1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
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3. Μεταδιδακτορικοί ερευνητές

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Cronos: a machine learning pipeline for description and predictive modeling of microbial communities over time
Συγγραφέας:	[EL] Λιτος, Αριστείδης[EN] Litos, Aristeidis [EL] Ιντζέ, Ευαγγελία[EN] Intze, Evangelia [EL] Παυλίδης, Παύλος[EN] Pavlidis, Pavlos [EL] Λαγκουβάρδος, Ηλίας[EN] Lagkouvardos, Ilias
Επικεφαλής ερευνητικής ομάδας:	[EL] Λαγκουβάρδος, Ηλίας[EN] Lagkouvardos, Ilias
Ημερομηνία:	09/08/2022
Περίληψη:	Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.
Γλώσσα:	Αγγλικά
Σελίδες:	11
DOI:	10.3389/fbinf.2022.866902
EISSN:	2673-7647
Θεματική κατηγορία:	[EL] Βιοπληροφορική[EN] Bioinformatics
Λέξεις-κλειδιά:	microbial profiles; microbiome; machine learning; De novo clustering; microbial communities; infant gut maturation; multinomial logistic regression; time-series
Κάτοχος πνευματικών δικαιωμάτων:	© 2022 Litos, Intze, Pavlidis and Lagkouvardos
Όροι και προϋποθέσεις δικαιωμάτων:	This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.frontiersin.org/articles/10.3389/fbinf.2022.866902/full
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.frontiersin.org/journals/bioinformatics
Τίτλος πηγής δημοσίευσης:	Frontiers in Bioinformatics
Τεύχος:	August 2022
Τόμος:	2
Σελίδες τεκμηρίου (στην πηγή):	Article no 866902
Σημειώσεις:	This article was submitted to Genomic Analysis, a section of the journal Frontiers in Bioinformatics This research was co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Program “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (IKY).
Εμφανίζεται στις συλλογές:	Μεταδιδακτορικοί ερευνητές