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False-positive identifications are a significant problem in metagenomics classification. We present KrakenUniq, a novel metagenomics classifier that combines the fast k -mer-based classification of Kraken with an efficient algorithm for assessing the coverage of unique k -mers found in each species in a dataset. On various test datasets, KrakenUniq gives better recall and precision than other methods and effectively classifies and distinguishes pathogens with low abundance from false positives in infectious disease samples. By using the probabilistic cardinality estimator HyperLogLog, KrakenUniq runs as fast as Kraken and requires little additional memory. KrakenUniq is freely available at https://github.com/fbreitwieser/krakenuniq .

Although Kraken’s k -mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.

Metagenomic strategy serves as the foundation for the ecological exploration of novel bioresources (e.g., industrial enzymes and bioactive molecules) and biohazards (e.g., pathogens and antibiotic resistance genes) in natural and engineered microbial systems across multiple disciplines. Recent advancements in sequencing technology have fostered rapid development in the field of microbiome research where an increasing number of studies have applied both illumina short reads (SRs) and nanopore long reads (LRs) sequencing in their metagenomic workflow. However, given the high complexity of an environmental microbiome data set and the bioinformatic challenges caused by the unique features of these sequencing technologies, integrating SRs and LRs is not as straightforward as one might assume. The fast renewal of existing tools and growing diversity of new algorithms make access to this field even more difficult. Therefore, here we systematically summarized the complete workflow from DNA extraction to data processing strategies for applying illumina and nanopore‐integrated metagenomics in the investigation in environmental microbiomes. Overall, this review aims to provide a timely knowledge framework for researchers that are interested in or are struggling with the SRs and LRs integration in their metagenomic analysis. The discussions presented will facilitate improved ecological understanding of community functionalities and assembly of natural, engineered, and human microbiomes, benefiting researchers from multiple disciplines. Strategies and tools in illumina and nanopore‐integrated metagenomic analysis of microbiome data. Highlights A timely knowledge framework to integrate nanopore long reads and illumina short reads is provided. Workflow of common strategies for illumina and nanopore integration is illustrated in Figure 1. Algorithm basis and application properties of tools are summarized in Table 3.

The human microbiome refers to the community of microorganisms, including prokaryotes, viruses, and microbial eukaryotes, that populate the human body. The National Institutes of Health launched an initiative that focuses on describing the diversity of microbial species that are associated with health and disease. The first phase of this initiative includes the sequencing of hundreds of microbial reference genomes, coupled to metagenomic sequencing from multiple body sites. Here we present results from an initial reference genome sequencing of 178 microbial genomes. From 547,968 predicted polypeptides that correspond to the gene complement of these strains, previously unidentified (\"novel\") polypeptides that had both unmasked sequence length greater than 100 amino acids and no BLASTP match to any nonreference entry in the nonredundant subset were defined. This analysis resulted in a set of 30,867 polypeptides, of which 29,987 (̃97%) were unique. In addition, this set of microbial genomes allows for ̃40% of random sequences from the microbiome of the gastrointestinal tract to be associated with organisms based on the match criteria used. Insights into pan-genome analysis suggest that we are still far from saturating microbial species genetic data sets. In addition, the associated metrics and standards used by our group for quality assurance are presented.

Standards for sequencing the microbial 'uncultivated majority', namely bacterial and archaeal single-cell genome sequences, and genome sequences from metagenomic datasets, are proposed. We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health.Clinical metagenomic next-generation sequencing (mNGS) is rapidly moving from bench to bedside. This Review discusses the clinical applications of mNGS, including infectious disease diagnostics, microbiome analyses, host response analyses and oncology applications. Moreover, the authors review the challenges that need to be overcome for mNGS to be successfully implemented in the clinical laboratory and propose solutions to maximize the benefits of clinical mNGS for patients.

In this study, Padhi et al. demonstrate the potential of metagenomics-based approaches for the discovery of natural products from complex ecosystems.

Background Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits. Results We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions. Conclusions Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples.