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MinePath: mining for phenotype differential sub-paths in molecular pathways

Koumakis Eleftherios, Kanterakis Alexandros, Kartsaki Evgenia, Chatzimina Maria Evangelia, Zervakis Michail, Tsiknakis Manolis N., Vassou Despoina, Kafetzopoulos Dimitris, Marias Kostas, Moustakis Vasilis, Potamias, Georgios, 1953-

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/6A920C4E-7E9D-4248-BFDC-2BA8656FFEAF-
Αναγνωριστικόhttp://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005187-
Αναγνωριστικόhttps://doi.org/10.1371/journal.pcbi.1005187-
Γλώσσαen-
Μέγεθος41 pagesen
ΤίτλοςMinePath: mining for phenotype differential sub-paths in molecular pathwaysen
ΔημιουργόςKoumakis Eleftheriosen
ΔημιουργόςΚουμακης Ελευθεριοςel
ΔημιουργόςKanterakis Alexandrosen
ΔημιουργόςKartsaki Evgeniaen
ΔημιουργόςChatzimina Maria Evangeliaen
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςTsiknakis Manolis N.en
ΔημιουργόςVassou Despoinaen
ΔημιουργόςKafetzopoulos Dimitrisen
ΔημιουργόςMarias Kostasen
ΔημιουργόςMoustakis Vasilisen
ΔημιουργόςΜουστακης Βασιληςel
ΔημιουργόςPotamias, Georgios, 1953-en
ΕκδότηςPublic Library of Scienceen
ΠερίληψηPathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-06-28-
Ημερομηνία Δημοσίευσης2016-
Θεματική ΚατηγορίαPathway analysisen
Θεματική ΚατηγορίαGSAen
Θεματική ΚατηγορίαGene Set Analysisen
Θεματική ΚατηγορίαMinePathen
Βιβλιογραφική Αναφορά L. Koumakis, A. Kanterakis, E. Kartsaki, M. Chatzimina, M. Zervakis, M. Tsiknakis, D. Vassou, D. Kafetzopoulos, K. Marias, V. Moustakis and G. Potamias, "MinePath: mining for phenotype differential sub-paths in molecular pathways," PLoS Comput. Biol., vol. 12, no. 11, Nov. 2016. doi: 10.1371/journal.pcbi.1005187en

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