Who is Who in Phylogenetic Networks

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1 Andrew R. Francis, Daniel H. Huson and Mike Steel. Normalising phylogenetic networks. 2020. Keywords: explicit network, from network, normal network, phylogenetic network, phylogeny, polynomial, Program PhyloSketch, reconstruction, tree-child network. Note: https://arxiv.org/abs/2008.07797.         2 Michael Fuchs, Guan-Ru Yu and Louxin Zhang. On the Asymptotic Growth of the Number of Tree-Child Networks. 2020. Keywords: counting, explicit network, phylogenetic network, phylogeny, tree-child network. Note: https://arxiv.org/abs/2003.08049.         3 Marefatollah Mansouri. Counting General Phylogenetic networks. 2020. Keywords: counting, explicit network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/2005.14547.         4 Marefatollah Mansouri. Combinatorial properties of phylogenetic networks. PhD thesis, Vienna University of Technology (Austria), Institute of Discrete Mathematics and Geometry, 2020. Keywords: counting, explicit network, galled tree, level k phylogenetic network, normal network, phylogenetic network, phylogeny. Note: https://dmg.tuwien.ac.at/bgitten/Theses/mansouri.pdf.         5 Jonathan Klawitter and Peter Stumpf. Drawing Tree-Based Phylogenetic Networks with Minimum Number of Crossings. 2020. Keywords: explicit network, FPT, minimum number, NP complete, phylogenetic network, phylogeny, polynomial, visualization. Note: https://arxiv.org/abs/2008.08960.         6 Elizabeth Gross, Leo van Iersel, Remie Janssen, Mark Jones, Colby Long and Yukihiro Murakami. Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes. 2020. Keywords: characterization, distinguishability, explicit network, galled tree, phylogenetic network, population genetics, semidirected network, statistical model, uniqueness. Note: https://arxiv.org/abs/2007.08782.         7 Stefan Forcey and Drew Scalzo. Phylogenetic networks as circuits with resistance distance. 2020. Keywords: circular split system, from distances, level k phylogenetic network, NeighborNet, nested network, phylogenetic network, phylogeny, split network. Note: https://arxiv.org/abs/2007.13574.         8 Stefan Forcey and Drew Scalzo. Galois connections for phylogenetic networks and their polytopes. 2020. Keywords: characterization, from distances, galled tree, planar, reconstruction, split network. Note: https://arxiv.org/abs/2004.11944.         9 Katharina Huber, Simone Linz and Vincent Moulton. Weakly displaying trees in temporal tree-child network. 2020. Keywords: cherry-picking, from rooted trees, phylogenetic network, phylogeny, reconstruction, rigidly displaying, time consistent network, tree-child network, weakly displaying. Note: https://arxiv.org/abs/2004.02634.         10 Remie Janssen, Mark Jones and Yukihiro Murakami. Combining Networks Using Cherry Picking Sequences. In AlCoB20, Vol. 12099:77-92 of LNCS, Springer, 2020. Keywords: cherry-picking, explicit network, FPT, from network, hybridization, orchard network, phylogenetic network, phylogeny, tree-child network.         11 Remie Janssen and Yukihiro Murakami. Linear Time Algorithm for Tree-Child Network Containment. In AlCoB20, Vol. 12099:93-107 of LNCS, Springer, 2020. Keywords: explicit network, from network, isomorphism, phylogenetic network, phylogeny, polynomial, reconstruction, tree-child network, tree-child sequence. Note: https://doi.org/10.1007/978-3-030-42266-0_8.         12 Hannah Brown, Lei Zuo and Dan Gusfield. Comparing Integer Linear Programming to SAT-Solving for Hard Problems in Computational and Systems Biology. In AlCoB2020, Vol. 12099:63-76 of LNCS, Springer, 2020. Keywords: from binary characters, History bound, integer linear programming, minimum number, phylogeny, SAT. Note: https://doi.org/10.1007/978-3-030-42266-0_6.         13 Louxin Zhang. Recent Progresses in the Combinatorial and Algorithmic Study of Rooted Phylogenetic Networks. In WALCOM20, Vol. 12049:22-27 of LNCS, Springer, 2020. Keywords: cluster containment, galled network, galled tree, nearly-stable network, phylogenetic network, phylogeny, polynomial, reticulation-visible network, survey, time consistent network, tree containment, tree-based network, tree-child network.         14 Katharina Huber and Guillaume Scholz. Phylogenetic networks that are their own fold-ups. In AAM, Vol. 113(101959):1-24, 2020. Keywords: characterization, explicit network, FU-stable network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/1804.01841.         15 Leo van Iersel, Vincent Moulton and Yukihiro Murakami. Reconstructibility of unrooted level-k phylogenetic networks from distances. In AAM, Vol. 120(102075):1-30, 2020. Keywords: from distances, galled tree, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: https://doi.org/10.1016/j.aam.2020.102075.         16 Andreas Gunawan, Jeyaram Rathin and Louxin Zhang. Counting and enumerating galled networks. In DAM, Vol. 283:644-654, 2020. Keywords: counting, enumeration, explicit network, galled network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/1812.08569.         17 Jonathan Klawitter. The agreement distance of unrooted phylogenetic networks. In DMTCS, Vol. 21(2):22.1-23, 2020. Keywords: agreement forest, distance between networks, explicit network, from network, phylogenetic network, phylogeny. Note: https://dmtcs.episciences.org/6567/pdf.         18 Momoko Hayamizu, Katharina Huber, Vincent Moulton and Yukihiro Murakami. Recognizing and realizing cactus metrics. In IPL, Vol. 157(105916):1-5, 2020. Keywords: cactus graph, from distances, level k phylogenetic network, optimal realization, polynomial. Note: https://doi.org/10.1016/j.ipl.2020.105916.         19 Gabriel Cardona and Louxin Zhang. Counting and Enumerating Tree-Child Networks and Their Subclasses. In JCSS, Vol. 114:84-104, 2020. Keywords: counting, enumeration, explicit network, galled network, galled tree, normal network, phylogenetic network, phylogeny, tree-child network.         20 Mathilde Bouvel, Philippe Gambette and Marefatollah Mansouri. Counting Phylogenetic Networks of Level 1 and 2. In JOMB, 2020. Keywords: counting, explicit network, level k phylogenetic network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/1909.10460, to appear.         21 Cassandra Durell and Stefan Forcey. Level-1 phylogenetic networks and their balanced minimum evolution polytopes. In JOMB, Vol. 80:1235-1263, 2020. Keywords: from distances, galled tree, phylogenetic network, phylogeny, reconstruction, split network. Note: https://arxiv.org/pdf/1905.09160.pdf.         22 Simone Linz and Charles Semple. Caterpillars on three and four leaves are sufficient to reconstruct binary normal networks. In JOMB, 2020. Keywords: explicit network, from rooted trees, from triplets, normal network, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: https://arxiv.org/abs/2002.00483, to appear.         23 Hadi Poormohammadi, Mohsen Sardari Zarchi and Hossein Ghaneai. NCHB: A method for constructing rooted phylogenetic networks from rooted triplets based on height function and binarization. In JTB, Vol. 489(110144), 2020. Keywords: explicit network, from triplets, heuristic, phylogenetic network, phylogeny, Program Simplistic, Program TripNet, reconstruction. Note: https://doi.org/10.1016/j.jtbi.2019.110144.         24 Hadi Poormohammadi and Mohsen Sardari Zarchi. Netcombin: An algorithm for constructing optimal phylogenetic network from rooted triplets. In PLoS ONE, Vol. 15(9):e0227842.1-25, 2020. Keywords: explicit network, from triplets, heuristic, phylogenetic network, phylogeny, Program Simplistic, Program TripNet, reconstruction. Note: https://doi.org/10.1371/journal.pone.0227842.         25 Leo van Iersel, Remie Janssen, Mark Jones, Yukihiro Murakami and Norbert Zeh. Polynomial-Time Algorithms for Phylogenetic Inference Problems Involving Duplication and Reticulation. In TCBB, Vol. 17(1):14-26, 2020. Keywords: hybridization, minimum number, parental hybridization, phylogenetic network, phylogeny, reconstruction, weakly displaying. Note: http://pure.tudelft.nl/ws/portalfiles/portal/71270795/08798653.pdf.         26 Joan Carles Pons, Celine Scornavacca and Gabriel Cardona. Generation of Level-k LGT Networks. In TCBB, Vol. 17(1):158-164, 2020. Keywords: explicit network, level k phylogenetic network, LGT network, phylogenetic network, phylogeny.         27 Konstantinos Mampentzidis. Comparison and Construction of Phylogenetic Trees and Networks. PhD thesis, Aarhus University, Denmark, 2019. Keywords: distance between networks, explicit network, from network, phylogenetic network, phylogeny, polynomial, triplet distance. Note: https://pure.au.dk/ws/files/172250681/thesis_Konstantinos_Mampentzidis.pdf.         28 Katharina Huber, Leo van Iersel, Remie Janssen, Mark Jones, Vincent Moulton, Yukihiro Murakami and Charles Semple. Rooting for phylogenetic networks. 2019. Keywords: explicit network, from network, level k phylogenetic network, orchard network, orientation, phylogenetic network, phylogeny, reconstruction, stack-free network, tree-based network, tree-child network, valid network. Note: https://arxiv.org/abs/1906.07430.         29 R. A. Leo Elworth, Huw A. Ogilvie, Jiafan Zhu and Luay Nakhleh. Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization. In Tandy Warnow editor, Bioinformatics and Phylogenetics. Seminal Contributions of Bernard Moret, Vol. 29 of Computational Biology, Springer, 2019. Keywords: explicit network, phylogenetic network, phylogeny, Program Dendroscope, Program PhyloNet, Program PhyloNetworks SNaQ, Program PIRN, Program SplitsTree, reconstruction, survey. Note: https://bioinfocs.rice.edu/sites/g/files/bxs266/f/ElworthZhuOgilvieNakhleh.pdf         30 Louxin Zhang. Clusters, Trees, and Phylogenetic Network Classes. In Tandy Warnow editor, Bioinformatics and Phylogenetics. Seminal Contributions of Bernard Moret, Vol. 29:277-315 of Computational Biology, Springer, 2019. Keywords: cluster containment, explicit network, phylogenetic network, phylogeny, polynomial, tree containment.         31 Juan Wang and Maozu Guo. IGNet: Constructing Rooted Phylogenetic Networks Based on Incompatible Graphs. In ICNC-FSKD19, Vol. 1075:894-900 of Advances in Intelligent Systems and Computing, Springer, 2019. Keywords: explicit network, from rooted trees, phylogenetic network, phylogeny, Program BIMLR, Program IGNet, Program LNetwork, reconstruction, software.         32 Jesper Jansson, Konstantinos Mampentzidis, Ramesh Rajaby and Wing-Kin Sung. Computing the Rooted Triplet Distance Between Phylogenetic Networks. In IWOCA19, Vol. 11638:290-303 of LNCS, Springer, 2019. Keywords: distance between networks, from network, phylogenetic network, phylogeny, polynomial, triplet distance.         33 Simone Linz and Charles Semple. Attaching leaves and picking cherries to characterise the hybridisation number for a set of phylogenies. In AAM, Vol. 105:102-129, 2019. Keywords: agreement forest, cherry-picking, explicit network, from rooted trees, phylogenetic network, phylogeny, reconstruction, tree-child network. Note: https://doi.org/10.1016/j.aam.2019.01.004.         34 Michael Fuchs, Bernhard Gittenberger and Marefatollah Mansouri. Counting Phylogenetic Networks with Few Reticulation Vertices: Tree-Child and Normal Networks. In Australasian Journal of Combinatorics, Vol. 73(2):385-423, 2019. Keywords: counting, explicit network, normal network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/1803.11325, see also erratum at https://arxiv.org/abs/2006.15784.         35 Yukihiro Murakami, Leo van Iersel, Remie Janssen, Mark Jones and Vincent Moulton. Reconstructing Tree-Child Networks from Reticulate-Edge-Deleted Subnetworks. In BMB, Vol. 81:3823-3863, 2019. Keywords: from subnetworks, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction, tree-child network, uniqueness, valid network. Note: https://doi.org/10.1007/s11538-019-00641-w.         36 Louxin Zhang. Generating normal networks via leaf insertion and nearest neighbor interchange. In BMC Bioinformatics, Vol. 20(642):1-9, 2019. Keywords: enumeration, explicit network, generation, NNI moves, phylogenetic network, phylogeny. Note: https://link.springer.com/content/pdf/10.1186/s12859-019-3209-3.pdf.         37 Juan Wang and Maozu Guo. A review of metrics measuring dissimilarity for rooted phylogenetic networks. In Briefings in Bioinformatics, Vol. 20(6):1972-1980, 2019. Keywords: distance between networks, explicit network, from network, mu distance, phylogenetic network, phylogeny, survey, tree sibling network, tree-child network.         