Merge pull request #90 from SPAAM-community/phylogenomics

Updating the Phylogenomics section

Author: jfy133

assets/images/chapters/phylogenomics/ancestral_features.png

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+@article{baum2008reading,
+  title={Reading a phylogenetic tree: the meaning of monophyletic groups},
+  author={Baum, David and others},
+  journal={Nature Education},
+  volume={1},
+  number={1},
+  pages={190},
+  year={2008},
+  url = {https://www.nature.com/scitable/topicpage/reading-a-phylogenetic-tree-the-meaning-of-41956/}
+}
+
+@article{stamatakisRAxMLVersionTool2014,
+	title = {{RAxML} {Version} 8: a tool for phylogenetic analysis and post-analysis of large phylogenies},
+	volume = {30},
+	shorttitle = {{RAxML} {Version} 8},
+	url = {http://bioinformatics.oxfordjournals.org/content/early/2014/01/21/bioinformatics.btu033.short},
+	number = {9},
+	urldate = {2014-06-06},
+	journal = {Bioinformatics},
+	author = {Stamatakis, Alexandros},
+	year = {2014},
+	pages = {1312--1313},
+	file = {[PDF] à partir de oxfordjournals.org:/Users/akocher/Zotero/storage/29FI2FDT/Stamatakis - 2014 - RAxML Version 8 A tool for Phylogenetic Analysis .pdf:application/pdf;Snapshot:/Users/akocher/Zotero/storage/VUZP6N2M/bioinformatics.btu033.html:text/html},
+}
+
+@article{rambautExploringTemporalStructure2016,
+	title = {Exploring the temporal structure of heterochronous sequences using {TempEst} (formerly {Path}-{O}-{Gen})},
+	volume = {2},
+	url = {https://academic.oup.com/ve/article/2/1/vew007/1753488},
+	doi = {10.1093/ve/vew007},
+	abstract = {Abstract.  Gene sequences sampled at different points in time can be used to infer molecular phylogenies on a natural timescale of months or years, provided tha},
+	language = {en},
+	number = {1},
+	urldate = {2020-01-31},
+	journal = {Virus Evolution},
+	author = {Rambaut, Andrew and Lam, Tommy T. and Max Carvalho, Luiz and Pybus, Oliver G.},
+	month = jan,
+	year = {2016},
+	pages = {vew007},
+	file = {Full Text PDF:/Users/akocher/Zotero/storage/T2TAFK6M/Rambaut et al. - 2016 - Exploring the temporal structure of heterochronous.pdf:application/pdf},
+}
+
+@article{bouckaertBEASTSoftwarePlatform2014,
+	title = {{BEAST} 2: {A} {Software} {Platform} for {Bayesian} {Evolutionary} {Analysis}},
+	volume = {10},
+	shorttitle = {{BEAST} 2},
+	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3985171/},
+	doi = {10.1371/journal.pcbi.1003537},
+	abstract = {We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as ...},
+	language = {en},
+	number = {4},
+	urldate = {2020-01-31},
+	journal = {PLoS Computational Biology},
+	author = {Bouckaert, Remco and Heled, Joseph and Kühnert, Denise and Vaughan, Tim and Wu, Chieh-Hsi and Xie, Dong and Suchard, Marc A. and Rambaut, Andrew and Drummond, Alexei J.},
+	year = {2014},
+	pmid = {24722319},
+	pages = {e1003537},
+	file = {Full Text:/Users/akocher/Zotero/storage/NAIVV8SZ/Bouckaert et al. - 2014 - BEAST 2 A Software Platform for Bayesian Evolutio.pdf:application/pdf;Snapshot:/Users/akocher/Zotero/storage/JMWKGBUD/PMC3985171.html:text/html},
+}
+
+@article{heledLookingTreesForest2013,
+	title = {Looking for trees in the forest: summary tree from posterior samples},
+	volume = {13},
+	issn = {1471-2148},
+	shorttitle = {Looking for trees in the forest},
+	url = {https://doi.org/10.1186/1471-2148-13-221},
+	doi = {10.1186/1471-2148-13-221},
+	abstract = {Bayesian phylogenetic analysis generates a set of trees which are often condensed into a single tree representing the whole set. Many methods exist for selecting a representative topology for a set of unrooted trees, few exist for assigning branch lengths to a fixed topology, and even fewer for simultaneously setting the topology and branch lengths. However, there is very little research into locating a good representative for a set of rooted time trees like the ones obtained from a BEAST analysis.},
+	language = {en},
+	number = {1},
+	urldate = {2020-10-16},
+	journal = {BMC Evolutionary Biology},
+	author = {Heled, Joseph and Bouckaert, Remco R.},
+	month = oct,
+	year = {2013},
+	pages = {221},
+	file = {Springer Full Text PDF:/Users/akocher/Zotero/storage/VXW93PUL/Heled and Bouckaert - 2013 - Looking for trees in the forest summary tree from.pdf:application/pdf},
+}
+
+@article{rambautPosteriorSummarizationBayesian2018,
+	title = {Posterior {Summarization} in {Bayesian} {Phylogenetics} {Using} {Tracer} 1.7},
+	volume = {67},
+	issn = {1063-5157},
+	url = {https://doi.org/10.1093/sysbio/syy032},
+	doi = {10.1093/sysbio/syy032},
+	abstract = {Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.},
+	number = {5},
+	urldate = {2022-03-24},
+	journal = {Systematic Biology},
+	author = {Rambaut, Andrew and Drummond, Alexei J and Xie, Dong and Baele, Guy and Suchard, Marc A},
+	month = sep,
+	year = {2018},
+	pages = {901--904},
+	file = {Full Text PDF:/Users/akocher/Zotero/storage/SZCHX2ME/Rambaut et al. - 2018 - Posterior Summarization in Bayesian Phylogenetics .pdf:application/pdf;Snapshot:/Users/akocher/Zotero/storage/K72A2AEZ/4989127.html:text/html},
+}
+
+@article{cornuaultRoadMapPhylogenetic2022,
