Tiara: deep learning-based classification system for eukaryotic sequences

Enhancing metagenomic classification with compression-based features

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.

Microbial acidification by N, S, Fe and Mn oxidation as a key mechanism for deterioration of subsea tunnel sprayed concrete

The deterioration of fibre-reinforced sprayed concrete was studied in the Oslofjord subsea tunnel (Norway). At sites with intrusion of saline groundwater resulting in biofilm growth, the concrete exhibited significant concrete deterioration and steel fibre corrosion. Using amplicon sequencing and shotgun metagenomics, the microbial taxa and surveyed potential microbial mechanisms of concrete degradation at two sites over five years were identified. The concrete beneath the biofilm was investigated with polarised light microscopy, scanning electron microscopy and X-ray diffraction. The oxic environment in the tunnel favoured aerobic oxidation processes in nitrogen, sulfur and metal biogeochemical cycling as evidenced by large abundances of metagenome-assembled genomes (MAGs) with potential for oxidation of nitrogen, sulfur, manganese and iron, observed mild acidification of the concrete, and the presence of manganese- and iron oxides. These results suggest that autotrophic microbial populations involved in the cycling of several elements contributed to the corrosion of steel fibres and acidification causing concrete deterioration.

Decoupling of strain- and intrastrain-level interactions of microbiomes in a sponge holobiont

Holobionts are highly organized assemblages of eukaryotic hosts, cellular microbial symbionts, and viruses, whose interactions and evolution involve complex biological processes. It is largely unknown which specific determinants drive similarity or individuality in genetic diversity between holobionts. Here, we combine short- and long-read sequencing and DNA-proximity-linkage technologies to investigate intraspecific diversity of the microbiomes, including host-resolved viruses, in individuals of a model marine sponge. We find strong impacts of the sponge host and the cellular hosts of viruses on strain-level organization of the holobiont, whereas substantial overlap in nucleotide diversity between holobionts suggests frequent exchanges of microbial cells and viruses at intrastrain level in the local sponge population. Immune-evasive arms races likely restricted virus-host co-evolution at the intrastrain level, generated holobiont-specific genome variations, and linked virus-host genetics through recombination. Our work shows that a decoupling of strain- and intrastrain-level interactions is a key factor in the genetic diversification of holobionts.

4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or more minor classes from bacteria and archaea in metagenome assemblies. However, these classifiers often overlook the issue of class imbalance, leading to low precision in identifying the minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

Insights into gut fungi in pigs: A comprehensive review

Fungi in the gut microbiota of mammals play a crucial role in host physiological regulation, including intestinal homeostasis and host immune regulation. However, our understanding of gut fungi in mammals remains limited, especially in economically valuable animals, such as pigs. Therefore, this review first describes the classification and characterisation of fungi, provides insights into the methods used to study gut fungi, and summarises the recent progress on pig gut fungi. Additionally, it discusses the challenges in the study of pig gut fungi and highlights potential perspectives. The aim of this review is to serve as a valuable reference for advancing our knowledge of gut fungi in animals.

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

First Report and Genome Resource of Monilinia vaccinii-corymbosi, causal agent of Mummy Berry Disease of Black Huckleberry (Vaccinium membranaceum)

Monilinia vacccinii-corymbosi (phylum Ascomycota, family Sclerotiniaceae) causes fruit disease 'mummy berry' on berry crops and responsible for yield losses and quality of fruits. We reported mummy berry disease of black huckleberry (Vaccinium membranaceum) first time in British Columbia, Canada. We have performed sequencing and genome assembly of M. vacccinii-corymbosi from infected fruits of huckleberry. The resulting genome was 33.8 Mbp in size and consisted of 2,437 scaffolds with an N50 of 33,816 bp. To our best knowledge, this is the first report of resource announcement of whole genome sequence of mummy berry pathogen (M. vacccinii-corymbosi) infecting black huckleberry. The genome resource will be valuable for future studies to understand the genomic structure of pathogen, and mechanisms associated with black huckleberry-M. vacccinii-corymbosi interactions.