38 Janosch Döcker, Leo van Iersel, Steven Kelk and Simone Linz. Deciding the existence of a cherry-picking sequence is hard on two trees. In DAM, Vol. 260:131-143, 2019. Keywords: cherry-picking, explicit network, hybridization, minimum number, NP complete, phylogenetic network, phylogeny, reconstruction, temporal-hybridization number, time consistent network, tree-child network. Note: https://arxiv.org/abs/1712.02965.         39 Jonathan Klawitter. The agreement distance of rooted phylogenetic networks. In DMTCS, Vol. 21(3):19.1-24, 2019. Keywords: agreement forest, distance between networks, explicit network, from network, phylogenetic network, phylogeny, SPR distance. Note: https://arxiv.org/abs/1806.05800.         40 Jonathan Klawitter and Simone Linz. On the Subnet Prune and Regraft Distance. In ELJC, Vol. 26(2):P2.3.1-23, 2019. Keywords: agreement forest, explicit network, phylogenetic network, phylogeny, reticulation-visible network, SPR distance, tree-based network, tree-child network. Note: https://arxiv.org/abs/1805.07839.         41 Yan Hong and Juan Wang. Frin: An Efficient Method for Representing Genome Evolutionary History. In Frontiers in Genetics, Vol. 10(1261):1-8, 2019. Keywords: explicit network, from clusters, phylogenetic network, phylogeny, Program Frin, reconstruction. Note: https://doi.org/10.3389/fgene.2019.01261.         42 Andreas Gunawan, Hongwei Yan and Louxin Zhang. Compression of Phylogenetic Networks and Algorithm for the Tree Containment Problem. In JCB, Vol. 25(3), 2019. Keywords: explicit network, phylogenetic network, phylogeny, polynomial, quasi-reticulation-visible network, reticulation-visible network, tree containment, tree-child network. Note: https://arxiv.org/abs/1806.07625.         43 Katharina Huber, Vincent Moulton and Taoyang Wu. Hierarchies from lowest stable ancestors in nonbinary phylogenetic networks. In JOC, Vol. 36:200-231, 2019. Keywords: characterization, explicit network, phylogenetic network, phylogeny. Note: https://link.springer.com/content/pdf/10.1007/s00357-018-9279-5.pdf.         44 Steven Kelk, Fabio Pardi, Celine Scornavacca and Leo van Iersel. Finding the most parsimonious or likely tree in a network with respect to an alignment. In JOMB, Vol. 78:527-547, 2019. Keywords: APX hard, from network, from sequences, likelihood, NP complete, parsimony. Note: https://arxiv.org/abs/1707.03648.         45 Joan Carles Pons, Charles Semple and Mike Steel. Tree-based networks: characterisations, metrics, and support trees. In JOMB, Vol. 78(4):899-918, 2019. Keywords: characterization, explicit network, from network, phylogenetic network, phylogeny, time consistent network, tree-based network. Note: https://arxiv.org/abs/1710.07836.         46 Péter L. Erdös, Leo van Iersel and Mark Jones. Not all phylogenetic networks are leaf-reconstructible. In JOMB, Vol. 79(5):1623-1638, 2019. Keywords: from subnetworks, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: https://doi.org/10.1007/s00285-019-01405-9.         47 Gabriel Cardona, Joan Carles Pons and Celine Scornavacca. Generation of Binary Tree-Child phylogenetic networks. In PLoS Computational Biology, Vol. 15(10):e1007440.1-29, 2019. Keywords: enumeration, explicit network, generation, phylogenetic network, phylogeny, Program PhyloNetwork, Program TCGenerators, software, tree-child network. Note: https://doi.org/10.1371/journal.pcbi.1007440.         48 Christopher Blair and Cécile Ané. Phylogenetic trees and networks can serve as powerful and complementary approaches for analysis of genomic data. In SB, Vol. 69(3):593-601, 2019. Keywords: likelihood, phylogenetic network, phylogeny, Program PhyloNetwork, Program PhyloNetworks SNaQ, survey.         49 Janosch Döcker, Simone Linz and Charles Semple. Displaying trees across two phylogenetic networks. In TCS, Vol. 796:129-146, 2019. Keywords: display set, explicit network, normal network, phylogenetic network, phylogeny, time consistent network, tree-child network. Note: https://arxiv.org/abs/1901.06612.         50 Sajad Mirzaei. Efficient Algorithms for Trees and Networks in Evolutionary Genomics. PhD thesis, University of Connecticut, 2018. Keywords: branch length, explicit network, haplotype network, heuristic, Program PIRN, reconstruction. Note: https://opencommons.uconn.edu/dissertations/1853.         51 Guillaume Scholz. New algorithms and mathematical tools for phylogenetics beyond trees. PhD thesis, University of East Anglia, 2018. Keywords: circular split system, explicit network, explicit network, from splits, galled tree, phylogenetic network, phylogeny, polynomial, reconstruction, split network, uniqueness. Note: https://ueaeprints.uea.ac.uk/id/eprint/66952.         52 Andreas Gunawan. On the tree and cluster containment problems for phylogenetic networks. PhD thesis, National University of Singapore, 2018. Keywords: cluster containment, explicit network, galled network, genetically stable network, nearly-stable network, phylogenetic network, phylogeny, reticulation-visible network, tree containment. Note: https://scholarbank.nus.edu.sg/handle/10635/144270.         53 Kuang-Yu Chang, Wing-Kai Hon and Sharma V. Thankachan. Compact Encoding for Galled-Trees and its Applications. In 2018 Data Compression Conference, Pages 297-306, 2018. Keywords: compression, counting, explicit network, galled tree, phylogenetic network, polynomial.         54 Andreas Gunawan. Solving the Tree Containment Problem for Reticulation-visible Networks in Linear Time. In AlCoB18, Vol. 10849:24-36 of LNCS, Springer, 2018. Keywords: explicit network, from network, from rooted trees, phylogenetic network, phylogeny, polynomial, reticulation-visible network, tree containment. Note: https://arxiv.org/abs/1702.04088.         55 Leo van Iersel, Remie Janssen, Mark Jones, Yukihiro Murakami and Norbert Zeh. Polynomial-Time Algorithms for Phylogenetic Inference Problems. In AlCoB18, Vol. 10849:37-49 of LNCS, Springer, 2018. Keywords: hybridization, minimum number, parental hybridization, phylogenetic network, phylogeny, polynomial, reconstruction, weakly displaying. Note: https://research.tudelft.nl/files/53686721/10.1007_978_3_319_91938_6_4.pdf.         56 Mathias Weller. Linear-Time Tree Containment in Phylogenetic Networks. In RECOMB-CG18, Vol. 11183:309-323 of LNCS, Springer, 2018. Keywords: explicit network, from network, from rooted trees, nearly-stable network, phylogenetic network, phylogeny, polynomial, reconstruction, reticulation-visible network, tree containment. Note: https://arxiv.org/abs/1702.06364.         57 Hussein A. Hejase, Natalie VandePol, Gregory A. Bonito and Kevin J. Liu. Fast and accurate statistical inference of phylogenetic networks using large-scale genomic sequence data. In RECOMB-CG18, Vol. 11183:242-259 of LNCS, Springer, 2018. Keywords: explicit network, from rooted trees, heuristic, phylogenetic network, phylogeny, Program FastNet, reconstruction. Note: http://biorxiv.org/content/early/2017/05/01/132795.         58 Sebastien Roch and Kun-Chieh Wang. Circular Networks from Distorted Metrics. In RECOMB18, Vol. 10812:167-176 of LNCS, Springer, 2018. Keywords: abstract network, circular split system, from distances, NeighborNet, phylogenetic network, phylogeny, reconstruction, split network. Note: https://arxiv.org/abs/1707.05722.         59 Andrew R. Francis, Charles Semple and Mike Steel. New characterisations of tree-based networks and proximity measures. In Advances in Applied Mathematics, Vol. 93:93-107, 2018. Keywords: characterization, explicit network, phylogenetic network, phylogeny, time consistent network, tree-based network. Note: https://arxiv.org/abs/1611.04225.         60 Leo van Iersel, Steven Kelk, Giorgios Stamoulis, Leen Stougie and Olivier Boes. On unrooted and root-uncertain variants of several well-known phylogenetic network problems. In ALG, Vol. 80(11):2993-3022, 2018. Keywords: explicit network, FPT, from network, from unrooted trees, NP complete, phylogenetic network, phylogeny, reconstruction, tree containment. Note: https://hal.inria.fr/hal-01599716.         61 Magnus Bordewich, Charles Semple and Nihan Tokac. Constructing tree-child networks from distance matrices. In Algorithmica, Vol. 80(8):2240-2259, 2018. Keywords: compressed network, explicit network, from distances, phylogenetic network, phylogeny, polynomial, reconstruction, tree-child network, uniqueness. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/BSN17.pdf.         62 Sarah Bastkowski, Daniel Mapleson, Andreas Spillner, Taoyang Wu, Monika Balvociute and Vincent Moulton. SPECTRE: a Suite of PhylogEnetiC Tools for Reticulate Evolution. In BIO, Vol. 34(6):1057-1058, 2018. Keywords: abstract network, NeighborNet, phylogenetic network, phylogeny, Program FlatNJ, Program QNet, Program SplitsTree, reconstruction, software, split network. Note: https://doi.org/10.1101/169177.         63 Hongwei Yan, Andreas Gunawan and Louxin Zhang. S-Cluster++: a fast program for solving the cluster containment problem for phylogenetic networks. In BIO, Vol. 34(17):i680–i686, 2018. Keywords: cluster containment, explicit network, phylogenetic network, phylogeny, SAT. Note: http://dx.doi.org/10.1093/bioinformatics/bty594.         64 Andrew R. Francis, Katharina Huber and Vincent Moulton. Tree-based unrooted phylogenetic networks. In BMB, Vol. 80(2):404-416, 2018. Keywords: characterization, explicit network, NP complete, phylogenetic network, phylogeny, tree containment, tree-based network, unrooted tree-based network. Note: https://arxiv.org/abs/1704.02062.         65 Remie Janssen, Mark Jones, Péter L. Erdös, Leo van Iersel and Celine Scornavacca. Exploring the tiers of rooted phylogenetic network space using tail moves. In BMB, Vol. 80(8):2177-2208, 2018. Keywords: distance between networks, explicit network, from network, NNI moves, orientation, phylogenetic network, phylogeny, SPR distance. Note: https://arxiv.org/abs/1708.07656.         66 Katharina Huber, Vincent Moulton, Charles Semple and Taoyang Wu. Quarnet inference rules for level-1 networks. In BMB, Vol. 80:2137-2153, 2018. Keywords: explicit network, from quarnets, from subnetworks, galled tree, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction. Note: https://arxiv.org/abs/1711.06720.         67 Charles Semple and Jack Simpson. When is a phylogenetic network simply an amalgamation of two trees? In BMB, Vol. 80(9):2338-2348, 2018. Keywords: characterization, phylogenetic network, phylogeny, reticulation-visible network, stack-free network, tree-based network. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/SS18.pdf.         68 Philippe Gambette, Andreas Gunawan, Anthony Labarre, Stéphane Vialette and Louxin Zhang. Solving the Tree Containment Problem in Linear Time for Nearly Stable Phylogenetic Networks. In DAM, Vol. 246:62-79, 2018. Keywords: explicit network, from network, from rooted trees, nearly-stable network, phylogenetic network, phylogeny, polynomial, tree containment. Note: https://hal-upec-upem.archives-ouvertes.fr/hal-01575001/en/.         69 Magnus Bordewich and Charles Semple. A universal tree-based network with the minimum number of reticulations. In DAM, Vol. 250:357-362, 2018. Keywords: explicit network, phylogenetic network, phylogeny, tree-based network. Note: https://arxiv.org/abs/1707.08274.         70 Kuang-Yu Chang, Yun Cui, Siu-Ming Yiu and Wing-Kai Hon. Reconstructing One-Articulated Networks with Distance Matrices. In JCB, Vol. 25(3):253-269, 2018. Keywords: explicit network, from distances, k-reticulated, phylogenetic network, phylogeny, reconstruction.         71 Jonathan Klawitter. The SNPR neighbourhood of tree-child networks. In JGAA, Vol. 22(2):329-355, 2018. Keywords: distance between networks, phylogenetic network, phylogeny, SPR distance, tree-child network. Note: https://arxiv.org/abs/1707.09579         72 Andrew R. Francis, Katharina Huber, Vincent Moulton and Taoyang Wu. Bounds for phylogenetic network space metrics. In JOMB, Vol. 76(5):1229-1248, 2018. Keywords: bound, distance between networks, from network, NNI distance, NNI moves, phylogenetic network, phylogeny, SPR distance, TBR distance. Note: https://arxiv.org/abs/1702.05609.         73 Magnus Bordewich, Katharina Huber, Vincent Moulton and Charles Semple. Recovering normal networks from shortest inter-taxa distance information. In JOMB, Vol. 77(3):571-594, 2018. Keywords: explicit network, from distances, normal network, phylogenetic network, phylogeny, polynomial, reconstruction, uniqueness. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/BHMS18.pdf.         74 Andrew R. Francis and Vincent Moulton. Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution. In JTB, Vol. 446:160-167, 2018. Keywords: explicit network, from unrooted trees, phylogenetic network, phylogeny, tree-child network, uniqueness. Note: https://arxiv.org/abs/1712.04223.         75 Kristina Wicke and Mareike Fischer. Phylogenetic diversity and biodiversity indices on phylogenetic networks. In MB, Vol. 298:80-90, 2018. Keywords: diversity, explicit network, from network, normal network, phylogenetic network, phylogeny. Note: https://arxiv.org/abs/1706.05279.         76 Chi Zhang, Huw A. Ogilvie, Alexei J. Drummond and Tanja Stadler. Bayesian Inference of Species Networks from Multilocus Sequence Data. In MBE, Vol. 35(2):504-517, 2018. Keywords: bayesian, explicit network, from sequences, phylogenetic network, phylogeny, reconstruction, statistical model. Note: https://dx.doi.org/10.1093/molbev/msx307.         77 Michael Hendriksen. Tree-based unrooted nonbinary phylogenetic networks. In MBIO, Vol. 302:131-138, 2018. Keywords: characterization, phylogenetic network, phylogeny, tree-based network, unrooted tree-based network.         78 Jiafan Zhu, Dingqiao Wen, Yun Yu, Heidi M. Meudt and Luay Nakhleh. Bayesian inference of phylogenetic networks from bi-allelic genetic markers. In PLoS Computational Biology, Vol. 14(1):e1005932.1-32, 2018. Keywords: bayesian, explicit network, from multistate characters, phylogenetic network, phylogeny, Program PhyloNet. Note: https://doi.org/10.1371/journal.pcbi.1005932.         79 Paul Bastide, Claudia Solís-Lemus, Ricardo Kriebel, Kenneth William Sparks and Cécile Ané. Phylogenetic Comparative Methods on Phylogenetic Networks with Reticulations. In SB, Vol. 67(5):800-820, 2018. Keywords: ancestral trait reconstruction, from network, likelihood, Program PhyloNetworks SNaQ, software, statistical model, statistical test. Note: https://doi.org/10.1101/194050.         80 James H. Degnan. Modeling Hybridization Under the Network Multispecies Coalescent. In SB, Vol. 67(5):786-799, 2018. Keywords: distance between networks, phylogenetic network, phylogeny, reconstruction, survey. Note: https://academic.oup.com/sysbio/article/67/5/786/5017269.         81 Leo van Iersel, Mark Jones and Celine Scornavacca. Improved maximum parsimony models for phylogenetic networks. In SB, Vol. 67(3):518-542, 2018. Keywords: explicit network, FPT, from sequences, NP complete, parsimony, phylogenetic network, phylogeny, reconstruction, weakly displaying. Note: https://leovaniersel.files.wordpress.com/2017/12/improved_parsimony_networks.pdf.         82 Dingqiao Wen, Yun Yu, Jiafan Zhu and Luay Nakhleh. Inferring Phylogenetic Networks Using PhyloNet. In SB, Vol. 67(4):735-740, 2018. Keywords: bayesian, likelihood, parsimony, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software.         83 Dingqiao Wen and Luay Nakhleh. Coestimating Reticulate Phylogenies and Gene Trees from Multilocus Sequence Data. In SB, Vol. 67(3):439-457, 2018. Keywords: bayesian, explicit network, from sequences, incomplete lineage sorting, phylogenetic network, phylogeny, Program PhyloNet, reconstruction. Note: https://doi.org/10.1093/sysbio/syx085.         84 Elizabeth Gross and Colby Long. Distinguishing Phylogenetic Networks. In SIAM Journal on Applied Algebra and Geometry, Vol. 2(1):72-93, 2018. Keywords: distinguishability, phylogenetic network, phylogeny, semidirected network, statistical model, uniqueness. Note: https://arxiv.org/abs/1706.03060.         85 Leo van Iersel and Vincent Moulton. Leaf-reconstructibility of phylogenetic networks. In SIAM Journal on Discrete Mathematics, Vol. 32(3):2047-2066, 2018. Keywords: explicit network, from network, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: https://arxiv.org/abs/1701.08982.         86 Laura Jetten and Leo van Iersel. Nonbinary tree-based phylogenetic networks. In TCBB, Vol. 15(1):205-217, 2018. 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Size of a phylogenetic network. In DAM, Vol. 217(2):362-367, 2017. Keywords: compressed network, number of vertices, phylogenetic network, phylogeny, stack-free network. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/S16.pdf.         107 Olga K. Kamneva and Noah A. Rosenberg. Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting. In Evolutionary Bioinformatics, Vol. 13(1176934317691935):1-16, 2017. Keywords: evaluation, explicit network, incomplete lineage sorting, simulation. Note: https://dx.doi.org/10.1177%2F1176934317691935.         108 Andreas Gunawan, Bhaskar DasGupta and Louxin Zhang. A decomposition theorem and two algorithms for reticulation-visible networks. In Information and Computation, Vol. 252:161-175, 2017. Keywords: cluster containment, explicit network, from clusters, from network, from rooted trees, phylogenetic network, phylogeny, polynomial, reticulation-visible network, tree containment. 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PhD thesis, University of Tasmania, Australia, 2016. Keywords: phylogenetic network, phylogeny, statistical model. Note: http://arxiv.org/abs/1606.07160.         125 Hussein A. Hejase and Kevin J. Liu. A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. Vol. 17(422):1-12, 2016. Keywords: abstract network, evaluation, from sequences, phylogenetic network, phylogeny, Program PhyloNet, Program PhyloNetworks SNaQ, reconstruction, simulation, unicyclic network. Note: http://dx.doi.org/10.1186/s12859-016-1277-1.         126 Benjamin Albrecht. Computing Hybridization Networks Using Agreement Forests. PhD thesis, Ludwig-Maximilians-Universität München, 2016. Note: https://edoc.ub.uni-muenchen.de/19444/.         127 Nihan Tokac. Efficiency of Algorithms in Phylogenetics. PhD thesis, Durham University, U.K., 2016. 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Keywords: duplication, explicit network, from rooted trees, from species tree, lateral gene transfer, loss, phylogenetic network, phylogeny, reconstruction.         157 Magnus Bordewich and Charles Semple. Determining phylogenetic networks from inter-taxa distances. In JOMB, Vol. 73(2):283-303, 2016. Keywords: from distances, phylogenetic network, phylogeny, reconstruction, reticulation-visible network, time consistent network, tree-child network, uniqueness. Note: http://132.181.26.35/~c.semple/papers/BS15b.pdf.         158 Katharina Huber, Vincent Moulton and Taoyang Wu. Transforming phylogenetic networks: Moving beyond tree space. In JTB, Vol. 404:30-39, 2016. Keywords: distance between networks, level k phylogenetic network, NNI moves, phylogenetic network, phylogeny. Note: http://arxiv.org/abs/1601.01788.         159 Momoko Hayamizu. On the existence of infinitely many universal tree-based networks. In JTB, Vol. 396:204-206, 2016. Keywords: explicit network, phylogenetic network, phylogeny, tree-based network. Note: http://arxiv.org/abs/1512.02402.         160 James Oldman, Taoyang Wu, Leo van Iersel and Vincent Moulton. TriLoNet: Piecing together small networks to reconstruct reticulate evolutionary histories. In MBE, Vol. 33(8):2151-2162, 2016. Keywords: explicit network, from subnetworks, from trinets, galled tree, phylogenetic network, phylogeny, Program LEV1ATHAN, Program TriLoNet, reconstruction.         161 Claudia Solís-Lemus and Cécile Ané. Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting. In PLOS Genetics, Vol. 12(3):e1005896, 2016. Keywords: explicit network, from quartets, from unrooted trees, likelihood, phylogenetic network, phylogeny, Program PhyloNetworks SNaQ. Note: http://arxiv.org/abs/1509.06075v1.         162 Dingqiao Wen, Yun Yu and Luay Nakhleh. Bayesian Inference of Reticulate Phylogenies under the Multispecies Network Coalescent. In PLoS Genetics, Vol. 12(5):e1006006, 2016. Keywords: bayesian, coalescent, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software.         163 Juan Wang. A Survey of Methods for Constructing Rooted Phylogenetic Networks. In PLoS ONE, Vol. 11(11):e0165834, 2016. Keywords: evaluation, explicit network, from clusters, phylogenetic network, phylogeny, Program BIMLR, Program Dendroscope, Program LNetwork, reconstruction, survey. Note: http://dx.doi.org/10.1371/journal.pone.0165834.         164 Leo van Iersel, Steven Kelk, Nela Lekic, Chris Whidden and Norbert Zeh. Hybridization Number on Three Rooted Binary Trees is EPT. In SIDMA, Vol. 30(3):1607-1631, 2016. Keywords: agreement forest, explicit network, FPT, from rooted trees, hybridization, minimum number, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1402.2136.         165 Sajad Mirzaei and Yufeng Wu. Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees. In TCBB, Vol. 13(3):565-570, 2016. Keywords: bound, explicit network, from rooted trees, heuristic, phylogenetic network, phylogeny, Program PIRN, reconstruction, software. Note: http://www.engr.uconn.edu/~ywu/Papers/PIRNs-preprint.pdf.         166 Quan Nguyen. Likelihood-based Phylogenetic Network Inference by Approximate Structural Expectation Maximization. Master's thesis, University of Helsinki, 2015. Keywords: BIC, likelihood, phylogenetic network, phylogeny, Program PhyloDAG, reconstruction, software. Note: http://urn.fi/URN:NBN:fi-fe2015062910525.         167 Maxime Morgado. Propriétés structurelles et relations des classes de réseaux phylogénétiques. Master's thesis, ENS Cachan, 2015. Keywords: compressed network, distinct-cluster network, explicit network, galled network, galled tree, level k phylogenetic network, nested network, normal network, phylogenetic network, phylogeny, regular network, spread, tree containment, tree sibling network, tree-based network, tree-child network, unicyclic network.         168 Dwueng-Chwuan Jhwueng and Brian O'Meara. Trait Evolution on Phylogenetic Networks. 2015. Keywords: explicit network, from network, hybridization, phylogenetic network, phylogeny, Program BMhyd, statistical model. Note: http://dx.doi.org/10.1101/023986.         169 Andreas Gunawan and Louxin Zhang. Bounding the Size of a Network Defined By Visibility Property. 2015. Keywords: bound, explicit network, galled network, nearly-stable network, phylogenetic network, phylogeny, reticulation-visible network, stable-child network. Note: http://arxiv.org/abs/1510.00115.         170 Laura Jetten. Characterising tree-based phylogenetic networks. Bachelor thesis, 2015. Keywords: characterization, explicit network, phylogenetic network, phylogeny, tree-based network. Note: http://resolver.tudelft.nl/uuid:fda2636d-0ed5-4dd2-bacf-8abbbad8994e.         171 Benjamin Albrecht. Computing a Relevant Set of Nonbinary Maximum Acyclic Agreement Forests. 2015. Keywords: agreement forest, explicit network, exponential algorithm, from rooted trees, phylogenetic network, phylogeny, Program Hybroscale, reconstruction, software. Note: http://arxiv.org/abs/1512.05703.         172 Benjamin Albrecht. Fast computation of all maximum acyclic agreement forests for two rooted binary phylogenetic trees. 2015. Keywords: agreement forest, explicit network, from rooted trees, phylogenetic network, phylogeny, Program Hybroscale, reconstruction, software. Note: http://arxiv.org/abs/1512.05656.         173 Nela Lekic. Trees, agreement forests and treewidth: combinatorial algorithms for constructing phylogenetic networks. PhD thesis, Maastricht University, The Netherlands, 2015. Keywords: explicit network, from rooted trees, phylogenetic network, phylogeny, reconstruction.         