TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

High-throughput sequencing (HTS) is becoming the new norm of diagnostics in plant quarantine settings. HTS can be used to detect, in theory, all pathogens present in any given sample. The technique’s success depends on various factors, including methods for sample management/preparation and suitable bioinformatic analysis. The Limit of Detection (LoD) of HTS for plant diagnostic tests can be higher than that of PCR, increasing the risk of false negatives in the case of low titer of the target pathogen. Several solutions have been suggested, particularly for RNA viruses, including rRNA depletion of the host, dsRNA, and siRNA extractions, which increase the relative pathogen titer in a metagenomic sample. However, these solutions are costly and time-consuming. Here we present a faster and cost-effective alternative method with lower HTS-LoD similar to or lower than PCR. The technique is called TArget-SPecific Reverse Transcript (TASPERT) pool. It relies on pathogen-specific reverse primers, targeting all RNA viruses of interest, pooled and used in double-stranded cDNA synthesis. These reverse primers enrich the sample for only pathogens of interest. Evidence on how TASPERT is significantly superior to oligodT, random 6-mer, and 20-mer in generating metagenomic libraries containing the pathogen of interest is presented in this proof of concept.

MMMVI: Detecting SARS-CoV-2 Variants of Concern in Metagenomic Samples

Motivation: SARS-CoV-2 is the causative agent of the COVID-19 pandemic. Variants of Concern (VOCs) and Variants of Interest (VOIs) are lineages that represent a greater risk to public health, and can be differentiated from the wildtype virus based on unique profiles of signature mutations. Wastewater surveillance by metagenomic sequence analysis can capture signal for these variants that may not be detected by public health testing and/or sequencing initiatives. However, because multiple viral genomes are likely to be present in a metagenomic sample, additional analytical scrutiny of the sequencing reads beyond variant calling can provide more confident diagnostic determinations. Results: We have developed MMMVI to aggregate and report cases where multiple mutations are present on a given read. These reads can be used as enhanced biomarkers to more confidently confirm the presence of a VOC/VOI in the sample. Availability: MMMVI is implemented in Python, and is available under the MIT licence from https://github.com/dorbarker/voc-identify/

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly

Background The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagenome assembler that partitions a multi-species de Bruijn graph into single-species sub-graphs. This study aimed to improve the performance of MetaVelvet-SL by using a deep learning-based model to predict the partition nodes in a multi-species de Bruijn graph. Results This study showed that the recent advances in deep learning offer the opportunity to better exploit sequence information and differentiate genomes of different species in a metagenomic sample. We developed an extension to MetaVelvet-SL, which we named MetaVelvet-DL, that builds an end-to-end architecture using Convolutional Neural Network and Long Short-Term Memory units. The deep learning model in MetaVelvet-DL can more accurately predict how to partition a de Bruijn graph than the Support Vector Machine-based model in MetaVelvet-SL can. Assembly of the Critical Assessment of Metagenome Interpretation (CAMI) dataset showed that after removing chimeric assemblies, MetaVelvet-DL produced longer single-species contigs, with less misassembled contigs than MetaVelvet-SL did. Conclusions MetaVelvet-DL provides more accurate de novo assemblies of whole metagenome data. The authors believe that this improvement can help in furthering the understanding of microbiomes by providing a more accurate description of the metagenomic samples under analysis.

Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.

Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir

Ancient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is particularly important for large-scale ecological and evolutionary studies of individual taxa and communities of both microbes and eukaryotes. AncientMetagenomeDir (archived at 10.5281/zenodo.3980833) is a collection of annotated metagenomic sample lists derived from published studies that provide basic, standardised metadata and accession numbers to allow rapid data retrieval from online repositories. These tables are community-curated and span multiple sub-disciplines to ensure adequate breadth and consensus in metadata definitions, as well as longevity of the database. Internal guidelines and automated checks facilitate compatibility with established sequence-read archives and term-ontologies, and ensure consistency and interoperability for future meta-analyses. This collection will also assist in standardising metadata reporting for future ancient metagenomic studies.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

Background Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. Results We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. Conclusions Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

