Mia Ruppel, Sven K. Nelson, Grace Sidberry, Madison Mitchell, Daniel Kick, Shawn K. Thomas, Katherine E. Guill, Melvin J. Oliver, Jacob D. Washburn
Premise
Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.
Methods and Results
RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.
Conclusions
The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.
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McAssey, E. V., Downs, C., Yorkston, M., Morden, C., and Heyduk, K. 2023. A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming. Applications in Plant Sciences 11(3): e11527
A statistical error was found after article publication. The relevant text from the Results section is provided below, with the corrected values shown in bold text. The error does not affect the findings of the study.
“Herbarium tissue library samples had significantly smaller insert sizes of mapped chloroplast reads compared to their freezer-stored DNA paired samples, taking into account covariates of read numbers and year (F1,25 = 229.243, P < 0.001). There was also a significant interaction effect between library size and sampling year (F1,25 = 9.753, P < 0.01). Similarly, herbarium tissue samples also had higher amounts of adapter sequences in the reads (F1,25 = 85.009, P < 0.001), with sampling year a significant covariate in the model (F1,25 = 6.378, P < 0.05).”
We apologize for this error.
mccassey, e.v.， Downs, C, Yorkston, M, Morden, C, and Heyduk, K. 2023。用于叶绿体组装和基因组脱脂的冷冻储存DNA和植物标本组织样本的比较。植物科学应用11(3):e11527文章发表后发现统计误差。结果部分的相关文本如下所示，更正后的值以粗体显示。这个错误不影响研究的结果。考虑到读取数和年份的协变量(F1,25 = 229.243, P < 0.001)，植物标本馆组织文库样本的叶绿体图谱插入尺寸明显小于冷冻保存的DNA配对样本。文库规模与采样年份之间也存在显著的交互效应(F1,25 = 9.753, P < 0.01)。同样，植物标本组织样本在reads中也有较高数量的适配器序列(F1,25 = 85.009, P < 0.001)，采样年份在模型中是一个显著的协变量(F1,25 = 6.378, P < 0.05)。我们为这个错误道歉。
{"title":"Correction to “A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming”","authors":"","doi":"10.1002/aps3.11540","DOIUrl":"10.1002/aps3.11540","url":null,"abstract":"<p>McAssey, E. V., Downs, C., Yorkston, M., Morden, C., and Heyduk, K. 2023. A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming. <i>Applications in Plant Sciences</i> 11(3): e11527</p><p>A statistical error was found after article publication. The relevant text from the Results section is provided below, with the corrected values shown in bold text. The error does not affect the findings of the study.</p><p>“Herbarium tissue library samples had significantly smaller insert sizes of mapped chloroplast reads compared to their freezer-stored DNA paired samples, taking into account covariates of read numbers and year (<b><i>F</i><sub>1,25</sub> = 229.243</b>, <i><b>P</b></i> < <b>0.001</b>). There was also a significant interaction effect between library size and sampling year (<b><i>F</i><sub>1,25</sub> = 9.753</b>, <i>P</i> < 0.01). Similarly, herbarium tissue samples also had higher amounts of adapter sequences in the reads (<b><i>F</i><sub>1,25</sub> = 85.009</b>, <i>P</i> < 0.001), with sampling year a significant covariate in the model (<b><i>F</i><sub>1,25</sub> = 6.378</b>, <i>P</i> < 0.05).”</p><p>We apologize for this error.</p>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439819/pdf/APS3-11-e11540.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10034571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrea J. Tirrell, Aaron E. Putnam, Michael I. J. Cianchette, Jacquelyn L. Gill
Premise
Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible.
Methods
In 2021 and 2022, we took iPhone photographs to record species presence in 1-m2 plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs.
Results
Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates.
Discussion
In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.
