Taxonomic Labels Research Articles

Disentangling Cobionts and Contamination in Long-Read Genomic Data using Sequence Composition.

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well-represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualising two-dimensional representations of read tetranucleotide composition learned by a Variational Autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualisation tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

Leveraging machine learning for taxonomic classification of emerging astroviruses.

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

The impact of transitive annotation on the training of taxonomic classifiers.

A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified-formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationally-generated data-a process commonly referred to as "transitive annotation." In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project's Bayesian classifier in our case) on data that itself has been computationally generated. We generate new training examples based on 16S rRNA data from a metagenomic experiment, and evaluate the extent to which the taxonomic labels predicted by the classifier change after re-training. We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. We conclude with a discussion of key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper.

Taxonomic Analysis of Asteroids with Artificial Neural Networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to the limits of ground-based observational instruments. In the near future, the Chinese Space Survey Telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 and 23 mag, respectively. With the aim of analyzing the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus–Binzel taxonomic system, our ANN classification tool composed of five individual ANNs is constructed, and the accuracy of this classification system is higher than 92%. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained by us in 2006 and 2007 with the 2.16 m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering its accuracy and stability, our ANN tool can be applied to analyze CSST asteroid spectra in the future.

MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

BackgroundWhole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles.ResultsHere, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality.ConclusionsMetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro. The software is freely available under the GNU general public license v3.9LNNdXLsCA96o8Y-9qmMqKVideo

An initial genomic blueprint of the healthy human oesophageal microbiome.

The oesophageal microbiome is thought to contribute to the pathogenesis of oesophageal cancer. However, investigations using culture and molecular barcodes have provided only a low-resolution view of this important microbial community. We therefore explored the potential of culturomics and metagenomic binning to generate a catalogue of reference genomes from the healthy human oesophageal microbiome, alongside a comparison set from saliva. Twenty-two distinct colonial morphotypes from healthy oesophageal samples were genome-sequenced. These fell into twelve species clusters, eleven of which represented previously defined species. Two isolates belonged to a novel species, which we have named Rothia gullae. We performed metagenomic binning of reads generated from UK samples from this study alongside reads generated from Australian samples in a recent study. Metagenomic binning generated 136 medium or high-quality metagenome-assembled genomes (MAGs). MAGs were assigned to 56 species clusters, eight representing novel Candidatus species, which we have named Ca. Granulicatella gullae, Ca. Streptococcus gullae, Ca. Nanosynbacter quadramensis, Ca. Nanosynbacter gullae, Ca. Nanosynbacter colneyensis, Ca. Nanosynbacter norwichensis, Ca. Nanosynococcus oralis and Ca. Haemophilus gullae. Five of these novel species belong to the recently described phylum Patescibacteria . Although members of the Patescibacteria are known to inhabit the oral cavity, this is the first report of their presence in the oesophagus. Eighteen of the metagenomic species were, until recently, identified only by hard-to-remember alphanumeric placeholder designations. Here we illustrate the utility of a set of recently published arbitrary Latinate species names in providing user-friendly taxonomic labels for microbiome analyses.Our non-redundant species catalogue contained 63 species derived from cultured isolates or MAGs. Mapping revealed that these species account for around half of the sequences in the oesophageal and saliva metagenomes. Although no species was present in all oesophageal samples, 60 species occurred in at least one oesophageal metagenome from either study, with 50 identified in both cohorts. Recovery of genomes and discovery of new species represents an important step forward in our understanding of the oesophageal microbiome. The genes and genomes that we have released into the public domain will provide a base line for future comparative, mechanistic and intervention studies.

Simultaneous profiling of host expression and microbial abundance by spatial metatranscriptome sequencing.

We developed an analysis pipeline that can extract microbial sequences from spatial transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline spatial metatranscriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from these novel data that showed host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively showed the capture efficiency and recall of our methods. This proof-of-concept work shows the feasibility of SMT analysis and paves the way for further experimental optimization and application.

A Guide to Gene-Centric Analysis Using TreeSAPP.

Gene-centric analysis is commonly used to chart the structure, function, and activity of microbial communities in natural and engineered environments. A common approach is to create custom ad hoc reference marker gene sets, but these come with the typical disadvantages of inaccuracy and limited utility beyond assigning query sequences taxonomic labels. The Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software package standardizes analysis of phylogenetic and functional marker genes and improves predictive performance using a classification algorithm that leverages information-rich reference packages consisting of a multiple sequence alignment, a profile hidden Markov model, taxonomic lineage information, and a phylogenetic tree. Here, we provide a set of protocols that link the various analysis modules in TreeSAPP into a coherent process that both informs and directs the user experience. This workflow, initiated from a collection of candidate reference sequences, progresses through construction and refinement of a reference package to marker identification and normalized relative abundance calculations for homologous sequences in metagenomic and metatranscriptomic datasets. The alpha subunit of methyl-coenzyme M reductase (McrA) involved in biological methane cycling is presented as a use case given its dual role as a phylogenetic and functional marker gene driving an ecologically relevant process. These protocols fill several gaps in prior TreeSAPP documentation and provide best practices for reference package construction and refinement, including manual curation steps from trusted sources in support of reproducible gene-centric analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating reference packages Support Protocol 1: Installing TreeSAPP Support Protocol 2: Annotating traits within a phylogenetic context Basic Protocol 2: Updating reference packages Basic Protocol 3: Calculating relative abundance of genes in metagenomic and metatranscriptomic datasets.

Image‐based taxonomic classification of bulk insect biodiversity samples using deep learning and domain adaptation

AbstractComplex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high‐throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, for example, from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family‐level classification of bulk samples of Coleoptera, as a critical first step in the characterization of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within‐dataset classification accuracy reached 98% and depended on the number and quality of training images, and on dataset complexity. The accuracy of between‐datasets predictions (across disparate source–target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardization of the imaging procedure. An algorithm for domain adaptation, domain adversarial training of neural networks (DANN), significantly improved the prediction performance of models trained by non‐standardized, low‐quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.

Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

Microbiome analysis is quickly moving towards high-throughput methods such as metagenomic sequencing. Accurate taxonomic classification of metagenomic data relies on reference sequence databases, and their associated taxonomy. However, for understudied environments such as the rumen microbiome many sequences will be derived from novel or uncultured microbes that are not present in reference databases. As a result, taxonomic classification of metagenomic data from understudied environments may be inaccurate. To assess the accuracy of taxonomic read classification, this study classified metagenomic data that had been simulated from cultured rumen microbial genomes from the Hungate collection. To assess the impact of reference databases on the accuracy of taxonomic classification, the data was classified with Kraken 2 using several reference databases. We found that the choice and composition of reference database significantly impacted on taxonomic classification results, and accuracy. In particular, NCBI RefSeq proved to be a poor choice of database. Our results indicate that inaccurate read classification is likely to be a significant problem, affecting all studies that use insufficient reference databases. We observed that adding cultured reference genomes from the rumen to the reference database greatly improved classification rate and accuracy. We also demonstrated that metagenome-assembled genomes (MAGs) have the potential to further enhance classification accuracy by representing uncultivated microbes, sequences of which would otherwise be unclassified or incorrectly classified. However, classification accuracy was strongly dependent on the taxonomic labels assigned to these MAGs. We therefore highlight the importance of accurate reference taxonomic information and suggest that, with formal taxonomic lineages, MAGs have the potential to improve classification rate and accuracy, particularly in environments such as the rumen that are understudied or contain many novel genomes.

Management of DNA reference libraries for barcoding and metabarcoding studies with the R package refdb.

