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.

Predicting biosynthetic gene clusters (BGCs) is critically important for discovery of antibiotics and other natural products. While BGC prediction from complete genomes is a well-studied problem, predicting BGCs in fragmented genomic assemblies remains challenging. The existing BGC prediction tools often assume that each BGC is encoded within a single contig in the genome assembly, a condition that is violated for most sequenced microbial genomes where BGCs are often scattered through several contigs, making it difficult to reconstruct them. The situation is even more severe in shotgun metagenomics, where the contigs are often short, and the existing tools fail to predict a large fraction of long BGCs. While it is difficult to assemble BGCs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding long BGCs. We describe biosyntheticSPAdes, a tool for predicting BGCs in assembly graphs and demonstrate that it greatly improves the reconstruction of BGCs from genomic and metagenomics data sets.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Public databases contain a planetary collection of nucleic acid sequences, but their systematic exploration has been inhibited by a lack of efficient methods for searching this corpus, now exceeding multiple petabases and growing exponentially [1, 2]. We developed a cloud computing infrastructure, Serratus , to enable ultra-high throughput sequence alignment at the petabase scale. We searched 5.7 million biologically diverse samples (10.2 petabases) for the hallmark gene RNA dependent RNA polymerase, identifying well over 10 ⁵ novel RNA viruses and thereby expanding the number of known species by roughly an order of magnitude. We characterised novel viruses related to coronaviruses and to hepatitis δ virus, respectively and explored their environmental reservoirs. To catalyse a new era of viral discovery, we established a free and comprehensive database of these data and tools. Expanding the known sequence diversity of viruses can reveal the evolutionary origins of emerging pathogens and improve pathogen surveillance for the anticipation and mitigation of future pandemics.

Motivation: Emerging Linked-Read (aka read-cloud) technologies such as the 10x Genomics Chromium system have great potential for accurate detection and phasing of largescale human genome structural variations (SVs). By leveraging the long-range information encoded in Linked-Read sequencing, computational techniques are able to detect and characterize complex structural variations that are previously undetectable by short-read methods. However, there is no available Linked-Read method for detection and assembly of novel sequence insertions, DNA sequences present in a given sequenced sample but missing in the reference genome, without requiring whole genome de novo assembly. In this paper, we propose a novel integrated alignment-based and local-assembly-based algorithm, Novel-X, that effectively uses the barcode information encoded in Linked-Read sequencing datasets to improve detection of such events without the need of whole genome de novo assembly. We evaluated our method on two haploid human genomes, CHM1 and CHM13, sequenced on the 10x Genomics Chromium system. These genomes have been also characterized with high coverage PacBio long-reads recently. We also tested our method on NA12878, the wellknown HapMap CEPH diploid genome and the child genome in a Yoruba trio (NA19240) which was recently studied on multiple sequencing platforms. Detecting insertion events is very challenging using short reads and the only viable available solution is by long-read sequencing (e.g. PabBio or ONT). Our experiments, however, show that Novel-X finds many insertions that cannot be found by state of the art tools using short-read sequencing data but present in PacBio data. Since Linked-Read sequencing is significantly cheaper than long-read sequencing, our method using Linked-Reads enables routine large-scale screenings of sequenced genomes for novel sequence insertions.Availability: Software is freely available at https://github.com/1dayac/novel_insertionsContact: imh2003@med.cornell.eduSupplementary information: Supplementary data are available at https://github.com/1dayac/novel_insertions_supplementary

Synthetic long read sequencing techniques such as UST’s TELL-Seq and Loop Genomics’ LoopSeq combine 3 $$'$$′ barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3 $$'$$′ unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.