{"title":"Using photogrammetry to create virtual permanent plots in rare and threatened plant communities","authors":"Andrea J. Tirrell, Aaron E. Putnam, Michael I. J. Cianchette, Jacquelyn L. Gill","doi":"10.1002/aps3.11534","DOIUrl":"10.1002/aps3.11534","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In 2021 and 2022, we took iPhone photographs to record species presence in 1-m<sup>2</sup> plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42880432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emily B. Sessa, Rishi R. Masalia, Nils Arrigo, Michael S. Barker, Jessie A. Pelosi
Premise
The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually.
Methods and Results
The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s).
Conclusions
GO annotations are a widely used “universal language” for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.
{"title":"GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data","authors":"Emily B. Sessa, Rishi R. Masalia, Nils Arrigo, Michael S. Barker, Jessie A. Pelosi","doi":"10.1002/aps3.11536","DOIUrl":"10.1002/aps3.11536","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>GO annotations are a widely used “universal language” for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10052130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flávia Fonseca Pezzini, Giada Ferrari, Laura L. Forrest, Michelle L. Hart, Kanae Nishii, Catherine A. Kidner
Recent technological advances in long-read high-throughput sequencing and assembly methods have facilitated the generation of annotated chromosome-scale whole-genome sequence data for evolutionary studies; however, generating such data can still be difficult for many plant species. For example, obtaining high-molecular-weight DNA is typically impossible for samples in historical herbarium collections, which often have degraded DNA. The need to fast-freeze newly collected living samples to conserve high-quality DNA can be complicated when plants are only found in remote areas. Therefore, short-read reduced-genome representations, such as target capture and genome skimming, remain important for evolutionary studies. Here, we review the pros and cons of each technique for non-model plant taxa. We provide guidance related to logistics, budget, the genomic resources previously available for the target clade, and the nature of the study. Furthermore, we assess the available bioinformatic analyses, detailing best practices and pitfalls, and suggest pathways to combine newly generated data with legacy data. Finally, we explore the possible downstream analyses allowed by the type of data generated using each technique. We provide a practical guide to help researchers make the best-informed choice regarding reduced genome representation for evolutionary studies of non-model plants in cases where whole-genome sequencing remains impractical.
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Katie Emelianova, Diego Mauricio Riaño-Pachón, Maria Fernanda Torres Jimenez
<p>Coined by Dutch theoretical biologists in the 1970s, the term bioinformatics originally denoted a broad concept relating to the study of information processing in biological systems, such as ecosystem interaction, neuronal messaging, and transfer of genetic information (Hogeweg, <span>2011</span>). Subsequently co-opted to describe the sequencing and analysis of molecules (from nucleic acids to proteins), bioinformatics has diverse applications including the analysis, visualization, storage, and generation of data relating to living organisms and the molecular information they carry. Plant biology has reaped dividends from the development and maturation of bioinformatics; it has not only extended our understanding of model plant species such as <i>Arabidopsis thaliana</i> (Cantó-Pastor et al., <span>2021</span>) but also driven innovative solutions to characterize non-model species (Nevado et al., <span>2014</span>). Both avenues of discovery contribute to key objectives in improving food security, conservation, and biotechnology.