174 Claudia Solís-Lemus. Statistical methods to infer population structure with coalescence and gene flow. PhD thesis, University of Wisconsin-Madison, 2015. Keywords: explicit network, from quartets, from unrooted trees, likelihood, phylogenetic network, phylogeny, Program PhyloNetworks SNaQ, semidirected network. Note: https://www.stat.wisc.edu/~claudia/thesis.pdf.         175 Quan Nguyen and Teemu Roos. Likelihood-based inference of phylogenetic networks from sequence data by PhyloDAG. In AlCoB15, Vol. 9199:126-140 of LNCS, springer, 2015. Keywords: BIC, explicit network, from sequences, likelihood, phylogenetic network, phylogeny, Program PhyloDAG, reconstruction, software. Note: http://www.cs.helsinki.fi/u/ttonteri/pub/alcob2015.pdf.         176 Vladimir Ulyantsev and Mikhail Melnik. Constructing Parsimonious Hybridization Networks from Multiple Phylogenetic Trees Using a SAT-solver. In AlCoB15, Vol. 9199:141-153 of LNCS, springer, 2015. Keywords: explicit network, from rooted trees, from trees, phylogenetic network, phylogeny, Program PIRN, reconstruction. Note: https://www.researchgate.net/profile/Vladimir_Ulyantsev/publication/282816862_Constructing_Parsimonious_Hybridization_Networks_from_Multiple_Phylogenetic_Trees_Using_a_SAT-Solver/links/561d372c08aecade1acb3699/Constructing-Parsimonious-Hybridization-Networks-from-Multiple-Phylogenetic-Trees-Using-a-SAT-Solver.pdf.         177 Dan Gusfield. Persistent Phylogeny: A Galled-Tree and Integer Linear Programming Approach. In BCB15, Pages 443-451, 2015. Keywords: explicit network, from binary characters, galled tree, integer linear programming, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1506.00678.         178 Misagh Kordi and Mukul S. Bansal. On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees. In ISBRA15, Vol. 9096:187-198 of LNCS, springer, 2015. Keywords: duplication, from rooted trees, from species tree, lateral gene transfer, loss, NP complete, phylogenetic network, phylogeny, reconstruction. Note: http://compbio.engr.uconn.edu/papers/Kordi_ISBRA2015.pdf.         179 Yun Yu and Luay Nakhleh. A Distance-Based Method for Inferring Phylogenetic Networks in the Presence of Incomplete Lineage Sorting. In ISBRA15, Vol. 9096:378-389 of LNCS, springer, 2015. Keywords: bootstrap, explicit network, from distances, heuristic, incomplete lineage sorting, phylogenetic network, phylogeny, reconstruction. Note: http://bioinfo.cs.rice.edu/sites/bioinfo.cs.rice.edu/files/YuNakhleh-ISBRA15.pdf.         180 Jittat Fakcharoenphol, Tanee Kumpijit and Attakorn Putwattana. A Faster Algorithm for the Tree Containment Problem for Binary Nearly Stable Phylogenetic Networks. In Proceedings of the The 12th International Joint Conference on Computer Science and Software Engineering (JCSSE'15), Pages 337-342, IEEE, 2015. Keywords: dynamic programming, explicit network, from network, from rooted trees, nearly-stable network, phylogenetic network, phylogeny, polynomial, tree containment.         181 Yun Yu and Luay Nakhleh. A maximum pseudo-likelihood approach for phylogenetic networks. In RECOMB-CG15, Vol. 16(Suppl 10)(S10):1-10 of BMC Genomics, BioMed Central, 2015. Keywords: explicit network, from rooted trees, hybridization, incomplete lineage sorting, likelihood, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, tripartition distance. Note: http://dx.doi.org/10.1186/1471-2164-16-S10-S10.         182 Thu-Hien To and Celine Scornavacca. Efficient algorithms for reconciling gene trees and species networks via duplication and loss events. In RECOMB-CG15, Vol. 16(Suppl 10)(S6):1-14 of BMC Genomics, BioMed Central, 2015. Keywords: explicit network, from network, from rooted trees, phylogenetic network, phylogeny, polynomial, reconstruction. Note: http://dx.doi.org/10.1186/1471-2164-16-S10-S6.         183 Philippe Gambette, Andreas Gunawan, Anthony Labarre, Stéphane Vialette and Louxin Zhang. Locating a Tree in A Phylogenetic Network in Quadratic Time. In RECOMB15, Vol. 9029:96-107 of LNCS, Springer, 2015. Keywords: evaluation, explicit network, from network, from rooted trees, genetically stable network, nearly-stable network, phylogenetic network, phylogeny, polynomial, tree containment. Note: https://hal.archives-ouvertes.fr/hal-01116231/en.         184 Johannes Fischer and Daniel Peters. A Practical Succinct Data Structure for Tree-Like Graphs. In WALCOM15, Vol. 8973:65-76 of LNCS, springer, 2015. Keywords: compression, from network, phylogenetic network, phylogeny.         185 Gabriel Cardona, Joan Carles Pons and Francesc Rosselló. A reconstruction problem for a class of phylogenetic networks with lateral gene transfers. In ALMOB, Vol. 10(28):1-15, 2015. Keywords: explicit network, from rooted trees, lateral gene transfer, phylogenetic network, phylogeny, Program LGTnetwork, reconstruction, software, tree-based network. Note: http://dx.doi.org/10.1186/s13015-015-0059-z.         186 Colin McDiarmid, Charles Semple and Dominic Welsh. Counting phylogenetic networks. In Annals of Combinatorics, Vol. 19(1):205-224, 2015. Keywords: counting, explicit network, normal network, phylogenetic network, phylogeny, tree-child network. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/MSW13.pdf.         187 Benjamin Albrecht. Computing all hybridization networks for multiple binary phylogenetic input trees. In BMCB, Vol. 16(236):1-15, 2015. Keywords: agreement forest, explicit network, exponential algorithm, FPT, from rooted trees, phylogenetic network, phylogeny, Program Hybroscale, Program PIRN, reconstruction. Note: http://dx.doi.org/10.1186/s12859-015-0660-7.         188 Ward C Wheeler. Phylogenetic network analysis as a parsimony optimization problem. In BMCB, Vol. 16(296):1-9, 2015. Keywords: explicit network, from sequences, parsimony, phylogenetic network, phylogeny, reconstruction. Note: http://dx.doi.org/10.1186/s12859-015-0675-0.         189 Sha Zhu, James H. Degnan, Sharyn J. Goldstein and Bjarki Eldon. Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees. In BMCB, Vol. 16(292):1-7, 2015. Keywords: explicit network, from network, phylogenetic network, phylogeny, Program Hybrid-Lambda, simulation, software. Note: http://dx.doi.org/10.1186/s12859-015-0721-y.         190 Marc Thuillard and Didier Fraix-Burnet. Phylogenetic Trees and Networks Reduce to Phylogenies on Binary States: Does It Furnish an Explanation to the Robustness of Phylogenetic Trees against Lateral Transfers? In Evolutionary Bioinformatics, Vol. 11:213-221, 2015. [Abstract] Keywords: circular split system, explicit network, from multistate characters, outerplanar, perfect, phylogenetic network, phylogeny, planar, polynomial, reconstruction, split. Note: http://dx.doi.org/10.4137%2FEBO.S28158.         191 Andrew R. Francis and Mike Steel. Tree-like Reticulation Networks - When Do Tree-like Distances Also Support Reticulate Evolution? In MBIO, Vol. 259:12-19, 2015. Keywords: from distances, phylogenetic network, phylogeny. Note: http://arxiv.org/abs/1405.2965.         192 Jessica W. Leigh and David Bryant. PopART: full-feature software for haplotype network construction. In Methods in Ecology and Evolution, Vol. 6(9):1110-1116, 2015. Keywords: abstract network, from sequences, haplotype network, MedianJoining, phylogenetic network, phylogeny, population genetics, Program PopART, Program TCS, software. Note: http://dx.doi.org/10.1111/2041-210X.12410.         193 Gergely J. Szöllösi, Adrián Arellano Davín, Eric Tannier, Vincent Daubin and Bastien Boussau. Genome-scale phylogenetic analysis finds extensive gene transfer among fungi. In Philosophical Transactions of the Royal Society of London B: Biological Sciences, Vol. 370(1678):1-11, 2015. Keywords: duplication, from sequences, lateral gene transfer, loss, phylogenetic network, phylogeny, Program ALE, reconstruction. Note: http://dx.doi.org/10.1098/rstb.2014.0335.         194 Fabio Pardi and Celine Scornavacca. Reconstructible Phylogenetic Networks: Do Not Distinguish the Indistinguishable. In PLoS Computational Biology, Vol. 11(4):e1007137.1-23, 2015. Keywords: branch length, explicit network, from rooted trees, identifiability, phylogenetic network, phylogeny, reconstruction. Note: http://dx.doi.org/10.1371/journal.pcbi.1004135.         195 Mareike Fischer, Leo van Iersel, Steven Kelk and Celine Scornavacca. On Computing The Maximum Parsimony Score Of A Phylogenetic Network. In SIDMA, Vol. 29(1):559-585, 2015. Keywords: APX hard, cluster containment, explicit network, FPT, from network, from sequences, integer linear programming, level k phylogenetic network, NP complete, parsimony, phylogenetic network, phylogeny, polynomial, Program MPNet, reconstruction, software. Note: http://arxiv.org/abs/1302.2430.         196 Katharina Huber, Leo van Iersel, Vincent Moulton and Taoyang Wu. How much information is needed to infer reticulate evolutionary histories? In Systematic Biology, Vol. 64(1):102-111, 2015. Keywords: explicit network, from network, from rooted trees, from subnetworks, from trinets, identifiability, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: http://dx.doi.org/10.1093/sysbio/syu076.         197 Andrew R. Francis and Mike Steel. Which phylogenetic networks are merely trees with additional arcs? In Systematic Biology, Vol. 64(5):768-777, 2015. Keywords: explicit network, phylogenetic network, phylogeny, polynomial, tree-based network. Note: http://arxiv.org/abs/1502.07045.         198 Dan Gusfield. ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks. MIT Press, 2014. Keywords: ARG, explicit network, phylogenetic network, phylogeny, survey. Note: http://mitpress.mit.edu/books/recombinatorics.         199 Josh Voorkamp né Collins. Untangling Evolution. PhD thesis, University of Otago, New Zealand, 2014. Keywords: agreement forest, explicit network, from rooted trees, generation, phylogenetic network, phylogeny, reconstruction. Note: http://otago.ourarchive.ac.nz/handle/10523/4802.         200 Yi-Chieh Wu. Computational evolutionary genomics : phylogenomic models spanning domains, genes, individuals, and species. PhD thesis, Massachusetts Institute of Technology, U.S.A., 2014. Keywords: duplication, from sequences, from species tree, lateral gene transfer, loss, phylogeny, Program TreeFix-DTL, reconstruction. Note: http://hdl.handle.net/1721.1/87937.         201 Benjamin Albrecht. Computing Hybridization Networks for Multiple Rooted Binary Phylogenetic Trees by Maximum Acyclic Agreement Forests. 2014. Keywords: agreement forest, from rooted trees, minimum number, phylogenetic network, phylogeny, polynomial, Program Hybroscale, reconstruction. Note: http://arxiv.org/abs/1408.3044.         202 Puspal Bhabak and Asish Mukhopadhyay. A 3-factor approximation algorithm for a Minimum Acyclic Agreement Forest on k rooted, binary phylogenetic trees. 2014. Keywords: agreement forest, approximation, explicit network, from rooted trees, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1407.7125.         203 Zhijiang Li. Fixed-Parameter Algorithm for Hybridization Number of Two Multifurcating Trees. Master's thesis, Dalhousie University, Canada, 2014. Keywords: agreement forest, explicit network, FPT, from rooted trees, minimum number, phylogenetic network, phylogeny, reconstruction. Note: http://hdl.handle.net/10222/53976.         204 Yun Yu. Models and Methods for Evolutionary Histories Involving Hybridization and Incomplete Lineage Sorting. PhD thesis, Rice University, U.S.A., 2014. Keywords: hybridization, incomplete lineage sorting, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software. Note: http://hdl.handle.net/1911/77583.         205 Julia Matsieva. A Static Formulation of the History Bound Problem. Master's thesis, UC Davis, 2014. Keywords: bound, dynamic programming, explicit network, from binary characters, from clusters, phylogenetic network, phylogeny, polynomial. Note: https://escholarship.org/uc/item/3741t064.         206 Leo van Iersel, Celine Scornavacca and Steven Kelk. Exact reconciliation of undated trees. 2014. Keywords: duplication, explicit network, integer linear programming, loss, phylogenetic network, phylogeny, Program ILPEACE, reconstruction. Note: https://arxiv.org/abs/1410.7004.         207 Vladimir Makarenkov, Alix Boc and Pierre Legendre. A New Algorithm for Inferring Hybridization Events Based on the Detection of Horizontal Gene Transfers. In Fuad Aleskerov, Boris Goldengorin and Panos M. Pardalos editors, Clusters, Orders, and Trees: Methods and Applications, Vol. 92 of Springer Optimization and Its Applications, Springer, 2014. Keywords: explicit network, phylogenetic network, phylogeny, reconstruction.         