+	title = {A road map for phylogenetic models of species trees},
+	issn = {1055-7903},
+	url = {https://www.sciencedirect.com/science/article/pii/S1055790322000963},
+	doi = {10.1016/j.ympev.2022.107483},
+	abstract = {The field of phylogenetics has burgeoned into a great diversity of statistical models, providing researchers with a vast amount of analytical tools for investigating the evolutionary theory. This abundance of theoretical work has the merit that many different aspects of evolution can be investigated using various types of data. However, empiricists may sometimes struggle to find the right model for their needs amid such variety. In particular, some computer programs gather the theory of many different models, published in hundreds of different papers, within the same operational framework. This makes it particularly difficult for users to obtain comprehensive information about the assumptions and structure of various models. Yet, a large part of phylogenetic models are structured in individual modules that can be linked together in the same conceptual framework, akin to some sort of phylogenetic supermodel. In this paper, we propose to browse through the network of phylogenetic models, emphasizing their modular structure, with the purpose to outline the commonalities and differences of individual models. Focusing on probabilistic models, we describe how to go from the model assumptions to the corresponding probability distributions as pedagogically as possible. To achieve this task, we resort heavily on graph theory to represent the probabilistic relationships among parameters and data, and present the models in their most elementary form (i.e. including parameters that are generally marginalized out), which simplifies the mathematics considerably. We concentrate on models designed for species trees, but evoke the link with other types of trees (e.g. gene trees).},
+	language = {en},
+	urldate = {2022-05-10},
+	journal = {Molecular Phylogenetics and Evolution},
+	author = {Cornuault, Josselin and Sanmartín, Isabel},
+	month = apr,
+	year = {2022},
+	volume = {173},
+	pages = {107483},
+	file = {ScienceDirect Snapshot:/Users/akocher/Zotero/storage/QQWJNSQ7/S1055790322000963.html:text/html},
+}
+
+@article{tamuraMEGA11MolecularEvolutionary2021,
+	title = {{MEGA11}: {Molecular} {Evolutionary} {Genetics} {Analysis} {Version} 11},
+	volume = {38},
+	issn = {1537-1719},
+	shorttitle = {{MEGA11}},
+	url = {https://doi.org/10.1093/molbev/msab120},
+	doi = {10.1093/molbev/msab120},
+	abstract = {The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.},
+	number = {7},
+	urldate = {2024-07-30},
+	journal = {Molecular Biology and Evolution},
+	author = {Tamura, Koichiro and Stecher, Glen and Kumar, Sudhir},
+	month = jul,
+	year = {2021},
+	pages = {3022--3027},
+	file = {Full Text PDF:/Users/akocher/Zotero/storage/Y558C684/Tamura et al. - 2021 - MEGA11 Molecular Evolutionary Genetics Analysis V.pdf:application/pdf;Snapshot:/Users/akocher/Zotero/storage/MUX5FSSL/6248099.html:text/html},
+}
+
+@book{drummondBayesianEvolutionaryAnalysis2015,
+	address = {Cambridge},
+	title = {Bayesian {Evolutionary} {Analysis} with {BEAST}},
+	isbn = {978-1-107-01965-2},
+	url = {https://www.cambridge.org/core/books/bayesian-evolutionary-analysis-with-beast/81F5894F05E87F13C688ADB00178EE00},
+	abstract = {What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide:Addresses the theoretical aspects of the field Advises on how to prepare and perform phylogenetic analysisHelps with interpreting analyses and visualisation of phylogeniesDescribes the software architecture Helps developing BEAST 2.2 extensions to allow these models to be extended further.With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users – from those using phylogenetic tools, to computational biologists and Bayesian statisticians.},
+	urldate = {2024-07-30},
+	publisher = {Cambridge University Press},
+	author = {Drummond, Alexei J. and Bouckaert, Remco R.},
+	year = {2015},
+	doi = {10.1017/CBO9781139095112},
+	file = {Snapshot:/Users/akocher/Zotero/storage/J8764TUY/81F5894F05E87F13C688ADB00178EE00.html:text/html},
+}
+
+@book{yangMolecularEvolutionStatistical2014,
+	title = {Molecular {Evolution}: {A} {Statistical} {Approach}},
+	isbn = {978-0-19-960260-5},
+	shorttitle = {Molecular {Evolution}},
+	abstract = {Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation. Molecular Evolution: a statistical approach presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. Written by an expert in the field, the book emphasizes conceptual understanding rather than mathematical proofs. The text is enlivened with numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative. This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.},
+	language = {en},
+	address = {Oxford},
+	publisher = {Oxford University Press},
+	author = {Yang, Ziheng},
+	year = {2014},
+	note = {Google-Books-ID: yILnCwAAQBAJ},
+	keywords = {Computers / Mathematical \& Statistical Software, Mathematics / Probability \& Statistics / General, Science / Life Sciences / Biology, Science / Life Sciences / Evolution, Science / Life Sciences / Molecular Biology},
+}