</p><p>The size and complexity of many plant genomes has historically made their analysis financially and computationally difficult. Frequent polyploidy and repeat element expansion make the elucidation of plant genome sequences challenging (Soltis et al., <span>2015</span>). Furthermore, high heterozygosity in wild populations, pervasive hybridization, and a lack of inbred lines present roadblocks to analyses such as read mapping and assembly (Kajitani et al., <span>2019</span>). Long-read technologies have become ever more accessible in recent years, and algorithmic advances have accommodated sequential updates to error models, read lengths, and library types (Michael and VanBuren, <span>2020</span>). Moreover, novel methods to scaffold contigs and obtain long-range interaction information have driven impressive improvements in genome assembly quality, making telomere-to-telomere genome sequencing projects an achievable goal for many labs (Kress et al., <span>2022</span>).</p><p>Long-read technologies paired with novel mapping algorithms have fueled discovery of new transposable element (TE) dynamics, and there has been an associated resurgence of interest in their role in adaptive trait evolution and phenotypic variation (Schrader and Schmitz, <span>2019</span>; Pimpinelli and Piacentini, <span>2020</span>). Bioinformatics developments in this field have led to vast improvements in our ability to detect complex TE mobilization patterns such as nested insertions and structural variants (Bree et al., <span>2022</span>; Lemay et al., <span>2022</span>). Despite these advancements, characterization and annotation of genomic features such as genes and repetitive elements remain challenging due to species-specific genomic configurations, taxonomically patchy reference databases, and a lack of robust benchmarking and quality control. While structural and functional annotation methods still have significant obstacles to ov
生物信息学一词由荷兰理论生物学家于20世纪70年代创立，最初表示一个与生物系统中的信息处理研究有关的广泛概念，如生态系统相互作用、神经元信息传递和遗传信息传递（Hogeweg，2011）。随后，生物信息学被用于描述分子（从核酸到蛋白质）的测序和分析，具有多种应用，包括分析、可视化、存储和生成与生物体及其携带的分子信息有关的数据。植物生物学从生物信息学的发展和成熟中获得了红利；它不仅扩展了我们对拟南芥等模式植物物种的理解（Cantó‐Pastor et al.，2021），还推动了表征非模式物种的创新解决方案（Nevado et al.，2014）。这两种发现途径都有助于实现改善粮食安全、保护和生物技术的关键目标。许多植物基因组的大小和复杂性在历史上使其分析在财务和计算上都很困难。频繁的多倍体和重复元件扩增使植物基因组序列的阐明具有挑战性（Soltis等人，2015）。此外，野生种群中的高杂合性、普遍的杂交和近交系的缺乏阻碍了读取图谱和组装等分析（Kajitani等人，2019）。近年来，长读技术变得越来越容易获得，算法的进步适应了对错误模型、读取长度和库类型的顺序更新（Michael和VanBuren，2020）。此外，构建重叠群和获得长距离相互作用信息的新方法推动了基因组组装质量的显著提高，使端粒到端粒基因组测序项目成为许多实验室可以实现的目标（Kress等人，2022）。长读技术与新的映射算法相结合，推动了新的转座元件（TE）动力学的发现，人们对其在适应性性状进化和表型变异中的作用重新产生了兴趣（Schrader和Schmitz，2019；Pimpinelli和Piacentini，2020）。该领域的生物信息学发展极大地提高了我们检测复杂TE动员模式（如嵌套插入和结构变体）的能力（Bree等人，2022；Lemay等人，2022）。尽管取得了这些进展，但由于物种特异性基因组配置、分类学上不完整的参考数据库以及缺乏强有力的基准和质量控制，基因和重复元素等基因组特征的表征和注释仍然具有挑战性。尽管结构和功能注释方法仍有重大障碍需要克服，但在改进这些方法的比较和优化方面做出了许多重要贡献（Caballero和Wegrzyn，2019）。此外，现有基因、变体和重复注释软件的扩展和聚合开始使研究人员能够组合和策划不同的算法方法和数据库（Nelson等人，2017；Kirsche等人，2023）。然而，要表征的植物多样性规模仍然是一个挑战，结合来自保存的、非模型的或难以获得的材料的样本需要创新的湿实验室和生物信息学解决方案（Lang等人，2020）。简化表示测序（RRS）方法是研究非模型植物的重要工具；这种对新兴测序技术的适应使得能够进行成本效益高的种群研究、使用植物标本馆标本分析历史多样性以及大规模的系统发育学探索（Kersey，2019；千植物转录组倡议，2019）。随着软件和方法的不断改进，与RRS相关的局限性，如同源基因、编码和非编码序列的不同选择景观以及数据缺失，越来越多地被考虑在内（Johnson等人，2016），在线门户网站中非模式分类群的组学数据的整合为研究人员描述世界植物群创造了一个更加容易访问的环境（Goodstein等人，2012）。生物信息学自在生物学应用中诞生以来，一直是一个不断变化的领域，技术、测序平台、算法和技术的更替率很高，植物科学中生物信息学的现状也不例外。这期《植物科学应用》特刊发表了五篇论文，探讨了生物信息学方法，以解决植物生物学中的问题，如基因组组装、减少代表性测序以及结构和功能注释。我们在这里总结这些论文。
