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This record includes training materials associated with the Australian BioCommons webinar ‘Conflict in multi-gene datasets: why it happens and what to do about it - deep coalescence, paralogy and reticulation’. This webinar took place on 20 May 2021.
Multi-gene datasets used in phylogenetic analyses, such as those produced by the sequence capture or target enrichment used in the Genomics for Australian Plants: Australian Angiosperm Tree of Life project, often show discordance between individual gene trees and between gene and species trees. This webinar explores three different forms of discordance: deep coalescence, paralogy, and reticulation. In each case, it considers underlying biological processes, how discordance presents in the data, and what bioinformatic or phylogenetic approaches and tools are available to address these challenges. It covers Yang and Smith paralogy resolution and general information on options for phylogenetic analysis.
This webinar is part of a series of webinars and workshops developed by the Genomics for Australian Plants (GAP) Initiative that focused on the analysis of target capture sequence data. In addition to two public webinars, the GAP bioinformatics working group is offering training workshops in the use of newly developed and existing scripts in an integrated workflow to participants in the 2021 virtual Australasian Systematic Botany Society Conference.
The materials are shared under a Creative Commons 4.0 International agreement unless otherwise specified and were current at the time of the event.
Files and materials included in this record:
Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.
Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.
Schmidt-Lebuhn - paralogy lineage sorting reticulation - slides (PDF): Slides presented during the webinar
Materials shared elsewhere:
A recording of the webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/1bw81q898z8
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Event metadata.pdf
md5:66e544c047a0b8f5a3729391a2696c36 |
105.4 kB | Download |
Index of training materials.pdf
md5:51464e239240f08ca02b801db830a440 |
90.9 kB | Download |
Schmidt-Lebuhn - paralogy lineage sorting reticulation-slides.pdf
md5:0e7402bab16a57da635da51f39393323 |
9.0 MB | Download |
Altenhoff AM, Dessimoz C, 2012. Inferring Orthology and Paralogy, pp. 259-279 in: Maria Anisimova (ed.), Evolutionary Genomics: Statistical and Computational Methods, Volume 1,Methods in Molecular Biology, vol. 855, Springer. https://people.inf.ethz.ch/adriaal/orthology-bookchapter.pdf
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Joly S, McLenachan PA, Lockhart PJ, 2009. A statistical approach for distinguishing hybridization and incomplete lineage sorting. Amer. Nat. 174: E54-E70. https://doi.org/10.1086/600082
Morel B, Schade P, Lutteropp S, Williams TA, Sz llősi GJ, Stamatakis A, 2021. SpeciesRax: A tool for maximum likelihood species tree inference from gene family trees under duplication, transfer, and loss. https://doi.org/10.1101/2021.03.29.437460
Nauheimer L, Weigner N, Joyce E, Crayn D, Clarke C, Nargar K, 2020. HybPhaser: a workflow for the detection and phasing of hybrids in target capture datasets. BioRxiv. https://doi.org/10.1101/2020.10.27.354589
Sol s-Lemus C, An C, 2016. Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting. PLoS Genetics 12: e1005896. https://doi.org/10.1371/journal.pgen.1005896
Yang Y, Smith SA, 2014. Orthology inference in nonmodel organisms using transcriptomes and low-coverage genomes: improving accuracy and matrix occupancy for phylogenomics. Mol. Biol. Evol. 31: 3081–3092. https://doi.org/10.1093/molbev/msu245
Zhang C, Rabiee M, Sayyari E, Mirarab S, 2018. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19: 153. https://doi.org/10.1186/s12859-018-2129-y
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