BioMetadata: How to describe Biological data lecture for the Summer school of multi-omics (UFZ) - NFDI4Microbiota

Page 1 BioMetadata: How to describe Biological Data?* *All slides are based on the material provided by: Pauvert, C., & Magel, M. (2024, June 7). Workshop Biometadata-02: How to describe biological data? A primer to a FAIR approach for now and the future. Zenodo.2 https://doi.org/10.5281/zenodo.11527597. CC BY 4.0 QR to original workshop material QR to NFDI4Microbiota homepage QR to NFDI4Microbiota KnowledgeBase QR to N4M MetadataStandards git Page 2 Checklist before we begin ●You are sitting in the classroom ●You have a functioning laptop ●You have a ORCID account* ●You are connected to the internet ●Notifications are turned off (email, phone, pager,...) *if not please create one now: https://orcid.org/signin Page 3 Stand up if: ●You are an early-stage researcher (PhD student) Page 4 Stand up if: ●You are an early-stage researcher (PhD student) ●You are a post-doc Page 5 Stand up if: ●You are an early-stage researcher (PhD student) ●You are a post-doc ●You uploaded data to a public repository Page 6 Stand up if: ●You are an early-stage researcher (PhD student) ●You are a post-doc ●You uploaded data to a public repository ●Know at least one generalist and one field-specific repository Page 7 Stand up if: ●You are an early-stage researcher (PhD student) ●You are a post-doc ●You uploaded data to a public repository ●Know at least one generalist and one field-specific repository ●Know what ENVO, UBERON, FMA, PO (or any one of these are) Page 8 Stand up if: ●You are an early-stage researcher (PhD student) ●You are a post-doc ●You uploaded data to a public repository ●Know at least one generalist and one field-specific repository ●Know what ENVO, UBERON, FMA, PO (or any one of these are) ●Know what FAIR stands for Page 9 Data-centric approach in modern biology Page 16 Arguments for data curation ●So others can find it stored online/archive (F) ●So others can scrutinize it and point out possible misconduct (A) ●So others have biological context to interpret it (I) ●So others can reanalyze it and extract new knowledge in the future (R) Page 17 Arguments for data curation ●So others can find it stored online/archive (F) ●So others can scrutinize it and point out possible misconduct (A) ●So others have biological context to interpret it (I) ●So others can reanalyze it and extract new knowledge in the future (R) Can you think of any other arguments yourself? Page 18 Arguments for data curation ●So others can find it stored online/archive (F) ●So others can scrutinize it and point out possible misconduct (A) ●So others have biological context to interpret it (I) ●So others can reanalyze it and extract new knowledge in the future (R) Can you think of any other arguments yourself? Can you identify which is the most important for your research? Page 19 Arguments for data curation ●So others can find it stored online/archive (F) ●So others can scrutinize it and point out possible misconduct (A) ●So others have biological context to interpret it (I) ●So others can reanalyze it and extract new knowledge in the future (R) Can you think of any other arguments yourself? Can you identify which is the most important for your research? Today we will talk more about the biological context. Page 20 Biological context As biology is highly based on context, the key point is to curate data so it can travel across research investigations. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 21 Biological context As biology is highly based on context, the key point is to curate data so it can travel across research investigations. Biological systems are complex and highly variable, so generalizations often do no hold. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 22 Biological context For biological data to be reusable and meaningful across studies, it must undergo a careful process of decontextualization and recontextualization* — because biology is too nuanced to be understood without context.** **Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. *Decontextualization is removing specific details – make data general enough to be used in other contexts Recontextualization is adding new, relevant ones – make data relevant in a new research context Page 23 Decontextualization Subtropical broadleaf forest biome Page 24 Decontextualization Subtropical broadleaf forest biome Source:https://education.nationalgeographic.org/resource/rain-forest/ Source: Buttigieg et al. (2013)* *Buttigieg, Pier, Norman Morrison, Barry Smith, Christopher J Mungall, Suzanna E Lewis, and the ENVO Consortium. 