Text-mining with the SimText toolset

  • How can I automatically collect PubMed data for a set of biomedical entities such as genes?

  • How can I analyze similarities among biomedical entities based on PubMed data on large-scale?

  • Learn how to use the SimText toolset

  • Upload a table with biomedical entities in Galaxy

  • Retrieve PubMed data for each of the biomedical entities

  • Extract biomedical terms from the PubMed data for each biomedical entity

  • Analyze the similarity among the biomedical entities based on the extracted data in an interactive app

Time estimation: 1 hour
Supporting Materials:
Last modification: Oct 18, 2022
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License The GTN Framework is licensed under MIT


Literature exploration in PubMed on a large number of biomedical entities (e.g., genes, diseases, or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we use SimText, a toolset for literature research that allows you to collect text from PubMed for any given set of biomedical entities, extract associated terms, and analyze similarities among them and their key characteristics in an interactive tool.

This tutorial is based on a proof-of-concept example given in Gramm et al. 2020. We are going to analyze similarities among 95 genes based on their associated biomedical terms in the literature, and compare their pre-existing disorder categories to their grouping based on the literature.

The workflow combines 3 main steps, starting with the retrieval of PubMed data for each of the genes. We then use the PubMed data from each gene to extract related scientific terms that are all combined in one large binary matrix. Finally, we explore the generated data in an interactive tool that performs different unsupervised machine-learning algorithms to analyze the similarities/ the grouping among the genes based on their extracted terms from the literature.

Tutorial overview.
Figure 1: Schematic presentation of the workflow.

In this tutorial, we will cover:

  1. Introduction
  2. Input data
  3. Retrieval of PubMed data
  4. Extraction of biomedical terms from PubMed abstracts
  5. Exploration of data in interactive tool
  6. Conclusion

Input data

The input data is a simple table with the genes we want to analyze as well as their pre-existing grouping (the grouping is required later on to compare it to our text-based gene grouping). In order for the tools to recognize the column with the biomedical entities of interest, our 95 genes, the column name should start with “ID_”, and for the grouping variable with “GROUPING_”.

Hands-on: Data upload
  1. Create a new history for this tutorial

    Click the new-history icon at the top of the history panel.

    If the new-history is missing:

    1. Click on the galaxy-gear icon (History options) on the top of the history panel
    2. Select the option Create New from the menu
  2. Import the input file from Zenodo

    • Copy the link location
    • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

    • Select Paste/Fetch Data
    • Paste the link into the text field

    • Press Start

    • Close the window

Retrieval of PubMed data

In the first step we collect PubMed data for each of the genes. The genes are used as search queries to download a defined number of PMIDs, here up to 500, from PubMed. The PMIDs are saved in additional columns of our input data.

To speed up the the download of PubMed data users can obtain an API key from the settings page of their NCBI account (to create an account, visit http://www.ncbi.nlm.nih.gov/account/) and add it to the Galaxy user-preferences (User → Preferences → Manage Information).

Hands-on: Step 1: PubMed query tool
  1. Run PubMed query Tool: toolshed.g2.bx.psu.edu/repos/iuc/pubmed_by_queries/pubmed_by_queries/0.0.2 with the following parameters:
    • param-file “Input file with query terms”: Input dataset
    • “Number of PMIDs (or abstracts) to save per ID”: 500

    The tool is also able to save the abstracts as text instead of their PMIDs. This feature is used for another type of analysis (see Gramm et al. 2020), or can be used if the user wants to use the tool independent from a workflow to retrieve many abstract texts at once. For the next step of our example in this tutorial, only PMIDs are required.

Extraction of biomedical terms from PubMed abstracts

Next, we extract the 100 most frequent ‘Disease’ and ‘Gene’ terms (PubTator annotations) from the PubMed data. All genes with their 100 associated terms are then combined in one large binary matrix. Each row represents a gene and each column one of the extracted terms. This matrix is later used to find similar genes, i.e. genes that have many common terms associated with them.

Hands-on: Extraction of PubTator annotations
  1. Run PMIDs to PubTator Tool: toolshed.g2.bx.psu.edu/repos/iuc/pmids_to_pubtator_matrix/pmids_to_pubtator_matrix/0.0.2 with the following parameters:
    • param-file “Input file with PMID IDs”: output of PubMed query tool
    • “categories”: Genes Diseases
    • “Number of most frequent terms/IDs to extract.”: 100
    Comment: PubTator

    PubTator annotates terms of the following categories: Gene, Disease, Mutation, Species and Chemical. In this example we chose to only extract gene and disease terms but you can also select other categories if you are interested in those.

Exploration of data in interactive tool

After generating the large binary matrix, we can explore the similarities/ the grouping among the genes in the interactive SimText tool. The following features are generated:

  1. Word clouds for each gene
  2. Dimension reduction and hierarchical clustering of the binary matrix
  3. Calculation of the adjusted rand index (similarity between text-based grouping and the pre-existing disorder categories)
  4. Table with terms and their frequency among the genes
Hands-on: Explore data interactively
  1. Run interactive_tool_simtext_app Tool: interactive_tool_simtext_app with the following parameters:
    • param-file “Input file”: initial input file with genes and pre-existing grouping
    • param-file “Matrix file”: output of PMIDs to PubTator tool
  2. Open interactive tool

    1. Go to User > Active InteractiveTools
    2. Wait for the SimText app to be running (Job Info)
    3. Click on SimText app
Figure 2: Left: For all genes a wordcloud with their associated terms cen be generated. Right: In the dimension reduction plot the similarities among the genes can be explored. The colouring corresponds to the pre-existing disorder categories.
Figure 3: Hierarchical clustering of the matrix. Different clustering methods can be selected and the adjusted rand index be calculated.


Key points
  • The SimText toolset allows large-scale literature analysis of a set of biomedical entities such as genes or diseases

  • The similarities among the biomedical entities can be explored interactively

  • The litertaure based grouping can be compared to an existing grouping to discover similarities/ relationships hidden in the literature

Frequently Asked Questions

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Statistics and machine learning topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum


  1. Gramm, M., E. Perez-Palma, S. Schumacher-Bass, J. Dalton, C. Leu et al., 2020 SimText: A text mining framework for interactive analysis and visualization of similarities among biomedical entities. BioRxiv. 10.1101/2020.07.06.190629


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Citing this Tutorial

  1. Marie Gramm, Dennis Lal group, Daniel Blankenberg, Text-mining with the SimText toolset (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/statistics/tutorials/text-mining_simtext/tutorial.html Online; accessed TODAY
  2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

author = "Marie Gramm and Dennis Lal group and Daniel Blankenberg",
title = "Text-mining with the SimText toolset (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/statistics/tutorials/text-mining_simtext/tutorial.html}",
note = "[Online; accessed TODAY]"
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}


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