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Advancing phylogenomics in Amaranthaceae sensu stricto: Development and application of a new nuclear target enrichment bait set 苋科植物系统基因组学研究进展:新型核靶富集诱饵的研制与应用
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-08-13 DOI: 10.1002/aps3.70019
Tina Kiedaisch, Gudrun Kadereit, Anže Žerdoner Čalasan, Diego F. Morales-Briones

Premise

Current phylogenies of Amaranthaceae sensu stricto (s.s.) are inadequately sampled and resolved to reflect the entire evolutionary history of the lineage, which is likely complex due to at least three whole-genome duplication events, occasionally followed by subsequent additional polyploidization events and rapid diversification of individual sublineages. We designed a new target enrichment bait set to overcome these challenges when reconstructing a phylogeny and demonstrated its applicability to the entire Amaranthaceae s.s. lineage.

Methods

We analyzed 12,775 orthologous and low-copy genes from a previous comprehensive transcriptomic study for marker selection. Following a newly developed approach that allows the selection of long exons and thus avoids the assembly of chimeric loci, we selected 1000 orthologous exons for phylogenomic analyses.

Results

Our in vivo application showed a high locus recovery rate across all major clades of Amaranthaceae s.s., generated a robust phylogenetic tree, and clarified previously ambiguous relationships of the genera Bosea and Charpentiera. Gene tree conflict analysis revealed mainly high levels of gene tree concordance within the lineage, with a few notable exceptions.

Discussion

The Amaranthaceae1000 kit will provide the basis for a phylogenetic tree across the Amaranthaceae s.s., facilitating future studies on systematics, diversification, and genome evolution within this economically important lineage.

目前对苋科严格感觉植物(s.s.)系统发育的采样和解析不足以反映整个谱系的进化史,这可能是由于至少三个全基因组复制事件,偶尔伴随着随后的额外多倍体事件和单个亚谱系的快速多样化而复杂的。为了克服这些困难,我们设计了一套新的目标富集诱饵,并证明了它在苋科整个谱系中的适用性。方法分析了12775个同源和低拷贝基因,用于标记选择。根据一种新开发的方法,允许选择长外显子,从而避免嵌合位点的组装,我们选择了1000个同源外显子进行系统基因组分析。结果我们的体内应用表明,在苋科所有主要分支中,位点回收率都很高,生成了一个健全的系统发育树,并澄清了以前不明确的Bosea属和Charpentiera属的关系。基因树冲突分析显示,谱系内基因树高度一致,少数例外。Amaranthaceae1000试剂盒将为Amaranthaceae s.s的系统发育树提供基础,促进未来对这一经济上重要谱系的系统学、多样化和基因组进化的研究。
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引用次数: 0
A practical and easy-to-scale protocol for removing chlorophylls from leaf extracts 从叶片提取物中去除叶绿素的一种实用且易于扩展的方案
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-07-29 DOI: 10.1002/aps3.70018
Alice Fossati, Valeria Cavalloro, Daniela Rossi, Simona Collina, Emanuela Martino

Premise

Leaf extracts are valuable sources of bioactive compounds. However, co-extracted chlorophylls interfere with analyses, including spectroscopic and biochemical assays. Existing methods for chlorophyll removal often have limitations, including the use of hazardous solvents, low specificity, or high costs.

Methods and Results

A solid-phase extraction protocol for chlorophyll removal from leaf extracts of Corylus avellana was developed using commercially available cartridges. The method requires standard equipment, can be completed within 10 minutes, and is scalable from analytical to preparative quantities. We validated this protocol across 20 taxa, demonstrating the removal of 85% of chlorophylls, successful scale up of quantities, cartridge reusability, and low solvent consumption.

Conclusions

Key innovations of the protocol include simplified elution steps and the possibility of multiple reuse cycles. The simplicity, sustainability, and scalability of this new protocol make it particularly valuable for high-throughput applications and process development.

