Metagenomic Tools Research Articles

Metagenomic assemblies tend to break around antibiotic resistance genes

BackgroundAssembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies.ResultsThe results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample.ConclusionsOur study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database should be performed as a complementary strategy to get accurate ARG abundance and diversity measures.

Using artificial intelligence to document the hidden RNA virosphere.

Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.

Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review.

This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.

The micro-ecological feature of colonies is a potential strategy for Phaeocystis globosa bloom formation

Phaeocystis globosa is among the dominant microalgae associated with harmful algal blooms. P. globosa has a polymorphic life cycle and its ecological success has been attributed to algal colony formation, however, few studies have assessed differences in microbial communities and their functional profiles between intra- and extra-colonies during P. globosa blooms. To address this, environmental and metagenomics tools were used to conduct a time-series analysis of the bacterial composition and metabolic characteristics of intra- and extra-colonies during a natural P. globosa bloom. The results show that bacterial composition, biodiversity, and network interactions differed significantly between intra- and extra-colonies. Dominant extra-colonial bacteria were Bacteroidia and Saccharimonadis, while dominant intra-colonial bacteria included Alphaproteobacteria and Gammaproteobacteria. Despite the lower richness and diversity observed in the intra-colonial bacterial community, relative to extra-colonies, the complexity and interconnectedness of the intra-colonial networks were higher. Regarding bacterial function, more functional genes were enriched in substance metabolism (polysaccharides, iron element and dimethylsulfoniopropionate) and signal communication (quorum sensing, indoleacetic acid-IAA) pathways in intra- than in extra-colonies. Conceptual model construction showed that microbial cooperative synthesis of ammonium, vitamin B12, IAA, and siderophores were strongly related to the P. globosa bloom, particularly in the intra-colonial environment. Overall, our data highlight the differences in bacterial structure and functions within and outside the colony during P. globosa blooms. These findings represent fundamental information indicating that phenotypic heterogeneity is a selective strategy that improves microbial population competitiveness and environmental adaptation, benefiting P. globosa bloom formation and persistence.

MetaBIDx: a new computational approach to bacteria identification in microbiomes.

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases.

Ten common issues with reference sequence databases and how to mitigate them.

Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review.

Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.

A Landscape View of the Female Genital Tract Microbiome in Healthy Controls and Women With Reproductive Health Conditions Associated With Ectopic Pregnancy.

Disruption of the female genital microbiome is associated with several pregnancy complications, including miscarriage, preterm onset of labour, and tubal pregnancy. Ectopic pregnancy is a known cause of maternal morbidity and mortality, but early diagnosis and treatment of ectopic pregnancy remain a challenge. Despite growing established associations between genital microbiome and female reproductive health, few studies have specifically focused on its link with ectopic pregnancy. Therefore, the current review aims to provide a comprehensive account of the female genital microbiome in healthy and fertile women compared to those in ectopic pregnancy and its associated risk factors. The microbial diversity from various sites of the female genital tract was explored for a reliable proxy of female reproductive health in sequencing-based ectopic pregnancy research. Our report confirmed the predominance of Lactobacillus in the vagina and the cervix among healthy women. The relative abundance decreased in the vaginal and cervical microbiome in the disease state. In contrast, there were inconsistent findings on the uterine microbiome across studies. Additionally, we explore a spectrum of opportunities to enhance our understanding of the female genital tract microbiome and reproductive conditions. In conclusion, this study identifies gaps within the field and emphasises the need for visionary solutions in metagenomic tools for the early detection of ectopic pregnancy and other gynaecological diseases.

Targeted Metagenomic Databases Provide Improved Analysis of Microbiota Samples.

We report on Moonbase, an innovative pipeline that builds upon the established tools of MetaPhlAn and Kraken2, enhancing their capabilities for more precise taxonomic detection and quantification in diverse microbial communities. Moonbase enhances the performance of Kraken2 mapping by providing an efficient method for constructing project-specific databases. Moonbase was evaluated using synthetic metagenomic samples and compared against MetaPhlAn3 and generalized Kraken2 databases. Moonbase significantly improved species precision and quantification, outperforming marker genes and generalized databases. Construction of a phylogenetic tree from 16S genome data in Moonbase allowed for the incorporation of UniFrac-type phylogenetic information into diversity calculations of samples. We demonstrated that the resulting analysis increased statistical power in distinguishing microbial communities. This study highlights the continual evolution of metagenomic tools with the goal of improving metagenomic analysis and highlighting the potential of the Moonbase pipeline.