The complete mitogenome of the Atlantic longnose chimaera Rhinochimaera atlantica (Holt & Byrne, 1909)

Holocephali is a subclass of chondrichthyans with ample geographic distribution in marine ecosystems. Holocephalan species are organized into three families: Callorhinchidae, Chimaeridae, and Rhinochimaeridae. Despite the critical ecological and evolutionary importance, genomic information from holocephalans is still scarce, particularly from rhinochimaerids. The present study provides the first complete mitogenome of the Atlantic longnose chimaera Rhinochimaera atlantica (Holt & Byrne, 1909). The whole mitogenome was sequenced from an R. atlantica specimen, collected on the Porcupine Bank (NE Atlantic), by Illumina high-throughput sequencing. The R. atlantica mitogenome has 17,852 nucleotides with 13 protein-coding genes, 22 transfer RNA, and two ribosomal RNA genes. Nine of these genes are in the complementary strand. This mitogenome has a GC content of 41.5% and an AT content of 58.5%. The phylogenetic reconstruction provided here, using all the available complete and partial Holocephali mitogenomes, places R. atlantica in the Rhinochimaeridae family, as expected. This genomic resource will be useful in the genomic characterization of this species.

DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses

Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.

The genome of the rayed Mediterranean limpet Patella caerulea (Linnaeus, 1758)

Patella caerulea (Linnaeus, 1758) is a mollusc limpet species of the class Gastropoda. Endemic to the Mediterranean Sea, it is considered a keystone species due to its primary role in structuring and regulating the ecological balance of tidal and subtidal habitats. It is currently being used as a bioindicator to assess the environmental quality of coastal marine waters and as a model species to understand adaptation to ocean acidification. Here, we provide a high-quality reference genome assembly and annotation for P. caerulea. We generated ∼30 Gb of Pacific Biosciences high-fidelity data from a single individual and provide a final 749.8 Mb assembly containing 62 contigs, including the mitochondrial genome (14,938 bp). With an N50 of 48.8 Mb and 98% of the assembly contained in the 18 largest contigs, this assembly is near chromosome-scale. Benchmarking Universal Single-Copy Orthologs scores were high (Mollusca, 87.8% complete; Metazoa, 97.2% complete) and similar to metrics observed for other chromosome-level Patella genomes, highlighting a possible bias in the Mollusca database for Patellids. We generated transcriptomic Illumina data from a second individual collected at the same locality and used it together with protein evidence to annotate the genome. A total of 23,938 protein-coding gene models were found. By comparing this annotation with other published Patella annotations, we found that the distribution and median values of exon and gene lengths was comparable with other Patella species despite different annotation approaches. The present high-quality P. caerulea reference genome, available on GenBank (BioProject: PRJNA1045377; assembly: GCA_036850965.1), is an important resource for future ecological and evolutionary studies.

Single-cell genomics revealed Candidatus Grellia alia sp. nov. as an endosymbiont of Eutreptiella sp. (Euglenophyceae)

Though endosymbioses between protists and prokaryotes are widespread, certain host lineages have received disproportionate attention what may indicate either a predisposition to such interactions or limited studies on certain protist groups due to lack of cultures. The euglenids represent one such group in spite of microscopic observations showing intracellular bacteria in some strains. Here, we perform a comprehensive molecular analysis of a previously identified endosymbiont in the Eutreptiella sp. CCMP3347 using a single cell approach and bulk culture sequencing. The genome reconstruction of this endosymbiont allowed the description of a new endosymbiont Candidatus Grellia alia sp. nov. from the family Midichloriaceae. Comparative genomics revealed a remarkably complete conjugative type IV secretion system present in three copies on the plasmid sequences of the studied endosymbiont, a feature missing in the closely related Grellia incantans. This study addresses the challenge of limited host cultures with endosymbionts by showing that the genomes of endosymbionts reconstructed from single host cells have the completeness and contiguity that matches or exceeds those coming from bulk cultures. This paves the way for further studies of endosymbionts in euglenids and other protist groups. The research also provides the opportunity to study the diversity of endosymbionts in natural populations.

Increasing transposase abundance with ocean depth correlates with a particle-associated lifestyle

