BRIDGEcereal
BRIDGEcereal accelerates large-indel discovery and structural haplotype visualization across pan-genome de novo assemblies of major cereal crops to support comparative genomics and candidate gene prioritization.
Key Features:
- Unsupervised machine learning: Two unsupervised modules perform comparative structural analyses across multiple assemblies.
- CHOICE: CHOICE (clustering HSPs for ortholog identification via coordinates and equivalence) extracts orthologous gene segments using high-scoring segment pairs (HSPs) as anchors.
- CLIPS: CLIPS (clustering via large-indel permuted slopes) clusters extracted segments by shared large-indel patterns to derive concise haplotype groupings.
- Large-indel detection: Targets large insertions and deletions that are difficult to characterize by short-read alignment against a single reference.
- Indel-level outputs: Reports indel locations, sizes, and inferred relationships among haplotypes for comparative interpretation.
- Search-window and ordering refinement: Supports iterative refinement of upstream/downstream search windows and haplotype ordering for intergenic indels with uncertain boundaries.
- Pan-genome scale: Integrates 120 assemblies from wheat, barley, maize, sorghum, and rice, including complex polyploid genomes.
Scientific Applications:
- QTL and GWAS mapping: Mapping structural haplotypes in QTL and GWAS intervals in wheat to link structural variation to phenotypic variation.
- Structural variant interpretation: Interpreting large indels that reshape gene structure and modulate expression in crop genomes.
- Comparative genomics: Comparative analysis of pan-genome assemblies across wheat, barley, maize, sorghum, and rice.
- Candidate gene discovery: Prioritizing candidate causal genes by associating structural haplotypes with phenotypic signals.
Methodology:
Two unsupervised machine learning modules are applied: CHOICE extracts orthologous segments from multiple de novo assemblies using HSPs as anchors, and CLIPS clusters those segments by shared indel patterns to derive haplotype groupings.
Topics
Details
- License:
- CC-BY-NC-ND-4.0
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R
- Added:
- 8/7/2023
- Last Updated:
- 11/24/2024
Operations
Data Inputs & Outputs
Alignment
Publications
Zhang B, Huang H, Tibbs-Cortes LE, Vanous A, Zhang Z, Sanguinet K, Garland-Campbell KA, Yu J, Li X. Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes. Molecular Plant. 2023;16(6):975-978. doi:10.1016/j.molp.2023.05.005. PMID:37202927.