Plant Genetic and Mutation Studies

The paper sequences 4.3 million promoter mutations and 4.7 million coding mutations in wheat to understand gene function and improve agronomic traits.

The paper identifies and analyzes de novo mutations and somatic gene conversions in an apple tree's sport mutation, linking them to fruit coloration changes and genomic biases.

The paper sequenced whole genomes of an oilseed rape mutant population to catalog mutations induced by EMS mutagenesis and provides an online tool for mutation identification.

The paper shows that parental age effects on somatic mutation rates in Arabidopsis persist across generations, with varying patterns in mutation rates depending on the generation and type of mutation.

The paper introduces a high-resolution method to detect EMS-induced mutations in sorghum mutants, improving SNP detection accuracy and uncovering additional false-negative mutations.

The paper generates a large-scale genomic resource of sorghum EMS mutants, identifying disruptive mutations in over 15,500 genes and making data accessible via BSAseq integration and a web tool.

This study shows that long-term high temperatures increase mutation rates and alter mutation patterns in Arabidopsis plants.

The paper assesses mutational effects of gamma radiation on Arabidopsis thaliana, finding increased de novo mutations with radiation dose and higher mutagenic effects during the reproductive stage.

This study analyzes small insertions and deletions (indels) in soybean lines mutagenized by fast neutron irradiation, finding that while SNPs are likely spontaneous, indels are enriched and increase loss-of-function mutations.

The paper analyzes a yellow leaf mutant in sesame to identify genetic and proteomic changes affecting leaf color and chlorophyll content.

The paper improves whole genome sequence accuracy by using replication to detect false positives in mutagenized sorghum, identifying 1.2 million high-quality EMS-induced SNPs.

The paper develops a pipeline for mapping point mutations using next-generation sequencing data, validated in Arabidopsis.

The paper develops a Bayesian method to identify causative mutations from multiple candidate mutations in mutants, validated by genetic data and enriched for conserved functions.