2013.2 “The Environment Ontology: Contextualising Biological and Biomedical Entities.”2Journal of Biomedical Semantics24 (1): 43.2https://doi.org/10.1186/2041-1480-4-43. Page 25 Decontextualization Source: Buttigieg et al. (2013)* *Buttigieg, Pier, Norman Morrison, Barry Smith, Christopher J Mungall, Suzanna E Lewis, and the ENVO Consortium. 2013.2 “The Environment Ontology: Contextualising Biological and Biomedical Entities.”2Journal of Biomedical Semantics24 (1): 43.2https://doi.org/10.1186/2041-1480-4-43. The labeling of data through2bioontologies2ensures that they are at least2temporarily decoupled2from information about the local features of their production. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 32 Definition of metadata Metadata are data about the data, or a “love note to the future” * Metadata are “reliability labels” ** * Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. ** Scott, Jason. 2011. “The Metadata Mania.” ASCII by Jason Scott. http://ascii.textfiles.com/archives/3181. CC Attribution 2.0 generic source: https://en.wikipedia.org/wiki/File:Metadata_is_a_love_note_to_the_future_(8071729256).jpg Page 33 Types of metadata (general) ●Descriptive: what is the data? e.g., title, description Page 34 Types of metadata (general) ●Descriptive: what is the data? e.g., title, description ●Structural: how the data is organized? e.g., file, collection Page 35 Types of metadata (general) ●Descriptive: what is the data? e.g., title, description ●Structural: how the data is organized? e.g., file, collection ●Administrative: what is the provenance? e.g., versions, license Page 36 Types of metadata (general) ●Descriptive: what is the data? e.g., title, description ●Structural: how the data is organized? e.g., file, collection ●Administrative: what is the provenance? e.g., versions, license ●Quality: How good is the data? e.g., quality rank Page 37 Exercise 1: On your pad write down 1-4 metadata that you have already encountered in you everyday life. Page 38 In essence ●Ontologies help scientists and machines to use common terms to help generalize your data. (Decontextualization) Page 39 In essence ●Ontologies help scientists and machines to use common terms to help generalize your data. (Decontextualization) ●Metadata are important for re-usability of your data. (Recontextualization) Page 40 In essence ●Ontologies help scientists and machines to use common terms to help generalize your data. (Decontextualization) ●Metadata are important for re-usability of your data. (Recontextualization) Now, let’s continue to talk about Metadata and Metadata fields Page 41 Metadata Fields Metadata Field Type of (possible) constraints Study description free-text lat_lon (geolocation) Coordinates ([ISO8601] 2compliant) env_broad_scale Ontology term (controlled vocabulary) Now, let’s continue to talk about Metadata and Metadata fields Page 48 Metadata Standards Ideally a metadata standard indicates: ●Description of the field Page 49 Metadata Standards Ideally a metadata standard indicates: ●Description of the field ●Level of requirement (mandatory, recommended, optional) Page 50 Metadata Standards Ideally a metadata standard indicates: ●Description of the field ●Level of requirement (mandatory, recommended, optional) ●Cardinality (range of expected values) Page 51 Metadata Standards Ideally a metadata standard indicates: ●Description of the field ●Level of requirement (mandatory, recommended, optional) ●Cardinality (range of expected values) ●Persistent identifier of the field (PID) Page 52 Exercise 2: Find Metadata Standards Go to Fairsharing.org and find at least 2 metadata standards for two different omics types (genomic, proteomics, metabolomics) and note them in your pad. Page 53 Go to Fairsharing.org and find at least 2 metadata standards for two different omics types (genomic, proteomics, metabolomics) and note them in your pad. Do they check all the boxes from the previous slide? Exercise 2: Find Metadata Standards Page 54 Exercise 3: Find your checklist Go to https://www.ebi.ac.uk/ena/browser/checklists and find a checklist that matches your model system/biome or type of sample. Identify the mandatory fields and decide if they would be enough to recontextualise your dataset. After this consideration, add what you would think should be additional mandatory fields. Page 55 Minimal requirements What you just did, was find the minimal requirements (minimal metadata fields) for your sample/biome. Filling all of course takes time and resources but: Page 56 Minimal requirements What you just did, was find the minimal requirements (minimal metadata fields) for your sample/biome. Filling all of course takes time and resources but: Working FAIRly takes time and effort* * 0-to-20 is already good and better than 0! (Charlie Pauvert) Page 57 Exercise 4: Metadata Standards Origins Go to: https://genomicsstandardsconsortium.github.io/mixs/ Try to identify from which checklist or extension your ENA checklist came