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

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.

PMID: 37202927
Funding: - Agricultural Research Service: 2090-21000-033-00D

Documentation

Links