LongGF

LongGF detects gene fusions from long-read RNA sequencing, leveraging Nanopore direct mRNA and PacBio cDNA reads to identify fusion events and map breakpoints for cancer research.

Key Features:

• High accuracy in gene fusion detection: Identifies candidate gene fusions from long-read RNA-seq data including Nanopore direct mRNA and PacBio cDNA datasets.
• Error tolerance for long reads: Manages high basecalling error rates and alignment errors inherent to long-read sequencing through specialized algorithms.
• Benchmark and real-data validation: Demonstrated superior performance on simulated long-read RNA-seq datasets and validated on a Nanopore direct mRNA dataset and a PacBio dataset from a mixture of 10 cancer cell lines.
• Base-resolution breakpoint mapping: Pinpoints exact translocation breakpoints at base resolution in an acute myeloid leukemia Nanopore cDNA dataset with Sanger sequencing confirmation.
• Cancer fusion discovery: Enables discovery of candidate fusion events in cancer samples to support studies of oncogenic mechanisms and potential targeted therapies.
• Efficient implementation: Implemented in C++ and optimized for fast processing while maintaining high accuracy.

Scientific Applications:

• Cancer fusion discovery: Detection of gene fusions in cancer samples and mixed cancer cell line datasets to identify candidate oncogenic events.
• Acute myeloid leukemia breakpoint characterization: Base-resolution mapping and validation of translocation breakpoints in AML using Nanopore cDNA data and Sanger sequencing.
• Method benchmarking: Evaluation and comparison of fusion-detection performance on simulated long-read RNA-seq datasets.

Methodology:

Implemented in C++ and employs advanced algorithms for long-read sequencing data that include error correction and alignment optimization, with design considerations for fast processing while maintaining high accuracy.