dataesr/scientific-paragraphs-categorization huggingface dataset:
PROTOCOL NATURE PROTOCOLS | www. nature. com/nprot it is made public on MassIVE. The dataset will not appear publicly in either repository until you click the 'Make Public' button on your dataset's status page (see below). • 'Generate a DOI'-Use this if you want a digital object identifier to be generated and assigned to this dataset. This is encouraged for all public datasets and can be used in publications. 17 Disregard the section titled 'Advanced Global FDR Settings'; this is not applicable to small-molecule workflows. It is currently used for global FDRs across submitted files in proteomics datasets. 18 In the 'Workflow submission' section, enter an email address at which you will receive notifications when workflow jobs are completed. c CRITICAL STEP Make your dataset public, as shown in Step 19; this is not automatic and must be done explicitly after submitting data and generating a dataset MSV accession number.
19 Once a dataset is submitted to MassIVE, it will have an MSV accession number and will be a private dataset in the repository, accessible only to the submitter through their personal user interface or via a user-approved password-protected link (e. g. , perhaps during a review for publications). To make a dataset public, first select the 'Jobs' tab of the user workspace portal (Box 3) to find the dataset. In the list of all job submissions, MassIVE dataset submissions will appear as 'MASSIVE-COMPLETE' workflows. Click on 'DONE' next to the MassIVE dataset to be made public and choose 'Make Dataset Public'. On the MassIVE website, to enable immediate use of the MassIVE dataset for GNPS workflows, click on the 'Convert Spectra' tab. This converts the uploaded files to. mzML files in a new folder called 'ccms peak'.
Choice of workflow c CRITICAL Once MS data files are uploaded as datasets into GNPS-MassIVE, they are available to use for analysis workflows within GNPS. Here, we highlight how to execute the molecular networking workflow. A dataset can be recalled from either private or public domains in MassIVE for networking analysis. Once data files have been added, they will be populated in the 'Basic Options' section of the workflow selection. The user must then input a number of parameters before running the GNPS job in both the 'Basic Options' section and in a number of 'Advanced Options' sections. The advanced parameters are dependent on analysis platform, experimental setup, and conditions for acquisition of mass spectra, and will require the user to understand their ionization methods, fragmentation conditions and energies, mobile and stationary phases, and the fragmentation behavior of molecules of interest.
Suggested settings for a variety of platforms are provided in the experimental section (Supplementary Methods). A GNPS job will take ~10 min for small datasets (up to 4 LC/MS files), 1 h for medium datasets (5-400 LC/MS files), and several hours (to days) for larger datasets (400+ LC/MS files). 20 Decide whether you are going to follow the 'molecular networking flow' (Steps 20-31), or whether you will proceed by choosing a Network Parameter Preset. In the 'Networking Parameter Presets' section (which resides directly under the 'Workflow Selection' and 'Workflow Description' sections), one of three options can be selected to set the networking parameters to approximately appropriate values depending on the size of your dataset. Clicking on one of these three options will open a workflow input form in a new tab.
The default workflow settings are for 'medium data'. 'Small data' refers to a dataset of up to 4 LC-MS files, 'medium data' corresponds to datasets of 5-400 LC-MS files, and 'large data' is applicable to datasets of >400 LC-MS files (e. g. , MSV000083437 is a medium dataset with 113 files in total). Because readers following the tutorial on the dataset MSV000083437 are guided through selection of parameters, no 'Parameter Preset' should be chosen for this example. Network Parameter presets were developed for low-resolution data to ensure a 1% FDR; on the basis of this, readers with high-resolution data or with special requirements are advised to use custom parameters. c CRITICAL STEP If the user selects an option in the 'Networking Parameter Presets' section, Steps 21-30 can be bypassed. Because readers following the tutorial on the dataset MSV000083437 are guided through selection of parameters, no parameter preset should be chosen for this example.
