Biology subjects

Watson-Haigh, N.

Publications and source records attributed to Watson-Haigh, N..

3 recordsLinked to original sources

bioRxiv2022

SAI: Fast and automated quantification of stomatal parameters on microscope images

Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results.

plant biology↗

bioRxiv2019

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↗

bioRxiv2018

Optical and physical mapping with local finishing enables megabase-scale resolution of agronomically important regions in the wheat genome

BackgroundNumerous scaffold-level sequences for wheat are now being released and, in this context, we report on a strategy for improving the overall assembly to a level comparable to that of the human genome.\n\nResultsUsing chromosome 7A of wheat as a model, sequence-finished megabase scale sections of this chromosome were established by combining a new independent assembly based on a BAC-based physical map, BAC pool paired end sequencing, chromosome arm specific mate-pair sequencing and Bionano optical mapping with the IWGSC RefSeq v1.0 sequence and its underlying raw data. The combined assembly results in 18 super-scaffolds across the chromosome. The value of finished genome regions is demonstrated for two approximately 2.5 Mb regions associated with yield and the grain quality phenotype of fructan carbohydrate grain levels. In addition, the 50 Mb centromere region analysis incorporates cytological data highlighting the importance of non-sequence data in the assembly of this complex genome region.\n\nConclusionsSufficient genome sequence information is shown to be now available for the wheat community to produce sequence-finished releases of each chromosome of the reference genome. The high-level completion identified that an array of seven fructosyl transferase genes underpins grain quality and yield attributes are affected by five f-box-only-protein-ubiquitin ligase domain and four root-specific lipid transfer domain genes. The completed sequence also includes the centromere.

genomics↗