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Editorial Molecular Phylogenetics 2016 Vassily Lyubetsky, 1 William H. Piel, 2 and Peter F. Stadler 3 1 Russian Academy of Sciences, Moscow, Russia 2 Yale-NUS College and National University of Singapore, Singapore 3 Bioinformatics, Institute for Informatics, Leipzig University, Leipzig, Germany Correspondence should be addressed to Vassily Lyubetsky; [email protected] Received 5 December 2016; Accepted 12 December 2016 Copyright © 2016 Vassily Lyubetsky et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Knowledge of phylogeny is of fundamental importance for understanding evolution. It has become an indispensable tool in modern genomics as a framework for interpreting genomes and metagenomes, for understanding the evolution of genes, proteins, and noncoding RNAs, as well as gene regulation by secondary RNA and protein structures, or for reconstructing ancestral genomes [1]. e era of next- generation sequencing (NGS) brought about an influx of data but also posed new theoretical challenges, for example, in reducing systematic error, insuring gene orthology, and working with incomplete datasets [2]. e contents of the spe- cial issue exemplify the wide range of uses for phylogenetics: traditional medicines, climate change, functional genomics, and microbial resistance to heavy metals and drugs. Some topics of modern phylogenetics are to be men- tioned. Traditionally, studies of species evolution to a large extent relied on the comparative analysis of genomic regions coding for rRNAs and proteins apart from the analysis of morphological characters. Later, analyses made use of regulatory elements and the structure of the genome as a whole. More recently, phylogenetic analyses are incor- porating ultraconserved elements (UCEs) and highly con- served elements (HCEs). Models of evolution of the genome structure and HCE initially faced considerable algorithmic challenges, which gave rise to (oſten unnatural) constraints in these models even for conceptually simple tasks such as the calculation of distance between two structures or the identification of UCEs. ese constraints are now being addressed with fast and efficient solutions with no constraints on the underlying models [3, 4]. ese approaches have led to an unexpected result: at least for some organelles and taxa, the genome and HCE structures, despite themselves containing relatively little information, still adequately resolve the evo- lution of species. e HCEs identification is also important in searching for promoters and regulatory elements that characterize the functional evolution of the genome. Another fundamental question is the resolution of ancient taxa with obscure and recalcitrant relationships. A classic example is the question of monophyly of the Mesozoa, specifically with respect to the parasitic phyla Orthonectida and Dicyemida. is question is aggravated by a well-known and yet still unsolved problem of long branch attraction. Of particular interest is the statistical view on such questions that leads to the problem of a formal description of the classes of trees for a given supermatrix which are generated by popular programs such as PhyloBayes and RAxML. Also of interest is the development of statistical tests for monophyly within this framework which retain accuracy despite increasingly large, genome-scale, datasets. Using regulatory elements for phylogeny is a complex problem. A key question is how to estimate statistical support for phylogenetic signal derived from regulatory elements that are highly dynamic and not easily aligned. Even sim- ple computation of distances between genes, leader genes, or hairpins in RNA can be nontrivial [5]. An alternative approach is to extract phylogenetic signal from syntenic patterns of regulatory elements [6], but this comes with its own computational challenges. Apart from classic molecular systematic applications to infer taxon phylogenies, the trend is to approach molecular Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 9029306, 2 pages http://dx.doi.org/10.1155/2016/9029306

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EditorialMolecular Phylogenetics 2016

Vassily Lyubetsky,1 William H. Piel,2 and Peter F. Stadler3

1Russian Academy of Sciences, Moscow, Russia2Yale-NUS College and National University of Singapore, Singapore3Bioinformatics, Institute for Informatics, Leipzig University, Leipzig, Germany

Correspondence should be addressed to Vassily Lyubetsky; [email protected]

Received 5 December 2016; Accepted 12 December 2016

Copyright © 2016 Vassily Lyubetsky et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Knowledge of phylogeny is of fundamental importance forunderstanding evolution. It has become an indispensabletool in modern genomics as a framework for interpretinggenomes and metagenomes, for understanding the evolutionof genes, proteins, and noncoding RNAs, as well as generegulation by secondary RNA and protein structures, orfor reconstructing ancestral genomes [1]. The era of next-generation sequencing (NGS) brought about an influx ofdata but also posed new theoretical challenges, for example,in reducing systematic error, insuring gene orthology, andworkingwith incomplete datasets [2].The contents of the spe-cial issue exemplify the wide range of uses for phylogenetics:traditional medicines, climate change, functional genomics,and microbial resistance to heavy metals and drugs.

