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Abstract
Syntenies are genomic segments of consecutive genes identified by a certain conservation in gene content and order. The notion of conservation may vary from one definition to another, the more constrained requiring identical gene contents and gene orders, while more relaxed definitions just require a certain similarity in gene content, and not necessarily in the same order. Regardless of the way they are identified, the goal is to characterize homologous genomic regions, i.e., regions deriving from a common ancestral region, reflecting a certain gene co-evolution that can enlighten important functional properties. In addition of being able to identify them, it is also necessary to infer the evolutionary history that has led from the ancestral segment to the extant ones. In this field, most algorithmic studies address the problem of inferring rearrangement scenarios explaining the disruption in gene order between segments with the same gene content, some of them extending the evolutionary model to gene insertion and deletion. However, syntenies also evolve through other events modifying their content in genes, such as duplications, losses or horizontal gene transfers, i.e., the movement of genes from one species to another. Although the reconciliation approach between a gene tree and a species tree addresses the problem of inferring such events for single-gene families, little effort has been dedicated to the generalization to segmental events and to syntenies. This paper reviews some of the main algorithmic methods for inferring ancestral syntenies and focus on those integrating both gene orders and gene trees.
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Velandia-Huerto CA, Berkemer SJ, Hoffmann A, Retzlaff N, Romero Marroquín LC, Hernández-Rosales M, Stadler PF, Bermúdez-Santana CI. Orthologs, turn-over, and remolding of tRNAs in primates and fruit flies. BMC Genomics 2016; 17:617. [PMID: 27515907 PMCID: PMC4981973 DOI: 10.1186/s12864-016-2927-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 07/11/2016] [Indexed: 12/26/2022] Open
Abstract
Background Transfer RNAs (tRNAs) are ubiquitous in all living organism. They implement the genetic code so that most genomes contain distinct tRNAs for almost all 61 codons. They behave similar to mobile elements and proliferate in genomes spawning both local and non-local copies. Most tRNA families are therefore typically present as multicopy genes. The members of the individual tRNA families evolve under concerted or rapid birth-death evolution, so that paralogous copies maintain almost identical sequences over long evolutionary time-scales. To a good approximation these are functionally equivalent. Individual tRNA copies thus are evolutionary unstable and easily turn into pseudogenes and disappear. This leads to a rapid turnover of tRNAs and often large differences in the tRNA complements of closely related species. Since tRNA paralogs are not distinguished by sequence, common methods cannot not be used to establish orthology between tRNA genes. Results In this contribution we introduce a general framework to distinguish orthologs and paralogs in gene families that are subject to concerted evolution. It is based on the use of uniquely aligned adjacent sequence elements as anchors to establish syntenic conservation of sequence intervals. In practice, anchors and intervals can be extracted from genome-wide multiple sequence alignments. Syntenic clusters of concertedly evolving genes of different families can then be subdivided by list alignments, leading to usually small clusters of candidate co-orthologs. On the basis of recent advances in phylogenetic combinatorics, these candidate clusters can be further processed by cograph editing to recover their duplication histories. We developed a workflow that can be conceptualized as stepwise refinement of a graph of homologous genes. We apply this analysis strategy with different types of synteny anchors to investigate the evolution of tRNAs in primates and fruit flies. We identified a large number of tRNA remolding events concentrated at the tips of the phylogeny. With one notable exception all phylogenetically old tRNA remoldings do not change the isoacceptor class. Conclusions Gene families evolving under concerted evolution are not amenable to classical phylogenetic analyses since paralogs maintain identical, species-specific sequences, precluding the estimation of correct gene trees from sequence differences. This leaves conservation of syntenic arrangements with respect to “anchor elements” that are not subject to concerted evolution as the only viable source of phylogenetic information. We have demonstrated here that a purely synteny-based analysis of tRNA gene histories is indeed feasible. Although the choice of synteny anchors influences the resolution in particular when tight gene clusters are present, and the quality of sequence alignments, genome assemblies, and genome rearrangements limits the scope of the analysis, largely coherent results can be obtained for tRNAs. In particular, we conclude that a large fraction of the tRNAs are recent copies. This proliferation is compensated by rapid pseudogenization as exemplified by many very recent alloacceptor remoldings. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2927-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Cristian A Velandia-Huerto
- Biology Department, Universidad Nacional de Colombia, Carrera 45 # 26-85, Edif. Uriel Gutiérrez, Bogotá, D.C, Colombia
| | - Sarah J Berkemer
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany.,Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18D-04107, Leipzig, Germany
| | - Anne Hoffmann
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18D-04107, Leipzig, Germany
| | - Nancy Retzlaff
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany.,Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18D-04107, Leipzig, Germany
| | - Liliana C Romero Marroquín
- Biology Department, Universidad Nacional de Colombia, Carrera 45 # 26-85, Edif. Uriel Gutiérrez, Bogotá, D.C, Colombia
| | - Maribel Hernández-Rosales
- CONACYT - Instituto de Matemáticas, UNAM Juriquilla, Av. Juriquilla #3001, Santiago de Querétaro, MX-76230, QRO, México
| | - Peter F Stadler
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany. .,Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18D-04107, Leipzig, Germany. .,Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1, Leipzig, D-04103, Germany. .,Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, Vienna, A-1090, Austria. .,Center for non-coding RNA in Technology and Health, Grønegårdsvej 3, Frederiksberg C, DK-1870, Denmark. .,Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501, USA.
