While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets.

SPAdes—St. Petersburg genome Assembler—was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single‐cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole‐genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand‐specificity of RNA‐seq data

We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

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Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species

word count: 203 68 3 Main text word count: 3280 69 70 Abstract: Although much work has linked the human microbiome to specific phenotypes and 71 lifestyle variables, data from different projects have been challenging to integrate and the extent 72 of microbial and molecular diversity in human stool remains unknown. Using standardized 73 protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-74 scientists, together with an open research network, we compare human microbiome specimens 75 primarily from the USA, UK, and Australia to one another and to environmental samples. Our 76 results show an unexpected range of beta-diversity in human stool microbiomes as compared to 77 environmental samples, demonstrate the utility of procedures for removing the effects of 78 overgrowth during room-temperature shipping for revealing phenotype correlations, uncover 79 new molecules and kinds of molecular communities in the human stool metabolome, and 80 examine emergent associations among the microbiome, metabolome, and the diversity of plants 81 that are consumed (rather than relying on reductive categorical variables such as veganism, 82 which have little or no explanatory power). We also demonstrate the utility of the living data 83 resource and cross-cohort comparison to confirm existing associations between the microbiome 84 and psychiatric illness, and to reveal the extent of microbiome change within one individual 85 during surgery, providing a paradigm for open microbiome research and education. 86 87Importance: We show that a citizen-science, self-selected cohort shipping samples through the 88 mail at room temperature recaptures many known microbiome results from clinically collected 89 cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the 90 population data, showing that the extent of microbiome change after events such as surgery can 91 4 exceed differences between distinct environmental biomes, and the effect of diverse plants in the 92 diet which we confirm with untargeted metabolomics on hundreds of samples. 93 94 Introduction 95The human microbiome plays a fundamental role in human health and disease. While 96 many studies link microbiome composition to phenotypes, we lack understanding of the 97 boundaries of bacterial diversity within the human population, and the relative importance of 98 lifestyle, health conditions, and diet, to underpin precision medicine or to educate the broader 99 community about this key aspect of human health. 100 We launched the American Gut Project (AGP; http://americangut.org) in November of 101 2012 as a collaboration between the Earth Microbiome Project (EMP) (1) and the Human Food 102 Project (HFP; http://humanfoodproject.com/) to discover the kinds of microbes and microbiomes 103 "in the wild" via a self-selected citizen-scientist cohort. The EMP is tasked with characterizing 104 the global microbial taxonomic and functional diversity, and the HFP is focused on 105 understanding microbial diversity a...

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin–angiotensin–aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.