{"title":"Making sense of complexity: Advances in bioinformatics for plant biology","authors":"Katie Emelianova, Diego Mauricio Riaño-Pachón, Maria Fernanda Torres Jimenez","doi":"10.1002/aps3.11538","DOIUrl":"10.1002/aps3.11538","url":null,"abstract":"<p>Coined by Dutch theoretical biologists in the 1970s, the term bioinformatics originally denoted a broad concept relating to the study of information processing in biological systems, such as ecosystem interaction, neuronal messaging, and transfer of genetic information (Hogeweg, <span>2011</span>). Subsequently co-opted to describe the sequencing and analysis of molecules (from nucleic acids to proteins), bioinformatics has diverse applications including the analysis, visualization, storage, and generation of data relating to living organisms and the molecular information they carry. Plant biology has reaped dividends from the development and maturation of bioinformatics; it has not only extended our understanding of model plant species such as <i>Arabidopsis thaliana</i> (Cantó-Pastor et al., <span>2021</span>) but also driven innovative solutions to characterize non-model species (Nevado et al., <span>2014</span>). Both avenues of discovery contribute to key objectives in improving food security, conservation, and biotechnology.</p><p>The size and complexity of many plant genomes has historically made their analysis financially and computationally difficult. Frequent polyploidy and repeat element expansion make the elucidation of plant genome sequences challenging (Soltis et al., <span>2015</span>). Furthermore, high heterozygosity in wild populations, pervasive hybridization, and a lack of inbred lines present roadblocks to analyses such as read mapping and assembly (Kajitani et al., <span>2019</span>). Long-read technologies have become ever more accessible in recent years, and algorithmic advances have accommodated sequential updates to error models, read lengths, and library types (Michael and VanBuren, <span>2020</span>). Moreover, novel methods to scaffold contigs and obtain long-range interaction information have driven impressive improvements in genome assembly quality, making telomere-to-telomere genome sequencing projects an achievable goal for many labs (Kress et al., <span>2022</span>).</p><p>Long-read technologies paired with novel mapping algorithms have fueled discovery of new transposable element (TE) dynamics, and there has been an associated resurgence of interest in their role in adaptive trait evolution and phenotypic variation (Schrader and Schmitz, <span>2019</span>; Pimpinelli and Piacentini, <span>2020</span>). Bioinformatics developments in this field have led to vast improvements in our ability to detect complex TE mobilization patterns such as nested insertions and structural variants (Bree et al., <span>2022</span>; Lemay et al., <span>2022</span>). Despite these advancements, characterization and annotation of genomic features such as genes and repetitive elements remain challenging due to species-specific genomic configurations, taxonomically patchy reference databases, and a lack of robust benchmarking and quality control. While structural and functional annotation methods still have significant obstacles to ov","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44733584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vidya S. Vuruputoor, Daniel Monyak, Karl C. Fetter, Cynthia Webster, Akriti Bhattarai, Bikash Shrestha, Sumaira Zaman, Jeremy Bennett, Susan L. McEvoy, Madison Caballero, Jill L. Wegrzyn
Premise
Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein-coding gene predictions.
Methods
The impact of repeat masking, long-read and short-read inputs, and de novo and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity.
Results
Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended.
Discussion
While the annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions.
