ChiTaH

ChiTaH identifies known human chimeras and fusion genes from DNA-Seq or RNA-Seq next-generation sequencing (NGS) data to detect and quantify chimeric transcripts relevant to cancer and other complex diseases.

Key Features:

• Data support: Processes DNA-Seq and RNA-Seq data generated by next-generation sequencing (NGS).
• Reference-based mapping: Maps sequencing reads against a comprehensive reference database of 43,466 non-redundant known human chimeras.
• Known chimera identification: Identifies known human chimeras and fusion genes from mapped reads.
• Junction read quantification: Performs quantification of junction reads to address sensitivity and specificity in chimera detection.
• Performance: Reported to outperform other methods in accuracy and speed on simulated and real sequencing datasets.
• Heterogeneity detection: Detects heterogeneity of specific chimeras, exemplified by BCR-ABL1 in bulk and single-cell analyses of the K-562 cell line, with experimental validation.

Scientific Applications:

• Cancer fusion detection: Detection of driver fusion genes and chimeric transcripts relevant to cancer research, diagnosis, and treatment.
• Single-cell and bulk heterogeneity analysis: Characterization of intra-sample heterogeneity of chimeric transcripts in bulk and single-cell sequencing, including BCR-ABL1 in K-562.
• Method benchmarking: Benchmarking and evaluation of chimera prediction performance using simulated and real sequencing datasets.

Methodology:

Reference-based mapping of DNA-Seq or RNA-Seq reads to a database of 43,466 non-redundant known human chimeras and quantification of junction reads.