DNA barcoding and metabarcoding are revolutionizing the study and survey of biodiversity. In order to assign taxonomic labels to the DNA sequence data retrieved, these methods are strongly dependent on comprehensive and accurate reference databases. Producing reliable databases linking biological sequences and taxonomic data can be-and often has been-done using mainstream tools such as spreadsheet software. However, spreadsheets quickly become insufficient when the amount of data increases to thousands of taxa and sequences to be matched, and validation operations become more complex and are error prone if done in a manual way. Thus, there is a clear need for providing scientists with user-friendly, reliable and powerful tools to manipulate and manage DNA reference databases in tractable, sound and efficient ways. Here, we introduce the R package refdb as an environment for semi-automatic and assisted construction of DNA reference libraries. The refdb package is a reference database manager offering a set of powerful functions to import, organize, clean, filter, audit and export the data. It is broadly applicable in metabarcoding data generally obtained in biodiversity and biomonitoring studies. We present the main features of the package and outline how refdb can speed up reference database generation, management and handling, and thus contribute to standardization and repeatability in barcoding and metabarcoding studies.

Extending Recognition for Taxonomic Curation Beyond the Traditional Authorities

Taxonomy is at the center of modern biodiversity science, since it defines the dual name and meaning of species that jointly allows biologists to study and classify organisms while linking observations from multiple sources. With the accelerating digitization of biodiversity data has come the increased need for readily available taxonomic products, as is reflected in the number of initiatives dedicated to curating and publishing digital lists of accepted names, checklists of taxa occurring in different regions, and systematic classifications of species in different groups. Taxonomic curation can be described as a collage effort, whereby contributors work to assemble a diverse range of evidence and scientific resources into a harmonious understanding of biodiversity. However, traditionally, who receives recognition and credit for taxonomic curation has been based primarily on those holding official academic positions and credentials. Similarly, the results have generally been provided to end-users as only a list of valid accepted or synonymous species names without explicit evidence regarding how those decisions were made. In contrast, we propose a functional view of who should be recognized as a Taxonomic Curator (TC), casting a broad net to capture contributions made by individuals and organizations that might not self-identify as scientific authorities about which taxa exist in a group or region. While academic credentials are useful indicators of quality training and knowledge, expertise can be acquired through other pathways such as field experience or self-schooling. Similarly, authoring new information is essential to being a TC, but is not limited to publishing full-length articles or edited volumes. In light of the rapid pace of science, the qualities of trustworthiness, accountability, and responsiveness are ultimately more important. A person’s authoritativeness as a TC should therefore be tied to their participation in a social process of self-correction and engagement rather than academic expertise at one point in time. Similarly, this extended view of taxonomic curation makes it inherent to any project where information is validated according to an internally coherent set of taxonomic units (e.g., ‘cleaning’ data from the Global Biodiversity Information Facility prior to analysis, or vetting iNaturalist records). We argue that this view better represents the importance of the services TCs provide for innovative collections-based biological research; e.g., the Extended Specimen approach (Lendemer et al. 2019). The digitization of biodiversity data has been possible in large part due to the development of content management software and tools for natural history collections (e.g., Arctos, Symbiota). In general, these tools enable effective taxonomic curation through the linking between species names, observational data, and scientific literature. In this way, digitizing specimen information directly involves the work of TCs (usually many) to clean, validate, interpret, and annotate the taxonomic labels for the species identified. Although not generally published as formal academic scholarship, these efforts functionally author new species lists in which the related occurrences (specimens or observations) form the body of evidence for each taxonomic concept established in the research group or institution responsible for publishing the data. While contributors to biodiversity data portals or citizen science projects such as iNaturalist may not identify as traditional taxonomic authorities, they often exhibit exactly the qualities of iterative social accountability and improvement needed for progress in biodiversity data science today. Therefore, innovative credit models such as nanopublications that mint Digital Object Identifiers should be tested to encourage the continued valuable work of TCs in formal and informal networks.