{"title":"Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes","authors":"Vidya S. Vuruputoor, Daniel Monyak, Karl C. Fetter, Cynthia Webster, Akriti Bhattarai, Bikash Shrestha, Sumaira Zaman, Jeremy Bennett, Susan L. McEvoy, Madison Caballero, Jill L. Wegrzyn","doi":"10.1002/aps3.11533","DOIUrl":"10.1002/aps3.11533","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein-coding gene predictions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The impact of repeat masking, long-read and short-read inputs, and de novo and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>While the annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10050062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Universal target enrichment probe kits are used to circumvent the individual identification of loci suitable for phylogenetic studies in a given taxon. Under certain circumstances, however, target capture can be inefficient and costly, and lower numbers of marker loci may be sufficient. We therefore propose a computational pipeline that enables the easy identification of a subset of promising candidate loci for a taxon of interest.
Methods and Results
Target sequences used for probe design are filtered based on an assembled reference genome, resulting in presumably intron-containing single-copy loci as present in the reference taxon. The applicability of the proposed approach is demonstrated based on two probe kits (universal and family-specific) in combination with several publicly available reference genomes.
Conclusions
Guided by commercial probe kits, LoCoLotive enables fast and cost-efficient marker mining. Its accuracy mainly depends on the quality of the reference genome and its relatedness to the taxa under study.
{"title":"LoCoLotive: In silico mining for low-copy nuclear loci based on target capture probe sets and arbitrary reference genomes","authors":"Ulrich Lautenschlager, Agnes Scheunert","doi":"10.1002/aps3.11535","DOIUrl":"10.1002/aps3.11535","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Universal target enrichment probe kits are used to circumvent the individual identification of loci suitable for phylogenetic studies in a given taxon. Under certain circumstances, however, target capture can be inefficient and costly, and lower numbers of marker loci may be sufficient. We therefore propose a computational pipeline that enables the easy identification of a subset of promising candidate loci for a taxon of interest.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>Target sequences used for probe design are filtered based on an assembled reference genome, resulting in presumably intron-containing single-copy loci as present in the reference taxon. The applicability of the proposed approach is demonstrated based on two probe kits (universal and family-specific) in combination with several publicly available reference genomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Guided by commercial probe kits, LoCoLotive enables fast and cost-efficient marker mining. Its accuracy mainly depends on the quality of the reference genome and its relatedness to the taxa under study.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46096480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chris Jackson, Todd McLay, Alexander N. Schmidt-Lebuhn
Premise
The HybPiper pipeline has become one of the most widely used tools for the assembly of target capture data for phylogenomic analysis. After the production of locus sequences and before phylogenetic analysis, the identification of paralogs is a critical step for ensuring the accurate inference of evolutionary relationships. Algorithmic approaches using gene tree topologies for the inference of ortholog groups are computationally efficient and broadly applicable to non-model organisms, especially in the absence of a known species tree.
Methods and Results
We containerized and expanded the functionality of both HybPiper and a pipeline for the inference of ortholog groups, providing novel options for the treatment of target capture sequence data, and allowing seamless use of the outputs of the former as inputs for the latter. The Singularity container presented here includes all dependencies, and the corresponding pipelines (hybpiper-nf and paragone-nf, respectively) are implemented via two Nextflow scripts for easier deployment and to vastly reduce the number of commands required for their use.
Conclusions
The hybpiper-nf and paragone-nf pipelines are easily installed and provide a user-friendly experience and robust results to the phylogenetic community. They are used by the Australian Angiosperm Tree of Life project. The pipelines are available at https://github.com/chrisjackson-pellicle/hybpiper-nf and https://github.com/chrisjackson-pellicle/paragone-nf.
