Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23747
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process
Authors: Maiolo, Massimo
Gatti, Lorenzo
Frei, Diego
Leidi, Tiziano
Gil, Manuel
Anisimova, Maria
et. al: No
DOI: 10.1186/s12859-021-04442-8
10.21256/zhaw-23747
Published in: BMC Bioinformatics
Volume(Issue): 22
Issue: 1
Page(s): 518
Issue Date: 24-Oct-2021
Publisher / Ed. Institution: BioMed Central
ISSN: 1471-2105
Language: English
Subjects: Alignment software; Dynamic programming; Evolutionary alignment; Indel evolution; Multiple sequence alignmnet; Poisson Indel Process; Algorithm; Phylogeny; Sequence alignment; Software; Evolution, molecular; INDEL mutation
Subject (DDC): 005: Computer programming, programs and data
Abstract: Background Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. Results We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model—the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. Conclusions The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.
URI: https://digitalcollection.zhaw.ch/handle/11475/23747
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: Fast joint estimation of alignment and phylogeny from genomics sequences in a frequentist framework
Appears in collections:Publikationen Life Sciences und Facility Management

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Maiolo, M., Gatti, L., Frei, D., Leidi, T., Gil, M., & Anisimova, M. (2021). ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process. BMC Bioinformatics, 22(1), 518. https://doi.org/10.1186/s12859-021-04442-8
Maiolo, M. et al. (2021) ‘ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process’, BMC Bioinformatics, 22(1), p. 518. Available at: https://doi.org/10.1186/s12859-021-04442-8.
M. Maiolo, L. Gatti, D. Frei, T. Leidi, M. Gil, and M. Anisimova, “ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process,” BMC Bioinformatics, vol. 22, no. 1, p. 518, Oct. 2021, doi: 10.1186/s12859-021-04442-8.
MAIOLO, Massimo, Lorenzo GATTI, Diego FREI, Tiziano LEIDI, Manuel GIL und Maria ANISIMOVA, 2021. ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process. BMC Bioinformatics. 24 Oktober 2021. Bd. 22, Nr. 1, S. 518. DOI 10.1186/s12859-021-04442-8
Maiolo, Massimo, Lorenzo Gatti, Diego Frei, Tiziano Leidi, Manuel Gil, and Maria Anisimova. 2021. “ProPIP : A Tool for Progressive Multiple Sequence Alignment with Poisson Indel Process.” BMC Bioinformatics 22 (1): 518. https://doi.org/10.1186/s12859-021-04442-8.
Maiolo, Massimo, et al. “ProPIP : A Tool for Progressive Multiple Sequence Alignment with Poisson Indel Process.” BMC Bioinformatics, vol. 22, no. 1, Oct. 2021, p. 518, https://doi.org/10.1186/s12859-021-04442-8.


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