five

Data from: The effect of gene flow on coalescent-based species-tree inference

收藏
Mendeley Data2024-06-25 更新2024-06-27 收录
下载链接:
https://zenodo.org/records/4984251
下载链接
链接失效反馈
资源简介:
Most current methods for inferring species-level phylogenies under the coalescent model assume that no gene flow occurs following speciation. Several studies have examined the impact of gene flow (e.g., Eckert and Carstens (2008); Chung and Ane (2011); Leache et al. (2014); Solis-Lemus et al. (2016)) and of ancestral population structure (DeGeorgio and Rosenberg, 2016) on the performance of species-level phylogenetic inference, and analytic results have been proven for network models of gene flow (e.g., Solis-Lemus et al. (2016); Zhu et al. (2016)). However, there are few analytic results for a continuous model of gene flow following speciation, despite the development of mathematical tools that could facilitate such study (e.g., Hobolth et al. (2011); Andersen et al. (2014); Tian and Kubatko (2016)). In this paper, we consider a three-taxon isolation-with-migration model that allows gene flow between sister taxa for a brief period following speciation, as well as variation in the effective population sizes across the species tree. We derive the probabilities of each of the three gene tree topologies under this model, and show that for certain choices of the gene flow and effective population size parameters, anomalous gene trees (i.e., gene trees that are discordant with the species tree but that have higher probability than the gene tree concor- dant with the species tree) exist. We characterize the region of parameter space producing anomalous trees, and show that the probability of the gene tree that is concordant with the species tree can be arbitrarily small. We then show that there is theoretical support for using SVDQuartets with an outgroup to infer the rooted three-taxon species tree in a model of gene flow between sister taxa. We study the performance of SVDQuartets on simulated data and compare it to three other commonly-used methods for species tree inference, AS- TRAL, MP-EST, and concatenation. The simulations show that ASTRAL, MP-EST, and concatenation can be statistically inconsistent when gene flow is present, while SVDQuartets performs well, though large sample sizes may be required for certain parameter choices.
创建时间:
2023-06-28
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4099个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

Global Flood Database (GFD)

全球洪水数据库(GFD)是一个包含全球范围内洪水事件记录的数据集。该数据集提供了详细的洪水事件信息,包括洪水发生的时间、地点、影响范围、受灾人口和财产损失等。数据集的目的是为了支持洪水风险评估、灾害管理和气候变化研究。

global-flood-database.cloudtostreet.info 收录

AIS数据集

该研究使用了多个公开的AIS数据集,这些数据集经过过滤、清理和统计分析。数据集涵盖了多种类型的船舶,并提供了关于船舶位置、速度和航向的关键信息。数据集包括来自19,185艘船舶的AIS消息,总计约6.4亿条记录。

github 收录

MeSH

MeSH(医学主题词表)是一个用于索引和检索生物医学文献的标准化词汇表。它包含了大量的医学术语和概念,用于描述医学文献中的主题和内容。MeSH数据集包括主题词、副主题词、树状结构、历史记录等信息,广泛应用于医学文献的分类和检索。

www.nlm.nih.gov 收录

HazyDet

HazyDet是由解放军工程大学等机构创建的一个大规模数据集,专门用于雾霾场景下的无人机视角物体检测。该数据集包含383,000个真实世界实例,收集自自然雾霾环境和正常场景中人工添加的雾霾效果,以模拟恶劣天气条件。数据集的创建过程结合了深度估计和大气散射模型,确保了数据的真实性和多样性。HazyDet主要应用于无人机在恶劣天气条件下的物体检测,旨在提高无人机在复杂环境中的感知能力。

arXiv 收录

DIV2K

displayName: DIV2K labelTypes: [] license: - DIV2K Custom mediaTypes: - Image paperUrl: https://doi.org/10.1109/CVPRW.2017.150 publishDate: "2017" publishUrl: https://data.vision.ee.ethz.ch/cvl/DIV2K/ publisher: - ETH Zurich tags: - RGB Image taskTypes: - Image Super-resolution --- # 数据集介绍 ## 简介 DIV2K数据集分为: 列车数据: 从800高清高分辨率图像开始,我们获得相应的低分辨率图像,并为2、3和4个降尺度因子提供高分辨率和低分辨率图像 验证数据: 100高清晰度高分辨率图像用于生成低分辨率对应图像,低分辨率从挑战开始提供,并用于参与者从验证服务器获得在线反馈; 当挑战的最后阶段开始时,高分辨率图像将被释放。 测试数据: 100多样的图像用于生成低分辨率的相应图像; 参与者将在最终评估阶段开始时收到低分辨率图像,并在挑战结束并确定获胜者后宣布结果。 ## 引文 ``` @inproceedings{agustsson2017ntire, title={Ntire 2017 challenge on single image super-resolution: Dataset and study}, author={Agustsson, Eirikur and Timofte, Radu}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops}, pages={126--135}, year={2017} } ``` ## Download dataset :modelscope-code[]{type="git"}

魔搭社区 收录