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Pathway-specific model estimation for improved pathway annotation by network crosstalk
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
Rekke forfattare: 22020 (engelsk)Inngår i: Scientific Reports, E-ISSN 2045-2322, Vol. 10, nr 1, artikkel-id 13585Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2020. Vol. 10, nr 1, artikkel-id 13585
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-185368DOI: 10.1038/s41598-020-70239-zISI: 000563534600007PubMedID: 32788619OAI: oai:DiVA.org:su-185368DiVA, id: diva2:1477086
Tilgjengelig fra: 2020-10-16 Laget: 2020-10-16 Sist oppdatert: 2022-09-15bibliografisk kontrollert
Inngår i avhandling
1. From networks to pathway analysis
Åpne denne publikasjonen i ny fane eller vindu >>From networks to pathway analysis
2021 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2021. s. 77
HSV kategori
Forskningsprogram
biokemi med inriktning mot bioinformatik
Identifikatorer
urn:nbn:se:su:diva-195048 (URN)978-91-7911-564-7 (ISBN)978-91-7911-565-4 (ISBN)
Disputas
2021-09-17, Air and Fire, SciLifeLab, Tomtebodavägen 23, Solna, 14:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2021-08-25 Laget: 2021-08-03 Sist oppdatert: 2025-02-07bibliografisk kontrollert

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Castresana-Aguirre, MiguelSonnhammer, Erik L. L.

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