RESUMO
We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.
Assuntos
Cromossomos de Plantas/genética , Evolução Molecular , Fabaceae/genética , Genoma de Planta , Pisum sativum/genética , Proteínas de Plantas/genética , Locos de Características Quantitativas , Mapeamento Cromossômico , Fabaceae/classificação , Regulação da Expressão Gênica de Plantas , Variação Genética , Genômica , Fenótipo , Filogenia , Padrões de Referência , Sequências Repetitivas de Ácido Nucleico , Proteínas de Armazenamento de Sementes/genética , Sequenciamento Completo do GenomaRESUMO
Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering.