A large sequenced mutant library - valuable reverse genetic resource that covers 98% of sorghum genes.

Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum, a drought and heat-tolerant C4 species, requires a vast, large-scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large-scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)-induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis-regulatory elements. A comparison of the distribution of mutations in the large-scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS-induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5' UTR, gene body, and 3'-UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the Ka /Ks value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB (https://www.depts.ttu.edu/igcast/sorbmutdb.php) and SorghumBase (https://sorghumbase.org/). This current large-scale sequence-indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis.

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

SorghumBase: a web-based portal for sorghum genetic information and community advancement.

MAIN CONCLUSION: SorghumBase provides a community portal that integrates genetic, genomic, and breeding resources for sorghum germplasm improvement. Public research and development in agriculture rely on proper data and resource sharing within stakeholder communities. For plant breeders, agronomists, molecular biologists, geneticists, and bioinformaticians, centralizing desirable data into a user-friendly hub for crop systems is essential for successful collaborations and breakthroughs in germplasm development. Here, we present the SorghumBase web portal ( https://www.sorghumbase.org ), a resource for the sorghum research community. SorghumBase hosts a wide range of sorghum genomic information in a modular framework, built with open-source software, to provide a sustainable platform. This initial release of SorghumBase includes: (1) five sorghum reference genome assemblies in a pan-genome browser; (2) genetic variant information for natural diversity panels and ethyl methanesulfonate (EMS)-induced mutant populations; (3) search interface and integrated views of various data types; (4) links supporting interconnectivity with other repositories including genebank, QTL, and gene expression databases; and (5) a content management system to support access to community news and training materials. SorghumBase offers sorghum investigators improved data collation and access that will facilitate the growth of a robust research community to support genomics-assisted breeding.

BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations.

SUMMARY: With the advance of next-generation sequencing technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest. AVAILABILITY AND IMPLEMENTATION: BSAseq is freely available on https://www.sciapps.org/page/bsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SciApps: a cloud-based platform for reproducible bioinformatics workflows.

Motivation: The rapid accumulation of both sequence and phenotype data generated by high-throughput methods has increased the need to store and analyze data on distributed storage and computing systems. Efficient data management across these heterogeneous systems requires a workflow management system to simplify the task of analysis through automation and make large-scale bioinformatics analyses accessible and reproducible. Results: We developed SciApps, a web-based platform for reproducible bioinformatics workflows. The platform is designed to automate the execution of modular Agave apps and support execution of workflows on local clusters or in a cloud. Two workflows, one for association and one for annotation, are provided as exemplar scientific use cases. Availability and implementation: https://www.sciapps.org. Supplementary information: Supplementary data are available at Bioinformatics online.

The maize methylome influences mRNA splice sites and reveals widespread paramutation-like switches guided by small RNA.

The maize genome, with its large complement of transposons and repeats, is a paradigm for the study of epigenetic mechanisms such as paramutation and imprinting. Here, we present the genome-wide map of cytosine methylation for two maize inbred lines, B73 and Mo17. CG (65%) and CHG (50%) methylation (where H = A, C, or T) is highest in transposons, while CHH (5%) methylation is likely guided by 24-nt, but not 21-nt, small interfering RNAs (siRNAs). Correlations with methylation patterns suggest that CG methylation in exons (8%) may deter insertion of Mutator transposon insertion, while CHG methylation at splice acceptor sites may inhibit RNA splicing. Using the methylation map as a guide, we used low-coverage sequencing to show that parental methylation differences are inherited by recombinant inbred lines. However, frequent methylation switches, guided by siRNA, persist for up to eight generations, suggesting that epigenetic inheritance resembling paramutation is much more common than previously supposed. The methylation map will provide an invaluable resource for epigenetic studies in maize.

Gramene 2013: comparative plant genomics resources.

Gramene (http://www.gramene.org) is a curated online resource for comparative functional genomics in crops and model plant species, currently hosting 27 fully and 10 partially sequenced reference genomes in its build number 38. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. Whole-genome alignments complemented by phylogenetic gene family trees help infer syntenic and orthologous relationships. Genetic variation data, sequences and genome mappings available for 10 species, including Arabidopsis, rice and maize, help infer putative variant effects on genes and transcripts. The pathways section also hosts 10 species-specific metabolic pathways databases developed in-house or by our collaborators using Pathway Tools software, which facilitates searches for pathway, reaction and metabolite annotations, and allows analyses of user-defined expression datasets. Recently, we released a Plant Reactome portal featuring 133 curated rice pathways. This portal will be expanded for Arabidopsis, maize and other plant species. We continue to provide genetic and QTL maps and marker datasets developed by crop researchers. The project provides a unique community platform to support scientific research in plant genomics including studies in evolution, genetics, plant breeding, molecular biology, biochemistry and systems biology.

Uncertainty quantification in neural-network based pain intensity estimation.