| | - Clara I Bermúdez-Santana
- Biology Department, Universidad Nacional de Colombia, Carrera 45 # 26-85, Edif. Uriel Gutiérrez, Bogotá, D.C, Colombia
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Brejová B, Kravec M, Landau GM, Vinař T. Fast computation of a string duplication history under no-breakpoint-reuse. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2014; 372:20130133. [PMID: 24751867 PMCID: PMC3996574 DOI: 10.1098/rsta.2013.0133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, we provide an O(n log(2) n log log n log* n) algorithm to compute a duplication history of a string under no-breakpoint-reuse condition. The motivation of this problem stems from computational biology, in particular, from analysis of complex gene clusters. The problem is also related to computing edit distance with block operations, but, in our scenario, the start of the history is not fixed, but chosen to minimize the distance measure.
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Affiliation(s)
- Broňa Brejová
- Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia
| | - Martin Kravec
- Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia
| | - Gad M. Landau
- Department of Computer Science, University of Haifa, Haifa 31905, Israel
- Department of Computer Science and Engineering, NYU-Poly, Six MetroTech Center, Brooklyn, NY 11201-3840, USA
| | - Tomáš Vinař
- Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia
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Abstract
The purpose of this chapter is to provide a comprehensive review of the field of genome rearrangement, i.e., comparative genomics, based on the representation of genomes as ordered sequences of signed genes. We specifically focus on the "hard part" of genome rearrangement, how to handle duplicated genes. The main questions are: how have present-day genomes evolved from a common ancestor? What are the most realistic evolutionary scenarios explaining the observed gene orders? What was the content and structure of ancestral genomes? We aim to provide a concise but complete overview of the field, starting with the practical problem of finding an appropriate representation of a genome as a sequence of ordered genes or blocks, namely the problems of orthology, paralogy, and synteny block identification. We then consider three levels of gene organization: the gene family level (evolution by duplication, loss, and speciation), the cluster level (evolution by tandem duplications), and the genome level (all types of rearrangement events, including whole genome duplication).
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Affiliation(s)
- Nadia El-Mabrouk
- Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, QC, Canada
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Tremblay Savard O, Bertrand D, El-Mabrouk N. Evolution of orthologous tandemly arrayed gene clusters. BMC Bioinformatics 2011; 12 Suppl 9:S2. [PMID: 22152029 PMCID: PMC3283317 DOI: 10.1186/1471-2105-12-s9-s2] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Tandemly Arrayed Gene (TAG) clusters are groups of paralogous genes that are found adjacent on a chromosome. TAGs represent an important repertoire of genes in eukaryotes. In addition to tandem duplication events, TAG clusters are affected during their evolution by other mechanisms, such as inversion and deletion events, that affect the order and orientation of genes. The DILTAG algorithm developed in 1 makes it possible to infer a set of optimal evolutionary histories explaining the evolution of a single TAG cluster, from an ancestral single gene, through tandem duplications (simple or multiple, direct or inverted), deletions and inversion events. RESULTS We present a general methodology, which is an extension of DILTAG, for the study of the evolutionary history of a set of orthologous TAG clusters in multiple species. In addition to the speciation events reflected by the phylogenetic tree of the considered species, the evolutionary events that are taken into account are simple or multiple tandem duplications, direct or inverted, simple or multiple deletions, and inversions. We analysed the performance of our algorithm on simulated data sets and we applied it to the protocadherin gene clusters of human, chimpanzee, mouse and rat. CONCLUSIONS Our results obtained on simulated data sets showed a good performance in inferring the total number and size distribution of duplication events. A limitation of the algorithm is however in dealing with multiple gene deletions, as the algorithm is highly exponential in this case, and becomes quickly intractable.
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Affiliation(s)
| | - Denis Bertrand
- Computational and Mathematical Biology, Genome Institute of Singapore, Singapore
| | - Nadia El-Mabrouk
- Department of Computer Science (DIRO), University of Montreal, Montreal, Quebec, Canada
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Statistical approaches for the analysis of DNA methylation microarray data. Hum Genet 2011; 129:585-95. [PMID: 21519831 DOI: 10.1007/s00439-011-0993-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2011] [Accepted: 04/13/2011] [Indexed: 12/25/2022]
Abstract
Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.
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