Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: ARPIP : ancestral sequence reconstruction with insertions and deletions under the poisson indel process
Authors: Jowkar, Gholamhossein
Pecerska, Julija
Maiolo, Massimo
Gil, Manuel
Anisimova, Maria
et. al: No
DOI: 10.1093/sysbio/syac050
Published in: Systematic Biology
Issue Date: 22-Jul-2022
Publisher / Ed. Institution: Oxford University Press
ISSN: 1063-5157
1076-836X
Language: English
Subjects: Poisson indel process; SARS-CoV; Ancestral sequence; Dynamic programming; Evolutionary stochastic process; Indel; Joint ancestral sequence reconstruction; Maximum likelihood; Phylogeny
Subject (DDC): 004: Computer science
572: Biochemistry
Abstract: Modern phylogenetic methods allow inference of ancestral molecular sequences given an alignment and phylogeny relating present day sequences. This provides insight into the evolutionary history of molecules, helping to understand gene function and to study biological processes such as adaptation and convergent evolution across a variety of applications. Here we propose a dynamic programming algorithm for fast joint likelihood-based reconstruction of ancestral sequences under the Poisson Indel Process (PIP). Unlike previous approaches, our method, named ARPIP, enables the reconstruction with insertions and deletions based on an explicit indel model. Consequently, inferred indel events have an explicit biological interpretation. Likelihood computation is achieved in linear time with respect to the number of sequences. Our method consists of two steps, namely finding the most probable indel points and reconstructing ancestral sequences. First, we find the most likely indel points and prune the phylogeny to reflect the insertion and deletion events per site. Second, we infer the ancestral states on the pruned subtree in a manner similar to FastML. We applied ARPIP on simulated datasets and on real data from the Betacoronavirus genus. ARPIP reconstructs both the indel events and substitutions with a high degree of accuracy. Our method fares well when compared to established state-of-the-art methods such as FastML and PAML. Moreover, the method can be extended to explore both optimal and suboptimal reconstructions, include rate heterogeneity through time and more. We believe it will expand the range of novel applications of ancestral sequence reconstruction.
Further description: Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)
URI: https://digitalcollection.zhaw.ch/handle/11475/26176
Related research data: https://doi.org/10.5061/dryad.wstqjq2nj
https://github.com/acg-team/bpp-ARPIP
Fulltext version: Published version
License (according to publishing contract): CC BY-NC 4.0: Attribution - Non commercial 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: Frequentist estimation of the evolutionary history of sequences with substitutions and indels
Appears in collections:Publikationen Life Sciences und Facility Management

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