208 Riccardo Dondi and Yuri Pirola. Beyond Evolutionary Trees. In Ming-Yang Kao editor, Encyclopedia of Algorithms, Pages 1-7, Springer, 2014. Keywords: explicit network, phylogenetic network, phylogeny, reconstruction, survey.         209 Ran Libeskind-Hadas, Yi-Chieh Wu, Mukul S. Bansal and Manolis Kellis. Pareto-optimal phylogenetic tree reconciliation. In ISMB14, Vol. 30:i87-i95 of BIO, 2014. Keywords: duplication, lateral gene transfer, loss, phylogenetic network, phylogeny, polynomial, Program Xscape, reconstruction. Note: http://dx.doi.org/10.1093/bioinformatics/btu289.         Toggle abstract "Motivation: Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem. Results: We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies. © 2014 The Author. Published by Oxford University Press. All rights reserved." 210 Leo van Iersel and Steven Kelk. Kernelizations for the hybridization number problem on multiple nonbinary trees. In WG14, Vol. 8747:299-311 of LNCS, springer, 2014. Keywords: explicit network, from rooted trees, kernelization, minimum number, phylogenetic network, phylogeny, Program Treeduce, reconstruction. Note: http://arxiv.org/abs/1311.4045.         211 David A. Morrison. Rooted Phylogenetic Networks for Exploratory Data Analysis. In Advances in Research, Vol. 2(3):145-152, 2014. Keywords: abstract network, explicit network, phylogenetic network, reconstruction.         212 Steven Kelk and Celine Scornavacca. Constructing minimal phylogenetic networks from softwired clusters is fixed parameter tractable. In ALG, Vol. 68(4):886-915, 2014. Keywords: explicit network, FPT, from clusters, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1108.3653.         Toggle abstract "Here we show that, given a set of clusters C on a set of taxa X, where |X|=n, it is possible to determine in time f(k)×poly(n) whether there exists a level-≤k network (i.e. a network where each biconnected component has reticulation number at most k) that represents all the clusters in C in the softwired sense, and if so to construct such a network. This extends a result from Kelk et al. (in IEEE/ACM Trans. Comput. Biol. Bioinform. 9:517-534, 2012) which showed that the problem is polynomial-time solvable for fixed k. By defining "k-reticulation generators" analogous to "level-k generators", we then extend this fixed parameter tractability result to the problem where k refers not to the level but to the reticulation number of the whole network. © 2012 Springer Science+Business Media New York." 213 David A. Morrison. Phylogenetic Networks: A Review of Methods to Display Evolutionary History. In Annual Research & Review in Biology, Vol. 4(10):1518-1543, 2014. Keywords: explicit network, phylogenetic network, phylogeny, reconstruction, survey.         214 Johann-Mattis List, Shijulal Nelson-Sathi, Hans Geisler and William Martin. Networks of lexical borrowing and lateral gene transfer in language and genome evolution. In BioEssays, Vol. 36(2):141-150, 2014. Keywords: explicit network, minimal lateral network, phylogenetic network, Program lingpy. Note: http://dx.doi.org/10.1002/bies.201300096.         Toggle abstract "Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution. Editor's suggested further reading in BioEssays Linguistic evidence supports date for Homeric epics. © 2014 The Authors. BioEssays Published by WILEY Periodicals, Inc." 215 Judith Keijsper and Rudi Pendavingh. Reconstructing a phylogenetic level-1 network from quartets. In BMB, Vol. 76(10):2517-2541, 2014. Keywords: explicit network, from quartets, galled tree, phylogenetic network, phylogeny, polynomial, reconstruction. Note: http://arxiv.org/abs/1308.5206.         216 Paul Cordue, Simone Linz and Charles Semple. Phylogenetic Networks that Display a Tree Twice. In BMB, Vol. 76(10):2664-2679, 2014. Keywords: from rooted trees, normal network, phylogenetic network, phylogeny, reconstruction, tree-child network. Note: http://www.math.canterbury.ac.nz/~c.semple/papers/CLS14.pdf.         Toggle abstract "In the last decade, the use of phylogenetic networks to analyze the evolution of species whose past is likely to include reticulation events, such as horizontal gene transfer or hybridization, has gained popularity among evolutionary biologists. Nevertheless, the evolution of a particular gene can generally be described without reticulation events and therefore be represented by a phylogenetic tree. While this is not in contrast to each other, it places emphasis on the necessity of algorithms that analyze and summarize the tree-like information that is contained in a phylogenetic network. We contribute to the toolbox of such algorithms by investigating the question of whether or not a phylogenetic network embeds a tree twice and give a quadratic-time algorithm to solve this problem for a class of networks that is more general than tree-child networks. © 2014, Society for Mathematical Biology." 217 Jialiang Yang, Stefan Grünewald, Yifei Xu and Xiu-Feng Wan. Quartet-based methods to reconstruct phylogenetic networks. In BMC Systems Biology, Vol. 80(21), 2014. Keywords: abstract network, from quartets, phylogenetic network, phylogeny, Program QuartetMethods, Program QuartetNet, Program SplitsTree, reconstruction. Note: http://dx.doi.org/10.1186/1752-0509-8-21 .         Toggle abstract "Background: Phylogenetic networks are employed to visualize evolutionary relationships among a group of nucleotide sequences, genes or species when reticulate events like hybridization, recombination, reassortant and horizontal gene transfer are believed to be involved. In comparison to traditional distance-based methods, quartet-based methods consider more information in the reconstruction process and thus have the potential to be more accurate.Results: We introduce QuartetSuite, which includes a set of new quartet-based methods, namely QuartetS, QuartetA, and QuartetM, to reconstruct phylogenetic networks from nucleotide sequences. We tested their performances and compared them with other popular methods on two simulated nucleotide sequence data sets: one generated from a tree topology and the other from a complicated evolutionary history containing three reticulate events. We further validated these methods to two real data sets: a bacterial data set consisting of seven concatenated genes of 36 bacterial species and an influenza data set related to recently emerging H7N9 low pathogenic avian influenza viruses in China.Conclusion: QuartetS, QuartetA, and QuartetM have the potential to accurately reconstruct evolutionary scenarios from simple branching trees to complicated networks containing many reticulate events. These methods could provide insights into the understanding of complicated biological evolutionary processes such as bacterial taxonomy and reassortant of influenza viruses. © 2014 Yang et al.; licensee BioMed Central Ltd." 218 Leo van Iersel, Steven Kelk, Nela Lekic and Celine Scornavacca. A practical approximation algorithm for solving massive instances of hybridization number for binary and nonbinary trees. In BMCB, Vol. 15(127):1-12, 2014. Keywords: agreement forest, approximation, explicit network, from rooted trees, phylogenetic network, phylogeny, Program CycleKiller, Program TerminusEst, reconstruction. Note: http://dx.doi.org/10.1186/1471-2105-15-127.         219 Ward C Wheeler. Phyletic groups on networks. In Cladistics, Vol. 30(4):447-451, 2014. Keywords: explicit network, from network, phylogenetic network, phylogeny. Note: http://dx.doi.org/10.1111/cla.12062.         Toggle abstract "Three additional phyletic group types, "periphyletic," "epiphyletic", and "anaphyletic" (in addition to Hennigian mono-, para-, and polyphyletic) are defined in terms of trees and phylogenetic networks (trees with directed reticulate edges) via a generalization of the algorithmic definitions of Farris. These designations concern groups defined as monophyletic on trees, but with additional gains or losses of members from network edges. These distinctions should be useful in discussion of systems with non-vertical inheritance such as recombination between viruses, horizontal exchange between bacteria, hybridization in plants and animals, as well as human linguistic evolution. Examples are illustrated with Indo-European language groups. © The Willi Hennig Society 2013." 220 Sven Herrmann and Vincent Moulton. Computing the blocks of a quasi-median graph. In DAM, Vol. 179:129-138, 2014. Keywords: abstract network, from sequences, phylogenetic network, phylogeny, polynomial, Program QuasiDec, quasi-median network, reconstruction. Note: http://arxiv.org/abs/1206.6135.         221 David A. Morrison. Next generation sequencing and phylogenetic networks. In EMBnet.journal, Vol. 20(e760):1-4, 2014. Keywords: abstract network, from NGS data, phylogenetic network, phylogeny, Program SplitsTree, reconstruction.         222 Adrià Alcalà Mena, Mercè Llabrés, Francesc Rosselló and Pau Rullan. Tree-Child Cluster Networks. In Fundamenta Informaticae, Vol. 134(1-2):1-15, 2014. Keywords: explicit network, from clusters, phylogenetic network, phylogeny, Program PhyloNetwork, reconstruction, tree-child network.         223 Juan Wang. A new algorithm to construct phylogenetic networks from trees. In Genetics and Molecular Research, Vol. 13(1):1456-1464, 2014. Keywords: explicit network, from clusters, heuristic, phylogenetic network, Program LNetwork, Program QuickCass, reconstruction. Note: http://dx.doi.org/10.4238/2014.March.6.4.         Toggle abstract "Developing appropriate methods for constructing phylogenetic networks from tree sets is an important problem, and much research is currently being undertaken in this area. BIMLR is an algorithm that constructs phylogenetic networks from tree sets. The algorithm can construct a much simpler network than other available methods. Here, we introduce an improved version of the BIMLR algorithm, QuickCass. QuickCass changes the selection strategy of the labels of leaves below the reticulate nodes, i.e., the nodes with an indegree of at least 2 in BIMLR. We show that QuickCass can construct simpler phylogenetic networks than BIMLR. Furthermore, we show that QuickCass is a polynomial-time algorithm when the output network that is constructed by QuickCass is binary. © FUNPEC-RP." 224 Sarah Bastkowski, Andreas Spillner and Vincent Moulton. Fishing for minimum evolution trees with Neighbor-Nets. In IPL, Vol. 114(1-2):3-18, 2014. Keywords: circular split system, from distances, NeighborNet, phylogeny, polynomial.         Toggle abstract "In evolutionary biology, biologists commonly use a phylogenetic tree to represent the evolutionary history of some set of species. A common approach taken to construct such a tree is to search through the space of all possible phylogenetic trees on the set so as to find one that optimizes some score function, such as the minimum evolution criterion. However, this is hampered by the fact that the space of phylogenetic trees is extremely large in general. Interestingly, an alternative approach, which has received somewhat less attention in the literature, is to instead search for trees within some set of bipartitions or splits of the set of species in question. Here we consider the problem of searching through a set of splits that is circular. Such sets can, for example, be generated by the NeighborNet algorithm for constructing phylogenetic networks. More specifically, we present an O(n4) time algorithm for finding an optimal minimum evolution tree in a circular set of splits on a set of species of size n. In addition, using simulations, we compare the performance of this algorithm when applied to NeighborNet output with that of FastME, a leading method for searching for minimum evolution trees in tree space. We find that, even though a circular set of splits represents just a tiny fraction of the total number of possible splits of a set, the trees obtained from circular sets compare quite favorably with those obtained with FastME, suggesting that the approach could warrant further investigation. © 2013 Elsevier B.V." 225 Matthieu Willems, Nadia Tahiri and Vladimir Makarenkov. A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle. In JBCB, Vol. 12(5), 2014. Keywords: explicit network, from distances, heuristic, phylogenetic network, phylogeny, reconstruction.         