前提是叶提取物是生物活性化合物的宝贵来源。然而,共提取叶绿素干扰分析,包括光谱和生化分析。现有的叶绿素去除方法通常有局限性,包括使用有害溶剂、特异性低或成本高。方法与结果建立了一种利用市售胶筒从榛叶提取物中提取叶绿素的固相萃取方法。该方法需要标准设备,可在10分钟内完成,并且可从分析量扩展到制备量。我们在20个分类群中验证了该方案,证明了85%的叶绿素被去除,数量成功扩大,墨盒可重复使用,溶剂消耗低。结论该方案的关键创新在于简化了洗脱步骤和多次重复使用的可能性。这种新协议的简单性、可持续性和可伸缩性使其对高吞吐量应用程序和流程开发特别有价值。
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引用次数: 0
catalogoUCsBR: A new R package and application for creating comprehensive plant species lists for Brazilian Protected Areas catalogoUCsBR:一个新的R包和应用程序,用于创建巴西保护区的综合植物物种列表
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-07-17 DOI: 10.1002/aps3.70016
Thuane Bochorny, Pablo H. A. de Melo, Rafaela C. Forzza

Premise

The increased online publication of data associated with Brazilian botanical collections and biodiversity information systems has significantly improved access to information on plant species occurring in Brazil. However, information about Brazilian flora within Protected Areas is fragmented.

Methods and Results

The catalogoUCsBR package—developed in R and as a Shiny application—performs several data refinement processes to facilitate creating comprehensive plant checklists of Brazilian Protected Areas. Functions include combining herbarium records into a desired format and removing duplicates from different databases, extracting information on accepted species names, facilitating taxonomic revision through digitized images and labels from herbarium collections, and publishing the plant species list in the Catálogo de Plantas das Unidades de Conservação do Brasil.

Conclusions

The catalogoUCsBR package is an open platform designed for creating taxonomically verified plant species lists that enhances the effectiveness of Brazilian conservation efforts, ensuring that they are based on accurate and up-to-date biodiversity information.

