Analyzing rare mutations in metagenomes assembled using long and accurate reads.

The advent of long and accurate "HiFi" reads has greatly improved our ability to generate complete metagenome-assembled genomes (MAGs), enabling "complete metagenomics" studies that were nearly impossible to conduct with short reads. In particular, HiFi reads simplify the identification and phasing of mutations in MAGs: It is increasingly feasible to distinguish between positions that are prone to mutations and positions that rarely ever mutate, and to identify co-occurring groups of mutations. However, the problems of identifying rare mutations in MAGs, estimating the false-discovery rate (FDR) of these identifications, and phasing identified mutations remain open in the context of HiFi data. We present strainFlye, a pipeline for the FDR-controlled identification and analysis of rare mutations in MAGs assembled using HiFi reads. We show that deep HiFi sequencing has the potential to reveal and phase tens of thousands of rare mutations in a single MAG, identify hotspots and coldspots of these mutations, and detail MAGs' growth dynamics.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets.

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Nutrition and the Gut Microbiota in 10- to 18-Month-Old Children Living in Urban Slums of Mumbai, India.

In this cross-sectional study, we describe the composition and diversity of the gut microbiota among undernourished children living in urban slums of Mumbai, India, and determine how nutritional status, including anthropometric measurements, dietary intakes from complementary foods, feeding practices, and micronutrient concentrations, is associated with their gut microbiota. We collected rectal swabs from children aged 10 to 18 months living in urban slums of Mumbai participating in a randomized controlled feeding trial and conducted 16S rRNA sequencing to determine the composition of the gut microbiota. Across the study cohort, Proteobacteria dominated the gut microbiota at over 80% relative abundance, with Actinobacteria representation at <4%, suggesting immaturity of the gut. Increased microbial α-diversity was associated with current breastfeeding, greater head circumference, higher fat intake, and lower hemoglobin concentration and weight-for-length Z-score. In redundancy analyses, 47% of the variation in Faith's phylogenetic diversity (Faith's PD) could be accounted for by age and by iron and polyunsaturated fatty acid intakes. Differences in community structure (ß-diversity) of the microbiota were observed among those consuming fats and oils the previous day compared to those not consuming fats and oils the previous day. Our findings suggest that growth, diet, and feeding practices are associated with gut microbiota metrics in undernourished children, whose gut microbiota were comprised mainly of Proteobacteria, a phylum containing many potentially pathogenic taxa.IMPORTANCE The impact of comprehensive nutritional status, defined as growth, nutritional blood biomarkers, dietary intakes, and feeding practices, on the gut microbiome in children living in low-resource settings has remained underreported in microbiome research. Among undernourished children living in urban slums of Mumbai, India, we observed a high relative abundance of Proteobacteria, a phylum including many potentially pathogenic species similar to the composition in preterm infants, suggesting immaturity of the gut, or potentially a high inflammatory burden. We found head circumference, fat and iron intake, and current breastfeeding were positively associated with microbial diversity, while hemoglobin and weight for length were associated with lower diversity. Findings suggest that examining comprehensive nutrition is critical to gain more understanding of how nutrition and the gut microbiota are linked, particularly in vulnerable populations such as children in urban slum settings.

Visualizing 'omic feature rankings and log-ratios using Qurro.

Many tools for dealing with compositional ' 'omics' data produce feature-wise values that can be ranked in order to describe features' associations with some sort of variation. These values include differentials (which describe features' associations with specified covariates) and feature loadings (which describe features' associations with variation along a given axis in a biplot). Although prior work has discussed the use of these 'rankings' as a starting point for exploring the log-ratios of particularly high- or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a 'rank plot') alongside a plot of selected features' log-ratios within samples (a 'sample plot'). Qurro's interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here, we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

MetaCarvel: linking assembly graph motifs to biological variants.

Reconstructing genomic segments from metagenomics data is a highly complex task. In addition to general challenges, such as repeats and sequencing errors, metagenomic assembly needs to tolerate the uneven depth of coverage among organisms in a community and differences between nearly identical strains. Previous methods have addressed these issues by smoothing genomic variants. We present a variant-aware metagenomic scaffolder called MetaCarvel, which combines new strategies for repeat detection with graph analytics for the discovery of variants. We show that MetaCarvel can accurately reconstruct genomic segments from complex microbial mixtures and correctly identify and characterize several classes of common genomic variants.

Current progress and future opportunities in applications of bioinformatics for biodefense and pathogen detection: report from the Winter Mid-Atlantic Microbiome Meet-up, College Park, MD, January 10, 2018.

The Mid-Atlantic Microbiome Meet-up (M3) organization brings together academic, government, and industry groups to share ideas and develop best practices for microbiome research. In January of 2018, M3 held its fourth meeting, which focused on recent advances in biodefense, specifically those relating to infectious disease, and the use of metagenomic methods for pathogen detection. Presentations highlighted the utility of next-generation sequencing technologies for identifying and tracking microbial community members across space and time. However, they also stressed the current limitations of genomic approaches for biodefense, including insufficient sensitivity to detect low-abundance pathogens and the inability to quantify viable organisms. Participants discussed ways in which the community can improve software usability and shared new computational tools for metagenomic processing, assembly, annotation, and visualization. Looking to the future, they identified the need for better bioinformatics toolkits for longitudinal analyses, improved sample processing approaches for characterizing viruses and fungi, and more consistent maintenance of database resources. Finally, they addressed the necessity of improving data standards to incentivize data sharing. Here, we summarize the presentations and discussions from the meeting, identifying the areas where microbiome analyses have improved our ability to detect and manage biological threats and infectious disease, as well as gaps of knowledge in the field that require future funding and focus.