Toggle abstract "Several algorithms and software have been developed for inferring phylogenetic trees. However, there exist some biological phenomena such as hybridization, recombination, or horizontal gene transfer which cannot be represented by a tree topology. We need to use phylogenetic networks to adequately represent these important evolutionary mechanisms. In this article, we present a new efficient heuristic algorithm for inferring hybridization networks from evolutionary distance matrices between species. The famous Neighbor-Joining concept and the least-squares criterion are used for building networks. At each step of the algorithm, before joining two given nodes, we check if a hybridization event could be related to one of them or to both of them. The proposed algorithm finds the exact tree solution when the considered distance matrix is a tree metric (i.e. it is representable by a unique phylogenetic tree). It also provides very good hybrids recovery rates for large trees (with 32 and 64 leaves in our simulations) for both distance and sequence types of data. The results yielded by the new algorithm for real and simulated datasets are illustrated and discussed in detail. © Imperial College Press." 226 Lavanya Kannan and Ward C Wheeler. Exactly Computing the Parsimony Scores on Phylogenetic Networks Using Dynamic Programming. In JCB, Vol. 21(4):303-319, 2014. Keywords: explicit network, exponential algorithm, from network, from sequences, parsimony, phylogenetic network, phylogeny, reconstruction.         Toggle abstract "Scoring a given phylogenetic network is the first step that is required in searching for the best evolutionary framework for a given dataset. Using the principle of maximum parsimony, we can score phylogenetic networks based on the minimum number of state changes across a subset of edges of the network for each character that are required for a given set of characters to realize the input states at the leaves of the networks. Two such subsets of edges of networks are interesting in light of studying evolutionary histories of datasets: (i) the set of all edges of the network, and (ii) the set of all edges of a spanning tree that minimizes the score. The problems of finding the parsimony scores under these two criteria define slightly different mathematical problems that are both NP-hard. In this article, we show that both problems, with scores generalized to adding substitution costs between states on the endpoints of the edges, can be solved exactly using dynamic programming. We show that our algorithms require O(mpk) storage at each vertex (per character), where k is the number of states the character can take, p is the number of reticulate vertices in the network, m = k for the problem with edge set (i), and m = 2 for the problem with edge set (ii). This establishes an O(nmpk2) algorithm for both the problems (n is the number of leaves in the network), which are extensions of Sankoff's algorithm for finding the parsimony scores for phylogenetic trees. We will discuss improvements in the complexities and show that for phylogenetic networks whose underlying undirected graphs have disjoint cycles, the storage at each vertex can be reduced to O(mk), thus making the algorithm polynomial for this class of networks. We will present some properties of the two approaches and guidance on choosing between the criteria, as well as traverse through the network space using either of the definitions. We show that our methodology provides an effective means to study a wide variety of datasets. © Copyright 2014, Mary Ann Liebert, Inc. 2014." 227 Josh Voorkamp né Collins. Maximal Acyclic Agreement Forests. In JCB, Vol. 21(10):723-731, 2014. Keywords: agreement forest, explicit network, from rooted trees, hybridization, minimum number, phylogenetic network, phylogeny, reconstruction.         228 Leo van Iersel and Vincent Moulton. Trinets encode tree-child and level-2 phylogenetic networks. In JOMB, Vol. 68(7):1707-1729, 2014. Keywords: explicit network, from subnetworks, from trinets, level k phylogenetic network, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1210.0362.         Toggle abstract "Phylogenetic networks generalize evolutionary trees, and are commonly used to represent evolutionary histories of species that undergo reticulate evolutionary processes such as hybridization, recombination and lateral gene transfer. Recently, there has been great interest in trying to develop methods to construct rooted phylogenetic networks from triplets, that is rooted trees on three species. However, although triplets determine or encode rooted phylogenetic trees, they do not in general encode rooted phylogenetic networks, which is a potential issue for any such method. Motivated by this fact, Huber and Moulton recently introduced trinets as a natural extension of rooted triplets to networks. In particular, they showed that level-1 phylogenetic networks are encoded by their trinets, and also conjectured that all "recoverable" rooted phylogenetic networks are encoded by their trinets. Here we prove that recoverable binary level-2 networks and binary tree-child networks are also encoded by their trinets. To do this we prove two decomposition theorems based on trinets which hold for all recoverable binary rooted phylogenetic networks. Our results provide some additional evidence in support of the conjecture that trinets encode all recoverable rooted phylogenetic networks, and could also lead to new approaches to construct phylogenetic networks from trinets. © 2013 Springer-Verlag Berlin Heidelberg." 229 Jesper Jansson and Andrzej Lingas. Computing the rooted triplet distance between galled trees by counting triangles. In Journal of Discrete Algorithms, Vol. 25:66-78, 2014. Keywords: distance between networks, explicit network, from network, galled network, phylogenetic network, phylogeny, polynomial, triplet distance.         Toggle abstract "We consider a generalization of the rooted triplet distance between two phylogenetic trees to two phylogenetic networks. We show that if each of the two given phylogenetic networks is a so-called galled tree with n leaves then the rooted triplet distance can be computed in o(n2.687) time. Our upper bound is obtained by reducing the problem of computing the rooted triplet distance between two galled trees to that of counting monochromatic and almost-monochromatic triangles in an undirected, edge-colored graph. To count different types of colored triangles in a graph efficiently, we extend an existing technique based on matrix multiplication and obtain several new algorithmic results that may be of independent interest: (i) the number of triangles in a connected, undirected, uncolored graph with m edges can be computed in o(m1.408) time; (ii) if G is a connected, undirected, edge-colored graph with n vertices and C is a subset of the set of edge colors then the number of monochromatic triangles of G with colors in C can be computed in o(n2.687) time; and (iii) if G is a connected, undirected, edge-colored graph with n vertices and R is a binary relation on the colors that is computable in O(1) time then the number of R-chromatic triangles in G can be computed in o(n2.687) time. © 2013 Elsevier B.V. All rights reserved." 230 Hadi Poormohammadi, Changiz Eslahchi and Ruzbeh Tusserkani. TripNet: A Method for Constructing Rooted Phylogenetic Networks from Rooted Triplets. In PLoS ONE, Vol. 9(9):e106531, 2014. Keywords: explicit network, from triplets, heuristic, level k phylogenetic network, phylogenetic network, phylogeny, Program TripNet, reconstruction, software. Note: http://arxiv.org/abs/1201.3722.         Toggle abstract "The problem of constructing an optimal rooted phylogenetic network from an arbitrary set of rooted triplets is an NP-hard problem. In this paper, we present a heuristic algorithm called TripNet, which tries to construct a rooted phylogenetic network with the minimum number of reticulation nodes from an arbitrary set of rooted triplets. Despite of current methods that work for dense set of rooted triplets, a key innovation is the applicability of TripNet to non-dense set of rooted triplets. We prove some theorems to clarify the performance of the algorithm. To demonstrate the efficiency of TripNet, we compared TripNet with SIMPLISTIC. It is the only available software which has the ability to return some rooted phylogenetic network consistent with a given dense set of rooted triplets. But the results show that for complex networks with high levels, the SIMPLISTIC running time increased abruptly. However in all cases TripNet outputs an appropriate rooted phylogenetic network in an acceptable time. Also we tetsed TripNet on the Yeast data. The results show that Both TripNet and optimal networks have the same clustering and TripNet produced a level-3 network which contains only one more reticulation node than the optimal network." 231 Kevin J. Liu, Jingxuan Dai, Kathy Truong, Ying Song, Michael H. Kohn and Luay Nakhleh. An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes. In PLoS ONE, Vol. 10(6):e1003649, 2014. Keywords: explicit network, from network, phylogenetic network, phylogeny, Program PhyloNet-HMM. Note: http://arxiv.org/abs/1310.7989.         Toggle abstract "One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on PhyloNet-HMM-a new comparative genomic framework for detecting introgression in genomes. PhyloNet-HMM combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (potentially reticulate) evolutionary history of the genomes and dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detected a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgressed genomic regions. Based on our analysis, it is estimated that about 9% of all sites within chromosome 7 are of introgressive origin (these cover about 13 Mbp of chromosome 7, and over 300 genes). Further, our model detected no introgression in a negative control data set. We also found that our model accurately detected introgression and other evolutionary processes from synthetic data sets simulated under the coalescent model with recombination, isolation, and migration. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism. © 2014 Liu et al." 232 Yun Yu, Jianrong Dong, Kevin J. Liu and Luay Nakhleh. Maximum likelihood inference of reticulate evolutionary histories. In PNAS, Vol. 111(46):16448-16453, 2014. Keywords: explicit network, likelihood, phylogenetic network, phylogeny, reconstruction. Note: http://dx.doi.org/10.1073/pnas.1407950111.         233 Leo van Iersel, Steven Kelk, Nela Lekic and Leen Stougie. Approximation algorithms for nonbinary agreement forests. In SIDMA, Vol. 28(1):49-66, 2014. Keywords: agreement forest, approximation, from rooted trees, hybridization, minimum number, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1210.3211.         Toggle abstract "Given two rooted phylogenetic trees on the same set of taxa X, the Maximum Agreement Forest (maf) problem asks to find a forest that is, in a certain sense, common to both trees and has a minimum number of components. The Maximum Acyclic Agreement Forest (maaf) problem has the additional restriction that the components of the forest cannot have conflicting ancestral relations in the input trees. There has been considerable interest in the special cases of these problems in which the input trees are required to be binary. However, in practice, phylogenetic trees are rarely binary, due to uncertainty about the precise order of speciation events. Here, we show that the general, nonbinary version of maf has a polynomial-time 4-approximation and a fixedparameter tractable (exact) algorithm that runs in O(4opoly(n)) time, where n = |X| and k is the number of components of the agreement forest minus one. Moreover, we show that a c-approximation algorithm for nonbinary maf and a d-approximation algorithm for the classical problem Directed Feedback Vertex Set (dfvs) can be combined to yield a d(c+3)-approximation for nonbinary maaf. The algorithms for maf have been implemented and made publicly available. © 2014 Society for Industrial and Applied Mathematics." 