.

BACKGROUND: Archaeobotanists and palaeoecologists extensively use geometric morphometrics to identify plant opal phytoliths. Particularly when applied to assemblages of phytoliths from concentrations retrieved from closed contexts, morphometric data from archaeological phytoliths compared with similar data from reference material may allow taxonomic attribution. Observer variation is one aspect of phytolith morphometry that has received little attention but may be an important source of error, and hence cause of potential misidentification of plant remains.
METHODS: To investigate inter- and intra-observer variation in phytolith morphometry, eight researchers (observers) from different laboratories measured 50 samples each from three phytolith morphotypes, Bilobate, Bulliform flabellate and Elongate dendritic, three times, under the auspices of the International Committee for Phytolith Morphometrics (ICPM).
METHODS: Data for 17 size and shape variables were collected for each phytolith by manually digitising a phytolith outline (mask) from a photograph, followed by measurement of the mask with open-source morphometric software.
RESULTS: Inter-observer variation ranged from 0 to 23% difference from the mean of all observers. Intra-observer variation ranged from 0 to 9% difference from the mean of individual observers per week. Inter- and intra-observer variation was generally higher among inexperienced researchers.
CONCLUSIONS: Scaling errors were a major cause of variation and occurred more with less experienced researchers, which is likely related to familiarity with data collection. The results indicate that inter- and intra-observer variation can be substantially reduced by providing clear instructions for and training with the equipment, photo capturing, software, data collection and data cleaning. In this paper, the ICPM provides recommendations to minimise variation.Advances in automatic data collection may eventually reduce inter- and intra-observer variation, but until this is common practice, the ICPM recommends that phytolith morphometric analyses adhere to standardised guidelines to assure that measured phytolith variables are accurate, consistent and comparable between different researchers and laboratories.

Banana wilt caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) is a devastating fungal disease. Biocontrol strategies hold immense potential for inhibiting the spread of Foc TR4. Here, 30 actinobacteria were isolated from soils and screened for their antagonistic activity against Foc TR4. Strain SCA4-21T was selected due to its strongest antagonistic activity against Foc TR4. Strain SCA4-21T also exhibited strong antagonistic activity against the other eight phytopathogenic fungi. The strain was identified as the genus Streptomyces according to its physiological, biochemical, and phenotypic characteristics. The phylogenetic trees of 16S rRNA sequences demonstrated that strain SCA4-21T formed a subclade with S. iranensis HM 35T and/or S. rapamycinicus NRRL B-5491T with low bootstrap values. Considering that 16S rRNAs did not provide sufficient resolution for species-level identification, the whole genome of strain SCA4-21T was sequenced. Multilocus sequence analysis (MLSA) based on five housekeeping gene alleles (atpD, gyrB, recA, rpoB, and trpB) revealed that strain SCA4-21T clustered into S. hygroscopicus subsp. hygroscopicus NBRC 13472T with 100% of bootstrap value. The analysis of the genome-based phylogeny also approved the results. Average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) were 91.26 and 44.30%, respectively, with values below the respective species level threshold of 95 and 70%. Hence, strain SCA 4-21T represented a novel species within the genus Streptomyces, named Streptomyces luomodiensis sp. nov. The type strain is SCA4-21T (=GDMCC4.340T = JCM36555T). By the CAZymes analysis, 348 carbohydrate-active enzymes (CAZymes) were detected, including 15 chitinases and eight β-1,3-glucanases. The fermentation broth of strain SCA4-21T, exhibiting strong antagonistic activity against Foc TR4, demonstrated high activities of chitinase and β-1,3-glucanase, which might be involved in antifungal activity. Our results showed an innovative potential biocontrol agent for managing plant fungal diseases, specifically banana fusarium wilt.

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

The aim of this study was to explore the taxonomic identification and evaluate the safety of a bacterium, Enterococcus lactis IDCC 2105, isolated from homemade cheese in Korea, using whole genome sequence (WGS) analysis. It sought to identify the species level of this Enterococcus spp., assess its antibiotic resistance, and evaluate its virulence potential. WGS analysis confirmed the bacterial strain IDCC 2105 as E. lactis and identified genes responsible for resistance to erythromycin and clindamycin, specifically msrC, and eatAv, which are chromosomally located, indicating a minimal risk for horizontal gene transfer. The absence of plasmids in E. lactis IDCC 2105 further diminishes the likelihood of resistance gene dissemination. Additionally, our investigation into seven virulence factors, including hemolysis, platelet aggregation, biofilm formation, hyaluronidase, gelatinase, ammonia production, and β-glucuronidase activity, revealed no detectable virulence traits. Although bioinformatic analysis suggested the presence of collagen adhesion genes acm and scm, these were not corroborated by phenotypic virulence assays. Based on these findings, E. lactis IDCC 2105 presents as a safe strain for potential applications, contributing valuable information on its taxonomy, antibiotic resistance profile, and lack of virulence factors, supporting its use in food products.

The dataset profiled in this research is built on sequencing of lactic acid bacteria 16S rDNA mined from Nono (N4 and N5), Kunu (K4 and K1) and Garri. The 16S rDNA sequences files are accessible under the data identification numbers: OK017047, OK017046, OK017044, OK017043, OK017045 at the GenBank database, NCBI. Taxonomic identification and phylogenetic tree analysis were done using the online BLAST (blastn) and MEGA11 software, respectively. The effect of the bacteriocin produced by these organisms on spoilage bacteria associated with salad was evaluated using an agar well diffusion assay. Limosilactobacillus pontis strain EOINONO, Limosilactobacillus pontis strain OGENONO, Limosilactobacillus pontis strain SEOGARI, Lactiplantibacillus plantarum strain MJIKUNU and Limosilactobacillus pontis strain EEIKUNU were the identified bacteriocinogenic organisms while Bacillus tequilensis strain SEOABACHA, Bacillus tequilensis strain EEIABACHA, Achromobacter xylosoxidans strain IMABACHA and Achromobacter insolitus strain MJIABACHA were the identified spoilage organisms.

UNASSIGNED: Taxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses.
UNASSIGNED: taxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered.
UNASSIGNED: taxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.

The Adoxophyes tea tortrix (Lepidoptera: Tortricidae) is a group of leaf rollers that cause enormous economic losses on tea and apple crops. In East Asia, taxonomic ambiguity of the Adoxophyes orana complex (AOC), which consists of A. orana, A. dubia, A. honmai, and A. paraorana, has persisted for decades because of vague diagnostic characters. In this study, differences in the AOC were examined to improve species identification, determine genetic variations, and develop control strategies. Analyses revealed that A. orana comprised 2 lineages, a widely distributed Palearctic lineage and an East Asian lineage that was nested with other Asian species. Genetic divergence of >3% is proposed to confirm the AOC species that would benefit subsequent taxonomic revision. The monophyletic Taiwanese A. sp. with 2.8-4% from other AOC species appeared to suggest it as an independent taxon, and low interspecific divergence between A. honmai and A. dubia of 0.3% indicated possibility of recent divergence or intraspecific variations. Our result further suggested that the Z9-14:Ac ratio of semiochemicals could be a reference for the reblending of pheromone attractants in Taiwanese tea plantations. Moreover, the AOC species appeared to have a tendency of specific geographic distributions, with A. dubia and A. honmai in Japan and China, A. paraorana in Korea, and A. sp. in Taiwan. Maintaining the unique genetic composition of Adoxophyes species in each geographic region and preventing the possible invasions into those AOC-free countries through the transportation of host plants are essential in managing the AOC in East Asia.

2-hydroxybutyric acid (2HB) serves as an important regulatory factor in a variety of diseases. The circulating level of 2HB in serum is significantly higher in multiple diseases, such as cancer and type 2 diabetes (T2D). However, there is currently no systematic study on 2HB-producing bacteria that demonstrates whether gut bacteria contribute to the circulating 2HB pool. To address this question, we used BLASTP to reveal the taxonomic profiling of 2HB-producing bacteria in the human microbiome, which are mainly distributed in the phylum Proteobacteria and Firmicutes. In vitro experiments showed that most gut bacteria (21/32) have at least one path to produce 2HB, which includes Aspartic acid, methionine, threonine, and 2-aminobutyric acid. Particularly, Fusobacterium nucleatum has the strongest ability to synthesize 2HB, which is sufficient to alter colon 2HB concentration in mice. Nevertheless, neither antibiotic (ABX) nor Fusobacterium nucleatum gavage significantly affected mouse serum 2HB levels during the time course of this study. Taken together, our study presents the profiles of 2HB-producing bacteria and demonstrates that gut microbiota was a major contributor to 2HB concentration in the intestinal lumen but a relatively minor contributor to serum 2HB concentration.