Although studies have shown that urban environments and mass-transit systems have distinct genetic profiles, there are no systematic studies of these dense, human/microbial ecosystems around the world. To address this gap in knowledge, we created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 58 cities, spanning 3,741 samples and 4,424 taxonomically-defined microorganisms collected for from 2015-2017. The map provides annotated, geospatial details about microbial strains, functional genetics, antimicrobial resistance, and novel genetic elements, including 10,928 novel predicted viral species. Urban microbiomes often resemble human commensal microbiomes from the skin and airways, but also contain a consistent "core" of 61 species which are predominantly not human commensal species. Conversely, samples may be accurately (91.4%) classified to their city-oforigin using a linear support vector machine over taxa. These data also show that AMR density across cities varies by several orders of magnitude, including many AMRs present on plasmids with specific cosmopolitan distributions. Together, these results constitute a high-resolution global metagenomic atlas, which enables the discovery of new genetic components of the built human environment, highlights potential forensic applications, and provides an essential first draft of the global AMR burden of the world's cities.

Background: De novo assemblies are critical for capturing the genetic composition of complex samples. Linked-read sequencing techniques such as 10x Genomics' Linked-Reads, UST's TELL-Seq, Loop Genomics' LoopSeq, and BGI's Long Fragment Read combines 30 barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. The application of linked-read sequencing to genome assembly has demonstrated that barcoding-based technologies balance the ffs between long-range linkage, per-base coverage, and costs. Linkedreads come with their own challenges, chief among them the association of multiple long fragments with the same 30 barcode. The lack of a unique correspondence between a long fragment and a barcode, in conjunction with low sequencing depth, confounds the assignment of linkage between short-reads. Results: We introduce Ariadne, a novel linked-read deconvolution algorithm based on assembly graphs, that can be used to extract single-species read-sets from a large linked-read dataset. Ariadne deconvolution of linked-read clouds increases the proportion of read clouds containing only reads from a single fragment by up to 37.5-fold. Using these enhanced read clouds in de novo assembly significantly improves assembly contiguity and the size of the largest aligned blocks in comparison to the non-deconvolved read clouds. Integrating barcode deconvolution tools, such as Ariadne, into the postprocessing pipeline for linked-read technologies increases the quality of de novo assembly for complex populations, such as microbiomes. Ariadne is intuitive, computationally efficient, and scalable to other large-scale linked-read problems, such as human genome phasing.