• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

ASTRAL-II：基于合并的数百个分类群和数千个基因的种系发生树估计。

ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes.

机构信息

Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA and Departments of Computer Science and Bioengineering, The University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.

出版信息

Bioinformatics. 2015 Jun 15;31(12):i44-52. doi: 10.1093/bioinformatics/btv234.

DOI:10.1093/bioinformatics/btv234
PMID:26072508
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4765870/
Abstract

MOTIVATION

The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed 'bipartitions'. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent.

RESULTS

We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL's running time is [Formula: see text], and ASTRAL-II's running time is [Formula: see text], where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space.

AVAILABILITY AND IMPLEMENTATION

ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/.

CONTACT

smirarab@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

由于不完全谱系分选，不同的基因座可能具有不同的树，因此物种系统发育的估计需要多个基因座，该模型由多物种合并模型建模。我们最近开发了一种基于合并的方法 ASTRAL，该方法在多物种合并模型下具有统计一致性，并且在我们检查的数据集上比其他基于合并的方法更准确。ASTRAL 通过使用一组允许的“二分法”来约束搜索空间，从而在多项式时间内运行。尽管受到允许二分法的限制，ASTRAL 仍然具有统计一致性。

结果

我们提出了 ASTRAL 的新版本，称为 ASTRAL-II。我们表明，ASTRAL-II 具有明显优于 ASTRAL 的优势：它速度更快，可以分析更大的数据集（多达 1000 个物种和 1000 个基因），并且在某些条件下具有更高的准确性。ASTRAL 的运行时间为[公式：见正文]，而 ASTRAL-II 的运行时间为[公式：见正文]，其中 n 是物种数，k 是基因座数，X 是搜索空间的允许二分法集。

可用性和实现

ASTRAL-II 可在开源网站 https://github.com/smirarab/ASTRAL 上获得，并且可在 http://www.cs.utexas.edu/~phylo/datasets/astral2/ 上获得使用的数据集。

联系方式

smirarab@gmail.com

补充信息

补充数据可在 Bioinformatics 在线获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/742137de24f4/btv234f6p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/3094eb3d7899/btv234f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/382c9edafac9/btv234f2p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/2d94891ec726/btv234f3p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/7f2798be7777/btv234f4p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/b39437f26eca/btv234f5p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/742137de24f4/btv234f6p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/3094eb3d7899/btv234f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/382c9edafac9/btv234f2p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/2d94891ec726/btv234f3p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/7f2798be7777/btv234f4p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/b39437f26eca/btv234f5p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402a/4765870/742137de24f4/btv234f6p.jpg

相似文献

1
ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes.ASTRAL-II：基于合并的数百个分类群和数千个基因的种系发生树估计。
Bioinformatics. 2015 Jun 15;31(12):i44-52. doi: 10.1093/bioinformatics/btv234.
2
ASTRAL: genome-scale coalescent-based species tree estimation.ASTRAL：基于基因组规模合并的物种树估计。
Bioinformatics. 2014 Sep 1;30(17):i541-8. doi: 10.1093/bioinformatics/btu462.
3
FASTRAL: improving scalability of phylogenomic analysis.FASTRAL：提升系统发育基因组学分析的可扩展性。
Bioinformatics. 2021 Aug 25;37(16):2317-2324. doi: 10.1093/bioinformatics/btab093.
4
ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees.ASTRAL-III：从部分解析的基因树重建多项式时间种系发生树。
BMC Bioinformatics. 2018 May 8;19(Suppl 6):153. doi: 10.1186/s12859-018-2129-y.
5
ASTRID: Accurate Species TRees from Internode Distances.ASTRID：基于节间距离的精确物种树
BMC Genomics. 2015;16 Suppl 10(Suppl 10):S3. doi: 10.1186/1471-2164-16-S10-S3. Epub 2015 Oct 2.
6
A comparative study of SVDquartets and other coalescent-based species tree estimation methods.SVDquartets与其他基于溯祖理论的物种树估计方法的比较研究。
BMC Genomics. 2015;16 Suppl 10(Suppl 10):S2. doi: 10.1186/1471-2164-16-S10-S2. Epub 2015 Oct 2.
7
Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data.小行星：一种在高比例缺失数据下从基因树推断物种树的新算法。
Bioinformatics. 2023 Jan 1;39(1). doi: 10.1093/bioinformatics/btac832.
8
To Include or Not to Include: The Impact of Gene Filtering on Species Tree Estimation Methods.包含还是不包含：基因过滤对物种树估计方法的影响。
Syst Biol. 2018 Mar 1;67(2):285-303. doi: 10.1093/sysbio/syx077.
9
The performance of coalescent-based species tree estimation methods under models of missing data.基于合并的种系发生树估计方法在缺失数据模型下的性能。
BMC Genomics. 2018 May 8;19(Suppl 5):286. doi: 10.1186/s12864-018-4619-8.
10
STELAR: a statistically consistent coalescent-based species tree estimation method by maximizing triplet consistency.STELAR：一种基于最大三重一致性的统计一致的合并物种树估计方法。
BMC Genomics. 2020 Feb 10;21(1):136. doi: 10.1186/s12864-020-6519-y.

引用本文的文献

1
 (Berberidaceae), a new riparian shrub from northern Sichuan, China.小檗科（Berberidaceae），一种来自中国四川北部的新河岸灌木。
PhytoKeys. 2025 Aug 15;261:165-174. doi: 10.3897/phytokeys.261.158475. eCollection 2025.
2
Insect Phylogenomics: From Experiment Planning to Post-phylogenetic Analyses.昆虫系统发育基因组学：从实验规划到系统发育后分析
Methods Mol Biol. 2025;2935:211-235. doi: 10.1007/978-1-0716-4583-3_9.
3
Insights into angiosperm evolution and lineage-specialized lignan biosynthesis from the early-diverging genome.

