e.g. BLAST, UniProt, MSH6, Albumin...

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Last updated: April 15, 2025

Content

Expasy contains information about bioinformatics resources which are relevant to the life sciences and created and/or maintained by a SIB member. These resources can be either:

  1. Software tools including command line software, graphical user interfaces, desktop and mobile applications, web-based services, application program interfaces (APIs) and infrastructure scripts that help to run services
  2. Databases or knowledgebases
  3. Data sets

News from the resources are also displayed in the Expasy home page.

Resources which are in the SIB portfolio are displayed in the section labelled "SIB Resources" of the Expasy home page. These resources also include the mention "supported by the SIB Swiss Institute of Bioinformatics" in their detailed view to indicate that they benefit from SIB support.

Resources in Expasy are either mature or legacy resources (Gabella et al, 2022). Examples:

  • The UniProt Knowledgebase resource created in 2003 is a mature resource.
  • The neXtProt resource which has been archived is a legacy state resource.

Resources in Expasy can either be a database, a software tool, or both. This is indicated by the two icons at the top right. Examples:

  • Rhea, an expert-curated knowledgebase of chemical and transport reactions of biological interest, is a database.
  • The viral genomics pipeline, V-pipe, is a software tool.
  • Bgee includes both a knowledgebase with expert curation (database) and a software tool for gene expression data.

Depending on their topic, resources in Expasy are assigned to one or more categories (designated by icons) or subcategories:

  1. Genes & Genomes - subcategories: Genomics, Metagenomics and Transcriptomics
  2. Proteins & Proteomes
  3. Evolution & Phylogeny - subcategories: Evolution biology and Population genetics
  4. Structural Biology - subcategories: Drug design, Medicinal chemistry and Structural analysis
  5. Systems Biology - subcategories: Glycomics, Lipidomics and Metabolomics
  6. Text mining & Machine learning

Resources are assigned one or more keywords from the EDAM ontology. EDAM (Ison et al, 2013 ; Black et al, 2022) is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. Targeting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format.

The EDAM Operation terms are displayed in the section entitled "What you can do with this resource", while the other EDAM terms are displayed in the section "Browse these keywords in Expasy". Note that EDAM topic terms which correspond to a category are excluded.

The following table summarizes the information displayed in Expasy, whether it is mandatory or not, whether it is displayed in the card view or the detailed view of the resource.

Information Mandatory (Y/N) Card view (Y/N) Detailed view (Y/N)
Resource name Y Y Y
Short description Y Y N
Long description Y N Y
Database or software icon(s) Y Y Y
Category icon(s) Y Y Y
URL Y N Y
Media URL N N Y
Documentation URL N N Y
Tutorial URL N N Y
SIB Group Y N Y
SIB support N N Y
ELIXIR or GCBR badge N N Y
License N N Y
Keywords N N Y

Filtering by topic

A filter panel is proposed on the left side of the home page. Select one or more categories or sub-categories to view all the resources with those categories or sub-categories. You will need to unselect your choice(s) if you want to filter by different categories. Examples:

  • Select the category Text mining & Machine learning to view all the resources matching that topic. All the resources found are not SIB resources.
  • Select the sub-category Glycomics to view all glycobiology resources. There is one SIB resource, Glyco@Expasy, as well as several other resources of SIB groups.
  • Select the sub-category Population genetics and the category Text mining & Machine learning to view all the resources on population genetics and/or text mining and machine learning. There are two SIB resources, V-pipe and mOTUs, as well as several other resources of SIB groups.
  • Select the sub-categories Glycomics and Lipidomics to view all the glycomics and lipidomics resources. There are two SIB resources, Glyco@Expasy and SwissLipids, as well as several other resources of SIB groups.

Searching in Expasy

A search bar is directly accessible from the top of each page allowing simultaneously two types of search: a regular search and a cross-resource search. The results are displayed jointly on one page.

The regular search returns all resources matching the search criteria. These are listed as cards in the search results. Each card displays the resource’s name, a short description, an icon for the category and a second icon indicating the type of resource (tool or database). Clicking on a card on the result page shows the detailed view of the corresponding resource. Examples:

  • Search for the resource name, i.e. blast, returns the UniProt BLAST tool.
  • Search for a word, i.e. command, returns several resources containing the word in the description.
  • Search for a SIB group name, i.e. "Bioinformatics Systems Biology group", returns all resources developed by that SIB group.
  • Search for Global Core Biodata Resource, i.e. "Global Core Biodata Resource", returns all the GCBR resources.

The cross-resource search search option allows the content of a various databases to be queried in parallel. The results are the number of hits obtained in each database. To view the results, you may need to select the + to view these results. Examples:

  • Search for a protein name, i.e. calmodulin returns hits in all four categories of databases.
  • Search for a disease name, i.e. Alzheimer returns hits in all four categories of databases.
  • Search for an anatomy term, i.e. brain returns hits in all four categories of databases.
  • Search for a UniProt AC, i.e. P01308 corresponding to human insulin, also returns hits in all four categories of databases.
  • Search for a PDB ID, i.e. 1AAP corresponding to a 3D structure of human APP protein, only returns hits in databases in the Evolution & Phylogeny and Proteins & Proteomes categories.
  • Search for a RefSeq AC, i.e. NM_007294.3 corresponding to a mRNA for human BRCA1 protein, only returns hits in databases in Proteins & Proteomes category.
  • Search for an Ensembl ID, i.e. ENSG00000142192.22 corresponding to human APP protein, only returns hits in databases in the Evolution & Phylogeny and Proteins & Proteomes categories.
  • Search for an ChEBI ID, i.e. CHEBI:27732 corresponding to caffeine, only returns hits in databases in the Evolution & Phylogeny and Proteins & Proteomes categories.
  • Search for an Enzyme Classification (EC), i.e. 2.7.11.13 corresponding to protein kinase C, returns hits in databases in all categories except for Genes & Genomes.

The keywords displayed in the detailed view of an entry can be used to search in Expasy. Examples:

A few EDAM term are particularly useful:

  • To find resources where you can deposit data, search for the term Deposition.
  • To find datasets, search for the term Data reference.
  • To find resources providing training material, search for the term Training material.

You might also be interested in

The detailed view for an entry displays a section "You might also be interested in" below the resource information. Suggestions of resources that share at least one topic which the resource being displayed are shown.

References

  • Gabella C, Duvaud S, Durinx C. Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics. Brief Bioinform. 2022 Jan 17;23(1):bbab478. DOI: 10.1093/bib/bbab478.
  • Ison J, Kalas M, Jonassen I, Bolser D, Uludag M, McWilliam H, Malone J, Lopez R, Pettifer S, Rice P. EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics. 2013 May 15;29(10):1325-32. DOI: 10.1093/bioinformatics/btt113.
  • Melissa Black, Lucie Lamothe, Hager Eldakroury, Mads Kierkegaard, Ankita Priya, Anne Machinda, Uttam Singh Khanduja, Drashti Patoliya, Rashika Rathi, Tawah Peggy Che Nico, Gloria Umutesi, Claudia Blankenburg, Anita Op, Precious Chieke, Omodolapo Babatunde, Steve Laurie, Steffen Neumann, Veit Schwämmle, Ivan Kuzmin, Chris Hunter, Jonathan Karr, Jon Ison, Alban Gaignard, Bryan Brancotte, Hervé Ménager, Matúš Kalaš (2022). EDAM: the bioscientific data analysis ontology (update 2021) [version 1; not peer reviewed]. F1000Research, 11(ISCB Comm J): 1. Poster. DOI: 10.7490/f1000research.1118900.1