Molecular networking workflow 21 Log in to GNPS (refer to Steps 2-8 for information about how to set up an account). The GNPS website banner contains tabs with which to navigate the platform, including tabs with which to navigate to MassIVE datasets, help documentation and the GNPS forum, as well as contact information (Supplementary Fig. 1 ). 22 Upload the desired dataset(s) to MassIVE (Steps 11-18 and Box 4). This step can be skipped if importing existing data files from MassIVE. Readers following the tutorial can omit this step because the GNPS-MassIVE dataset MSV000083437 already exists. 23 From the GNPS splash screen (home page), start a molecular networking job by clicking the 'Create Molecular Network' button (Supplementary Fig. 4a ). This will bring up the main workflow input page, which has a number of fillable fields to complete under each of ten sections (Supplementary Fig.
4b ). 24 In the 'Workflow Selection' section, enter a descriptive name for the job into the 'Title' field to facilitate retrieval of the workflow upon its completion (Box 5). Readers following the tutorial can type 'GF/SPF Mouse Duodenum Example' into the 'Title' field (Supplementary Fig. 4c ). 25 Under 'Basic Options', add the LC-MS files for the molecular networking workflow by choosing the 'Select Input Files' tab next to the 'Spectrum Files (Required)' field. A pop-up window with three tabs will appear: 'Select Input Files', 'Upload Files', and 'Share Files' (Supplementary Fig. 4c ). If multiple datasets will be analyzed together, repeat the above procedure with the other MSV numbers to import them into the user space. For readers following the dataset MSV000083437 tutorial, files can be imported by selecting the 'Share Files' tab.
In the 'Share Files' window, enter the MassIVE accession number for the dataset (MSV000083437) into the 'Import Data Share' box (Supplementary Fig. 4c ). After clicking 'Import', the dataset will appear in your GNPS user workspace and files can be selected for the GNPS networking workflow under the 'Select Input Files' tab as described below. 26 Choose the 'Select Input Files' tab (Supplementary Fig. 4f ) to input mass spectrometry files already in your user workspace. From the list of datasets towards the lower left of the window, select all the files you want to analyze by clicking on individual files or an entire folder. For readers following the tutorial, GF1, GF2, GF3, GF4, SPF1, SPF2, SPF3, and SPF4 should be selected from the folder labeled 'peak'. ? TROUBLESHOOTING 27 Click on the 'Spectrum Files G1' button (top of left-hand column list, with green arrow) to mark this folder/files for analysis.
Your selection(s) should appear in the 'Selected Spectrum Files G1' folder in the right-hand column of the window. For readers following the tutorial, folders containing data for GF1, GF2, GF3, GF4, SPF1, SPF2, SPF3, and SPF4 should now be under 'Selected Spectrum Files G1' (Supplementary Fig. 4g ). 28 Load the associated metadata file (see Box 3 for format) separately into the 'Selected Metadata File' folder. To do this, select the file from your workspace list (often within a MassIVE dataset in the folder labeled as 'other'), click on the 'Metadata File' tab with the green arrow, and check that the file appears in the right-hand 'Selected Metadata File' folder. For readers following the tutorial, '3DMouse_duodenum_metadata. txt" can be selected from the folder labeled 'other' (Supplementary Fig.
Box 5 | The importance of making your GNPS-MassIVE data public Many GNPS users do not realize that when they have a dataset with an MSV accession number, their data are not yet public and thus remain in their private space, in accordance with the GNPS-MassIVE philosophy that data depositors should define how much and when they want to share their data in the public domain. Alternatively, upon submission, users can choose to make a dataset entirely available or 'public' to the GNPS community for browsing, commenting, subscribing, and/or downloading. This not only promotes robustness and reproducibility in MS data analysis, but also provides the user with access to the knowledge of the entire community. Indeed, the utility of GNPS for all users increases as more data become public, and the information and knowledge gained by any one user from this free service to the community derives from contributions made by the rest of the GNPS community.