from? Which checklist would you chose from this resource? Page 64 In essence ontologies: ●increase findability of your dataset Page 65 In essence ontologies: ●increase findability of your dataset ●improve machine-readability of your datasets Page 66 In essence ontologies: ●increase findability of your dataset ●improve machine-readability of your datasets ●help others correctly categorize & re-use your datasets > recontextualization Page 67 In essence ontologies: ●increase findability of your dataset ●improve machine-readability of your datasets ●help others correctly categorize & re-use your datasets > recontextualization ●required by data repositories Page 68 Ontology terms have: ●have curated2textual definitions2and synonyms Adapted from: Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023.2 “Introduction to2Ontologies.”2https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 69 Ontology terms have: ●have curated2textual definitions2and synonyms ●are arranged in a2hierarchy2from general to specific Adapted from: Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023.2 “Introduction to2Ontologies.”2https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 70 Ontology terms have: ●have curated2textual definitions2and synonyms ●are arranged in a2hierarchy2from general to specific ●have defined2relationships2with others terms (e.g.,2is_a,2has_condition) Adapted from: Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023.2 “Introduction to2Ontologies.”2https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 71 Ontology terms have: ●have curated2textual definitions2and synonyms ●are arranged in a2hierarchy2from general to specific ●have defined2relationships2with others terms (e.g.,2is_a,2has_condition) ●have persistent2identifiers Adapted from: Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023.2 “Introduction to2Ontologies.”2https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 72 Ontology terms have: ●have curated2textual definitions2and synonyms ●are arranged in a2hierarchy2from general to specific ●have defined2relationships2with others terms (e.g.,2is_a,2has_condition) ●have persistent2identifiers ●can be2cross-referenced2with other resources (ontology or not) Adapted from: Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023.2 “Introduction to2Ontologies.”2https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies. Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press. Page 73 Selecting Onotologies ●has sound terms definitions that you agree with Adapted from: Rocca-Serra, Philippe, Susanna-Assunta Sansone, Danielle Welter, and Alasdair J. G. Gray. 2023. “Selecting Terminologies and Ontologies.” https://w3id.org/faircookbook/FCB020. Malone, James, Robert Stevens, Simon Jupp, Tom Hancocks, Helen Parkinson, and Cath Brooksbank. 2016.2 “Ten2Simple2Rules2for2Selecting2a2Bio-Ontology.”2PLOS Computational Biology212 (2): e1004743.2https://doi.org/10.1371/journal.pcbi.1004743. Page 80 The three musketeers of environmental metadata Metadata field Abbreviation Definition Recommended use of subclasses from broad-scale environmental context env_broad_scale global correlation; ecosystem biome [ENVO:00000428] local environmental context env_local_scale in local vicinity; causal influences deeper hierarchy than broad-scale (UBERON terms accepted) environmental medium env_medium immediate surroundings of your sample during sampling environmental material [ENVO:00010483] Page 81 Exercise 9: DARTAGNAN vs. the three musketeers ●Browse2ENVO or UBERON (see previous table) ●List2ontology terms fitting your data ●Fill out2the following template for yourself: –env_broad_scale –env_local_scale –env_medium Page 82 DataHarmonizer* ●Data Harmonizer (A tool helping metadata collection and validation) –Load metadata standards –Fill the template –Validate your metadata against the template * Gill, Ivan S., Emma J. Griffiths, Damion Dooley, Rhiannon Cameron, Sarah Savić Kallesøe, Nithu Sara John, Anoosha Sehar, et al. 2023.2 “The2DataHarmonizer: A Tool for Faster Data Harmonization, Validation, Aggregation and Analysis of Pathogen Genomics Contextual Information.”2 Microbial Genomics29 (1).2https://doi.org/10.1099/mgen.0.000908. Page 83 NMDC submission portal Leverage DataHarmonizer to lower barriers to collect, study and biosample data Not going to use it for data submission but data description! Page 84 Exercise X: Find your repository Go to: https://www.ebi.ac.uk/submission And find out which repository would “best suit” your data. Page 85 FAIR Data is a process “Even if you don’t know how to go all the way to zero-to-60 open science, zero-to-20 is also really good” * * Perkel, Jeffrey M. 2023. “How to Make Your Scientific Data Accessible, Discoverable and Useful.” Nature 618 (7967): 1098–99. https://doi.org/10.1038/d41586-023-01929-7.