dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples include LDB Lichen Database, MIADB Spectral Library 1,24,25 , Sumner Spectral Library  26  , CASMI Spectral Library  24, 27  , and MassBank (Japan (http://massbank. jp)  28  , EU (https://massbank. eu/MassBank/)  29 and North America (http://mona. fiehnlab. ucdavis. edu/ )), a large MS data library that is directly synced with GNPS. There are also tags and sample information (metadata) entries provided by the community in the GNPS knowledge base. Furthermore, all public data are periodically searched against the NIST 2017 spectral library and highconfidence spectral matches are annotated. GNPS-MassIVE now performs >6,000 analysis jobs and has >200,000 page views a month (excluding developers), with the predominant analysis being molecular networking. As a result, GNPS-based analysis has been used for the discovery of hundreds of new molecules in the past few years, ranging from immune regulators to antimicrobials, including antiviral agents and protease inhibitors  9, [30] [31] [32] [33].
Here, we provide a detailed protocol for generating a publishable and reproducible molecular network from a mass spectrometry dataset. This protocol will take the reader through the following steps: how to upload data, how to make the data public, how to subscribe to public data for living data updates, and how to reproducibly create publishable molecular networks using standardized sample information (metadata) through the GNPS infrastructure (Fig. 1 ); terminology associated with GNPS molecular networking is defined in Table  1. This protocol was previously made available as a preprint document  34.
Overview of the method This protocol aims to provide researchers with a basic workflow for reliably and reproducibly creating molecular networks from mass spectrometry data using GNPS. The workflows for GNPS molecular networking described in this protocol can be performed in any laboratory with access to a tandem mass spectrometer, which is usually connected to an HPLC system. The overall procedure consists of five main stages (Fig. 2 ): (i) collecting MS  2  spectra, (ii) converting instrument-specific raw data files to an open format, (iii) uploading data to the MassIVE public repository, (iv) submitting a job to GNPS, and (v) visualizing the resulting molecular network. We also discuss how to navigate the 'My User' and 'Jobs' options, including how to share the links of a job, how to clone a job (Step 50), and how to subscribe to datasets in the public domain to obtain living data updates ( Step 53).
Applications of the method GNPS molecular networking provides the ability to analyze and compare MS 2 spectra in one or more datasets acquired within the scope of a specific study, across datasets from multiple studies, and also to compare those datasets to all publicly available GNPS-MassIVE datasets, including communitycurated spectral libraries. In addition, ongoing contributions to spectral libraries and submissions of new public datasets enable continuous identification: the periodic and automated reanalysis of all public datasets. GNPS is being used to network data acquired on a number of different mass spectrometers in a wide variety of exploratory studies, with samples originating from diverse environments and used for various purposes. The environments range from the indoor environment  [36] [37] [38]  to dissolved organic matter in the oceans  39  ; from microbes in culture  9, [40] [41] [42] [43]  to mouse  44  or human microbiomes  45, 46  or infections  [47] [48] [49]  ; from clinical samples  22, 50, 51  to plants  52  , algae  53  , sponges  5, 54  , and corals  55  ; as well as a number of other sample types  16, 56.
In addition, molecular networking has been applied to NPs discovery from a variety of organisms  [57] [58] [59] [60]  , forensics  61  , small-molecule identification  24, 62  , and biological discovery in hypothesis-driven research  63. Furthermore, GNPS facilitates large-scale meta-analyses that can compare and potentially link studies from different laboratories by enabling rapid comparisons across multiple public datasets. Finally, to promote data Reference identification number for a MS 2 consensus cluster. In Cytoscape, this identification number is also called 'shared name' Consensus cluster A grouping of MS 2 spectra that are considered identical on the basis of the MS-Cluster algorithm  10, 12. Because GNPS brings together approaches from different scientific communities, there are terms, such as 'cluster', that have different meanings.
Dereplication-and variable dereplication-based library search is a key part of the GNPS platform and can be performed without performing a molecular networking job  11. Numerous commercial and non-commercial MS 2 reference databases exist, such as the NIST/ EPA/NIH Mass Spectral Library 73 ; METLIN  74  ; the MassBanks of Japan (http://massbank. jp)  28  , EU (https://massbank. eu/MassBank/)  29 and North America (http://mona. fiehnlab. ucdavis. edu/); mzCloud  75, 76  ; and ReSpect  77  , which potentially provides users with access to ~2. 4 million MS 2 reference spectra, when GC-MS and LC-MS reference spectra are both considered  64. Many of these reference databases have an integrated spectral-matching tool for compound identification; these include mzCloud, METLIN/XCMS Online  78, 79  , Metabox  80  , and MassBank.
The goal of GNPS is not only to provide a spectral-matching tool, but also to serve as a data storage and knowledge capture/ dissemination platform, as well as to provide access to a host of other analysis tools not covered in detail here, such as in silico-based dereplication  [81] [82] [83]  , network annotation propagation  84  , genome mining tools  85  , and MASST (Mass Search Tool) searches. GNPS is currently the only online platform that provides molecular networking, a computational tool that compares pairs of MS 2 spectra on the basis of their similarities and connects them to MS 2 reference spectral libraries. Molecular networking enables further propagation of annotations through mass spectral relations. MetGem 86 is a standalone software package that can be used for the generation of molecular networks, which works well for smaller datasets; it is not connected to a knowledge base, repository-wide analysis tools or the additional computational resources that GNPS provides.
Reproducibility, blanks, and controls A well-organized and well-thought-out experimental plan is essential to the successful creation of useful molecular networks, because molecular networks are only as meaningful as the experiment and data from which they originate. This includes providing sample information (metadata) tables and raw data files for the sample set (Box 1); metadata tables aid the creation of molecular networks that have increased interpretative value. To avoid pitfalls associated with large-scale mass spectrometry experiments, for example, batch effects  88  , sample carryover and/or contamination  89  , and high NATURE PROTOCOLS PROTOCOL NATURE PROTOCOLS | www. nature. com/nprot background signal  90  , and to maximize reproducibility and signal-to-noise ratio  91  , a dataset should include blanks, quality control (QC) samples, and experimental replicates.
When possible, one should add internal standard(s) to each sample to ensure that the system performs consistently. If the internal standard(s) are not within the user-defined limits of chromatography variability, the sample needs to be either removed from downstream analysis or rerun. This is particularly useful in applications in which Box 1 | Sample information (metadata) collation and input • Timing typically 1-2 h for a small dataset; up to a few days for large complex metadata entries of large datasets The inclusion of a metadata (sample information) table is extremely valuable for interpreting the molecular network that is generated using the data  138. Although a time-consuming step, it is also one of the most valuable steps for interpreting the final molecular network. The more time spent on curating sample information (metadata), the more useful the resulting molecular network will be.
The online tutorial on metadata formatting, which includes a template file, can be accessed at https://ccmsucsd. github. io/GNPSDocumentation/networking/#metadata. thousands of samples, such as NP extract libraries, are screened. Further, when acquiring data for many samples, especially when multiple batches are used, we advise acquiring data for additional QC samples to monitor batch and plate effects throughout the experiment to assess instrumental variation over time, such as retention-time drift. QC samples can either consist of aliquots from a subset of test samples pooled together (pooled QC) or can be mixtures of molecules specifically defined for quality assurance. For example, it is common to use the last column of a 96-well plate for the QC mixture to ensure that the instrument and chromatography behave in an identical fashion throughout an experiment.
Molecular networking parameters GNPS-based molecular networking parameters-the set of user-defined and/or pre-populated values selected for analysis-can be varied substantially and need to be set appropriately for the acquired dataset, on the basis of the sample (anticipated molecular masses and types of molecules), instrument resolution, and collision energies used for MS acquisition. Networking parameters and basic mass spectrometry/metabolomics terminology are described in detail in Table  2  and the Procedure and should be considered and selected carefully in order to obtain useful networks, which ultimately depend on the quality and quantity of MS 2 spectra.