Predicting Langmuir model parameters for tungsten adsorption in heterogeneous soils using compositional signatures

• Soil taxonomic designations were statistically discriminated by compositional data. • Stacked ensemble models showed greater accuracies than unstacked models. • Multiple W chemical interactions evident across diverse range of soil “types”. Metallic tungsten (W) is a highly dense material of increasing importance to the U.S. Army as a strategic, non-radioactive replacement for depleted uranium. While there is a growing body of evidence regarding the mechanistic behavior of ionic W (formed after the spontaneous oxidation of metal) in the environment, predicting its environmental fate remains challenging, owing to the widespread geochemical heterogeneity of soils. Therefore, we developed W adsorption prediction models by creating different functional “compositions” of the chemical and physical characteristics for different soil “types” (a non-specific yet commonly used to term to designate different soils). A relatively small dataset consisting of twenty soils (possessing six different soil “types” from across the U.S.) were evaluated for W adsorption behavior. Physical and chemical soil data were separated into water-extracted (WE), bulk, and particle-size distribution (PSD) compositions, and center log-ratio (clr) transformed. Classification models built using extremely randomized trees (ERT) showed that the compositions' accuracies were WE > Bulk > PSD at the Order and Suborder levels. W's adsorption isotherms were constructed using batch equilibrium experiments and modeled against the Langmuir model, where S max = calculated adsorption maximum, K 1/L = inverse Langmuir affinity coefficient. Afterward, both the ERT and ensemble, or stacked, ERT models (by addition of Order and/or Suborder taxonomic labels as ensemble classifiers) were developed for predicting the S max and K 1/L parameters based on the different compositions. In general, model accuracies were substantially increased by the addition of the labels (stacked models). Feature importance calculations pointed to a wide range of potential chemical mechanisms simultaneously controlling W adsorption, laying the groundwork for more detailed in-situ elemental speciation studies. Overall, this work showcased a new technological capability allowing for accurately predicting W adsorption on a wide variety of morphological soil designations. Capsule: This work found that soil morphological designations greatly improved the accuracy of Langmuir adsorption predictions of CoDA-transformed characterization data.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen.

Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem

High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA.

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

BackgroundSequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction.ResultsOn two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84.ConclusionsSBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers.

OP32 Stool microbiome communities predict remission in treatment-naïve Pediatric Crohn’s Disease patients

Abstract Background Early relapse in paediatric Crohn’s Disease (CD) is associated with severe disease course that heavily impairs quality of life. Changes in gut microbiome composition have been linked to active CD and disease course. This has led to development of microbiome-based prediction models for diagnosis and response to treatment. Our aim was to identify community-level microbiome signatures of treatment-naïve children with mild-to-moderate CD who did not require anti-TNF or surgery at diagnosis, with the goal of predicting need for re-induction or treatment escalation within the first year after diagnosis. Methods We selected de novo, treatment-naïve paediatric CD patients from the RISK cohort(Gevers 2014). Taxonomic labels were assigned to the 16s rRNA amplicon data using QIIME and closed OTU-picking. A hierarchical Bayesian model for microbial community structure was used to learn how baseline gut microbiomes differed according to treatment outcome. Model predictions were assessed using a leave-one-out analysis. We compared 16S rRNA sequences of CD patients with non-IBD controls(Gevers 2014) and healthy siblings of CD patients(Turpin 2016). Results Metadata and 16S rRNA amplicon data were available from 197 stool samples of de novo paediatric CD patients from the RISK cohort. We selected 44 out of 197 samples of patients that were treatment-naïve. Prior to treatment, PCDAI scores were similar between patients reaching remission and those that did not at 6 months. Bayesian analysis characterized 4 assemblages that accounted for 93% of the posterior probability distribution. The Bayesian model on pre-treatment stool microbiomes was able to predict 6-month outcome of patients that maintained remission and those that did not from the pre-treatment microbiome in 81% and 75% of samples (AUC=0.79). When comparing CD samples to 28 non-IBD controls (many with GI symptoms but negative for IBD during endoscopy, e.g. Irritable Bowel Syndrome), 6 assemblages were characterized with 44% of distributions shared between groups (AUC=0.61). In contrast, in CD samples compared to 728 healthy sibling samples (with increased genetic susceptibility), shared distribution within 4 characterized assemblages was less than 1% (AUC=1). Conclusion A Bayesian approach predicted clinical course in treatment-naïve children with CD in the first year after diagnosis with high accuracy, when ensuring only treatment-naïve faecal samples in the analysis. This classification level is comparable to previous findings using mucosal samples. Further study is needed to validate these pre-treatment microbiome signatures of newly diagnosed paediatric CD patients to allow identification of patients with mild-to-moderate disease who are most likely to require treatment escalation.