Improper pain management leads to severe physical or mental consequences, including suffering, a negative impact on quality of life, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is a challenging task because different individuals experience pain differently. To overcome this, many researchers in the field have employed machine learning models to evaluate pain intensity objectively using physiological signals. However, these efforts have primarily focused on pain point estimation, disregarding inherent uncertainty and variability in the data and model. A point estimate, which provides only partial information, is not sufficient for sound clinical decision-making. This study proposes a neural network-based method for objective pain interval estimation, and quantification of uncertainty. Our approach, which enables objective pain intensity estimation with desired confidence probabilities, affords clinicians a better understanding of a person's pain intensity. We explored three distinct algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results demonstrate that LossS outperforms the other two by providing narrower prediction intervals. For 50%, 75%, 85%, and 95% prediction interval coverage probability, LossS provides average interval widths that are 22.4%, 7.9%, 16.7%, and 9.1% narrower than those of LossL, and 19.3%, 21.1%, 23.6%, and 26.9% narrower than those of bootstrap. As LossS outperforms, we assessed its performance in three different model-building approaches: (1) a generalized approach using a single model for the entire population, (2) a personalized approach with separate models for each individual, and (3) a hybrid approach with models for clusters of individuals. Results demonstrate that the hybrid model-building approach provides the best performance.

The taxonomic name resolution service: an online tool for automated standardization of plant names.

BACKGROUND: The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. RESULTS: The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. CONCLUSIONS: We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.

Transformer encoder with multiscale deep learning for pain classification using physiological signals.

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model's superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.

Analysis of pain research literature through keyword Co-occurrence networks.

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.

Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice.

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.

SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data.

SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project.

Ten new high-quality genome assemblies for diverse bioenergy sorghum genotypes.

Introduction: Sorghum (Sorghum bicolor (L.) Moench) is an agriculturally and economically important staple crop that has immense potential as a bioenergy feedstock due to its relatively high productivity on marginal lands. To capitalize on and further improve sorghum as a potential source of sustainable biofuel, it is essential to understand the genomic mechanisms underlying complex traits related to yield, composition, and environmental adaptations. Methods: Expanding on a recently developed mapping population, we generated de novo genome assemblies for 10 parental genotypes from this population and identified a comprehensive set of over 24 thousand large structural variants (SVs) and over 10.5 million single nucleotide polymorphisms (SNPs). Results: We show that SVs and nonsynonymous SNPs are enriched in different gene categories, emphasizing the need for long read sequencing in crop species to identify novel variation. Furthermore, we highlight SVs and SNPs occurring in genes and pathways with known associations to critical bioenergy-related phenotypes and characterize the landscape of genetic differences between sweet and cellulosic genotypes. Discussion: These resources can be integrated into both ongoing and future mapping and trait discovery for sorghum and its myriad uses including food, feed, bioenergy, and increasingly as a carbon dioxide removal mechanism.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.

Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud.

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we enhanced SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of MaizeCODE data management. These include publicly accessible scientific workflows for the reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows the integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.

Influence of a transverse static magnetic field on the orientation and peritectic reaction of Cu-10.5 at.% Sn peritectic alloy.

Peritectic alloy Cu-10.5 at.% Sn was directionally solidified at various growth speeds under a transverse static magnetic field. The experimental results indicated that the magnetic field caused the deformation of macroscopic interface morphology, the crystal orientation of primary phase along solidification direction, and the occurrence of peritectic reaction. The numerical simulations showed that the application of the magnetic field induced the formation of a unidirectional thermoelectric magnetic convection (TEMC), which modified solute transport in the liquid phase thereby enriching the solute concentration both at the sample and tri-junction scales. The modification of solidification structures under the magnetic field should be attributed to TEMC driven heat transfer and solute transport.

Effect of a weak transverse magnetic field on the microstructure in directionally solidified peritectic alloys.

Effect of a weak transverse magnetic field on the microstructures in directionally solidified Fe-Ni and Pb-Bi peritectic alloys has been investigated experimentally. The results indicate that the magnetic field can induce the formation of banded and island-like structures and refine the primary phase in peritectic alloys. The above results are enhanced with increasing magnetic field. Furthermore, electron probe micro analyzer (EPMA) analysis reveals that the magnetic field increases the Ni solute content on one side and enhances the solid solubility in the primary phase in the Fe-Ni alloy. The thermoelectric (TE) power difference at the liquid/solid interface of the Pb-Bi peritectic alloy is measured in situ, and the results show that a TE power difference exists at the liquid/solid interface. 3 D numerical simulations for the TE magnetic convection in the liquid are performed, and the results show that a unidirectional TE magnetic convection forms in the liquid near the liquid/solid interface during directional solidification under a transverse magnetic field and that the amplitude of the TE magnetic convection at different scales is different. The TE magnetic convections on the macroscopic interface and the cell/dendrite scales are responsible for the modification of microstructures during directional solidification under a magnetic field.

Unveiling the complexity of the maize transcriptome by single-molecule long-read sequencing.

Zea mays is an important genetic model for elucidating transcriptional networks. Uncertainties about the complete structure of mRNA transcripts limit the progress of research in this system. Here, using single-molecule sequencing technology, we produce 111,151 transcripts from 6 tissues capturing ∼70% of the genes annotated in maize RefGen_v3 genome. A large proportion of transcripts (57%) represent novel, sometimes tissue-specific, isoforms of known genes and 3% correspond to novel gene loci. In other cases, the identified transcripts have improved existing gene models. Averaging across all six tissues, 90% of the splice junctions are supported by short reads from matched tissues. In addition, we identified a large number of novel long non-coding RNAs and fusion transcripts and found that DNA methylation plays an important role in generating various isoforms. Our results show that characterization of the maize B73 transcriptome is far from complete, and that maize gene expression is more complex than previously thought.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.