Although 6 is listed as default, a lower value could be used if the user wants to be less restrictive or if the sample largely contains molecules with a small number of fragment ions. The maximum number of significant annotations are found when this value is set to 4 or 5  100   Maximum number of nodes that can be connected in a single component (molecular family) of a molecular network. This process iteratively breaks up large 'hairball' networks (of false positives) by removing the lowest-scoring alignments (by cosine score) first, until the resulting pieces fall below the maximum size Default setting is 100; this value can be set to 0 to allow for an unlimited number of nodes, or a higher setting can be used for larger datasets or for datasets containing many structurally related molecules.
This workflow connects clustered MS 2 spectra as nodes on the basis of spectral similarities and makes use of MS 2 data only, even for quantitation. The chromatographic dimension and MS 1 data are not considered in classic molecular networking. However, in MS-based metabolomics studies, statistical analysis is done predominantly from MS 1 -based peak abundances from extracted ion chromatograms (XICs). Those chromatographic peaks with a specific, accurate mass-to-charge ratio are described as features. To bridge this gap between MS 1 abundance and MS 2 qualitative information, there is a workflow to link MS 1 peak areas derived from LC-MS features with MS 2 information from molecular networking  142, 143. This workflow is called feature-based molecular networking (FBMN; https://ccmsucsd. github.
Although this percentage has grown to an average of 5-6% of spectra being annotated, a large percentage of MS 2 spectra typically remain unannotated. The structures of these unannotated molecules or 'dark matter' 105 might be known, but their identity is not revealed because no reference spectra exist in library databases against which to compare. To improve annotation rates, in silico tools have been developed to match unknown MS 2 spectra to putative chemical structures  106. Several of these computational tools, which include MetFrag  107  , MetFusion  108  , SIRIUS  109, 110  , CSI:FingerID 111 , MS-Finder  112  , Network Annotation Propagation (NAP)  84  , and Dereplicator  81, 82  , can be integrated into GNPS molecular networking workflows to provide insight into the annotation; the application of such tools is beyond the immediate scope of the networking protocol presented here.
Example datasets c CRITICAL All LC-MS data used in this paper are publicly available at the GNPS-MassIVE repository under the following accession numbers: • MSV000083437 (germ-free (GF) and specific-pathogen-free (SPF) mice) • MSV000083359 (3D cartography of diseased human lung  47  ) • MSV000083381 (stenothricin-GNPS analogs  11  ) PROTOCOL NATURE PROTOCOLS Procedure c CRITICAL The data submission and molecular networking workflow can be followed as a tutorial using an untargeted metabolomics dataset for 3D molecular cartography of the mouse duodenum (data not shown; MassIVE dataset MSV000083437). This dataset is a subset of a collection of metabolomes analyzed from organs of GF and SPF mice that led to the discovery of new amide-conjugated bile acids made by bacteria that affect host metabolism via farnesoid X receptor (FXR) agonism.
The following procedure will take the reader through submission of dataset MSV000083437 to the molecular networking workflow in GNPS, through the molecular networking workflow in GNPS (including input parameters), and through visualization of the generated network using both in browser and Cytoscapebased visualization (Fig. 3 ). c CRITICAL All steps, albeit in less detail, are also described and continuously updated and maintained in the online GNPS documentation at: https://ccms-ucsd. github. io/GNPSDocumentation/. Documentation should be regularly checked for the most up-to-date information, as well as descriptions of new software releases and features.