Unraveling the potential of microbial communities for lake bioremediation via the metagenomics tool: a review

Abstract Characterization of ubiquitous microorganisms has encountered many challenges, such as determining unknown microorganisms, their interactions, and unresolved functions in natural environments. Evolution in metagenomics and tools, however, has revolutionized assessment methodologies. Metagenomics has enabled unearthing the secret microbial treasure in a culture-independent manner and has proven more successful than conventional methodologies. It has provided an unparalleled platform for functional and taxonomic characterization of microbiota dwellings in altered lakes. Of late, many research articles have used metagenomics to understand microorganisms’ role in environmental clean-up. Consequently, these studies have been consolidated in the form of review articles. However, most of them are related to microbial characterization, procedure of metagenomics, and wastewater treatment, whereas only a few were directly related to lake bioremediation. Therefore, this review highlights the applications of metagenomics for unraveling microbial potential for lake rejuvenation. A paradigm shift from conventional to recent breakthroughs in metagenomics is also emphasized. The review also discusses merits, demerits of previous molecular techniques, and potential of metagenomics for understanding the microbial world in contaminated environments. Furthermore, the review discusses recent metagenomic studies for lake rejuvenation. Finally, future research directions are proposed for designing possible strategies for lake rejuvenation.

From Whole Genome Shotgun Sequencing to Viral Community Profiling: The ViromeScan Tool.

ViromeScan is an innovative metagenomic analysis tool that allows characterizing the taxonomy of viral communities from raw data of metagenomics sequencing, efficiently denoising samples from reads of other microorganisms. This means that users can use the same shotgun metagenomic sequencing data to fully characterize complex microbial ecosystems, including bacteria and viruses. Here we describe the analysis procedure with some examples, illustrating the processes computed by ViromeScan from raw data to the final output.

Informed interpretation of metagenomic data by StrainPhlAn enables strain retention analyses of the upper airway microbiome.

The usage of 16S rRNA gene sequencing has become the state-of-the-art method for the characterization of the microbiota in health and respiratory disease. The method is reliable for low biomass samples due to prior amplification of the 16S rRNA gene but has limitations as species and certainly strain identification is not possible. However, the usage of metagenomic tools for the analyses of microbiome data from low biomass samples is not straight forward, and careful optimization is needed. In this work, we show that by validating StrainPhlAn 3 results with the data from bacterial cultures, the strain-level tracking of the respiratory microbiome is feasible despite the high content of host DNA being present when parameters are carefully optimized to fit low biomass microbiomes. This work further proposes that strain retention analyses are feasible, at least for more abundant species. This will help to better understand the longitudinal dynamics of the upper respiratory microbiome during health and disease.

Comparison of k-mer-based de novo comparative metagenomic tools and approaches.

Aim: Comparative metagenomic analysis requires measuring a pairwise similarity between metagenomes in the dataset. Reference-based methods that compute a beta-diversity distance between two metagenomes are highly dependent on the quality and completeness of the reference database, and their application on less studied microbiota can be challenging. On the other hand, de-novo comparative metagenomic methods only rely on the sequence composition of metagenomes to compare datasets. While each one of these approaches has its strengths and limitations, their comparison is currently limited. Methods: We developed sets of simulated short-reads metagenomes to (1) compare k-mer-based and taxonomy-based distances and evaluate the impact of technical and biological variables on these metrics and (2) evaluate the effect of k-mer sketching and filtering. We used a real-world metagenomic dataset to provide an overview of the currently available tools for de novo metagenomic comparative analysis. Results: Using simulated metagenomes of known composition and controlled error rate, we showed that k-mer-based distance metrics were well correlated to the taxonomic distance metric for quantitative Beta-diversity metrics, but the correlation was low for presence/absence distances. The community complexity in terms of taxa richness and the sequencing depth significantly affected the quality of the k-mer-based distances, while the impact of low amounts of sequence contamination and sequencing error was limited. Finally, we benchmarked currently available de-novo comparative metagenomic tools and compared their output on two datasets of fecal metagenomes and showed that most k-mer-based tools were able to recapitulate the data structure observed using taxonomic approaches. Conclusion: This study expands our understanding of the strength and limitations of k-mer-based de novo comparative metagenomic approaches and aims to provide concrete guidelines for researchers interested in applying these approaches to their metagenomic datasets.