234 Monika Balvociute, Andreas Spillner and Vincent Moulton. FlatNJ: A Novel Network-Based Approach to Visualize Evolutionary and Biogeographical Relationships. In Systematic Biology, Vol. 63(3):383-396, 2014. Keywords: abstract network, flat, phylogenetic network, phylogeny, Program FlatNJ, Program SplitsTree, split network. Note: http://dx.doi.org/10.1093/sysbio/syu001.         Toggle abstract "Split networks are a type of phylogenetic network that allow visualization of conflict in evolutionary data. We present a new method for constructing such networks called FlatNetJoining (FlatNJ). A key feature of FlatNJ is that it produces networks that can be drawn in the plane in which labels may appear inside of the network. For complex data sets that involve, for example, non-neutral molecular markers, this can allow additional detail to be visualized as compared to previous methods such as split decomposition and NeighborNet. We illustrate the application of FlatNJ by applying it to whole HIV genome sequences, where recombination has taken place, fluorescent proteins in corals, where ancestral sequences are present, and mitochondrial DNA sequences from gall wasps, where biogeographical relationships are of interest. We find that the networks generated by FlatNJ can facilitate the study of genetic variation in the underlying molecular sequence data and, in particular, may help to investigate processes such as intra-locus recombination. FlatNJ has been implemented in Java and is freely available at www.uea.ac.uk/computing/software/ flatnj. [flat split system; NeighborNet; Phylogenetic network; QNet; split; split network.] © The Author(s) 2014." 235 Joel Sjöstrand, Ali Tofigh, Vincent Daubin, Lars Arvestad, Bengt Sennblad and Jens Lagergren. A Bayesian Method for Analyzing Lateral Gene Transfer. In Systematic Biology, Vol. 63(3):409-420, 2014. Keywords: bayesian, duplication, from rooted trees, from sequences, from species tree, lateral gene transfer, loss, phylogenetic network, phylogeny, Program JPrIME-DLTRS, reconstruction. Note: http://dx.doi.org/10.1093/sysbio/syu007.         236 David A. Morrison. Is the Tree of Life the Best Metaphor, Model, or Heuristic for Phylogenetics? In Systematic Biology, Vol. 63(4):628-638, 2014. Keywords: abstract network, explicit network, phylogenetic network, phylogeny, survey.         237 Anthony Labarre and Sicco Verwer. Merging partially labelled trees: hardness and a declarative programming solution. In TCBB, Vol. 11(2):389-397, 2014. Keywords: abstract network, from unrooted trees, heuristic, NP complete, phylogenetic network, phylogeny, reconstruction. Note: https://hal-upec-upem.archives-ouvertes.fr/hal-00855669.         Toggle abstract "Intraspecific studies often make use of haplotype networks instead of gene genealogies to represent the evolution of a set of genes. Cassens et al. proposed one such network reconstruction method, based on the global maximum parsimony principle, which was later recast by the first author of the present work as the problem of finding a minimum common supergraph of a set of t partially labelled trees. Although algorithms have been proposed for solving that problem on two graphs, the complexity of the general problem on trees remains unknown. In this paper, we show that the corresponding decision problem is NP-complete for t=3. We then propose a declarative programming approach to solving the problem to optimality in practice, as well as a heuristic approach, both based on the idpsystem, and assess the performance of both methods on randomly generated data. © 2004-2012 IEEE." 238 Gabriel Cardona, Mercè Llabrés, Francesc Rosselló and Gabriel Valiente. The comparison of tree-sibling time consistent phylogenetic networks is graph-isomorphism complete. In The Scientific World Journal, Vol. 2014(254279):1-6, 2014. Keywords: abstract network, distance between networks, from network, isomorphism, phylogenetic network, tree sibling network. Note: http://arxiv.org/abs/0902.4640.         Toggle abstract "Several polynomial time computable metrics on the class of semibinary tree-sibling time consistent phylogenetic networks are available in the literature; in particular, the problem of deciding if two networks of this kind are isomorphic is in P. In this paper, we show that if we remove the semibinarity condition, then the problem becomes much harder. More precisely, we prove that the isomorphism problem for generic tree-sibling time consistent phylogenetic networks is polynomially equivalent to the graph isomorphism problem. Since the latter is believed not to belong to P, the chances are that it is impossible to define a metric on the class of all tree-sibling time consistent phylogenetic networks that can be computed in polynomial time. © 2014 Gabriel Cardona et al." 239 Sha Zhu, James H. Degnan and Bjarki Eldon. Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees. 2013. Keywords: explicit network, from network, phylogenetic network, phylogeny, Program Hybrid-Lambda, simulation, software. Note: http://arxiv.org/abs/1303.0673.         240 Willem Sonke. Reconstructing a level-1-network from quartets. 2013. Keywords: abstract network, from quartets, phylogenetic network, phylogeny, polynomial, Program Fylogenetica, reconstruction, software, visualization. Note: http://alexandria.tue.nl/extra1/afstversl/wsk-i/sonke2013.pdf.         241 Sha Zhu. Stochastic tree models and probabilistic modelling of gene trees of given species networks. PhD thesis, University of Canterbury, New Zealand, 2013. Keywords: from network, generation, phylogenetic network, phylogeny, simulation, statistical model. Note: http://hdl.handle.net/10092/7944.         242 Thi-Hau Nguyen. Réconciliations: corriger des arbres de gènes et inférer la fiabilité des événements évolutifs. PhD thesis, Université Montpellier 2, France, 2013. Keywords: duplication, explicit network, from rooted trees, heuristic, lateral gene transfer, phylogenetic network, phylogeny, Program Mowgli, Program MowgliNNI, reconstruction. Note: http://www.biu-montpellier.fr/florabium/servlet/DocumentFileManager?source=ged&document=ged:IDOCS:247665&resolution=&recordId=theses%3ABIU_THESE%3A1789&file=.         243 Steven Kelk, Simone Linz and David A. Morrison. Fighting network space: it is time for an SQL-type language to filter phylogenetic networks. 2013. Keywords: database, explicit network, from network, phylogenetic network, phylogeny. Note: http://arxiv.org/abs/1310.6844.         244 Leo van Iersel, Steven Kelk, Nela Lekic and Leen Stougie. A short note on exponential-time algorithms for hybridization number. 2013. Keywords: explicit network, exponential algorithm, from rooted trees, phylogenetic network, phylogeny, reconstruction. Note: http://arxiv.org/abs/1312.1255.         245 Chris Whidden. Efficient Computation and Application of Maximum Agreement Forests. PhD thesis, Dalhousie University, Canada, 2013. Keywords: agreement forest, explicit network, FPT, from rooted trees, minimum number, phylogenetic network, phylogeny, reconstruction. Note: http://hdl.handle.net/10222/35349.         246 Sarah Bastkowski. From Trees to Networks and Back. PhD thesis, University of East Anglia, 2013. Keywords: abstract network, NeighborNet, phylogenetic network, phylogeny, Program FlatNJ, Program QNet, Program SplitsTree, reconstruction, software, split network. Note: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/_downloads/spectre_bastkowskis_thesis.pdf.         247 Simone Linz. Reticulation. In Brenner's Encyclopedia of Genetics (Second Edition), 2013. Keywords: phylogenetic network, phylogeny, survey.         248 Zhi-Zhong Chen, Fei Deng and Lusheng Wang. Identifying Duplications and Lateral Gene Transfers Simultaneously and Rapidly. In CIBCB13, Pages 128-135, 2013. Keywords: duplication, FPT, from rooted trees, from species tree, phylogenetic network, phylogeny, reconstruction. Note: http://www.cs.cityu.edu.hk/~lwang/research/singaporet2013.pdf.         249 Hoa Vu, Francis Chin, Wing-Kai Hon, Henry Leung, Kunihiko Sadakane, Wing-Kin Sung and Siu-Ming Yiu. Reconstructing k-Reticulated Phylogenetic Network from a Set of Gene Trees. In ISBRA13, Vol. 7875:112-124 of LNCS, springer, 2013. Keywords: from rooted trees, k-reticulated, phylogenetic network, phylogeny, polynomial, Program ARTNET, Program CMPT, reconstruction. Note: http://grid.cs.gsu.edu/~xguo9/publications/2013_Cloud%20computing%20for%20de%20novo%20metagenomic%20sequence%20assembly.pdf#page=123.         Toggle abstract "The time complexity of existing algorithms for reconstructing a level-x phylogenetic network increases exponentially in x. In this paper, we propose a new classification of phylogenetic networks called k-reticulated network. A k-reticulated network can model all level-k networks and some level-x networks with x > k. We design algorithms for reconstructing k-reticulated network (k = 1 or 2) with minimum number of hybrid nodes from a set of m binary trees, each with n leaves in O(mn 2) time. The implication is that some level-x networks with x > k can now be reconstructed in a faster way. We implemented our algorithm (ARTNET) and compared it with CMPT. We show that ARTNET outperforms CMPT in terms of running time and accuracy. We also consider the case when there does not exist a 2-reticulated network for the input trees. We present an algorithm computing a maximum subset of the species set so that a new set of subtrees can be combined into a 2-reticulated network. © 2013 Springer-Verlag." 250 David A. Morrison. Phylogenetic networks are fundamentally different from other kinds of biological networks. In WenJun Zhang editor, Network Biology: Theories, Methods and Applications, Chapter 2, Nova Publishers, 2013. Keywords: abstract network, explicit network, phylogenetic network, phylogeny.         251 Yun Yu, Nikola Ristic and Luay Nakhleh. Fast algorithms and heuristics for phylogenomics under ILS and hybridization. In RECOMB-CG'13, Vol. 14(Suppl 15):S6 of BMCB, 2013. Keywords: from network, from rooted trees, heuristic, phylogenetic network, phylogeny, Program PhyloNet, reconstruction.         252 Yufeng Wu. An Algorithm for Constructing Parsimonious Hybridization Networks with Multiple Phylogenetic Trees. In RECOMB13, Vol. 7821:291-303 of LNCS, springer, 2013. Keywords: explicit network, exponential algorithm, from rooted trees, phylogenetic network, phylogeny, Program PIRN, reconstruction. Note: http://www.engr.uconn.edu/~ywu/Papers/ExactNetRecomb2013.pdf.         Toggle abstract "Phylogenetic network is a model for reticulate evolution. Hybridization network is one type of phylogenetic network for a set of discordant gene trees, and "displays" each gene tree. A central computational problem on hybridization networks is: given a set of gene trees, reconstruct the minimum (i.e. most parsimonious) hybridization network that displays each given gene tree. This problem is known to be NP-hard, and existing approaches for this problem are either heuristics or make simplifying assumptions (e.g. work with only two input trees or assume some topological properties). In this paper, we develop an exact algorithm (called PIRNC ) for inferring the minimum hybridization networks from multiple gene trees. The PIRNC algorithm does not rely on structural assumptions. To the best of our knowledge, PIRN C is the first exact algorithm for this formulation. When the number of reticulation events is relatively small (say four or fewer), PIRNC runs reasonably efficient even for moderately large datasets. For building more complex networks, we also develop a heuristic version of PIRNC called PIRNCH. Simulation shows that PIRNCH usually produces networks with fewer reticulation events than those by an existing method. © 2013 Springer-Verlag." 253 Mukul S. Bansal, Eric J. Alm and Manolis Kellis. Reconciliation Revisited: Handling Multiple Optima when Reconciling with Duplication, Transfer, and Loss. In RECOMB13, Vol. 7821:1-13 of LNCS, springer, 2013. Keywords: duplication, from rooted trees, from species tree, loss, phylogenetic network, phylogeny, polynomial, Program RANGER-DTL, reconstruction. Note: http://people.csail.mit.edu/mukul/Bansal_RECOMB2013.pdf.         