Thus, if you are a GNPS user benefiting from community contributions, by making your datasets public (and contributing network annotations, Step 45), you are giving back to the community. All users are encouraged to make their data public as early as possible, which provides the depositor with access to advanced features that are not available for private datasets. These features include being able to subscribe to the dataset, find related datasets, share datasets with collaborators, access living data, and utilize emerging features such as the Mass Spectrometry Search Tool or MASST (the equivalent of BLAST for small molecules  140  ). We expect that features will continue to be developed further, thereby continually increasing the value for the end user of both their own and other public datasets.
PROTOCOL NATURE PROTOCOLS | www. nature. com/nprot 29 Once files have been selected, close the popup window by clicking on 'Finish Selection'. Datasets from both your private workspace and the public domain can be recalled using either strategy. For readers following the tutorial, the final data input is shown in Supplementary Fig. 4i. 30 Fill in the 'Precursor Ion Mass Tolerance' (PIMT) and 'Fragment Ion Mass Tolerance' (FIMT) fields in the 'Basic Options' section. Take into consideration the instrument resolution and calibration, as well as the acquisition parameters and the targeted/anticipated molecular masses (Table  4 ). PIMT: This parameter is used for MS-Cluster  10, 12  and spectral library searching, and the value influences the clustering of nearly identical MS 2 spectra via MS-Cluster.
nist. gov/srd/nist-standard-reference-database-1a-v17) are also low resolution. In addition the NPs community contributes annotated spectra from a range of different spectrometers that may be high or low resolution (Supplementary Table  3 ). 31 Click 'Submit' to begin the job. The molecular networking job for the example dataset (MSV000083437) should take about 20 min. The following additional parameters can be also be specified before final submission of the job: • Finally, enter an email address under 'Workflow Submission' to receive notifications when workflow jobs are completed. Readers following the tutorial should do this to receive notification when the example job is completed. ? TROUBLESHOOTING 32 Visualize the molecular networks generated using either direct visualization in browser (option A) or Cytoscape visualization (option B) (Fig.
2 , stage 5); these options for data visualization in addition to other data analysis options are tabulated in Table  5. These methods are complementary to one another and the user should choose the preferred visualization strategy on the basis of their data analysis needs. The GNPS in-browser visualization tool is a quick, simple way to begin analyzing data, particularly if the user wants to view and compare MS 2 spectra within the network. However, in-browser visualization allows the user to view only one molecular family (subnetwork) at a time. For more advanced data analysis and formatting options, the user can visualize their network offline using third-party tools. One popular visualization tool for molecular networks generated in GNPS is Cytoscape  35  , a program originally introduced by the systems biology community to allow visualization of the complex relationships in biological sequence data.
With Cytoscape, one can visualize the chemical space that was detected in the mass spectrometry experiment as a molecular network; the tool provides a way to encode any property of the network (i. e. , node label, shape, color, or size, as well as edge label, thickness, and so on) with a metadata category (i. e. , cohort, cosine score, compound source). An online tutorial can be accessed at https://ccms-ucsd. github. io/ GNPSDocumentation/networking/#online-exploration-of-molecular-networks. The steps outlined in option B provide the user with a working knowledge of how to configure a network in Cytoscape. Readers following the tutorial example can not only reproduce the same properties described in the steps below to generate a publishable network, but also use this network to specifically focus on the cluster containing bile acids in order to discover novel compounds.
