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      RANGER-DTL 2.0: rigorous reconstruction of gene-family evolution by duplication, transfer and loss

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          Abstract

          Summary

          RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++ and Python.

          Availability and implementation

          Pre-compiled executables, source code (open-source under GNU GPL) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Rapid evolutionary innovation during an Archaean genetic expansion.

          Lawrence A David, Eric Alm (2011)
          The natural history of Precambrian life is still unknown because of the rarity of microbial fossils and biomarkers. However, the composition of modern-day genomes may bear imprints of ancient biogeochemical events. Here we use an explicit model of macroevolution including gene birth, transfer, duplication and loss events to map the evolutionary history of 3,983 gene families across the three domains of life onto a geological timeline. Surprisingly, we find that a brief period of genetic innovation during the Archaean eon, which coincides with a rapid diversification of bacterial lineages, gave rise to 27% of major modern gene families. A functional analysis of genes born during this Archaean expansion reveals that they are likely to be involved in electron-transport and respiratory pathways. Genes arising after this expansion show increasing use of molecular oxygen (P = 3.4 × 10(-8)) and redox-sensitive transition metals and compounds, which is consistent with an increasingly oxygenating biosphere.
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            Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees

            Maureen Stolzer, Jian-Han Lai, Minli Xu … (2012)
            Motivation: Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. Results: We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. Availability: Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. Contact: mstolzer@andrew.cmu.edu
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              Efficient algorithms for the reconciliation problem with gene duplication, horizontal transfer and loss

              Mukul S. Bansal, Eric Alm, Manolis Kellis (2012)
              Motivation: Gene family evolution is driven by evolutionary events such as speciation, gene duplication, horizontal gene transfer and gene loss, and inferring these events in the evolutionary history of a given gene family is a fundamental problem in comparative and evolutionary genomics with numerous important applications. Solving this problem requires the use of a reconciliation framework, where the input consists of a gene family phylogeny and the corresponding species phylogeny, and the goal is to reconcile the two by postulating speciation, gene duplication, horizontal gene transfer and gene loss events. This reconciliation problem is referred to as duplication-transfer-loss (DTL) reconciliation and has been extensively studied in the literature. Yet, even the fastest existing algorithms for DTL reconciliation are too slow for reconciling large gene families and for use in more sophisticated applications such as gene tree or species tree reconstruction. Results: We present two new algorithms for the DTL reconciliation problem that are dramatically faster than existing algorithms, both asymptotically and in practice. We also extend the standard DTL reconciliation model by considering distance-dependent transfer costs, which allow for more accurate reconciliation and give an efficient algorithm for DTL reconciliation under this extended model. We implemented our new algorithms and demonstrated up to 100 000-fold speed-up over existing methods, using both simulated and biological datasets. This dramatic improvement makes it possible to use DTL reconciliation for performing rigorous evolutionary analyses of large gene families and enables its use in advanced reconciliation-based gene and species tree reconstruction methods. Availability: Our programs can be freely downloaded from http://compbio.mit.edu/ranger-dtl/. Contact: mukul@csail.mit.edu; manoli@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Janet Kelso: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 September 2018
                Publication date (Electronic): 24 April 2018
                Publication date PMC-release: 24 April 2018
                Volume: 34
                Issue: 18
                Pages: 3214-3216
                Affiliations
                [1 ]Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
                [2 ]Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
                [3 ]Broad Institute, Cambridge, MA, USA
                Author notes
                To whom correspondence should be addressed. mukul.bansal@ 123456uconn.edu
                Article
                Publisher ID: bty314
                DOI: 10.1093/bioinformatics/bty314
                PMC ID: 6137995
                PubMed ID: 29688310
                SO-VID: 6fc60b00-d7cf-4860-8bc6-356f7a2ae834
                Copyright © © The Author(s) 2018. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Date received : 13 May 2017
                Date revision received : 27 March 2018
                Date accepted : 20 April 2018
                Page count
                Pages: 3
                Funding
                Funded by: U.S. National Science Foundation
                Award ID: 1553421
                Funded by: U.S. National Science Foundation
                Award ID: 1616514
                Award ID: 1615573
                Funded by: University of Connecticut Summer Undergraduate Research Fund award
                Categories
                Subject: Applications Notes
                Subject: Phylogenetics

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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