Data conversion • Timing 1 h up to a few days (depending on size of dataset and computer setup) 1 Manually convert the raw data to open file formats prior to uploading to GNPS-MassIVE (Fig. 2 , stage 1). The protocol for data conversion should be chosen on the basis of the instrument used for mass spectrometry acquisition. Mass spectrometry files must be converted to an open file format for analysis in GNPS. Open file formats include. mzXML,. mzML, and. mgf formats, with the preferred formats being. mzXML and. mzML, although it is also encouraged to co-submit the raw data to MassIVE. MSConvert can be used for the conversion to a GNPS-compatible format of mass spectrometry data acquired on AB SCIEX, Agilent, Shimadzu (after initial conversion, Supplementary Methods), Thermo Scientific, and Waters instruments.
For Bruker files, a separate workflow must be used that applies internal lockmass calibration to the output file. This Bruker workflow is described in more detail in the Supplementary Methods. For AB SCIEX raw files (. wiff files), the data can also be converted into. mzML format using the MS Data Converter (AB SCIEX v. 1. up a MassIVE account that uses the same login and password. To manipulate MS data files in GNPS, they must first be uploaded to MassIVE, which is an online repository for mass spectrometry datasets hosted by the UCSD Center for Computational Mass Spectrometry (CCMS). The user workspace in GNPS/MassIVE provides a personalized location where researchers can curate mass datasets, submit and monitor GNPS workflows, subscribe to datasets that have been made publicly available by others, or clone and reanalyze either their own or other public datasets.
Within this space, the 'My User' tab provides a way to view all MassIVE datasets and reference spectra deposited by the user, and the 'Jobs' button allows easy access to all jobs submitted by the user through the GNPS and MassIVE interfaces. Clicking on 'MassIVE datasets' allows the user to browse and subscribe (Step 53) to all public MassIVE datasets with GNPS in the title. In addition, this banner is a portal to all resources for help while using GNPS. The 'Documentation' link in the banner takes the user to the GNPS documentation website, which has additional step-by-step instructions and links to supplemental tutorial videos, as well as access to the 'legacy' documentation (from a menu on the right-hand side of the page) that can provide additional information to the user. The 'Forum' button opens a Google groups forum where users can post questions, have discussions, and report potential bugs.
The reader can follow along (Supplementary Fig. 3 ) because the mass spectrometry output files for the tutorial example have already been uploaded to MassIVE and can be found in the dataset MSV000083437. 12 In the 'Workflow Selection' section, enter a title for your dataset, noting that GNPS datasets must have a 'GNPS' prefix in the title in order for these GNPS-MassIVE datasets to be visible to GNPS users. To satisfy this requirement for the dataset that reader will use when following the example, MassIVE dataset MSV000083437 has been titled 'GNPS Example Dataset_GF vs. SPF Mouse Duodenum. ' c CRITICAL STEP Adding GNPS to the title is therefore absolutely necessary for the dataset to become a part of the community, ensures that the data become 'alive' (Step 53), and enables subscriptions and other analysis features specifically used for the GNPS community (Step 53).
If a 'GNPS' tag is not added at the beginning of the title, it will not be part of the GNPS analysis infrastructure. Currently all of MassIVE has almost 11,000 public mass spectrometry datasets (mostly proteomics), ~1,100 of which are also part of GNPS. If GNPS is not added at the beginning, it is possible to go to MassIVE, log in, and edit the title at a later time. 13 In the 'Dataset Metadata' section, enter relevant metadata. To minimize the burden of making datasets for GNPS analysis and to enable as much flexibility in what additional information the user wants to make available, very few metadata fields are absolutely required, although the user is encouraged to provide as much metadata as possible. It should be noted that the datasets that have the most information associated with them are also the datasets that are the most visible to the community.
Fields for metadata relevant to the dataset being submitted are listed in Table  3. The first three fields ('Species', 'Instrument', and 'Post-Translational Modifications') are backed by lists of standardized controlled vocabulary (CV) terms, maintained by organizations such as the HUPO Proteomics Standards Initiative  116  and many others, that the user can implement  116, 117. To search these terms, type at least three characters into any of these text boxes, and a drop-down list of supported terms that match your query will be displayed. To select a term, click on it in the dropdown list and it will be added to your dataset. If the term you want is not present in the list, you can type your custom text into the text box and click the adjacent 'Add' button to tag your dataset. Metadata (sample information) for MassIVE dataset MSV000083437 has been added as shown in Supplementary Fig.