与巴西植物收集和生物多样性信息系统相关数据的在线出版增加,大大改善了对巴西植物物种信息的获取。然而,关于保护区内巴西植物群的信息是支离破碎的。方法和结果catalogoUCsBR包是用R语言开发的,作为一个Shiny的应用程序,它执行了几个数据优化过程,以方便创建巴西保护区的综合植物清单。其功能包括将植物标本馆记录合并为所需格式并从不同数据库中删除重复的记录,提取公认的物种名称信息,通过植物标本馆馆藏的数字化图像和标签促进分类修订,以及在Catálogo de Plantas das Unidades de conserva o do Brasil上发布植物物种列表。catalogoUCsBR软件包是一个开放平台,旨在创建经过分类学验证的植物物种列表,提高巴西保护工作的有效性,确保它们基于准确和最新的生物多样性信息。
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引用次数: 0
Advances in analyzing and engineering plant metabolic diversity 植物代谢多样性分析与工程研究进展
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-07-11 DOI: 10.1002/aps3.70017
Kira J. Tiedge, Federico Roda, Stacey D. Smith, Gaurav D. Moghe
<p>The immense diversity of the members of the kingdom Plantae, so far comprising more than 400,000 known species (Guiry, <span>2024</span>), is reflected not only in their morphological and genetic variability but also in their metabolic complexity. Plant metabolic networks are dynamic and expanding systems that evolve in a lineage-specific manner and produce hundreds of thousands of structurally diverse metabolites, which can be broadly categorized into general (or core/primary) and specialized (or secondary) metabolites. General metabolites, such as carbohydrates, amino acids, and lipids, are essential for fundamental physiological processes like growth, development, and reproduction. In contrast, specialized metabolites like alkaloids, flavonoids, terpenoids, and other phenolic compounds often have an impact at different levels beyond central carbon metabolism—from allosteric regulation of proteins, subcellular organization, and intercellular interactions to organismal phenotypes, phylogeographic/interspecies diversification, biotic/abiotic interactions, and ecosystem maintenance (Weng et al., <span>2021</span>; Ono and Murata, <span>2023</span>).</p><p>The chemical diversity of plant metabolites constitutes a vast and largely untapped phytochemical space with significant potential for applications across multiple fields. In medicine, plant-derived compounds have been a cornerstone of drug discovery for centuries. For example, alkaloids, like morphine and quinine, and terpenoids, such as paclitaxel, have revolutionized the treatment of pain, malaria, and cancer, respectively (Newman and Cragg, <span>2020</span>; Atanasov et al., <span>2021</span>). In agriculture, phytochemicals are increasingly recognized for their contribution to plant defense against pests and diseases, reducing the need for synthetic pesticides while promoting sustainable farming practices and food security (Sousa et al., <span>2021</span>). Beyond medicine and agriculture, plant metabolites hold promise for applications in biotechnology and industrial processes. For instance, terpenoids and phenolic compounds are being investigated