Species network inference under the multispecies coalescent model
Dissertation (Ph.D.) University of Alaska Fairbanks, 2019 Species network inference is a challenging problem in phylogenetics. In this work, we present two results on this. The first shows that many topological features of a level-1 network are identifable under the network multispecies coalescent m...
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ftunivalaska:oai:scholarworks.alaska.edu:11122/10482 2023-05-15T17:14:03+02:00 Species network inference under the multispecies coalescent model Baños Cervantes, Hector Daniel Allman, Elizabeth S. Rhodes, John A. Barry, Ronald Faudree, Jill 2019-05 http://hdl.handle.net/11122/10482 en_US eng http://hdl.handle.net/11122/10482 Department of Mathematics and Statistics biology mathematical models molecular biology phylogeny Dissertation phd 2019 ftunivalaska 2023-02-23T21:37:29Z Dissertation (Ph.D.) University of Alaska Fairbanks, 2019 Species network inference is a challenging problem in phylogenetics. In this work, we present two results on this. The first shows that many topological features of a level-1 network are identifable under the network multispecies coalescent model (NMSC). Specifcally, we show that one can identify from gene tree frequencies the unrooted semidirected species network, after suppressing all cycles of size less than 4. The second presents the theory behind a new, statistically consistent, practical method for the inference of level-1 networks under the NMSC. The input for this algorithm is a collection of unrooted topological gene trees, and the output is an unrooted semidirected species network. Chapter 1: Introduction -- Chapter 2: The network multispecies coalescent model -- 1. The coalescent model -- 2. The network multispecies coalescent model (NMSC) -- Chapter 3: Identifying species network features from gene tree quartets under the coalescent model -- 1. Introduction -- 2. Phylogenetic networks -- 3. Structure of level-1 networks -- 4. The network multispecies coalescent model and quartet concordance factors -- 5. Computing quartet concordance factors -- 6. The cycle property -- 7. The big cycle property -- 8. Identifying cycles in networks -- 9. Further results in 32-cycles -- 10. Discussion -- 11. Appendix -- Chapter 4: NANUQ: A method for inferring species networks from gene trees under the coalescent model -- 1. Introduction -- 2. Phylogenetic networks -- 3. The network multispecies coalescent model and quartet concordance factors -- 4. Network split systems and distances -- 5. Quartet distance for level-1 networks -- 6. Split networks from the network quartet distance -- 7. The NANUQ algorithm for inference of phylogenetic networks -- 8. Examples -- Chapter 5: Conclusions and future work - References. Doctoral or Postdoctoral Thesis nanuq Alaska University of Alaska: ScholarWorks@UA Fairbanks |
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University of Alaska: ScholarWorks@UA |
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English |
topic |
biology mathematical models molecular biology phylogeny |
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biology mathematical models molecular biology phylogeny Baños Cervantes, Hector Daniel Species network inference under the multispecies coalescent model |
topic_facet |
biology mathematical models molecular biology phylogeny |
description |
Dissertation (Ph.D.) University of Alaska Fairbanks, 2019 Species network inference is a challenging problem in phylogenetics. In this work, we present two results on this. The first shows that many topological features of a level-1 network are identifable under the network multispecies coalescent model (NMSC). Specifcally, we show that one can identify from gene tree frequencies the unrooted semidirected species network, after suppressing all cycles of size less than 4. The second presents the theory behind a new, statistically consistent, practical method for the inference of level-1 networks under the NMSC. The input for this algorithm is a collection of unrooted topological gene trees, and the output is an unrooted semidirected species network. Chapter 1: Introduction -- Chapter 2: The network multispecies coalescent model -- 1. The coalescent model -- 2. The network multispecies coalescent model (NMSC) -- Chapter 3: Identifying species network features from gene tree quartets under the coalescent model -- 1. Introduction -- 2. Phylogenetic networks -- 3. Structure of level-1 networks -- 4. The network multispecies coalescent model and quartet concordance factors -- 5. Computing quartet concordance factors -- 6. The cycle property -- 7. The big cycle property -- 8. Identifying cycles in networks -- 9. Further results in 32-cycles -- 10. Discussion -- 11. Appendix -- Chapter 4: NANUQ: A method for inferring species networks from gene trees under the coalescent model -- 1. Introduction -- 2. Phylogenetic networks -- 3. The network multispecies coalescent model and quartet concordance factors -- 4. Network split systems and distances -- 5. Quartet distance for level-1 networks -- 6. Split networks from the network quartet distance -- 7. The NANUQ algorithm for inference of phylogenetic networks -- 8. Examples -- Chapter 5: Conclusions and future work - References. |
author2 |
Allman, Elizabeth S. Rhodes, John A. Barry, Ronald Faudree, Jill |
format |
Doctoral or Postdoctoral Thesis |
author |
Baños Cervantes, Hector Daniel |
author_facet |
Baños Cervantes, Hector Daniel |
author_sort |
Baños Cervantes, Hector Daniel |
title |
Species network inference under the multispecies coalescent model |
title_short |
Species network inference under the multispecies coalescent model |
title_full |
Species network inference under the multispecies coalescent model |
title_fullStr |
Species network inference under the multispecies coalescent model |
title_full_unstemmed |
Species network inference under the multispecies coalescent model |
title_sort |
species network inference under the multispecies coalescent model |
publishDate |
2019 |
url |
http://hdl.handle.net/11122/10482 |
geographic |
Fairbanks |
geographic_facet |
Fairbanks |
genre |
nanuq Alaska |
genre_facet |
nanuq Alaska |
op_relation |
http://hdl.handle.net/11122/10482 Department of Mathematics and Statistics |
_version_ |
1766071305615966208 |