IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0099030.html
   My bibliography  Save this article

Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation

Author

Listed:
  • Yi-An Chen
  • Lokesh P Tripathi
  • Benoit H Dessailly
  • Johan Nyström-Persson
  • Shandar Ahmad
  • Kenji Mizuguchi

Abstract

Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

Suggested Citation

  • Yi-An Chen & Lokesh P Tripathi & Benoit H Dessailly & Johan Nyström-Persson & Shandar Ahmad & Kenji Mizuguchi, 2014. "Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
  • Handle: RePEc:plo:pone00:0099030
    DOI: 10.1371/journal.pone.0099030
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099030
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0099030&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0099030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Duncan P Brown & Nandini Krishnamurthy & Kimmen Sjölander, 2007. "Automated Protein Subfamily Identification and Classification," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-13, August.
    2. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles Bettembourg & Christian Diot & Olivier Dameron, 2015. "Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
    2. Karamollah Bagherifard & Mohsen Rahmani & Vahid Rafe & Mehrbakhsh Nilashi, 2018. "A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-26, March.
    3. Kanchan Jha & Sriparna Saha & Pratik Dutta, 2024. "Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach," Annals of Operations Research, Springer, vol. 339(3), pages 1793-1811, August.
    4. Dongmin Bang & Sangsoo Lim & Sangseon Lee & Sun Kim, 2023. "Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. Peng Wang & Shangwei Ning & Qianghu Wang & Ronghong Li & Jingrun Ye & Zuxianglan Zhao & Yan Li & Teng Huang & Xia Li, 2013. "mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    6. Tom Narock & Lina Zhou & Victoria Yoon, 2013. "Semantic similarity of ontology instances using polarity mining," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 416-427, February.
    7. Xiaomei Wu & Erli Pang & Kui Lin & Zhen-Ming Pei, 2013. "Improving the Measurement of Semantic Similarity between Gene Ontology Terms and Gene Products: Insights from an Edge- and IC-Based Hybrid Method," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    8. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.
    9. Fran Supek & Matko Bošnjak & Nives Škunca & Tomislav Šmuc, 2011. "REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-9, July.
    10. Charles Bettembourg & Christian Diot & Olivier Dameron, 2014. "Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    11. Adrian M Altenhoff & Romain A Studer & Marc Robinson-Rechavi & Christophe Dessimoz, 2012. "Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-10, May.
    12. Tiago Grego & Francisco M Couto, 2013. "Enhancement of Chemical Entity Identification in Text Using Semantic Similarity Validation," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-9, May.
    13. Nurul Aswa Omar & Shahreen Kasim & Mohd. Farhan Md. Fuzzee & Azizul Azhar Ramli & Hairulnizam Mahdin & Seah Choon Sen, 2017. "A Review on Feature based Approach in Semantic Similarity for Multiple Ontology," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 1(1), pages 7-9, February.
    14. Elisa Boari de Lima & Wagner Meira Júnior & Raquel Cardoso de Melo-Minardi, 2016. "Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-32, June.
    15. Laia Subirats & Luigi Ceccaroni & Felip Miralles, 2012. "Knowledge Representation for Prognosis of Health Status in Rehabilitation," Future Internet, MDPI, vol. 4(3), pages 1-14, August.
    16. Augusto Anguita-Ruiz & Alberto Segura-Delgado & Rafael Alcalá & Concepción M Aguilera & Jesús Alcalá-Fdez, 2020. "eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-34, April.
    17. Duc-Hau Le, 2020. "UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-18, July.
    18. Ilya Plyusnin & Liisa Holm & Petri Törönen, 2019. "Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-27, November.
    19. Shibiao Wan & Man-Wai Mak & Sun-Yuan Kung, 2014. "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
    20. Stephen F Altschul & John C Wootton & Elena Zaslavsky & Yi-Kuo Yu, 2010. "The Construction and Use of Log-Odds Substitution Scores for Multiple Sequence Alignment," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-17, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0099030. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.