for their potential as biofuels, bioplastics, and natural food preservatives (Mewalal et al., <span>2017</span>). Even though less than 10% of plant species have been thoroughly investigated for their chemical composition (Li and Vederas, <span>2009</span>), it is estimated that plants produce over a million compounds, although pinpointing a specific number is challenging because of the heterogeneity of metabolite databases available (Wang et al., <span>2016</span>; Nguyen-Vo et al., <span>2020</span>; Hawkins et al., <span>2021</span>). Recent estimates suggest that the total number of unique structures across the entire plant kingdom likely spans into the millions to tens of millions (Engler Hart et al., <span>2025</span>), indicating that over 99% of the phytochemical space remains unexplored and highlighting its vast and largely untapped p
植物界成员的巨大多样性,迄今为止包括超过40万种已知物种(Guiry, 2024),不仅反映在它们的形态和遗传变异上,还反映在它们的代谢复杂性上。植物代谢网络是一个动态的、不断扩展的系统,它以一种特定谱系的方式进化,产生成千上万种结构多样的代谢物,这些代谢物可以大致分为一般(或核心/初级)代谢物和专门(或次级)代谢物。一般代谢物,如碳水化合物、氨基酸和脂质,是基本生理过程如生长、发育和繁殖所必需的。相比之下,生物碱、黄酮类化合物、萜类化合物和其他酚类化合物等特化代谢物通常在不同水平上影响中枢碳代谢之外的其他方面——从蛋白质的变构调节、亚细胞组织和细胞间相互作用到有机体表型、系统地理/物种间多样化、生物/非生物相互作用和生态系统维持(Weng等,2021;小野和村田,2023)。植物代谢物的化学多样性构成了一个巨大的尚未开发的植物化学空间,在多个领域具有巨大的应用潜力。在医学领域,几个世纪以来,植物衍生化合物一直是药物发现的基石。例如,生物碱,如吗啡和奎宁,萜类,如紫杉醇,分别彻底改变了疼痛、疟疾和癌症的治疗(Newman和Cragg, 2020;Atanasov et al., 2021)。在农业领域,植物化学物质对植物抵御病虫害、减少合成农药需求、同时促进可持续农业做法和粮食安全的贡献日益得到认可(Sousa等人,2021年)。除了医学和农业,植物代谢物在生物技术和工业过程中也有应用前景。例如,萜类化合物和酚类化合物正在被研究作为生物燃料、生物塑料和天然食品防腐剂的潜力(Mewalal等人,2017)。尽管对不到10%的植物物种的化学成分进行了彻底的研究(Li and Vederas, 2009),但据估计,植物产生的化合物超过100万种,尽管由于现有代谢物数据库的异质性,确定具体数量具有挑战性(Wang et al., 2016;Nguyen-Vo等,2020;Hawkins et al., 2021)。最近的估计表明,整个植物界的独特结构总数可能达到数百万到数千万(Engler Hart et al., 2025),这表明超过99%的植物化学空间仍未被探索,并突出了其巨大且基本上未开发的潜力。为了捕获这种广泛的代谢物多样性和功能,使用了各种技术,例如代谢物提取,质谱分析,计算代谢组学(包括化合物注释),化学信息学,生物测定,化学分类学,系统基因组学,祖先状态重建和化学生态学的专门协议。随着基因组学、大数据和人工智能(AI)革命的深入,越来越需要为上述技术开发高通量替代品,并利用人工智能来解决突出的障碍。同样,将不同的“部分”(如酶、调节因子、转运蛋白)结合起来进行代谢工程和重建复杂代谢途径的动力也更大。然而,由于提取和分析方法的差异,代谢组学数据集和实验的比较仍然很困难,这只是持续挑战的一个例子。此外,收集、保存和共享代谢组学数据的标准只是在缓慢发展(Alseekh等人,2021;Genesiska等人,2024),许多代谢物的生态作用仍然知之甚少(Kessler和Kalske, 2018),限制了我们充分利用其潜力的能力。在本期特刊中,我们重点介绍了这些方法的样本,为植物代谢多样性提供了更多的见解。下面提供了这些问题的文章摘要,大致分为新颖的工作台技术、数据应用和人工智能技术。我们预计这些方法的应用将促进我们对植物对自然环境作出反应的化学反应库的理解,并将有助于在作物中培育/设计这些化学性状。和G.D.M.发起了这个特刊,S.D.S.和F.R.为它的发展做出了贡献。所有作者都对本期特刊中包含的手稿的编辑职责做出了贡献。所有作者都为手稿贡献了文本,K.J.T.将这些贡献结合起来,主导了写作和编辑。 所有作者都认可了手稿的最终版本。
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引用次数: 0
From WGS to gels: Development and testing of PCR primers targeting toxic Digitalis in support of food safety 从WGS到凝胶:开发和测试针对有毒洋地黄的PCR引物,以支持食品安全
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-07-01 DOI: 10.1002/aps3.70013
Elizabeth Sage Hunter, Sydnee Fo, Robert Literman, Richard H. Uva, Jennifer L. Wolny, Sara M. Handy