Some topics of modern phylogenetics are to be men-tioned. Traditionally, studies of species evolution to a largeextent relied on the comparative analysis of genomic regionscoding for rRNAs and proteins apart from the analysisof morphological characters. Later, analyses made use ofregulatory elements and the structure of the genome asa whole. More recently, phylogenetic analyses are incor-porating ultraconserved elements (UCEs) and highly con-served elements (HCEs). Models of evolution of the genomestructure and HCE initially faced considerable algorithmicchallenges, which gave rise to (often unnatural) constraintsin these models even for conceptually simple tasks suchas the calculation of distance between two structures orthe identification of UCEs. These constraints are now beingaddressed with fast and efficient solutions with no constraintson the underlyingmodels [3, 4].These approaches have led to

an unexpected result: at least for some organelles and taxa, thegenome and HCE structures, despite themselves containingrelatively little information, still adequately resolve the evo-lution of species. The HCEs identification is also importantin searching for promoters and regulatory elements thatcharacterize the functional evolution of the genome.

Another fundamental question is the resolution ofancient taxa with obscure and recalcitrant relationships. Aclassic example is the question of monophyly of theMesozoa,specifically with respect to the parasitic phyla Orthonectidaand Dicyemida. This question is aggravated by a well-knownand yet still unsolved problem of long branch attraction. Ofparticular interest is the statistical view on such questions thatleads to the problem of a formal description of the classes oftrees for a given supermatrix which are generated by popularprograms such as PhyloBayes and RAxML. Also of interest isthe development of statistical tests for monophyly within thisframework which retain accuracy despite increasingly large,genome-scale, datasets.

Using regulatory elements for phylogeny is a complexproblem. A key question is how to estimate statistical supportfor phylogenetic signal derived from regulatory elementsthat are highly dynamic and not easily aligned. Even sim-ple computation of distances between genes, leader genes,or hairpins in RNA can be nontrivial [5]. An alternativeapproach is to extract phylogenetic signal from syntenicpatterns of regulatory elements [6], but this comes with itsown computational challenges.

Apart from classic molecular systematic applications toinfer taxon phylogenies, the trend is to approach molecular

Hindawi Publishing CorporationBioMed Research InternationalVolume 2016, Article ID 9029306, 2 pageshttp://dx.doi.org/10.1155/2016/9029306

2 BioMed Research International

and biodiversity assessment at different levels in variouscommunities, for example, at the intraspecific level andwith environmental samples, including systematic studiesof bacterial and viral pathogenic agents. Molecular markerssuch as mobile elements are being developed and exploitedin studies of population polymorphisms, and RNA secondarystructures are used to detect signatures of selection.

A historical profile of molecular phylogenetics with someextrapolations into the future, as well as a brief outline of hotspots in this field, can be found in this special issue [7, 8]. Wetruly hope that these contributions will be of use to scientistsin various areas in possibly helping them to find answers andpose new questions in their own research.

Acknowledgments

We gratefully thank all scientific reviewers who dedicatedtheir time to evaluate submissions in this special issue.

Vassily LyubetskyWilliam H. PielPeter F. Stadler

References

[1] E. V. Koonin, The Logic of Chance: The Nature and Origin ofBiological Evolution, FT Press, 2011.

[2] J. W. Wagele and T. Bartolomaeus, Eds., Deep MetazoanPhylogeny: The Backbone of the Tree of Life: New Insights fromAnalyses of Molecules, Morphology, andTheory of Data Analysis,Walter de Gruyter, 2014.

[3] L. I. Rubanov, A. V. Seliverstov, O. A. Zverkov, and V. A.Lyubetsky, “A method for identification of highly conservedelements and evolutionary analysis of superphylum Alveolata,”BMC Bioinformatics, vol. 17, article 385, 2016.

[4] V. Lyubetsky, R. Gershgorin, A. Seliverstov, and K. Gorbunov,“Algorithms for reconstruction of chromosomal structures,”BMC Bioinformatics, vol. 17, article 40, 2016.

[5] S. A. Korolev, O. A. Zverkov, A. V. Seliverstov, and V. A.Lyubetsky, “Ribosome reinitiation at leader peptides increasestranslation of bacterial proteins,” Biology Direct, vol. 11, articleno. 20, 2016.

[6] O. A. Zverkov, A. V. Seliverstov, andV.A. Lyubetsky, “A databaseof plastid protein families from red algae and apicomplexaand expression regulation of the moeB Gene,” BioMed ResearchInternational, vol. 2015, Article ID 510598, 5 pages, 2015.

[7] V. Lyubetsky, W. H. Piel, and P. F. Stadler, “Molecular phyloge-netics 2014,”BioMedResearch International, vol. 2015, Article ID919251, 2 pages, 2015.

[8] V. Lyubetsky, W. H. Piel, and D. Quandt, “Current advances inmolecular phylogenetics,” BioMed Research International, vol.2014, Article ID 596746, 2 pages, 2014.

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