Transposases are mobile genetic elements that move within and between genomes, promoting genomic plasticity in microorganisms. In marine microbial communities, the abundance of transposases increases with depth, but the reasons behind this trend remain unclear. Our analysis of metagenomes from the Tara Oceans and Malaspina Expeditions suggests that a particle-associated lifestyle is the main covariate for the high occurrence of transposases in the deep ocean, and this trend holds true for individual genomes as well as in a community-wide sense. We observed a strong and depth-independent correlation between transposase abundance and the presence of biofilm-associated genes, as well as the prevalence of secretory enzymes. This suggests that mobile genetic elements readily propagate among microbial communities within crowded biofilms. Furthermore, we show that particle association positively correlates with larger genome size, which is in turn associated with higher transposase abundance. Cassette sequences associated with transposons are enriched with genes related to defense mechanisms, which are more highly expressed in the deep sea. Thus, while transposons spread at the expense of their microbial hosts, they also introduce novel genes and potentially benefit the hosts in helping to compete for limited resources. Overall, our results suggest a new understanding of deep ocean particles as highways for gene sharing among defensively oriented microbial genomes.IMPORTANCEGenes can move within and between microbial genomes via mobile genetic elements, which include transposases and transposons. In the oceans, there is a puzzling increase in transposase abundance in microbial genomes as depth increases. To gain insight into this trend, we conducted an extensive analysis of marine microbial metagenomes and metatranscriptomes. We found a significant correlation between transposase abundance and a particle-associated lifestyle among marine microbes at both the metagenome and genome-resolved levels. We also observed a link between transposase abundance and genes related to defense mechanisms. These results suggest that as microbes become densely packed into crowded particles, mobile genes are more likely to spread and carry genetic material that provides a competitive advantage in crowded habitats. This may enable deep sea microbes to effectively compete in such environments.

Biofilm colonization and succession in a full-scale partial nitritation-anammox moving bed biofilm reactor

BACKGROUND

Partial nitritation-anammox (PNA) is a biological nitrogen removal process commonly used in wastewater treatment plants for the treatment of warm and nitrogen-rich sludge liquor from anaerobic digestion, often referred to as sidestream wastewater. In these systems, biofilms are frequently used to retain biomass with aerobic ammonia-oxidizing bacteria (AOB) and anammox bacteria, which together convert ammonium to nitrogen gas. Little is known about how these biofilm communities develop, and whether knowledge about the assembly of biofilms in natural communities can be applied to PNA biofilms.

RESULTS

We followed the start-up of a full-scale PNA moving bed biofilm reactor for 175 days using shotgun metagenomics. Environmental filtering likely restricted initial biofilm colonization, resulting in low phylogenetic diversity, with the initial microbial community comprised mainly of Proteobacteria. Facilitative priority effects allowed further biofilm colonization, with the growth of initial aerobic colonizers promoting the arrival and growth of anaerobic taxa like methanogens and anammox bacteria. Among the early colonizers were known 'oligotrophic' ammonia oxidizers including comammox Nitrospira and Nitrosomonas cluster 6a AOB. Increasing the nitrogen load in the bioreactor allowed colonization by 'copiotrophic' Nitrosomonas cluster 7 AOB and resulted in the exclusion of the initial ammonia- and nitrite oxidizers.

CONCLUSIONS

We show that complex dynamic processes occur in PNA microbial communities before a stable bioreactor process is achieved. The results of this study not only contribute to our knowledge about biofilm assembly and PNA bioreactor start-up but could also help guide strategies for the successful implementation of PNA bioreactors. Video Abstract.

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads

BACKGROUND

The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages.

RESULTS

We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome.

CONCLUSIONS

This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

Marine picoplankton metagenomes and MAGs from eleven vertical profiles obtained by the Malaspina Expedition

The Ocean microbiome has a crucial role in Earth's biogeochemical cycles. During the last decade, global cruises such as Tara Oceans and the Malaspina Expedition have expanded our understanding of the diversity and genetic repertoire of marine microbes. Nevertheless, there are still knowledge gaps regarding their diversity patterns throughout depth gradients ranging from the surface to the deep ocean. Here we present a dataset of 76 microbial metagenomes (MProfile) of the picoplankton size fraction (0.2-3.0 µm) collected in 11 vertical profiles covering contrasting ocean regions sampled during the Malaspina Expedition circumnavigation (7 depths, from surface to 4,000 m deep). The MProfile dataset produced 1.66 Tbp of raw DNA sequences from which we derived: 17.4 million genes clustered at 95% sequence similarity (M-GeneDB-VP), 2,672 metagenome-assembled genomes (MAGs) of Archaea and Bacteria (Malaspina-VP-MAGs), and over 100,000 viral genomic sequences. This dataset will be a valuable resource for exploring the functional and taxonomic connectivity between the photic and bathypelagic tropical and sub-tropical ocean, while increasing our general knowledge of the Ocean microbiome.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Active prokaryotic and eukaryotic viral ecology across spatial scale in a deep-sea brine pool

