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Sequence Analysis and Evolutionary Studies of Reelin Proteins
KTH, School of Computer Science and Communication (CSC).ORCID iD: 0000-0002-6664-1607
2015 (English)In: Bioinformatics and Biology Insights, ISSN 1177-9322, E-ISSN 1177-9322, Vol. 9, 187-193 p.Article in journal (Refereed) PublishedText
Abstract [en]

The reelin gene is conserved across many vertebrate species, including humans. The protein product of this gene plays several important roles in early brain development and regulation of neural network plasticity of a matured brain structure. With an extended structure of 3461 amino acid sequences, consisting of eight reelin repeats, the human reelin sequence stands out as an exceptional model for evolutionary studies. In this study, sequence analysis of the human reelin and its homologues and reelin sequences from 104 other species is described in detail. Interesting sequence conservation patterns of individual repeats have been highlighted. Sequence phylogeny of the reelin sequences indicates a pattern similar to the evolution of the species, thereby serving as a highly conserved family for evolutionary purposes. Multiple sequence alignment of different reelin domain repeats, derived from homologues, suggests specific functions for individual repeats and high sequence conservation across reelin repeats from different organisms, albeit with few unusual domain architectures. A three-dimensional structural model of the full-length human reelin is now available that provides clues on residues at the dimer interface.

Place, publisher, year, edition, pages
Libertas Academica , 2015. Vol. 9, 187-193 p.
Keyword [en]
reelin protein, glycoprotein, domain repeats, phylogeny, domain architecture, neurogenesis, 3D modeling
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-181010DOI: 10.4137/BBI.S26530ISI: 000367288300004PubMedID: 26715843ScopusID: 2-s2.0-84961266629OAI: oai:DiVA.org:kth-181010DiVA: diva2:897780
Note

QC 20160126

Available from: 2016-01-26 Created: 2016-01-26 Last updated: 2016-08-31Bibliographically approved
In thesis
1. Probabilistic Modelling of Domain and Gene Evolution
Open this publication in new window or tab >>Probabilistic Modelling of Domain and Gene Evolution
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Phylogenetic inference relies heavily on statistical models that have been extended and refined over the past years into complex hierarchical models to capture the intricacies of evolutionary processes. The wealth of information in the form of fully sequenced genomes has led to the development of methods that are used to reconstruct the gene and species evolutionary histories in greater and more accurate detail. However, genes are composed of evolutionary conserved sequence segments called domains, and domains can also be affected by duplications, losses, and bifurcations implied by gene or species evolution. This thesis proposes an extension of evolutionary models, such as duplication-loss, rate, and substitution, that have previously been used to model gene evolution, to model the domain evolution.

In this thesis, I am proposing DomainDLRS: a comprehensive, hierarchical Bayesian method, based on the DLRS model by Åkerborg et al., 2009, that models domain evolution as occurring inside the gene and species tree. The method incorporates a birth-death process to model the domain duplications and losses along with a domain sequence evolution model with a relaxed molecular clock assumption. The method employs a variant of Markov Chain Monte Carlo technique called, Grouped Independence Metropolis-Hastings for the estimation of posterior distribution over domain and gene trees. By using this method, we performed analyses of Zinc-Finger and PRDM9 gene families, which provides an interesting insight of domain evolution.

Finally, a synteny-aware approach for gene homology inference, called GenFamClust, is proposed that uses similarity and gene neighbourhood conservation to improve the homology inference. We evaluated the accuracy of our method on synthetic and two biological datasets consisting of Eukaryotes and Fungal species. Our results show that the use of synteny with similarity is providing a significant improvement in homology inference.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2016. 69 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 19
Keyword
Phylogenetics, Phylogenomics, Evolution, Domain Evolution, Gene tree, Domain tree, Bayesian Inference, Markov Chain Monte Carlo, Homology Inference, Gene families, C2H2 Zinc-Finger, Reelin Protein
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-191352 (URN)978-91-7729-091-9 (ISBN)
External cooperation:
Public defence
2016-09-26, Conference room Air, SciLifeLab, Tomtebodavägen 23A, Solna, Stockholm, Stockholm, 09:00 (English)
Opponent
Supervisors
Funder
Swedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20160904

Available from: 2016-09-04 Created: 2016-08-29 Last updated: 2016-09-04Bibliographically approved

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