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BackgroundSchools are high-risk settings for SARS-CoV-2 transmission, but necessary for children’s educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown.MethodsThe objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 – March 2021.FindingsIn 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88% - 98%); 67% were associated with a positive wastewater sample (95% CI: 57% - 77%), and 40% were associated with a positive surface sample (95% CI: 29% - 52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples.InterpretationPassive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy.FundingCounty of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org., Current protocols in bioinformatics, 70 (1)

The discovery that the machinery of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 bacterial immune system can be re-purposed to easily create deletions, insertions and replacements in the mammalian genome has revolutionized the field of genome engineering and re-invigorated the field of gene therapy. Many parallels have been drawn between the newly discovered CRISPR-Cas9 system and the RNA interference (RNAi) pathway in terms of their utility for understanding and interrogating gene function in mammalian cells. Given this similarity, the CRISPR-Cas9 field stands to benefit immensely from lessons learned during the development of RNAi technology. We examine how the history of RNAi can inform today's challenges in CRISPR-Cas9 genome engineering such as efficiency, specificity, high-throughput screening and delivery for in vivo and therapeutic applications.

This report summarizes the proceedings of the structure mapping working group meeting of the RNA Ontology Consortium (ROC), held in Kona, Hawaii on January 8-9, 2011. The ROC hosted this workshop to facilitate collaborations among those researchers formalizing con-cepts in RNA, those developing RNA-related software, and those performing genome annota-tion and standardization. The workshop included three software presentations, extended round-table discussions, and the constitution of two new working groups, the first to address the need for better software integration and the second to discuss standardization and ben-chmarking of existing RNA annotation pipelines. These working groups have subsequently pursued concrete implementation of actions suggested during the discussion. Further infor-mation about the ROC and its activities can be found at http://roc.bgsu.edu/.

International audience; Multiple sequence alignments are powerful tools for understanding the structures, functions, and evolutionary histories of linear biological macromolecules (DNA, RNA, and proteins), and for finding homologs in sequence databases. We address several ontological issues related to RNA sequence alignments that are informed by structure. Multiple sequence alignments are usually shown as two-dimensional (2D) matrices, with rows representing individual sequences, and columns identifying nucleotides from different sequences that correspond structurally, functionally, and/or evolutionarily. However, the requirement that sequences and structures correspond nucleotide-by-nucleotide is unrealistic and hinders representation of important biological relationships. High-throughput sequencing efforts are also rapidly making 2D alignments unmanageable because of vertical and horizontal expansion as more sequences are added. Solving the shortcomings of traditional RNA sequence alignments requires explicit annotation of the meaning of each relationship within the alignment. We introduce the notion of "correspondence," which is an equivalence relation between RNA elements in sets of sequences as the basis of an RNA alignment ontology. The purpose of this ontology is twofold: first, to enable the development of new representations of RNA data and of software tools that resolve the expansion problems with current RNA sequence alignments, and second, to facilitate the integration of sequence data with secondary and three-dimensional structural information, as well as other experimental information, to create simultaneously more accurate and more exploitable RNA alignments.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Pairing between the hexamer seed region of a small interfering RNA (siRNA) guide strand (nucleotides 2–7) and complementary sequences in the 3′ UTR of mature transcripts has been implicated as an important element in off-target gene regulation and false positive phenotypes. To better understand the association between seed sequences and off-target profiles we performed an analysis of all possible (4096) hexamers and identified a nonuniform distribution of hexamer frequencies across the 3′ UTR transcriptome. Subsequent microarray analysis of cells transfected with siRNAs having seeds with low, medium, or high seed complement frequencies (SCFs) revealed that duplexes with low SCFs generally induced fewer off-targets and off-target phenotypes than molecules with more abundant 3′ UTR complements. These findings provide the first experimentally validated strategy for designing siRNAs with enhanced specificity and allow for more accurate interpretation of high throughput screening data generated with existing siRNA/shRNA collections.

Although recent microarray studies have provided evidence of RNA interference (RNAi)-mediated off-target gene modulation, little is known about whether these changes induce observable phenotypic outcomes. Here we show that a fraction of randomly selected small inhibitory RNAs (siRNAs) can induce changes in cell viability in a target-independent fashion. The observed toxicity requires an intact RNAi pathway and can be eliminated by the addition of chemical modifications that reduce off-target effects. Furthermore, an analysis of toxic and nontoxic duplexes identifies a strong correlation between the toxicity and the presence of a 4-base-pair motif (UGGC) in the RISC-entering strand of toxic siRNA. This article provides further evidence of siRNA-induced off-target effects generating a measurable phenotype and also provides an example of how such undesirable phenotypes can be mitigated by addition of chemical modifications to the siRNA.

The discovery that the bacterial clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated protein 9 (Cas9) acquired immune system can be utilized to create double-strand breaks (DSBs) in eukaryotic genomes has resulted in the ability to create genomic changes more easily than with other genome engineering techniques. While there is significant potential for the CRISPR–Cas9 system to advance basic and applied research, several unknowns remain, including the specificity of the RNA-directed DNA cleavage by the small targeting RNA, the CRISPR RNA (crRNA). Here we describe a novel synthetic RNA approach that allows for high-throughput gene editing experiments. This was used with a functional assay for protein disruption to perform high-throughput analysis of crRNA activity and specificity. We performed a comprehensive test of target cleavage using crRNAs that contain one and two nucleotide mismatches to the DNA target in the 20mer targeting region of the crRNA, allowing for the evaluation of hundreds of potential mismatched target sites without the requirement for the off-target sequences and their adjacent PAMs to be present in the genome. Our results demonstrate that while many crRNAs are functional, less than 5% of crRNAs with two mismatches to their target are effective in gene editing; this suggests an overall high level of functionality but low level of off-targeting.

Human microbiome reference datasets provide epidemiological context for researchers, enabling them to uncover new insights into their own data through meta-analyses. In addition, large and comprehensive reference sets offer a means to develop or test hypotheses and can pave the way for addressing practical study design considerations such as sample size decisions. We discuss the importance of reference sets in human microbiome research, limitations of existing resources, technical challenges to employing reference sets, examples of their usage, and contributions of the American Gut Project to the development of a comprehensive reference set. Through engaging the general public, the American Gut Project aims to address many of the issues present in existing reference resources, characterizing health and disease, lifestyle, and dietary choices of the participants while extending its efforts globally through international collaborations.