{"title":"hybpiper-nf and paragone-nf: Containerization and additional options for target capture assembly and paralog resolution","authors":"Chris Jackson, Todd McLay, Alexander N. Schmidt-Lebuhn","doi":"10.1002/aps3.11532","DOIUrl":"10.1002/aps3.11532","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>The HybPiper pipeline has become one of the most widely used tools for the assembly of target capture data for phylogenomic analysis. After the production of locus sequences and before phylogenetic analysis, the identification of paralogs is a critical step for ensuring the accurate inference of evolutionary relationships. Algorithmic approaches using gene tree topologies for the inference of ortholog groups are computationally efficient and broadly applicable to non-model organisms, especially in the absence of a known species tree.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>We containerized and expanded the functionality of both HybPiper and a pipeline for the inference of ortholog groups, providing novel options for the treatment of target capture sequence data, and allowing seamless use of the outputs of the former as inputs for the latter. The Singularity container presented here includes all dependencies, and the corresponding pipelines (hybpiper-nf and paragone-nf, respectively) are implemented via two Nextflow scripts for easier deployment and to vastly reduce the number of commands required for their use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The hybpiper-nf and paragone-nf pipelines are easily installed and provide a user-friendly experience and robust results to the phylogenetic community. They are used by the Australian Angiosperm Tree of Life project. The pipelines are available at https://github.com/chrisjackson-pellicle/hybpiper-nf and https://github.com/chrisjackson-pellicle/paragone-nf.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f4/1c/APS3-11-e11532.PMC10439820.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10046655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yasushi Miki, Susumu Saito, Teruo Niki, Daniel K. Gladish
Premise
Previously we described methods for generating three-dimensional (3D) virtual reconstructions of plant tissues from transverse thin sections. Here, we report the applicability of longitudinal sections and improved image-processing steps that are simpler to perform and utilize free applications.
Methods
In order to obtain improved digital images and a virtual 3D object (cuboid), GIMP 2.10 and ImageJ 2.3.0 running on a laptop computer were used. Sectional views of the cuboid and 3D visualization were realized with use of the plug-ins “Volume Viewer” and “3D Viewer” in ImageJ.
Results
A 3D object was constructed and sectional views along several cutting planes were generated. The 3D object consisted of selected tissues inside the cuboid that were extracted and visualized from the original section data, and an animated video of the 3D construct was also produced.
Discussion
Virtual cuboids can be constructed by stacking longitudinal images along the transverse depth direction or stacking transverse images vertically along the organ axis, with both generating similar 3D objects. Which to use depends on the purpose of the investigation: if the vertical cell structures need close examination, the former method may be better, but for more general spatial evaluations or for evaluation of organs over longer tissue distances than can be accommodated with longitudinal sectioning, the latter method should be chosen.
{"title":"Improved image processing for 3D virtual object construction from serial sections reveals tissue patterns in root tips of Zea mays","authors":"Yasushi Miki, Susumu Saito, Teruo Niki, Daniel K. Gladish","doi":"10.1002/aps3.11531","DOIUrl":"10.1002/aps3.11531","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Previously we described methods for generating three-dimensional (3D) virtual reconstructions of plant tissues from transverse thin sections. Here, we report the applicability of longitudinal sections and improved image-processing steps that are simpler to perform and utilize free applications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In order to obtain improved digital images and a virtual 3D object (cuboid), GIMP 2.10 and ImageJ 2.3.0 running on a laptop computer were used. Sectional views of the cuboid and 3D visualization were realized with use of the plug-ins “Volume Viewer” and “3D Viewer” in ImageJ.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A 3D object was constructed and sectional views along several cutting planes were generated. The 3D object consisted of selected tissues inside the cuboid that were extracted and visualized from the original section data, and an animated video of the 3D construct was also produced.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Virtual cuboids can be constructed by stacking longitudinal images along the transverse depth direction or stacking transverse images vertically along the organ axis, with both generating similar 3D objects. Which to use depends on the purpose of the investigation: if the vertical cell structures need close examination, the former method may be better, but for more general spatial evaluations or for evaluation of organs over longer tissue distances than can be accommodated with longitudinal sectioning, the latter method should be chosen.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43230397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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