MetaTransformer: deep metagenomic sequencing read classification using self-attention models.

Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling. DeepMicrobes, a machine learning-based classifier, has recently been introduced for taxonomic prediction at species and genus level. However, it relies on complex models based on bidirectional long short-term memory cells resulting in slow runtimes and excessive memory requirements, hampering its effective usability. We present MetaTransformer, a self-attention-based deep learning metagenomic analysis tool. Our transformer-encoder-based models enable efficient parallelization while outperforming DeepMicrobes in terms of species and genus classification abilities. Furthermore, we investigate approaches to reduce memory consumption and boost performance using different embedding schemes. As a result, we are able to achieve 2× to 5× speedup for inference compared to DeepMicrobes while keeping a significantly smaller memory footprint. MetaTransformer can be trained in 9hours for genus and 16hours for species prediction. Our results demonstrate performance improvements due to self-attention models and the impact of embedding schemes in deep learning on metagenomic sequencing data.

Metagenome-assembled genomes for biomarkers of bio-functionalities in Laal dahi, an Indian ethnic fermented milk product

Laal dahi is a sweetened and soft pudding-like fermented milk product of the Eastern regions of India, which has not been studied for its microbial community structures and health promoting functionality in terms of ‘omics’ approaches. We applied metagenomic and metagenomes-assembled genomes (MAGs) tools to decipher the biomarkers for genes encoding for different health promoting functionalities in laal dahi. Abundance of bacterial domains was observed with negligible presence of eukaryotes and viruses. Bacillota was the most abundant phylum with different bacterial species viz., Enterococcus italicus, Lactococcus raffinolactis, Lactobacillus helveticus, Bifidobacterium mongoliense, Hafnia alvei, Lactococcus lactis, Acetobacter okinawensis, Streptococcus thermophilus, Thermus thermophilus, Leuconostoc citreum, Leuconostoc pseudomesenteroides, Acetobacter orientalis, Lactobacillus gallinarum, Lactococcus chungangensis and Lactobacillus delbrueckii. Comparison of laal dahi microbiome with that of similar fermented milk products was also carried out after retrieving the metagenomic datasets from public databases. Significant abundance of Lb. helveticus, E. italicus, Lc. raffinolactis and Lc. lactis in laal dahi. Interestingly, Bifidobacterium mongoliense, Lb. gallinarum, Lc. chungangensis and Acetobacter okinawensis were only detected in laal dahi but Streptococcus infantarius, Lacticaseibacillus rhamnosus and Lb. johnsonii were absent. Reconstruction of putative single environment-specific genomes from metagenomes in addition to subsampling of the abundant species resulted in five high-quality MAGs identified as Lactobacillus delbrueckii, Lactobacillus helveticus, Lactococcus chungangensis, Lactococcus lactis and Streptococcus thermophilus. All MAGs showed the presence of various genes with several putative functions corresponding to different probiotic and prebiotic functions, short-chain fatty acids production, immunomodulation, antitumor genes, essential amino acid and vitamin biosynthesis. Genes for γ-Aminobutyric acid (GABA) production were only detected in MAG of Lactococcus lactis. Gene clusters for secondary metabolites (antimicrobial peptides) were detected in all MAGs except Lc. chungangensis. Additionally, detection of clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) elements was observed only in Lactobacillus delbrueckii and Streptococcus thermophilus. Annotation of several genes with potential health beneficial properties in all five MAGs may support the need to explore the culturability of these MAGs for future use in controlled fermentation of functional dairy products.

Virus Pop-Expanding Viral Databases by Protein Sequence Simulation.