On the complexity of haplotyping a microbial community

Motivation Population-level genetic variation enables competitiveness and niche specialization in microbial communities. Despite the difficulty in culturing many microbes from an environment, we can still study these communities by isolating and sequencing DNA directly from an environment (metagenomics). Recovering the genomic sequences of all isoforms of a given gene across all organisms in a metagenomic sample would aid evolutionary and ecological insights into microbial ecosystems with potential benefits for medicine and biotechnology. A significant obstacle to this goal arises from the lack of a computationally tractable solution that can recover these sequences from sequenced read fragments. This poses a problem analogous to reconstructing the two sequences that make up the genome of a diploid organism (i.e. haplotypes), but for an unknown number of individuals and haplotypes. Results The problem of single individual haplotyping (SIH) was first formalised by Lancia et al. in 2001. Now, nearly two decades later, we discuss the complexity of “haplotyping” metagenomic samples, with a new formalisation of Lancia et al’s data structure that allows us to effectively extend the single individual haplotype problem to microbial communities. This work describes and formalizes the problem of recovering genes (and other genomic subsequences) from all individuals within a complex community sample, which we term the metagenomic individual haplotyping (MIH) problem. We also provide software implementations for a pairwise single nucleotide variant (SNV) co-occurrence matrix and greedy graph traversal algorithm. Availability and implementation Our reference implementation of the described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) are open source, MIT licensed and freely available online at github.com/samstudio8/hansel and github.com/samstudio8/gretel, respectively.

A novel algorithm to accurately classify metagenomic sequences

Widespread availability of next-generation sequencing (NGS) technologies has prompted a recent surge in interest in the microbiome. As a consequence, metagenomics is a fast growing field in bioinformatics and computational biology. An important problem in analyzing metagenomic sequenced data is to identify the microbes present in the sample and figure out their relative abundances. In this article we propose a highly efficient algorithm dubbed as “Hybrid Metagenomic Sequence Classifier” (HMSC) to accurately detect microbes and their relative abundances in a metagenomic sample. The algorithmic approach is fundamentally different from other state-of-the-art algorithms currently existing in this domain. HMSC judiciously exploits both alignment-free and alignment-based approaches to accurately characterize metagenomic sequenced data. To demonstrate the effectiveness of HMSC we used 8 metagenomic sequencing datasets (2 mock and 6 in silico bacterial communities) produced by 3 different sequencing technologies (e.g., HiSeq, MiSeq, and NovaSeq) with realistic error models and abundance distribution. Rigorous experimental evaluations show that HMSC is indeed an effective, scalable, and efficient algorithm compared to the other state-of-the-art methods in terms of accuracy, memory, and runtime.Availability of data and materialsThe implementations and the datasets we used are freely available for non-commercial purposes. They can be downloaded from: https://drive.google.com/drive/folders/132k5E5xqpkw7olFjzYwjWNjyHFrqJITe?usp=sharing

Bac-PULCE: Bacterial Strain and AMR Profiling Using Long Reads via CRISPR Enrichment

Rapid identification of bacterial pathogens and their antimicrobial resistance (AMR) profiles is critical for minimising patient morbidity and mortality. While many sequencing methods allow deep genomic and metagenomic profiling of samples, widespread use (for example atpoint-of-care settings) is impeded because substantial sequencing and computational infrastructure is required for sequencing and analysis. Here we present Bac-PULCE (Bacterial strain and antimicrobial resistance Profiling Using Long reads via CRISPR Enrichment), which combines CRISPR-cas9 based targeted sequence enrichment with long-read sequencing. We show that this method allows simultaneous bacterial strain-level identification and antimicrobial resistance profiling of single isolates or metagenomic samples with minimal sequencing throughput. In contrast to short read sequencing, long read sequencing used in Bac-PULCE enables strain-level resolution even when targeting and sequencing highly conserved genomic regions, such as 16S rRNA. We show that these long reads allow sequencing of additional AMR genes linked to the targeted region. Additionally, long reads can be used to identify which species in a metagenomic sample harbour specific AMR loci. The ability to massively multiplex crRNAs suggests that this method has the potential to substantially increase the speed and specificity of pathogen strain identification and AMR profiling, while ensuring low computational overhead.ImportanceThere is a critical need for rapid and identification of bacterial strains and antibiotic resistance profiles in clinical settings. However, most current methods require both substantial laboratory infrastructure (e.g. for DNA sequencing), substantial compute infrastructure (e.g. for bioinformatic analyses), or both. Here we present a new method, Bac-PULCE, (Bacterial strain and antimicrobial resistance Profiling Using Long reads via CRISPR Enrichment), which combines CRISPR-cas9 based targeted sequence enrichment with long-read sequencing on the Oxford Nanopore platform. This allows rapid profiling of bacterial strains and antibiotic resistance genes in a sample while requiring very little laboratory or computational infrastructure.

mixtureS: a novel tool for bacterial strain genome reconstruction from reads

Motivation It is essential to study bacterial strains in environmental samples. Existing methods and tools often depend on known strains or known variations, cannot work on individual samples, not reliable, or not easy to use, etc. It is thus important to develop more user-friendly tools that can identify bacterial strains more accurately. Results We developed a new tool called mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets. Availability and implementation The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/. Supplementary information Supplementary data are available at Bioinformatics online.