Deep-sea brine pools represent rare, extreme environments, providing unique insight into the limits of life on Earth, and by analogy, the plausibility of life beyond it. A distinguishing feature of many brine pools is presence of thick microbial mats that develop at the brine-seawater interface. While these bacterial and archaeal communities have received moderate attention, viruses and their host interactions in these environments remain underexplored. To bridge this knowledge gap, we leveraged metagenomic and metatranscriptomic data from three distinct zones within the NEOM brine pool system (Gulf of Aqaba) to reveal the active viral ecology around the pools. We report a remarkable diversity and activity of viruses infecting microbial hosts in this environment, including giant viruses, RNA viruses, jumbo phages, and Polinton-like viruses. Many of these form distinct clades-suggesting presence of untapped viral diversity in this ecosystem. Brine pool viral communities exhibit zone-specific differences in infection strategy-with lysogeny dominating the bacterial mat further away from the pool's center. We linked viruses to metabolically important prokaryotes-including association between a jumbo phage and a key manganese-oxidizing and arsenic-metabolizing bacterium. These foundational results illuminate the role of viruses in modulating brine pool microbial communities and biogeochemistry through revealing novel viral diversity, host associations, and spatial heterogeneity in viral dynamics.

The genome sequence of Pycnococcus provasolii (CCAP190/2) (Guillard, 1991)

We present a genome assembly from cultured Pycnococcus provasolii (a marine green alga; Chlorophyta; None; Pseudoscourfieldiales; Pycnococcaceae). The genome sequence is 32.2 megabases in span. Most of the assembly is scaffolded into 44 chromosomal pseudomolecules (99.67%). The mitochondrial and plastid genomes have also been assembled, and the length of the mitochondrial scaffold is 24.3 kilobases and of the plastid genome has been assembled and is 80.2 kilobases in length.

The genome sequence of the Heterolobosean amoeboflagellate, Tetramitus jugosus CCAP 1588/3C

We present a genome assembly from cultivated Tetramitus jugosus (Heterolobosea; Schizopyrenida; Vahlkampfiidae). The genome sequence is 26.3 megabases in span. Most of the assembly (99.3%) is scaffolded into 52 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 49.46 kilobases in length.

Identification of a non-canonical ciliate nuclear genetic code where UAA and UAG code for different amino acids

The genetic code is one of the most highly conserved features across life. Only a few lineages have deviated from the "universal" genetic code. Amongst the few variants of the genetic code reported to date, the codons UAA and UAG virtually always have the same translation, suggesting that their evolution is coupled. Here, we report the genome and transcriptome sequencing of a novel uncultured ciliate, belonging to the Oligohymenophorea class, where the translation of the UAA and UAG stop codons have changed to specify different amino acids. Genomic and transcriptomic analyses revealed that UAA has been reassigned to encode lysine, while UAG has been reassigned to encode glutamic acid. We identified multiple suppressor tRNA genes with anticodons complementary to the reassigned codons. We show that the retained UGA stop codon is enriched in the 3'UTR immediately downstream of the coding region of genes, suggesting that there is functional drive to maintain tandem stop codons. Using a phylogenomics approach, we reconstructed the ciliate phylogeny and mapped genetic code changes, highlighting the remarkable number of independent genetic code changes within the Ciliophora group of protists. According to our knowledge, this is the first report of a genetic code variant where UAA and UAG encode different amino acids.

Genome resources and whole genome resequencing of Phytophthora rubi isolates from red raspberry

Phytophthora rubi is a primary causal agent of Phytophthora root rot and wilting of raspberry (Rubus idaeus L.) worldwide. The disease is a major concern for raspberry growers in Canada and USA. To date, no information is available on genomic diversity of P. rubi population from raspberry in Canada. Using a PCR-free library prep with dual-indexing for an Illumina HiSEQX running a 2x150 bp configuration, we generated whole genome sequence data of P. rubi isolates (n = 25) recovered during 2018 to 2020 from nine fields, four locations and four cultivars of raspberry growing areas of British Columbia, Canada. The assembled genome of 24 isolates of P. rubi averaged 8,541 scaffolds, 309× coverage, and 65,960,000 bp. We exploited single nucleotide polymorphisms (SNPs) obtained from whole genome sequence data to analyze the genome structure and genetic diversity of the P. rubi isolates. Low heterozygosity among the 72% of pathogen isolates and standardized index of association revealed that those isolates were clonal. Principal component analysis, discriminant analysis of principal component, and phylogenetic tree revealed that P. rubi isolates clustered with the raspberry specific cultivars. This study provides novel resources and insight into genome structure, genetic diversity, and reproductive biology of P rubi isolated from red raspberry. The availability of the P. rubi genomes also provides valuable resources for future comparative genomic and evolutionary studies for oomycetes pathogens.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes

The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and β diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Metaphor-A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.

