Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics.

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Enhancing untargeted metabolomics using metadata-based source annotation.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

The emergence of SARS-CoV-2 variants of concern has prompted the need for near real-time genomic surveillance to inform public health interventions. In response to this need, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info, a platform that currently tracks over 40 million combinations of PANGO lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials, and the general public. We describe the interpretable and opinionated visualizations in the variant and location focussed reports available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data, and the server infrastructure that enables widespread data dissemination via a high performance API that can be accessed using an R package. We present a case study that illustrates how outbreak.info can be used for genomic surveillance and as a hypothesis generation tool to understand the ongoing pandemic at varying geographic and temporal scales. With an emphasis on scalability, interactivity, interpretability, and reusability, outbreak.info provides a template to enable genomic surveillance at a global and localized scale.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS.

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages.

In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations.