FusionMap

FusionMap detects fusion events in RNA-Seq and gDNA-Seq datasets by aligning junction-spanning single reads and paired-end reads directly to the genome to identify fusion breakpoints at transcript and genomic levels.

Key Features:

• RNA-Seq and gDNA-Seq support: Detects fusion events in both transcriptomic (RNA-Seq) and genomic (gDNA-Seq) datasets.
• Single-end and paired-end reads: Processes single-end reads and paired-end reads, including junction-spanning single reads.
• Junction-spanning read utilization: Leverages reads that span fusion junctions to improve detection sensitivity and breakpoint resolution.
• Genome-centric alignment: Aligns fusion reads directly to the genome without requiring prior knowledge of potential fusion regions.
• Reference indexing: Builds and uses reference indexes as part of the detection workflow.
• Read filtering: Implements read filtering prior to fusion alignment to reduce spurious candidates.
• Fusion alignment: Performs dedicated fusion alignment to map and resolve fusion junctions.
• Reporting: Produces reports of detected fusion events and breakpoint information.
• Simulation validation: Validated on simulated RNA-Seq datasets with 75 nt paired-end reads, showing improved sensitivity and specificity when inner distance between read pairs is minimal.
• Empirical validation: Empirically validated on the K562 chronic myeloid leukemia cell line for accurate fusion characterization.
• Base-pair resolution: Characterizes fusion breakpoints with base-pair resolution.

Scientific Applications:

• Fusion gene discovery: Identification of fusion genes in RNA-Seq and gDNA-Seq datasets.
• Breakpoint characterization: Precise mapping of fusion breakpoints at base-pair resolution for transcript and genomic analyses.
• Performance benchmarking: Evaluation of fusion detection sensitivity and specificity using simulated 75 nt paired-end RNA-Seq datasets.
• Cancer genomics: Empirical identification and characterization of fusion events in cancer cell lines such as K562 (chronic myeloid leukemia).

Methodology:

Aligns fusion reads directly to the genome without prior knowledge of fusion regions and integrates reference indexing, read filtering, fusion alignment, and reporting.