Reconstructing the History of Syntenies Through Super-Reconciliation

Delabre, Matteo, El-Mabrouk, Nadia, Huber, Katharina, Lafond, Manuel, Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435, Noutahi, Emmanuel and Sautie Castellanos, Miguel (2018) Reconstructing the History of Syntenies Through Super-Reconciliation. In: Comparative Genomics. Lecture Notes in Computer Science, 11183 . Springer, pp. 179-195. ISBN 978-3-030-00833-8

Preview

PDF (Accepted manuscript) - Accepted Version Download (2MB) | Preview

Abstract

Classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is clearly not suited for genes grouped in syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the Super-Reconciliation model, that extends the traditional Duplication-Loss model to the reconciliation of a set of trees, accounting for segmental duplications and losses. From a complexity point of view, we show that the associated decision problem is NP-hard. We then give an exact exponential-time algorithm for this problem, assess its time efficiency on simulated datasets, and give a proof of concept on the opioid receptor genes.

Item Type:	Book Section
Uncontrolled Keywords:	gene tree,reconciliation,duplication,loss,synteny
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	LivePure Connector
Date Deposited:	23 Aug 2018 10:32
Last Modified:	14 Jun 2023 14:23
URI:	https://ueaeprints.uea.ac.uk/id/eprint/68117
DOI:	10.1007/978-3-030-00834-5_10

Downloads

Actions (login required)

View Item