• Search Peptide. This is a function added to GNPS to support proteomic and peptidomic dataset analysis. If a peptide sequence is found to be associated with the molecular family and was found through automated peptide mining in MassIVE, then the amino acid sequence entered here will be searched. (B) Molecular network visualization in Cytoscape c CRITICAL There are a few options for exporting molecular networks for visualization in Cytoscape. Once molecular networks generated from GNPS are imported into Cytoscape, a number of simple commands can be used to make the network generated more informative, visually appealing, and accessible (Supplementary Fig. 6 ). Documentation on how to use Cytoscape (versions after the 3. 7 release) and a Cytoscape community forum are available to assist with troubleshooting and to provide information about the latest plugins (also called Cytoscape Apps): https://cytoscape.
org/documentation_users. html and https://cytoscape. org/ community. html. An online version of this tutorial is accessible at: https://ccms-ucsd. github. io/GNPSDocumentation/cytoscape/. (i) To begin using Cytoscape, download the latest version of the software from: https://cytoscape. org/ according to the instructions given at the website (Supplementary Fig. 6a ). (ii) Import a molecular network into Cytoscape for visualization. Once Cytoscape has been downloaded, molecular networks can be imported and visualized using two different strategies. The first option will show a network with no preset layout, whereas the second will show a network with default layout settings. Do one of the following: • To import data for a network with no layout present, click on 'Download GraphML for Cytoscape' in the GNPS Job Status window (Supplementary Fig.
However, GNPS also curates crowd-sourced knowledge from users in the community. All remaining reference spectra provided by the user community receive a 'bronze' rating to denote that the annotation is contributed by users including partial or putative annotations. The annotation within GNPS can be made directly from the data and thus relies on the expertise of the experimentalist, and purification of the molecules is not required. This gives access to a curated reference database that is crowd-sourced and does not rely on commercially available standards. For example, most NPs from microbes, food, and plants are not commercially available, but partial annotations and thus crowd-sourced knowledge capture provides a resource of information that is inaccessible any other way. The only other resource that currently accepts putative and partial annotations is MassBank EU (https://massbank.
An empirical cutoff for cosine scoring of 0. 7 with 6 MS 2 ions matching is the default setting in GNPS. On average, this gives rise to 91% accurate annotations and ~1% incorrect annotations, with the remainder being attributed to possible isomers (4%) or having not enough information by the user to judge (4%)  100. However, use of a target decoy-based method to estimate confidence measures of annotations and FDRs in large-scale metabolomics experiments revealed that the annotation quality is dataset dependent, as well as dependent on analysis settings such as number of ions that are required to match. The general trend was that when few MS 2 ions are required to match, a much higher cosine is required and fewer matches will be obtained at the same FDR compared to when more MS 2 ions are required to match the reference spectra.
When more ions are matched, the cosine score can be lowered. There is a dataset-dependent optimum for the maximum number of spectral library matches at a specific FDR that is typically ~4-6 minimum matched peaks  100. Although the confidence in the spectral matches increases when more MS 2 fragment peaks are required, there are fewer spectra that have a larger number of ions, resulting in a diminished number of annotations, especially for low-molecularweight compounds. Propagating annotations through manual interpretation of the networks • Timing days-weeks c CRITICAL A molecular network can be very useful in manually propagating annotations-using the information from one annotated node to inform the annotation of nearby, non-annotated nodesthrough manual interpretation of networks in parallel with raw MS 2 spectra.
ucsd. edu/ ProteoSAFe/static/gnps-theoretical. jsp. Capturing information by adding reference spectra from your data • Timing 10 min-days c CRITICAL Once an MS 2 spectrum has been fully annotated, it can be added as a reference spectrum to GNPS. Because the GNPS library database is crowd-sourced, users are encouraged to submit spectral annotations because knowledge they have is captured through these annotations of reference spectra and is reusable by others. This enables the creation of reference spectra from MS 2 spectra in the dataset without needing to purify the molecule. The assumption is made that the people who collected the data are experts with regard to their samples and thus are in the best position to curate. In addition, if the same user or lab then uploads another related dataset, and it contains the same molecule, it will be automatically annotated.