The improvement of our knowledge of the virosphere, which includes unknown viruses, is a key area in virology. Metagenomics tools, which perform taxonomic assignation from high throughput sequencing datasets, are generally evaluated with datasets derived from biological samples or in silico spiked samples containing known viral sequences present in public databases, resulting in the inability to evaluate the capacity of these tools to detect novel or distant viruses. Simulating realistic evolutionary directions is therefore key to benchmark and improve these tools. Additionally, expanding current databases with realistic simulated sequences can improve the capacity of alignment-based searching strategies for finding distant viruses, which could lead to a better characterization of the "dark matter" of metagenomics data. Here, we present Virus Pop, a novel pipeline for simulating realistic protein sequences and adding new branches to a protein phylogenetic tree. The tool generates simulated sequences with substitution rate variations that are dependent on protein domains and inferred from the input dataset, allowing for a realistic representation of protein evolution. The pipeline also infers ancestral sequences corresponding to multiple internal nodes of the input data phylogenetic tree, enabling new sequences to be inserted at various points of interest in the group studied. We demonstrated that Virus Pop produces simulated sequences that closely match the structural and functional characteristics of real protein sequences, taking as an example the spike protein of sarbecoviruses. Virus Pop also succeeded at creating sequences that resemble real sequences not included in the databases, which facilitated the identification of a novel pathogenic human circovirus not included in the input database. In conclusion, Virus Pop is helpful for challenging taxonomic assignation tools and could help improve databases to better detect distant viruses.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond.

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Metagenomic analyses reveal that mesophilic anaerobic digestion substantially reduces the abundance of antibiotic resistance genes and mobile genetic elements in dairy manures

Animal manure is considered one of the main reservoirs of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) that carry them. There is increasing concern regarding the dissemination of antibiotic resistance due to the application of manure as a soil amendment, and there is an urgent need to define alternatives to minimize this problem. Anaerobic digestion (AD) has shown the potential to reduce the levels of ARGs present in manure. However, little is known about the effect of AD on the entire set of MGEs, which play a crucial role in the dissemination of antibiotic resistance. In this study, we used a set of metagenomic and bioinformatics tools to evaluate the long-term effects of mesophilic anaerobic digestion on ARGs and MGEs in bovine dairy manure and the potential mechanisms driving these changes. The results indicated that the average ARG and MGE levels were significantly reduced by 53% and 74%, respectively, after AD. The results also suggested that microbial groups including Lactobacillales, Bacillales, Eubacteriales, Micrococcales, and Actinomycetales were main groups associated with ARGs and MGEs. The changes in ARG and MGE seemed to result from distinct microbial successions, such as the removal of aerobic species and the decline in facultative bacteria, which led to the establishment of typical microbial communities present in anaerobic digesters. The study also suggested that hydrolytic species are another important group that potentially harbors ARGs and MGEs. Overall, the results indicated that digested manure could have a reduced risk of dissemination of antibiotic resistance.

Identifying Taxonomic Units in Metagenomic DNA Streams on Mobile Devices.

With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis tasks, e.g., classification, taxonomic units assignment, etc., are compute and memory intensive, and the available methods are designed for batch processing, the current metagenomic tools are not well suited for mobile devices. In this work, we propose a new memory-efficient approach to identify Operational Taxonomic Units (OTUs) in metagenomic DNA streams on mobile devices. Our method is based on finding connected components in overlap graphs constructed over a real-time stream of long DNA reads as produced by the MinION platform. We propose an efficient algorithm to maintain connected components when an overlap graph is streamed and show how redundant information can be removed from the stream by transitive closures. We also propose how our algorithms can be integrated into a larger DNA analysis pipeline tailored for mobile computing. Through experiments on simulated and real-world metagenomic data, executed on the actual mobile device, we demonstrate that our resulting solution is able to recover OTUs with high precision. Our experiments also demonstrate the compounding benefits of introducing feedback loops in the DNA analysis pipeline.

Metagenome reveals caprine abomasal microbiota diversity at early and late stages of Haemonchus contortus infection

Haemonchus contortus is one of the most detrimental gastrointestinal nematode parasites for small ruminants, especially in tropics and subtropics. Gastrointestinal nematode and microbiota share the same microhabitat; thus they interact with each other and their host. Metagenomics tools provide a promising way to examine the alterations in the gastric microbial composition induces by gastrointestinal parasites. In this study, we used metagenomics tools to characterize the impact of H. contortus infection on the caprine abomasal microbiota at early and late stage of infection and compared it with non-infected control. Our results showed that H. contortus infection caused a significant increase in abomasal pH at early (7 days post-infection) and late stage of infection (56 days post-infection). The analysis of alpha and beta diversity showed that the microbiota diversity both in number and in proportion was significantly affected at early and late stage of infection. All microbiota classes are impacted by H. contortus infection but Clostridia and Bacteroidia are more concerned. In infected animals, the genera Prevotella decreased at 7 and 56 days post-infection. Here we showed that the abomasal microbiota was significantly affected early after H. contortus infection, and these changes persist at late stage of the infection.