Premise

This study capitalized on a library of single-nucleotide polymorphisms created via whole genome sequencing (WGS) to develop and test a PCR assay for detecting toxic Digitalis species in food products. Complex foods can be difficult to analyze, but safeguarding consumer well-being and public health necessitates that products regulated by the U.S. Food and Drug Administration are contaminant free.

Methods

Ten pairs of PCR primers were designed, optimized, and tested against a subset of vouchered specimens. Two primer sets were screened using 55 vouchered Plantaginaceae species, complex food matrices, five different plant tissues, a dilution series, and spiked food products with 0.5%, 1%, and 5% biomass of D. purpurea and D. lanata.

Results

At optimized annealing temperatures, these primers amplified only Digitalis spp. (5). Both primer sets could detect spikes of D. purpurea and D. lanata down to 0.5% biomass and across three orders of magnitude, as well as five tissue types of D. purpurea.

Discussion

This study provides an enhanced DNA-based method for detecting Digitalis in complex food products. This novel method of primer development from WGS data lays the groundwork for a larger, more comprehensive panel for the rapid identification of botanical contaminants that may pose risks to consumers.

本研究利用全基因组测序(WGS)建立的单核苷酸多态性文库,开发并测试了一种检测食品中有毒洋地黄物种的PCR方法。复杂的食品可能很难分析,但为了保护消费者的福祉和公众的健康,美国食品和药物管理局规定的产品必须是无污染物的。方法设计、优化了10对PCR引物,并对部分样本进行了检测。用55种车前草科植物、复杂的食物基质、5种不同的植物组织、稀释系列和添加了0.5%、1%和5%的D. purpurea和D. lanata生物量的食品,筛选了两组引物。结果在优化的退火温度下,这些引物仅扩增洋地黄spp.(5)。两组引物均能检测到0.5%生物量下的紫荆和大叶茅的穗状花序,并能检测到紫荆的5种组织类型和3个数量级。本研究提供了一种基于dna的检测复杂食品中洋地黄的强化方法。这种从WGS数据中开发引物的新方法为更大,更全面的快速识别可能对消费者构成风险的植物污染物的面板奠定了基础。
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引用次数: 0
Applying interpretable machine learning to assess intraspecific trait divergence under landscape-scale population differentiation 应用可解释机器学习评估景观尺度种群分化下种内性状分化
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-06-18 DOI: 10.1002/aps3.70015
Sambadi Majumder, Chase M. Mason

Premise

Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflower Helianthus annuus occupying populations across two contrasting ecoregions—the Great Plains versus the North American Deserts.

Methods

Recursive feature elimination was applied to functional trait data from the HeliantHOME database, followed by the application of the Boruta algorithm to detect the traits that are most predictive of ecoregion. Random forest and gradient boosting machine classifiers were then trained and validated, with results visualized using accumulated local effects plots.

Results

The most ecoregion-predictive functional traits span categories of leaf economics, plant architecture, reproductive phenology, and floral and seed morphology. Relative to the Great Plains, genotypes from the North American Deserts exhibit shorter stature, fewer leaves, higher leaf nitrogen content, and longer average length of phyllaries.

Discussion

This approach readily identifies traits predictive of ecoregion origin, and thus the functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.

在这里,我们展示了可解释的机器学习方法的应用,以不同基因型的向日葵太阳花(Helianthus annuus)为研究对象,研究了两个不同生态区域(大平原和北美沙漠)的广泛分布种群的种内功能性状差异。方法对HeliantHOME数据库的功能性状数据进行递归特征消去,然后应用Boruta算法检测对生态区域预测能力最强的性状。然后训练和验证随机森林和梯度增强机器分类器,并使用累积的局部效果图将结果可视化。结果最能预测生态区域的功能性状包括叶片经济学、植物结构、生殖物候、花和种子形态。与大平原相比,来自北美沙漠的基因型表现出更短的身材,更少的叶片,更高的叶片氮含量和更长的叶根平均长度。这种方法很容易识别出预测生态区域起源的特征,从而识别出最有可能在整个景观中负责对比生态策略的功能特征。这种类型的方法可用于在广泛的背景下解析大型植物性状数据集,包括在种内尺度上明确测试种间范式的适用性。
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引用次数: 0
A novel combination of in vitro propagation and hydroponic culture for hybrid cacao (Theobroma cacao) plants 杂交可可植株的离体繁殖与水培新组合
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-06-13 DOI: 10.1002/aps3.70014
Luis David Vera Pinargote, Elisabeth Jamet, Naga Raju Maddela

Premise

Currently, there is a lack of controlled cultivation methods for cacao (Theobroma cacao), a plant species with high commercial value. One major concern is the tendency of cacao plants to accumulate high concentrations of cadmium (Cd), a heavy metal with high toxicity to living organisms.

Methods and Results

We describe a new two-step method for the propagation of hybrid cacao plants, consisting of (1) in vitro germination for two weeks, followed by (2) transfer to a vertical hydroponic system for growth under controlled conditions. As a test case, two new cacao hybrids were cultivated in the presence of Cd and showed different levels of tolerance.

Conclusions

Our simple approach offers a new research direction for the controlled cultivation of cacao plants and can potentially be applied to other plants of agronomic interest. Moreover, this method allows the identification of plants that are resistant to various toxic substances, which could then be used in phytoremediation.

