PhyloForge: Unifying Micro- and Macroevolution With Comprehensive Genomic Signals.

IF 5.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Ecology Resources Pub Date : 2024-11-26 DOI:10.1111/1755-0998.14050
Ya Wang, Wei Dong, Yufan Liang, Weiwei Lin, Junhao Chen, Robert Henry, Fei Chen
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Abstract

The dimensions of phylogenetic research have expanded to encompass the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge, a robust tool designed to seamlessly integrate the demands of both micro- and macroevolution, comprehensively utilising diverse phylogenomic signals, such as genes, SNPs, and structural variations, as well as mitochondrial and chloroplast genomes. PhyloForge's innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling the unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customisable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants and fungi, attests to its broad applicability in the field of phylogenetics. In summary, PhyloForge has significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life. PhyloForge codes could be found in GitHub (https://github.com/wangyayaya/PhyloForge/), and the program could be installed in Conda (https://anaconda.org/wangxiaobei/phyloforge).

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PhyloForge:用全面的基因组信号统一微观和宏观进化。
系统发育研究的范围已经扩大到包括微观进化水平上的大规模种群研究和宏观进化水平上的不同物种或分类单元之间的比较。传统的系统发生学工具往往难以处理这些不同进化尺度所需的各种复杂数据。为了应对这一挑战,我们推出了 PhyloForge,这是一款强大的工具,旨在无缝整合微观和宏观进化的需求,全面利用各种系统发生学信号,如基因、SNPs 和结构变异,以及线粒体和叶绿体基因组。PhyloForge 的创新之处在于它能够无缝整合多种系统发生组信号,实现多维基因组数据的统一分析。这一独特功能使研究人员能够更全面地了解生物进化的各个方面。PhyloForge 不仅为经验丰富的研究人员提供了高度可定制的分析工具,还具有设计直观的界面,便于初学者轻松进行系统发育分析。在动物、植物和真菌等不同领域的广泛测试证明了它在系统发生学领域的广泛适用性。总之,PhyloForge 在大规模基因组学时代具有巨大潜力,为深入了解生命进化提供了新的视角和工具集。PhyloForge的代码可以在GitHub(https://github.com/wangyayaya/PhyloForge/)上找到,程序可以安装在Conda(https://anaconda.org/wangxiaobei/phyloforge)上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Ecology Resources
Molecular Ecology Resources 生物-进化生物学
CiteScore
15.60
自引率
5.20%
发文量
170
审稿时长
3 months
期刊介绍: Molecular Ecology Resources promotes the creation of comprehensive resources for the scientific community, encompassing computer programs, statistical and molecular advancements, and a diverse array of molecular tools. Serving as a conduit for disseminating these resources, the journal targets a broad audience of researchers in the fields of evolution, ecology, and conservation. Articles in Molecular Ecology Resources are crafted to support investigations tackling significant questions within these disciplines. In addition to original resource articles, Molecular Ecology Resources features Reviews, Opinions, and Comments relevant to the field. The journal also periodically releases Special Issues focusing on resource development within specific areas.
期刊最新文献
PhyloForge: Unifying Micro- and Macroevolution With Comprehensive Genomic Signals. Temporal Variability in Effective Size ( N ̂ e $$ {\hat{N}}_e $$ ) Identifies Potential Sources of Discrepancies Between Mark Recapture and Close Kin Mark Recapture Estimates of Population Abundance. Chromosomal-Level Genome Suggests Adaptive Constraints Leading to the Historical Population Decline in an Extremely Endangered Plant. Development of SNP Panels from Low-Coverage Whole Genome Sequencing (lcWGS) to Support Indigenous Fisheries for Three Salmonid Species in Northern Canada. Probe Capture Enrichment Sequencing of amoA Genes Improves the Detection of Diverse Ammonia-Oxidising Archaeal and Bacterial Populations.
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