本文引用的文献

1
On the Robustness to Gene Tree Estimation Error (or lack thereof) of Coalescent-Based Species Tree Methods.基于溯祖理论的物种树方法对基因树估计误差的稳健性（或缺乏稳健性）研究
Syst Biol. 2015 Jul;64(4):663-76. doi: 10.1093/sysbio/syv016. Epub 2015 Mar 25.
2
Disk covering methods improve phylogenomic analyses.磁盘覆盖方法改进了系统发育基因组学分析。
BMC Genomics. 2014;15 Suppl 6(Suppl 6):S7. doi: 10.1186/1471-2164-15-S6-S7. Epub 2014 Oct 17.
3
BBCA: Improving the scalability of *BEAST using random binning.BBCA：使用随机装箱提高BEAST的可扩展性。
从早期分化的基因组中洞察被子植物进化和谱系特异性木脂素生物合成。
Sci Adv. 2025 Aug 15;11(33):eadw0486. doi: 10.1126/sciadv.adw0486.
4
A subgeneric revision of the genus (, ) and novel taxa from Eastern Asia based on morphology and multigene phylogenies.基于形态学和多基因系统发育对（属名）属的亚属修订及来自东亚的新分类群
IMA Fungus. 2025 Jul 17;16:e144260. doi: 10.3897/imafungus.16.144260. eCollection 2025.
5
Concatenation fails to describe the anomalous radiation of giant cockroaches (Blattodea: Blaberidae) despite moderate to low discordance.尽管存在中度到低度的不一致性，但串联法仍无法描述巨型蟑螂（蜚蠊目：硕蠊科）的异常辐射。
BMC Ecol Evol. 2025 Jul 21;25(1):72. doi: 10.1186/s12862-025-02409-4.
6
ASTER: A Package for Large-Scale Phylogenomic Reconstructions.ASTER：一个用于大规模系统发育基因组重建的软件包。
Mol Biol Evol. 2025 Jul 30;42(8). doi: 10.1093/molbev/msaf172.
7
Strong bat predation and weak environmental constraints predict longer moth tails.强烈的蝙蝠捕食和微弱的环境限制预示着蛾类的尾巴更长。
Proc Biol Sci. 2025 May;292(2046):20242824. doi: 10.1098/rspb.2024.2824. Epub 2025 May 7.
8
Evolutionary history of magnoliid genomes and benzylisoquinoline alkaloid biosynthesis.木兰类植物基因组的进化历史与苄基异喹啉生物碱的生物合成
Nat Commun. 2025 Apr 29;16(1):4039. doi: 10.1038/s41467-025-59343-8.
9
Incomplete lineage sorting and introgression among genera and species of Liliaceae tribe Tulipeae: insights from phylogenomics.百合科郁金香族属间和种间的不完全谱系分选与基因渐渗：系统发育基因组学的见解
BMC Biol. 2025 Apr 28;23(1):113. doi: 10.1186/s12915-025-02204-z.
10
Phylogenetic Analysis and Expression Patterns of Triterpenoid Saponin Biosynthesis Genes in 19 Araliaceae Plants.19种五加科植物中三萜皂苷生物合成基因的系统发育分析及表达模式
Int J Mol Sci. 2025 Apr 7;26(7):3439. doi: 10.3390/ijms26073439.
BMC Genomics. 2014;15 Suppl 6(Suppl 6):S11. doi: 10.1186/1471-2164-15-S6-S11. Epub 2014 Oct 17.
4
Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent.基于比对序列数据集串联的似然法树重建可能在统计上不一致。
Theor Popul Biol. 2015 Mar;100C:56-62. doi: 10.1016/j.tpb.2014.12.005. Epub 2014 Dec 26.
5
Statistical binning enables an accurate coalescent-based estimation of the avian tree.统计分箱可实现基于合并的鸟类树的精确估计。
Science. 2014 Dec 12;346(6215):1250463. doi: 10.1126/science.1250463. Epub 2014 Dec 11.
6
Phylotranscriptomic analysis of the origin and early diversification of land plants.陆地植物起源与早期多样化的系统发育转录组学分析
Proc Natl Acad Sci U S A. 2014 Nov 11;111(45):E4859-68. doi: 10.1073/pnas.1323926111. Epub 2014 Oct 29.
7
Evaluating Summary Methods for Multilocus Species Tree Estimation in the Presence of Incomplete Lineage Sorting.在存在不完全谱系分选的情况下评估多位点物种树估计的总结方法。
Syst Biol. 2016 May;65(3):366-80. doi: 10.1093/sysbio/syu063. Epub 2014 Aug 26.
8
ASTRAL: genome-scale coalescent-based species tree estimation.ASTRAL：基于基因组规模合并的物种树估计。
Bioinformatics. 2014 Sep 1;30(17):i541-8. doi: 10.1093/bioinformatics/btu462.
9
Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum.深度时间尺度下的系统发育分析：不可靠的基因树、被忽视的隐藏支持以及合并/串联难题
Mol Phylogenet Evol. 2014 Nov;80:231-66. doi: 10.1016/j.ympev.2014.08.013. Epub 2014 Aug 22.
10
Coalescent versus concatenation methods and the placement of Amborella as sister to water lilies.溯祖法与串联法以及无油樟作为睡莲姐妹群的定位
Syst Biol. 2014 Nov;63(6):919-32. doi: 10.1093/sysbio/syu055. Epub 2014 Jul 30.