Composition and metabolic potential of microbiomes associated with mesopelagic animals from Monterey Canyon

There is growing recognition that microbiomes play substantial roles in animal eco-physiology and evolution. To date, microbiome research has largely focused on terrestrial animals, with far fewer studies on aquatic organisms, especially pelagic marine species. Pelagic animals are critical for nutrient cycling, yet are also subject to nutrient limitation and might thus rely strongly on microbiome digestive functions to meet their nutritional requirements. To better understand the composition and metabolic potential of midwater host-associated microbiomes, we applied amplicon and shotgun metagenomic sequencing to eleven mesopelagic animal species. Our analyses reveal that mesopelagic animal microbiomes are typically composed of bacterial taxa from the phyla Proteobacteria, Firmicutes, Bacteroidota and, in some cases, Campylobacterota. Overall, compositional and functional microbiome variation appeared to be primarily governed by host taxon and depth and, to a lesser extent, trophic level and diel vertical migratory behavior, though the impact of host specificity seemed to differ between migrating and non-migrating species. Vertical migrators generally showed lower intra-specific microbiome diversity (i.e., higher host specificity) than their non-migrating counterparts. These patterns were not linked to host phylogeny but may reflect differences in feeding behaviors, microbial transmission mode, environmental adaptations and other ecological traits among groups. The results presented here further our understanding of the factors shaping mesopelagic animal microbiomes and also provide some novel, genetically informed insights into their diets.

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes

BACKGROUND

With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth's natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.

RESULTS

In this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA's capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives.

CONCLUSIONS

The VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

How clear is our current view on microbial dark matter? (Re-)assessing public MAG & SAG datasets with MDMcleaner

As of today, the majority of environmental microorganisms remain uncultured and is therefore referred to as 'microbial dark matter' (MDM). Hence, genomic insights into these organisms are limited to cultivation-independent approaches such as single-cell- and metagenomics. However, without access to cultured representatives for verifying correct taxon-assignments, MDM genomes may cause potentially misleading conclusions based on misclassified or contaminant contigs, thereby obfuscating our view on the uncultured microbial majority. Moreover, gradual database contaminations by past genome submissions can cause error propagations which affect present as well as future comparative genome analyses. Consequently, strict contamination detection and filtering need to be applied, especially in the case of uncultured MDM genomes. Current genome reporting standards, however, emphasize completeness over purity and the de facto gold standard genome assessment tool, checkM, discriminates against uncultured taxa and fragmented genomes. To tackle these issues, we present a novel contig classification, screening, and filtering workflow and corresponding open-source python implementation called MDMcleaner, which was tested and compared to other tools on mock and real datasets. MDMcleaner revealed substantial contaminations overlooked by current screening approaches and sensitively detects misattributed contigs in both novel genomes and the underlying reference databases, thereby greatly improving our view on 'microbial dark matter'.

Whokaryote: distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure

Metagenomics has become a prominent technology to study the functional potential of all organisms in a microbial community. Most studies focus on the bacterial content of these communities, while ignoring eukaryotic microbes. Indeed, many metagenomics analysis pipelines silently assume that all contigs in a metagenome are prokaryotic, likely resulting in less accurate annotation of eukaryotes in metagenomes. Early detection of eukaryotic contigs allows for eukaryote-specific gene prediction and functional annotation. Here, we developed a classifier that distinguishes eukaryotic from prokaryotic contigs based on foundational differences between these taxa in terms of gene structure. We first developed Whokaryote, a random forest classifier that uses intergenic distance, gene density and gene length as the most important features. We show that, with an estimated recall, precision and accuracy of 94, 96 and 95 %, respectively, this classifier with features grounded in biology can perform almost as well as the classifiers EukRep and Tiara, which use k-mer frequencies as features. By retraining our classifier with Tiara predictions as an additional feature, the weaknesses of both types of classifiers are compensated; the result is Whokaryote+Tiara, an enhanced classifier that outperforms all individual classifiers, with an F1 score of 0.99 for both eukaryotes and prokaryotes, while still being fast. In a reanalysis of metagenome data from a disease-suppressive plant endospheric microbial community, we show how using Whokaryote+Tiara to select contigs for eukaryotic gene prediction facilitates the discovery of several biosynthetic gene clusters that were missed in the original study. Whokaryote (+Tiara) is wrapped in an easily installable package and is freely available from https://github.com/LottePronk/whokaryote.