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Biology subjects

Suchecki, R.

Publications and source records attributed to Suchecki, R..

6 recordsLinked to original sources

GeneWhisperer: Enhancing manual genome annotation with large language models

Genome annotation is critical for understanding functional elements within genomes. Manual curation is a common practice for identifying the functions of genes, particularly those missed by automated annotation pipelines. However, this process is notoriously labour-intensive and time-consuming. In response to these challenges, we present GeneWhisperer, an innovative assistant system designed to facilitate the manual gene functional annotation process. Utilizing a large language model (LLM) agent, GeneWhisperer provides users access to tools appropriate for specific curation tasks in genome annotation. Featuring a conversational interface, GeneWhisperer fosters effective collaboration between human experts and AI. By synergizing the capabilities of AI with human expertise, GeneWhisperer offers a promising approach to advancing our understanding of gene functions and streamlining the gene annotation process.

bioinformatics↗

OzWheat: a genome-to-phenome platform to resolve complex traits for wheat pre-breeding and research.

For over a century, Australian wheat breeders have successfully adapted wheat to a broad range of climatic conditions and crop management practices. The OzWheat genome-to-phenome (G2P) platform was established to capture this breeding history and explore traits, genes, and their interactions with the environment to enable ongoing research and deliver targets for wheat improvement. A panel of 285 cultivars and landraces were chosen through knowledge of breeding pedigrees to represent both global diversity and the historic flow of genetic variation over more than 100 years of selective breeding in Australia. Genetic characterisation of the panel included identification of genome-wide sequence variants and gene expression profiling across environments. Important traits for adaptation (flowering time and plant height) were assayed in controlled environments and at multiple field sites and years, with genome-wide association analyses (GWAS) using linear mixed models detecting both known and novel loci. Here, we report establishment of the OzWheat G2P platform as a powerful tool to integrate wheat genomes and phenomes and demonstrate its use to identify candidate genes and understand gene by environment interactions. This provides the wheat research and breeding community a new resource to support future cultivar development.

genomics↗

Genotypic and resistance profile analysis of two oat crown rust differential sets urge coordination and standardisation

Puccinia coronata f. sp. avenae (Pca) is the causal agent of the disease known as crown rust which represents a bottleneck in oat production worldwide. Characterisation of pathogen populations often involves race (pathotype) assignments using differential sets, which are not uniform across countries. This study compared virulence profiles of 25 Pca isolates from Australia using two host differential sets, one from Australia and one from the USA. These differential sets were also genotyped using DArT sequencing technology. Phenotypic and genotypic discrepancies were detected on eight out of 29 common lines between the two sets, indicating that pathogen race assignments based on those lines are not comparable. To further investigate molecular markers that could assist in the stacking of rust resistance genes important for Australia, four published Pc91-linked markers were validated across the differential sets and then screened across a collection of 150 oat cultivars. Drover, Aladdin, and Volta were identified as putative carriers of the Pc91 locus. This is the first report to confirm that the cultivar Volta carries Pc91 and demonstrates the value of implementing molecular markers to characterise materials in breeding pools of oat. Overall, our findings highlight the necessity of examining seed stocks using pedigree and molecular markers to ensure seed uniformity and bring robustness to surveillance methodologies.

plant biology↗

The wheat Seven in Absentia gene is associated with increases in biomass and yield in hot climates

Wheat productivity is severely reduced by high temperatures. Breeding of heat tolerant cultivars can be achieved by identifying genes controlling physiological and agronomical traits with high temperature and using these to select superior genotypes, but no gene underlying genetic variation for heat tolerance has previously been described. We completed the positional cloning of qYDH.3BL, a quantitative trait locus (QTL) on bread wheat chromosome 3B associated with increased yield in hot and dry climates. The delimited genomic region contained 12 putative genes and a sequence variant in the promoter region of one gene - seven in absentia, TaSINA. This was associated with the QTLs effects on early vigour, plant biomass and yield components in two distinct wheat populations grown under various growth conditions. Near isogenic lines carrying the positive allele at qYDH.3BL under-expressed TaSINA and had increased vigour and water use efficiency early in development, as well as increased biomass, grain number and grain weight following heat stress. A survey of worldwide distribution indicated that the positive allele became widespread from the 1950s through the CIMMYT wheat breeding programme but, to date, has been selected only in breeding programmes in Mexico and Australia.\n\nSignificance statementWheat is the worlds most widely grown crop and a staple of human diet. Even brief episodes of high temperature in the growing season cause severe yield reductions. Finding and deploying genes for heat stress tolerance in new varieties is a priority for food security with climate change. We narrowed a genetic locus to a small genomic region where genetic variation was present only in one gene that showed clear differences of expression and improved yield and physiology under stress in the populations. Using diagnostic markers to track the positive haplotype in nearly 750 accessions, we found many regions where the allele could be used in breeding programmes to increase wheats heat tolerance.

genetics↗

LNISKS: Reference-free mutation identification for large and complex crop genomes

Mutation discovery is often key to the identification of genes responsible for major phenotypic traits. In the context of bulked segregant analysis, common reference-based computational approaches are not always suitable as they rely on a genome assembly which may be incomplete or highly divergent from the studied accession. Reference-free methods based on short sequences of length k (k-mers), such as NIKS, exploit redundancy of information across pools of recombinant genomes. Building on concepts from NIKS we introduce LNISKS, a mutation discovery method which is suited for large and repetitive crop genomes. In our experiments, it rapidly and with high confidence, identified mutations from over 700 Gbp of bread wheat genomic sequence data. LNISKS is publicly available at https://github.com/rsuchecki/LNISKS.

bioinformatics↗

Integrating past, present and future wheat research with Pretzel

MotivationMajor advances have been made in the assembly of complex genomes such as wheat. Remaining challenges include linking these new resources to legacy research, as well as lowering the bar to entry for members of the research community who may lack specific bioinformatics skills.\n\nResultsPretzel aims to solve these problems by providing an interactive, online environment for data visualisation and analysis which, when loaded with appropriately curated data, can enable researchers with no bioinformatics training to exploit the latest genomic resources. We demonstrate that Pretzel can be used to answer common questions asked by researchers as well as for advanced curation of pseudomolecule structure.\n\nAvailabilityPretzel is implemented in JavaScript and is freely available under a GPLv3 license online at: https://github.com/plantinformatics/pretzel.\n\nContactgabriel.keeble-gagnere@ecodev.vic.gov.au

bioinformatics↗