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Pathway-specific model estimation for improved pathway annotation by network crosstalk
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Number of Authors: 22020 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 13585Article in journal (Refereed) Published
Abstract [en]

Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.

Place, publisher, year, edition, pages
2020. Vol. 10, no 1, article id 13585
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-185368DOI: 10.1038/s41598-020-70239-zISI: 000563534600007PubMedID: 32788619OAI: oai:DiVA.org:su-185368DiVA, id: diva2:1477086
Available from: 2020-10-16 Created: 2020-10-16 Last updated: 2022-09-15Bibliographically approved
In thesis
1. From networks to pathway analysis
Open this publication in new window or tab >>From networks to pathway analysis
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured.

FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links.

Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2021. p. 77
National Category
Bioinformatics and Computational Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-195048 (URN)978-91-7911-564-7 (ISBN)978-91-7911-565-4 (ISBN)
Public defence
2021-09-17, Air and Fire, SciLifeLab, Tomtebodavägen 23, Solna, 14:00 (English)
Opponent
Supervisors
Available from: 2021-08-25 Created: 2021-08-03 Last updated: 2025-02-07Bibliographically approved

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Castresana-Aguirre, MiguelSonnhammer, Erik L. L.

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