Toggle abstract "Phylogenetic tree reconciliation is a powerful approach for inferring evolutionary events like gene duplication, horizontal gene transfer, and gene loss, which are fundamental to our understanding of molecular evolution. While Duplication-Loss (DL) reconciliation leads to a unique maximum-parsimony solution, Duplication-Transfer-Loss (DTL) reconciliation yields a multitude of optimal solutions, making it difficult the infer the true evolutionary history of the gene family. Here, we present an effective, efficient, and scalable method for dealing with this fundamental problem in DTL reconciliation. Our approach works by sampling the space of optimal reconciliations uniformly at random and aggregating the results. We present an algorithm to efficiently sample the space of optimal reconciliations uniformly at random in O(mn 2) time, where m and n denote the number of genes and species, respectively. We use these samples to understand how different optimal reconciliations vary in their node mapping and event assignments, and to investigate the impact of varying event costs. © 2013 Springer-Verlag." 254 Katharina Huber and Vincent Moulton. Encoding and Constructing 1-Nested Phylogenetic Networks with Trinets. In ALG, Vol. 66(3):714-738, 2013. Keywords: explicit network, from subnetworks, from trinets, phylogenetic network, phylogeny, reconstruction, uniqueness. Note: http://arxiv.org/abs/1110.0728.         Toggle abstract "Phylogenetic networks are a generalization of phylogenetic trees that are used in biology to represent reticulate or non-treelike evolution. Recently, several algorithms have been developed which aim to construct phylogenetic networks from biological data using triplets, i.e. binary phylogenetic trees on 3-element subsets of a given set of species. However, a fundamental problem with this approach is that the triplets displayed by a phylogenetic network do not necessarily uniquely determine or encode the network. Here we propose an alternative approach to encoding and constructing phylogenetic networks, which uses phylogenetic networks on 3-element subsets of a set, or trinets, rather than triplets. More specifically, we show that for a special, well-studied type of phylogenetic network called a 1-nested network, the trinets displayed by a 1-nested network always encode the network. We also present an efficient algorithm for deciding whether a dense set of trinets (i.e. one that contains a trinet on every 3-element subset of a set) can be displayed by a 1-nested network or not and, if so, constructs that network. In addition, we discuss some potential new directions that this new approach opens up for constructing and comparing phylogenetic networks. © 2012 Springer Science+Business Media, LLC." 255 Alberto Apostolico, Matteo Comin, Andreas W. M. Dress and Laxmi Parida. Ultrametric networks: a new tool for phylogenetic analysis. In Algorithms for Molecular Biology, Vol. 8(7):1-10, 2013. Keywords: abstract network, from distances, phylogenetic network, phylogeny, Program Ultranet. Note: http://dx.doi.org/10.1186/1748-7188-8-7.         Toggle abstract "Background: The large majority of optimization problems related to the inference of distance-based trees used in phylogenetic analysis and classification is known to be intractable. One noted exception is found within the realm of ultrametric distances. The introduction of ultrametric trees in phylogeny was inspired by a model of evolution driven by the postulate of a molecular clock, now dismissed, whereby phylogeny could be represented by a weighted tree in which the sum of the weights of the edges separating any given leaf from the root is the same for all leaves. Both, molecular clocks and rooted ultrametric trees, fell out of fashion as credible representations of evolutionary change. At the same time, ultrametric dendrograms have shown good potential for purposes of classification in so far as they have proven to provide good approximations for additive trees. Most of these approximations are still intractable, but the problem of finding the nearest ultrametric distance matrix to a given distance matrix with respect to the L∞ distance has been long known to be solvable in polynomial time, the solution being incarnated in any minimum spanning tree for the weighted graph subtending to the matrix.Results: This paper expands this subdominant ultrametric perspective by studying ultrametric networks, consisting of the collection of all edges involved in some minimum spanning tree. It is shown that, for a graph with n vertices, the construction of such a network can be carried out by a simple algorithm in optimal time O(n2) which is faster by a factor of n than the direct adaptation of the classical O(n3) paradigm by Warshall for computing the transitive closure of a graph. This algorithm, called UltraNet, will be shown to be easily adapted to compute relaxed networks and to support the introduction of artificial points to reduce the maximum distance between vertices in a pair. Finally, a few experiments will be discussed to demonstrate the applicability of subdominant ultrametric networks.Availability: http://www.dei.unipd.it/~ciompin/main/Ultranet/Ultranet.html. © 2013 Apostolico et al.; licensee BioMed Central Ltd." 256 Thi-Hau Nguyen, Vincent Ranwez, Stéphanie Pointet, Anne-Muriel Chifolleau Arigon, Jean-Philippe Doyon and Vincent Berry. Reconciliation and local gene tree rearrangement can be of mutual profit. In ALMOB, Vol. 8(12), 2013. Keywords: duplication, explicit network, from rooted trees, heuristic, lateral gene transfer, phylogenetic network, phylogeny, Program Mowgli, Program MowgliNNI, Program Prunier, reconstruction, software.         Toggle abstract "Background: Reconciliation methods compare gene trees and species trees to recover evolutionary events such as duplications, transfers and losses explaining the history and composition of genomes. It is well-known that gene trees inferred from molecular sequences can be partly erroneous due to incorrect sequence alignments as well as phylogenetic reconstruction artifacts such as long branch attraction. In practice, this leads reconciliation methods to overestimate the number of evolutionary events. Several methods have been proposed to circumvent this problem, by collapsing the unsupported edges and then resolving the obtained multifurcating nodes, or by directly rearranging the binary gene trees. Yet these methods have been defined for models of evolution accounting only for duplications and losses, i.e. can not be applied to handle prokaryotic gene families.Results: We propose a reconciliation method accounting for gene duplications, losses and horizontal transfers, that specifically takes into account the uncertainties in gene trees by rearranging their weakly supported edges. Rearrangements are performed on edges having a low confidence value, and are accepted whenever they improve the reconciliation cost. We prove useful properties on the dynamic programming matrix used to compute reconciliations, which allows to speed-up the tree space exploration when rearrangements are generated by Nearest Neighbor Interchanges (NNI) edit operations. Experiments on synthetic data show that gene trees modified by such NNI rearrangements are closer to the correct simulated trees and lead to better event predictions on average. Experiments on real data demonstrate that the proposed method leads to a decrease in the reconciliation cost and the number of inferred events. Finally on a dataset of 30 k gene families, this reconciliation method shows a ranking of prokaryotic phyla by transfer rates identical to that proposed by a different approach dedicated to transfer detection [BMCBIOINF 11:324, 2010, PNAS 109(13):4962-4967, 2012].Conclusions: Prokaryotic gene trees can now be reconciled with their species phylogeny while accounting for the uncertainty of the gene tree. More accurate and more precise reconciliations are obtained with respect to previous parsimony algorithms not accounting for such uncertainties [LNCS 6398:93-108, 2010, BIOINF 28(12): i283-i291, 2012].A software implementing the method is freely available at http://www.atgc-montpellier.fr/Mowgli/. © 2013 Nguyen et al.; licensee BioMed Central Ltd." 257 Mukul S. Bansal, Guy Banay, Timothy J. Harlow, J. Peter Gogarten and Ron Shamir. Systematic inference of highways of horizontal gene transfer in prokaryotes. In BIO, Vol. 29(5):571-579, 2013. Keywords: duplication, explicit network, from species tree, from unrooted trees, lateral gene transfer, phylogenetic network, phylogeny, Program HiDe, Program RANGER-DTL, reconstruction. Note: http://people.csail.mit.edu/mukul/Bansal_Highways_Bioinformatics_2013.pdf.         258 Juan Wang, Maozu Guo, Xiaoyan Liu, Yang Liu, Chunyu Wang, Linlin Xing and Kai Che. LNETWORK: An Efficient and Effective Method for Constructing Phylogenetic Networks. In BIO, Vol. 29(18):2269-2276, 2013. Keywords: explicit network, from rooted trees, phylogenetic network, phylogeny, Program LNetwork, reconstruction, software.         Toggle abstract "Motivation: The evolutionary history of species is traditionally represented with a rooted phylogenetic tree. Each tree comprises a set of clusters, i.e. subsets of the species that are descended from a common ancestor. When rooted phylogenetic trees are built from several different datasets (e.g. from different genes), the clusters are often conflicting. These conflicting clusters cannot be expressed as a simple phylogenetic tree; however, they can be expressed in a phylogenetic network. Phylogenetic networks are a generalization of phylogenetic trees that can account for processes such as hybridization, horizontal gene transfer and recombination, which are difficult to represent in standard tree-like models of evolutionary histories. There is currently a large body of research aimed at developing appropriate methods for constructing phylogenetic networks from cluster sets. The Cass algorithm can construct a much simpler network than other available methods, but is extremely slow for large datasets or for datasets that need lots of reticulate nodes. The networks constructed by Cass are also greatly dependent on the order of input data, i.e. it generally derives different phylogenetic networks for the same dataset when different input orders are used.Results: In this study, we introduce an improved Cass algorithm, Lnetwork, which can construct a phylogenetic network for a given set of clusters. We show that Lnetwork is significantly faster than Cass and effectively weakens the influence of input data order. Moreover, we show that Lnetwork can construct a much simpler network than most of the other available methods. © The Author 2013." 259 Stephen J. Willson. Reconstruction of certain phylogenetic networks from their tree-average distances. In BMB, Vol. 75(10):1840-1878, 2013. Keywords: explicit network, from distances, galled tree, normal network, phylogenetic network, phylogeny, unicyclic network. Note: http://www.public.iastate.edu/~swillson/Tree-AverageReconPaper9.pdf.         Toggle abstract "Trees are commonly utilized to describe the evolutionary history of a collection of biological species, in which case the trees are called phylogenetic trees. Often these are reconstructed from data by making use of distances between extant species corresponding to the leaves of the tree. Because of increased recognition of the possibility of hybridization events, more attention is being given to the use of phylogenetic networks that are not necessarily trees. This paper describes the reconstruction of certain such networks from the tree-average distances between the leaves. For a certain class of phylogenetic networks, a polynomial-time method is presented to reconstruct the network from the tree-average distances. The method is proved to work if there is a single reticulation cycle. © 2013 Society for Mathematical Biology." 260 Peter J. Humphries, Simone Linz and Charles Semple. Cherry picking: a characterization of the temporal hybridization number for a set of phylogenies. In BMB, Vol. 75(10):1879-1890, 2013. Keywords: characterization, cherry-picking, from rooted trees, hybridization, NP complete, phylogenetic network, phylogeny, reconstruction,