Fast and sensitive taxonomic assignment to metagenomic contigs.

SummaryMMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments.Availability and implementationMMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com.Supplementary informationSupplementary data are available at Bioinformatics online.

Barcoding a can of worms: testing cox1 performance as a DNA barcode of Nematoda.

Accurate taxonomic identifications and species delimitations are a fundamental problem in biology. The complex taxonomy of Nematoda is primarily based on morphology, which is often dubious. DNA barcoding emerged as a handy tool to identify specimens and assess diversity, but its applications in Nematoda are incipient. We evaluated cytochrome c oxidase subunit I (cox1) efficiency as a DNA barcode for nematodes scrutinising 5241 sequences retrieved from BOLD and GenBank. The samples included genera with medical, agricultural, or ecological relevance: Anguillicola, Caenorhabditis, Heterodera, Meloidogyne, Onchocerca, Strongyloides, and Trichinella. We assessed cox1 performance through barcode gap and Probability of Correct Identification (PCI) analyses, and estimated species richness through Automatic Barcode Gap Discovery (ABGD). Each genus presented distinct gap ranges, mirroring the evolutionary diversity within Nematoda. Thus, to survey the diversity of the phylum, a careful definition of thresholds for lower taxonomic levels should be considered. PCIs were around 70% for both databases, highlighting operational biases and challenges in nematode taxonomy. ABGD inferred higher richness than the taxonomic labels informed by databases. The prevalence of specimen misidentifications and dubious species delimitations emphasise the value of integrative approaches to nematode taxonomy and systematics. Overall, cox1 is a relevant tool for integrative taxonomy of nematodes.

Mapping sequence to feature vector using numerical representation of codons targeted to amino acids for alignment-free sequence analysis

The phylogenetic analysis based on sequence similarity targeted to real biological taxa is one of the major challenging tasks. In this paper, we propose a novel alignment-free method, CoFASA (Codon Feature based Amino acid Sequence Analyser), for similarity analysis of nucleotide sequences. At first, we assign numerical weights to the four nucleotides. We then calculate a score of each codon based on the numerical value of the constituent nucleotides, termed as degree of codons. Accordingly, we obtain the degree of each amino acid based on the degree of codons targeted towards a specific amino acid. Utilizing the degree of twenty amino acids and their relative abundance within a given sequence, we generate 20-dimensional features for every coding DNA sequence or protein sequence. We use the features for performing phylogenetic analysis of the set of candidate sequences.We use multiple protein sequences derived from Beta-globin (BG), NADH dehydrogenase subunit 5 (ND5), Transferrins (TFs), Xylanases, low identity (<40%) and high identity (⩾40%) protein sequences (encompassing 533 and 1064 protein families) for experimental assessments. We compare our results with sixteen (16) well-known methods, including both alignment-based and alignment-free methods. Various assessment indices are used, such as the Pearson correlation coefficient, RF (Robinson-Foulds) distance and ROC score for performance analysis. While comparing the performance of CoFASA with alignment-based methods (ClustalW, ClustalΩ, MAFFT, and MUSCLE), it shows very similar results. Further, CoFASA shows better performance in comparison to well-known alignment-free methods, including LZW-Kernal, jD2Stat, FFP, spaced, and AFKS-D2s in predicting taxonomic relationship among candidate taxa. Overall, we observe that the features derived by CoFASA are very much useful in isolating the sequences according to their taxonomic labels. While our method is cost-effective, at the same time, produces consistent and satisfactory outcomes.