SpliceGrapher

SpliceGrapher constructs and refines splice graphs from RNA-Seq and EST alignments guided by existing gene models to improve detection and representation of alternative splicing.

Key Features:

• Integration with existing gene models: Incorporates evidence from RNA-Seq and expressed sequence tag (EST) alignments to augment curated gene models and represent transcript structures.
• Machine learning site classification: Applies a machine learning framework to distinguish genuine splice sites from spurious ones and filter erroneous splice junctions.
• Comparative performance: Demonstrated greater consistency with established gene models than TAU and Cufflinks in analyses using Arabidopsis thaliana RNA-Seq data.
• Cross-species utility: Applicable to datasets from multiple species, including plant and human RNA-Seq data.

Scientific Applications:

• Alternative splicing analysis: Enables detection and characterization of alternatively spliced transcripts to study splicing complexity and transcript diversity.
• Genomic annotation and transcript mapping: Refines gene models and transcript annotations by integrating alignment evidence to improve accuracy of transcript mapping.

Methodology:

Uses RNA-Seq and EST alignments to guide splice graph prediction and applies a machine learning framework to filter erroneous splice sites and align predictions with validated gene models.