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Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

SummaryThe Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped.Availability and implementationThe source code is available at https://github.com/czbiohub/MIDAS2.Supplementary informationSupplementary data are available at Bioinformatics online.

All-inorganic metal halide perovskite CsPbX3(X = Cl, Br, I) nanocrystals (NCs) have demonstrated attractive optoelectronic characteristics. However, their photocatalytic properties are limited by their poor stability and easy recombination of photogenerated carriers. Herein, we introduced a CsPbBr3@MSNs nanocomposite (CsPbBr3NCs embedded in dendritic mesoporous silica nanospheres (MSNs)) as photocatalysts for photoinduced electron transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. The CsPbBr3nanocrystals (∼8.1 nm; PLQY of 62 ± 2.1%) were embedded in dendritic MSNs using a nanoconfinement strategy. PET-RAFT polymerization was successfully initiated using the CsPbBr3@MSNs nanocomposite as the photocatalyst. Reaction variables, such as catalyst loading, monomer composition, and excitation light wavelengths, were varied to yield polymers with the desired control of molecular weight and dispersity as well as block copolymers with high chain-end fidelity. In addition, the perovskite-based photocatalysts could be readily separated and purified, which allowed effective and rapid recycling of the nanocomposites for multiple polymerization cycles.

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.

Shape and dimensionality control by ligands is an efficient way to fabricate high-quality perovskite nanocrystals (NCs) with optoelectronic properties. Herein, to realize oriented transformations between different morphologies of CsPbBr3nanocrystals, we conceived of the use of defects and bidentate ligands to synergistically drive the reorientation of CsPbBr3nanocrystals into CsPbBr3nanowires (NWs). By employing aminocaproic acid as the bidentate ligand, CsPbBr3NWs were reconstructed from the CsPbBr3NCs (∼7 nm) with 20-60 nm in width and several micrometers in length, as well as a 527 nm photoluminescence (PL) peak, 59% PL quantum yield, and 98.5 ns PL lifetime. The bidentate ligand-directed anisotropic shape evolution and growth of perovskite NWs from NCs was elucidated by transmission electronmicroscopy and PL studies. The photodetectors fabricated with the reconstructed CsPbBr3NWs displayed a photocurrent enhancement (2.75 × 10-11A) compared with that of the devices based on solution-synthesized NWs (2.28 × 10-11A). The device also showed a short response time, indicating its more sensitive photoelectric response. This process provides a viable approach to engineering diverse morphologies of perovskite nanocrystals for optoelectronic applications.

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

In the version of this Article initially published, there were errors in the author affiliations, Fig. 3, main text and Acknowledgements section.

Direct, amplification-free detection of RNA has the potential to transform molecular diagnostics by enabling simple on-site analysis of human or environmental samples. CRISPR-Cas nucleases offer programmable RNA-guided RNA recognition that triggers cleavage and release of a fluorescent reporter molecule, but long reaction times hamper their detection sensitivity and speed. Here, we show that unrelated CRISPR nucleases can be deployed in tandem to provide both direct RNA sensing and rapid signal generation, thus enabling robust detection of ~30 molecules per µl of RNA in 20 min. Combining RNA-guided Cas13 and Csm6 with a chemically stabilized activator creates a one-step assay that can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA extracted from respiratory swab samples with quantitative reverse transcriptase PCR (qRT-PCR)-derived cycle threshold (Ct) values up to 33, using a compact detector. This Fast Integrated Nuclease Detection In Tandem (FIND-IT) approach enables sensitive, direct RNA detection in a format that is amenable to point-of-care infection diagnosis as well as to a wide range of other diagnostic or research applications.