可可(Theobroma cacao)是一种具有较高商业价值的植物物种,目前缺乏可控的栽培方法。一个主要的担忧是可可树会积累高浓度的镉(Cd),这是一种对生物体具有高毒性的重金属。方法和结果我们描述了一种新的杂交可可植株的两步繁殖方法,包括:(1)离体萌发两周,然后(2)转移到垂直水培系统中在受控条件下生长。作为试验用例,在Cd存在的情况下培育了两个新的可可杂交种,表现出不同程度的耐受性。结论该方法为可可植物的控制栽培提供了新的研究方向,并可应用于其他农艺植物。此外,这种方法可以识别对各种有毒物质具有抗性的植物,然后可以用于植物修复。
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引用次数: 0
Improving computer vision for plant pathology through advanced training techniques 通过先进的训练技术提高植物病理学的计算机视觉
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-06-07 DOI: 10.1002/aps3.70010
Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

Premise

This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao.

Methods

Despite recent stagnation in accuracy improvements in computer vision for image classification, our research demonstrates significant advancements in performance through semi-supervised learning, specialised loss functions, and the inclusion of a non-cocoa class.

Results

Semi-supervised learning reduced overfitting and enhanced generalisability, particularly for subtle symptoms. The non-cocoa class exposed models to a broad range of relevant features, significantly improving model robustness and performance in difficult cases. Grad-CAM for qualitative assessment provided valuable insights into model behaviour, highlighting cases of overfitting missed by summary statistics. We also describe dynamic focal loss, a novel loss function that uses an empirical measure of difficulty to weight each image. Our results suggest that while PhytNet shows promise in terms of computational efficiency and superior handling of difficult images, ResNet18 with semi-supervised learning and dynamic focal loss emerged as the strongest contender for real-world deployment.

Discussion

This research underscores the potential of semi-supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. It also presents a new high-quality benchmark dataset of 7220 images of diseased and healthy cocoa trees, offering a much greater and more realistic challenge than the Plan Village dataset.

本研究探讨了先进的训练技术,以提高卷积神经网络在可可疾病检测中的性能。尽管最近在计算机视觉图像分类的准确性提高方面停滞不前,但我们的研究表明,通过半监督学习、专门的损失函数和包含非可可类,在性能方面取得了重大进展。结果半监督学习减少了过拟合,提高了普遍性,特别是对细微症状。非可可类将模型暴露在广泛的相关特征中,显著提高了模型在困难情况下的鲁棒性和性能。用于定性评估的Grad-CAM为模型行为提供了有价值的见解,突出了汇总统计错过的过拟合案例。我们还描述了动态焦点损失,这是一种新的损失函数,它使用困难的经验度量来加权每个图像。我们的研究结果表明,虽然PhytNet在计算效率和对困难图像的卓越处理方面表现出了希望,但具有半监督学习和动态焦点损失的ResNet18成为现实世界部署的最强竞争者。本研究强调了半监督学习和高级损失函数在增强深度学习模型在农业疾病管理中的适用性方面的潜力。它还提供了一个新的高质量基准数据集,包含7220张患病和健康的可可树图像,比Plan Village数据集提供了更大、更现实的挑战。
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引用次数: 0
infinitylists: A Shiny application and R package for rapid generation of place-based species checklists infinitylists:一个闪亮的应用程序和R包,用于快速生成基于地点的物种清单
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-06-04 DOI: 10.1002/aps3.70012
Thomas Mesaglio, Fonti Kar, Hervé Sauquet, William K. Cornwell

Premise

Biodiversity researchers often need to answer the question: “Which species of taxon X have been documented in (or near) spatial polygon Y?” Online databases with billions of occurrence records, including vouchered specimens and citizen science records, can provide the answer; however, quick spatial processing of huge biodiversity datasets can be difficult, and many general-purpose tools are constrained by dataset size.

Methods and Results

infinitylists is a Shiny application and R package that allows users to generate species checklists for a user-specified taxon and area. It downloads taxon–country datasets (e.g., Madagascan geckos) from biodiversity data providers and uses an open source, column-oriented data file for fast retrieval and visualization. Available as a mobile-friendly web tool with preloaded data, it can also be run locally in R for very flexible applications.