44 To upload a single reference spectrum, click on 'View All Clusters With IDs' in the Job Status page, then select the cluster desired for annotation from the 'ClusterIdx' column. 45 Select the 'AnnotatetoGNPS' button. This button brings up the workflow for annotation, where input files, sample parameters, desired annotation, advanced annotations, and library selections can be added and the job can be submitted. 46 To add a known spectrum to the library from a file uploaded to MassIVE, select 'Contribute' under the 'Add Your Spectrum' heading on the main page, even if molecular networking has not been performed on this file. 47 To upload >50 reference spectra to GNPS, perform a separate batch upload as detailed in the online help documentation at https://ccms-ucsd. github. io/GNPSDocumentation/batchupload/.
All annotations can be refined at a later step, and the provenance of each contribution is retained within the GNPS-MassIVE environment. For example one person may annotate that they think a compound is a lipid, the next person may update and specify that it is a phosphatidylcholine, and a third person may refine this to be 1-oleoyl-2-palmitoyl-phosphatidylcholine, and all of this is logged into the CCMS spectral library for each MS 2 spectrum. To annotate an existing spectrum, select "View All Spectra with Ids" then select "AnnotatetoGNPS" in the resulting table. Alternatively, a reference spectrum can be added by selecting "Add Your Spectrum" in the main GNPS page. Data sharing and reproducibility of molecular networking • Timing 1-a few h c CRITICAL Data sharing is essential for the reproducibility of molecular networking analysis; therefore, we encourage all users to make datasets public, share molecular networking job links, and so on.
PROTOCOL NATURE PROTOCOLS | www. nature. com/nprot Datasets uploaded to MassIVE ideally include all raw and peak-picked mass spectrometry data and associated sample information (metadata). GNPS records all data inputs, transformations, mathematical operations on, and analyses of the data, providing a historical record of the data and its origins. This data provenance promotes reproducibility and ultimately quality of the data and its annotations. 49 Deposit reference MS 2 spectra for all newly discovered NPs into the GNPS reference library (described above) and share the unique CCMSLIB identifier of the MS 2 spectra in the NP characterization data 3 upon publication. These are then included in the spectra used for dereplication, the identification of known substances. Cloning a job • Timing 10 min 50 Once a job's URL address is shared, any GNPS user can clone the job.
Accessing an existing dataset on GNPS • Timing 5 min 51 If a dataset is public, users are able to download all files for reanalysis, including raw data and the sample information table (metadata). To access a MassIVE dataset of interest, select 'MassIVE Datasets' in the GNPS workspace portal (Box 1) and enter the MassIVE accession number or defining keywords into the search bar. c CRITICAL STEP Private datasets can be viewed only by the user who uploaded the data and anyone who has a link to the Job Status page. The user can create a password-protected link. When downloading data from a private dataset, you will be prompted to enter a password for that MassIVE dataset ID. 52 Click on the MassIVE accession number highlighted in green to link to the 'MassIVE dataset information page', and select the 'FTP Download' link to download files.
Alternatively, this link can be pasted into the quick connect box of an FTP client. c CRITICAL STEP If you are accessing private datasets using an FTP client, you will need to enter the MassIVE ID as the username, followed by a password. If the submitter did not specify a password, then it should be accessible using the password 'a'. Subscribing to a dataset and living data • Timing 5 min into the future 53 Public datasets remain alive long after publication; for example, they will be searched periodically against the ever-growing annotated GNPS spectral libraries, potentially yielding new putative annotations within those datasets. If you subscribe to a dataset, you will receive email notifications of new identifications that are made within that dataset as well as about other datasets that exhibit chemical similarities to the subscribed dataset.
This allows for users to be connected via their research interest to similar datasets. Updates are sent out about once a month and only when there is new information associated with the dataset. To subscribe to a dataset, navigate to the 'MassIVE dataset information' page as described in Steps 11-19 and click 'Subscribe'. This feature changes the way we interact with data. Previously, data were periodically reanalyzed by the submission of new jobs, but in GNPS, data are automatically reanalyzed and updates are sent to the subscribers. Therefore, data may give rise to useful results a few weeks or even a few years later after they are uploaded or they may enable the dissemination of all the knowledge of this dataset to all lab members or collaborators. ? TROUBLESHOOTING.