2b. c CRITICAL STEP Using the official CV to tag your dataset greatly increases the likelihood that it will be found and processed correctly by any automated software that may interface with the MassIVE repository. 14 Add data files to the 'Dataset File Selection' section. In this section, there are 11 different file types that can be added, and these are organized into three different categories: required, recommended, and optional. Most of these file categories are not strictly required. The only official file requirement for a MassIVE dataset is that at least one file is submitted in either the 'Raw Spectrum Files' or 'Peak List Files' categories. If a submitted dataset does not meet the additional requirements for a 'complete' submission, then it is considered 'partial', which is currently standard for smallmolecule datasets that are a part of GNPS (link for the definition of complete submission: http://proteomics.
ucsd. edu/service/massive/documentation/submit-data/submission-workflow/#MassIVE DatasetSubmission-SubmissionTypes). Guidelines for which files to upload are summarized in Box 4. For readers who are following the example, Peak List files were uploaded previously for dataset MSV000083437, as illustrated in Supplementary Fig. 3b , where nine folders (Control, GF1, GF2, GF3, GF4, SPF1, SPF2, SPF3, SPF4) have been added. 15 Check that each spectrum (data) file referenced within a 'Result File' is associated with a file from the 'Peak List Files' category; this is required for a submission to qualify as 'complete'. 'Mapping Spectrum Files to Identification Files' is not necessary for small molecule workflows. This section is where these two types of files are associated with each other as appropriate.
16 Populate the 'Dataset Publication' section. This section has three optional fields: • 'Enter a Password'-this is used, for example, to share selectively with collaborators and manuscript reviewers. • 'Share on ProteomeXchange'-this is not applicable to small-molecule workflows; checking the box will submit and announce the dataset via the ProteomeXchange Consortium at the time that. mzXML,. mzML, and. mgf. This is the file from which GNPS analysis is enabled. Strongly encouraged for submissions to improve the ability to interpret the final molecular networks • Supplementary files. All remaining files relevant to this dataset that do not properly fit into any of the other listed file categories. A metadata file (sample information in a tab-delimited text format) with relevant attributes that can be used for visualizing the data in networks should be included here (see Box 1).
Required for 'Complete' Submission • Result files. These are not necessary for small-molecule workflows but can be included and this is encouraged. • Spectrum identifications in a standardized format. The following formats are recognized by MassIVE as valid for this category: mzIdentML  145  , mzTab  146  , and mzTab-M 147. • Search engine files. The output of any search engine or data analysis tools or pipelines that were used to analyze this dataset, unless provided in a standardized format recognized by the 'Result Files' category (see above).
Optional • License files. These specify how and under what conditions the dataset files can be downloaded and used. Multiple license files can be uploaded, if appropriate. By default, you can simply leave the 'Standard License' checkbox checked and your dataset will be submitted under the default Creative Commons CC0 1. 0 Universal license. However, if you wish to provide your own license, then you can uncheck this box and assign your own file to the 'License Files' category. • Spectral libraries. Any custom spectral library files that were searched against in the analysis of this dataset or that were generated using the spectrum files provided in this dataset, if applicable. • Methods and protocols. Any open-format files containing explanations or discussions of the experimental procedures used to obtain or analyze this dataset.
Optional, mostly relevant to peptidomics and proteomics projects • Quantification results. Any data or metadata generated by the analysis software used. Typically applied to the quantification analysis of peptides and proteins. • Gel images. Any gel image files generated in the event that 2D gel electrophoresis has been used as a separation method. • Sequence databases. Any files from protein or other sequence databases that were associated with or searched against in the analysis of this dataset, if applicable (usually. fasta format).