Conclusions

infinitylists is an easy-to-use tool with applications including supplementing survey data, planning collecting expeditions, and informing gap-filling. infinitylists is a complementary tool to existing databases to help field ecologists and naturalists globally.

生物多样性研究人员经常需要回答这样一个问题:“X分类群中有哪些物种被记录在(或附近)空间多边形Y中?”拥有数十亿发生记录的在线数据库,包括凭据标本和公民科学记录,可以提供答案;然而,大型生物多样性数据集的快速空间处理可能是困难的,许多通用工具受到数据集大小的限制。infinitylists是一个Shiny的应用程序和R包,允许用户为用户指定的分类单元和区域生成物种清单。它从生物多样性数据提供商那里下载分类-国家数据集(例如,马达加斯加壁虎),并使用开源的、面向列的数据文件进行快速检索和可视化。它是一个移动友好的web工具,带有预加载的数据,也可以在R中本地运行,用于非常灵活的应用程序。结论:infinitylists是一个易于使用的工具,其应用包括补充调查数据,计划收集探险,并告知空白填补。Infinitylists是现有数据库的补充工具,可以帮助全球的野外生态学家和自然主义者。
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引用次数: 0
The Computer-Assisted Sequence Annotation (CASA) workflow for enzyme discovery 酶发现的计算机辅助序列注释(CASA)工作流程
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-06-03 DOI: 10.1002/aps3.70009
Gemma R. Takahashi, Franchesca M. Cumpio, Carter T. Butts, Rachel W. Martin

Premise

With the advent of inexpensive nucleic acid sequencing and automated annotation at the level of basic functionality, the central problem of enzyme discovery is no longer finding active sequences, it is determining which ones are suitable for further study. This requires annotation that goes beyond sequence similarity to known enzymes and provides information at the sequence and structural levels.

Methods

Here we introduce a workflow for generating highly informative, richly annotated sequence alignments from protein sequence data. Computer-Assisted Sequence Annotation (CASA) is a freely available Python-based workflow designed to automate portions of novel protein characterization, while producing a human-interpretable final output.

Results

We demonstrate CASA using one enzyme from the Drosera capensis genome. The workflow generates detailed annotations providing comparisons to known reference sequences. In addition to sequence similarity and predicted function, user-specified features such as active site residues, disulfide bonds, and substrate-binding residues can be displayed, and these can then be combined with downstream analyses to gain new insights into enzyme structure and function.

Discussion

This work demonstrates the utility of detailed annotations and protein structure prediction for choosing protein targets for biochemistry or structural biology from nucleic acid sequence data. The toolchain is freely available along with instructions and representative examples.

随着廉价的核酸测序和基本功能水平的自动注释的出现,酶发现的中心问题不再是寻找活性序列,而是确定哪些序列适合进一步研究。这需要超越已知酶序列相似性的注释,并提供序列和结构级别的信息。在这里,我们介绍了一个工作流,用于从蛋白质序列数据中生成高信息量、丰富注释的序列比对。计算机辅助序列注释(CASA)是一种免费的基于python的工作流程,旨在自动化部分新蛋白质表征,同时产生人类可解释的最终输出。结果我们利用一种来自牛血清基因组的酶证明了CASA。工作流生成详细的注释,提供与已知参考序列的比较。除了序列相似性和预测功能外,还可以显示用户指定的特征,如活性位点残基、二硫键和底物结合残基,然后将这些特征与下游分析相结合,以获得对酶结构和功能的新见解。这项工作证明了详细注释和蛋白质结构预测在从核酸序列数据中选择生物化学或结构生物学蛋白质靶点方面的实用性。该工具链与说明和代表性示例一起免费提供。
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期刊
Applications in Plant Sciences
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