ST-Steiner: a spatio-temporal gene discovery algorithm

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buir.contributor.authorNorman, Utku
buir.contributor.authorÇiçek, A. Ercüment
dc.citation.epage3440en_US
dc.citation.issueNumber18en_US
dc.citation.spage3433en_US
dc.citation.volumeNumber35en_US
dc.contributor.authorNorman, Utkuen_US
dc.contributor.authorÇiçek, A. Ercümenten_US
dc.date.accessioned2020-02-12T07:12:22Z
dc.date.available2020-02-12T07:12:22Z
dc.date.issued2019en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMotivation: Whole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited. Results: Here, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan coexpression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.en_US
dc.description.provenanceSubmitted by Evrim Ergin (eergin@bilkent.edu.tr) on 2020-02-12T07:12:22Z No. of bitstreams: 1 ST-Steiner_A_spatio-temporal_gene_discovery_algorithm.pdf: 1227912 bytes, checksum: ac80e0fbfe1e009dee1b11f206009b24 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-02-12T07:12:22Z (GMT). No. of bitstreams: 1 ST-Steiner_A_spatio-temporal_gene_discovery_algorithm.pdf: 1227912 bytes, checksum: ac80e0fbfe1e009dee1b11f206009b24 (MD5) Previous issue date: 2019-09-15en
dc.identifier.doi10.1093/bioinformatics/btz110en_US
dc.identifier.eissn1460-2059
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11693/53297
dc.language.isoEnglishen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttps://doi.org/10.1093/bioinformatics/btz110en_US
dc.source.titleBioinformaticsen_US
dc.titleST-Steiner: a spatio-temporal gene discovery algorithmen_US
dc.typeArticleen_US

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