Genome-wide association studies on resistance to powdery mildew in cultivated emmer wheat.

Powdery mildew, caused by the fungal pathogen Blumeria graminis (DC.) E. O.

Chromosome-scale genome assembly of bread wheat's wild relative Triticum timopheevii.

Wheat (Triticum aestivum) is one of the most important food crops with an urgent need for increase in its production to feed the growing world. Triticum timopheevii (2n = 4x = 28) is an allotetraploid wheat wild relative species containing the A and G genomes that has been exploited in many pre-breeding programmes for wheat improvement. In this study, we report the generation of a chromosome-scale reference genome assembly of T.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity.

PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.

Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method.

Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.

Co-expression pan-network reveals genes involved in complex traits within maize pan-genome.

Background: With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes.

Experimental and computational studies of cellulases as bioethanol enzymes.

Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol conversion catalysts are microorganism-based cellulolytic enzymes, but lack optimization for high bioethanol conversion, due to biological and other factors.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences.

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction.

Background: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies.

GrainGenes: Tools and Content to Assist Breeders Improving Oat Quality.

GrainGenes is the USDA-ARS database and Web resource for wheat, barley, oat, rye, and their relatives. As a community Web hub and database for small grains, GrainGenes strives to provide resources for researchers, students, and plant breeders to improve traits such as quality, yield, and disease resistance. Quantitative trait loci (QTL), genes, and genetic maps for quality attributes in GrainGenes represent the historical approach to mapping genes for groat percentage, test weight, protein, fat, and β-glucan content in oat ( spp.

Multiple Variant Calling Pipelines in Wheat Whole Exome Sequencing.

The highly challenging hexaploid wheat () genome is becoming ever more accessible due to the continued development of multiple reference genomes, a factor which aids in the plight to better understand variation in important traits. Although the process of variant calling is relatively straightforward, selection of the best combination of the computational tools for read alignment and variant calling stages of the analysis and efficient filtering of the false variant calls are not always easy tasks. Previous studies have analyzed the impact of methods on the quality metrics in diploid organisms.

mirMachine: A One-Stop Shop for Plant miRNA Annotation.

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences.

Background: Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free.

Genome-wide discovery of G-quadruplexes in barley.

G-quadruplexes (G4s) are four-stranded nucleic acid structures with closely spaced guanine bases forming square planar G-quartets. Aberrant formation of G4 structures has been associated with genomic instability. However, most plant species are lacking comprehensive studies of G4 motifs.

Building a successful international research community through data sharing: The case of the Wheat Information System (WheatIS).

The International Wheat Information System (WheatIS) Expert Working Group (EWG) was initiated in 2012 under the Wheat Initiative with a broad range of contributing organizations. The mission of the WheatIS EWG was to create an informational infrastructure, establish data standards, and build a single portal that allows search, retrieval, and display of globally distributed wheat data sets that are indexed in standard data formats at servers around the world. The web portal at WheatIS.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants.

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat.

JBrowse Connect: A server API to connect JBrowse instances and users.

We describe JBrowse Connect, an optional expansion to the JBrowse genome browser, targeted at developers. JBrowse Connect allows live messaging, notifications for new annotation tracks, heavy-duty analyses initiated by the user from within the browser, and other dynamic features. We present example applications of JBrowse Connect that allow users 1) to specify and execute BLAST searches by either running on the same host as the webserver, with a self-contained BLAST module leveraging NCBI Blast+ commands, or via a managed Galaxy instance that can optionally run on a different host, and 2) to run the primer design service Primer3.

Genome-Wide Discovery of G-Quadruplexes in Wheat: Distribution and Putative Functional Roles.

G-quadruplexes are nucleic acid secondary structures formed by a stack of square planar G-quartets. G-quadruplexes were implicated in many biological functions including telomere maintenance, replication, transcription, and translation, in many species including humans and plants. For wheat, however, though it is one of the world's most important staple food, no G-quadruplex studies have been reported to date.

Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize.

Background: Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome.

PedigreeNet: a web-based pedigree viewer for biological databases.

Motivation: Plant breeding aims to improve current germplasm that can tolerate a wide range of biotic and abiotic stresses. To accomplish this goal, breeders rely on developing a deeper understanding of genetic makeup and relationships between plant varieties to make informed plant selections. Although rapid advances in genotyping technology generated a large amount of data for breeders, tools that facilitate pedigree analysis and visualization are scant, leaving breeders to use classical, but inherently limited, hierarchical pedigree diagrams for a handful of plant varieties.

Structural dynamics of lytic polysaccharide monoxygenases reveals a highly flexible substrate binding region.

Lytic polysaccharide monooxygenases (LPMOs), which are found in fungi, bacteria, and viruses, are redox enzymes utilizing copper to break glycosidic bonds in recalcitrant crystalline form of polysaccharides, such as chitin and cellulose. They are classified by the Carbohydrate-Active enZYmes (CAZy) database under various families. LPMOs's structure with a flat substrate binding region has been shown to contribute to its function, however, the role that LPMOs structural dynamics play during polysaccharide degradation and its mechanism of binding towards substrate are relatively unknown.