red clover

red clover identifies single nucleotide polymorphisms (SNPs) from targeted genomic amplicon sequencing and RNA-seq to support SNP-based marker development and genomic analysis in Trifolium pratense.

Key Features:

• SNP discovery from targeted amplicon sequencing: Identifies SNPs across 72 red clover genotypes using targeted genomic amplicon sequencing.
• RNA-seq integration with stringent filtering: Applies the same stringent filtering criteria to publicly available RNA-seq data to expand SNP identification across varieties.
• SNP catalog size and consequence classification: Reports 69,975 total SNPs, of which 19,116 (28%) are classified as missense mutations.
• Annotation against Medicago truncatula: Annotates missense SNPs using Medicago truncatula as the reference genome, identifying 2,909 affected protein-coding regions.
• Domain-level variant enrichment: Identifies three domains—homeobox domain, pentatricopeptide repeat containing plant-like, and regulator of Vps4 activity—each containing five or more missense SNPs.
• Pathway analysis: Performs pathway analysis to highlight biological processes influenced by missense mutations.
• SSR marker context: Contrasts SNP-based analysis with simple sequence repeat (SSR) markers, noting SSR sparsity and challenges for unambiguous mapping with short reads from next-generation sequencing technologies.
• Support for marker development: Produces SNP-based markers suitable for genomics-assisted breeding applications in red clover.

Scientific Applications:

• SNP-based marker development: Enables development of SNP markers for breeding and selection in Trifolium pratense.
• Genetic diversity and population analysis: Facilitates sequence diversity and population-level analyses across red clover varieties.
• Candidate gene identification for self-incompatibility: Identifies genes and domains with elevated missense SNP counts as candidates for studying self-incompatibility mechanisms.
• Functional interpretation of variants: Uses annotation and pathway analysis to link missense mutations to biological processes affecting crop performance.

Methodology:

SNPs were identified from targeted genomic amplicon sequencing and RNA-seq using stringent filtering criteria, annotated against the Medicago truncatula reference genome, and subjected to pathway analysis.