PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R.
PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R.
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DOI：
10.1186/s12862-016-0837-3
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发表时间：
2016-12-01
影响因子：
3.4
通讯作者：
Townsend JP
中科院分区：
文献类型：
--
作者：
Dornburg A;Fisk JN;Tamagnan J;Townsend JP
系统发育信息量分析是筛选潜在或现有数据集的重要一步，以确定其分子位点趋同或平行进化的倾向。然而，虽然已经开发了新的理论来预测序列数据的效用，但由于缺乏能够应用理论进步的软件，特别是对于大的下一代序列数据集，这些进步的采用受到阻碍。此外，有没有理论上的障碍，应用系统发育的信息或计算的四重节间分辨率概率在贝叶斯设置，更稳健的帐户的不确定性，但有没有软件，计算密集的贝叶斯实验设计的方法可以实施。我们介绍PhyInformR，一个开源的软件包，执行快速计算的系统发育信息的内容，使用最新进展的系统发育信息为基础的理论。这些进展包括修改，将不均匀的分支长度和任何模型的核苷酸取代，以提供任何给定的数据集或数据集分区的系统发育效用的评估。PhyInformR为数据可视化提供了新的工具，并为快速统计计算优化了例程，包括利用贝叶斯后验分布和并行处理的方法。通过在用户硬件上实现计算，PhyInformR增加了用户可以应用于筛选系统发育/基因组信息内容的数据集的潜在能力。PhyInformR提供了一种方法来实现不同的替代模型，并指定不均匀的分支长度，用于系统发育信息或计算，提供基于四元组的分辨率概率，产生新的可视化，并促进下一代序列数据集的分析，同时通过使用并行处理来合并系统发育的不确定性。作为一个开源程序，PhyInformR是完全可定制和可扩展的，从而使先进的方法可以很容易地集成到本地生物信息学管道。软件可以通过CRAN获得，包含软件的软件包，详细的手册和其他示例数据也可以通过github免费提供：https://github.com/carolinafishes/PhyInformR。本文的在线版本（doi：10.1186/s12862-016-0837-3）包含补充材料，可供授权用户使用。
Analyses of phylogenetic informativeness represent an important step in screening potential or existing datasets for their proclivity toward convergent or parallel evolution of molecular sites. However, while new theory has been developed from which to predict the utility of sequence data, adoption of these advances have been stymied by a lack of software enabling application of advances in theory, especially for large next-generation sequence data sets. Moreover, there are no theoretical barriers to application of the phylogenetic informativeness or the calculation of quartet internode resolution probabilities in a Bayesian setting that more robustly accounts for uncertainty, yet there is no software with which a computationally intensive Bayesian approach to experimental design could be implemented. We introduce PhyInformR, an open source software package that performs rapid calculation of phylogenetic information content using the latest advances in phylogenetic informativeness based theory. These advances include modifications that incorporate uneven branch lengths and any model of nucleotide substitution to provide assessments of the phylogenetic utility of any given dataset or dataset partition. PhyInformR provides new tools for data visualization and routines optimized for rapid statistical calculations, including approaches making use of Bayesian posterior distributions and parallel processing. By implementing the computation on user hardware, PhyInformR increases the potential power users can apply toward screening datasets for phylogenetic/genomic information content by orders of magnitude. PhyInformR provides a means to implement diverse substitution models and specify uneven branch lengths for phylogenetic informativeness or calculations providing quartet based probabilities of resolution, produce novel visualizations, and facilitate analyses of next-generation sequence datasets while incorporating phylogenetic uncertainty through the use parallel processing. As an open source program, PhyInformR is fully customizable and expandable, thereby allowing for advanced methodologies to be readily integrated into local bioinformatics pipelines. Software is available through CRAN and a package containing the software, a detailed manual, and additional sample data is also provided freely through github: https://github.com/carolinafishes/PhyInformR. The online version of this article (doi:10.1186/s12862-016-0837-3) contains supplementary material, which is available to authorized users.
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DOI：
10.1126/science.1253451
发表时间：
2014-12-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jarvis ED;Mirarab S;Aberer AJ;Li B;Houde P;Li C;Ho SY;Faircloth BC;Nabholz B;Howard JT;Suh A;Weber CC;da Fonseca RR;Li J;Zhang F;Li H;Zhou L;Narula N;Liu L;Ganapathy G;Boussau B;Bayzid MS;Zavidovych V;Subramanian S;Gabaldón T;Capella-Gutiérrez S;Huerta-Cepas J;Rekepalli B;Munch K;Schierup M;Lindow B;Warren WC;Ray D;Green RE;Bruford MW;Zhan X;Dixon A;Li S;Li N;Huang Y;Derryberry EP;Bertelsen MF;Sheldon FH;Brumfield RT;Mello CV;Lovell PV;Wirthlin M;Schneider MP;Prosdocimi F;Samaniego JA;Vargas Velazquez AM;Alfaro-Núñez A;Campos PF;Petersen B;Sicheritz-Ponten T;Pas A;Bailey T;Scofield P;Bunce M;Lambert DM;Zhou Q;Perelman P;Driskell AC;Shapiro B;Xiong Z;Zeng Y;Liu S;Li Z;Liu B;Wu K;Xiao J;Yinqi X;Zheng Q;Zhang Y;Yang H;Wang J;Smeds L;Rheindt FE;Braun M;Fjeldsa J;Orlando L;Barker FK;Jønsson KA;Johnson W;Koepfli KP;O'Brien S;Haussler D;Ryder OA;Rahbek C;Willerslev E;Graves GR;Glenn TC;McCormack J;Burt D;Ellegren H;Alström P;Edwards SV;Stamatakis A;Mindell DP;Cracraft J;Braun EL;Warnow T;Jun W;Gilbert MT;Zhang G
通讯作者：
Zhang G
影响因子：
4.1
作者：
Dornburg, Alex;Friedman, Matt;Near, Thomas J.
通讯作者：
Near, Thomas J.
影响因子：
6.5
作者：
Townsend, Jeffrey P.;Su, Zhuo;Tekle, Yonas I.
通讯作者：
Tekle, Yonas I.
影响因子：
6.6
作者：
Revell, Liam J.
通讯作者：
Revell, Liam J.
影响因子：
10.7
作者：
Romiguier, Jonathan;Ranwez, Vincent;Douzery, Emmanuel J. P.
通讯作者：
Douzery, Emmanuel J. P.