ModTect

ModTect detects RNA modifications de novo from standard RNA-sequencing data to map nucleotide-resolution modifications and reveal epitranscriptomic alterations relevant to disease.

Key Features:

• De Novo Identification: Identifies RNA modifications without prior knowledge to enable discovery of novel epitranscriptomic sites.
• Nucleotide-Resolution Detection: Localizes modifications at single-nucleotide resolution in RNA-seq data.
• Statistical Signal Analysis: Employs a statistical framework that analyzes deletion and mis-incorporation signals from standard RNA-sequencing reads.
• Known and Novel Modification Detection: Detects known modifications such as N^1-methyladenosine and novel modifications including N^2,N^2-dimethylguanosine.
• Detection of RNA–DNA Sequence Differences: Identifies RNA–DNA sequence differences that may be missed by Sanger sequencing.
• Broad Applicability and Scalability: Has been applied to large-scale datasets including 11,371 patient samples and 934 cell lines across 33 cancer types.
• Clinical Relevance: Reveals associations between RNA modification patterns and cancer progression and survival outcomes.

Scientific Applications:

• Epitranscriptome profiling in cancer: Maps RNA modification landscapes across cancer types to study dysregulation in tumors.
• Clinical association studies: Associates RNA modification events with progression and survival outcomes in large clinical cohorts.
• Biomarker and therapeutic target identification: Supports identification of candidate RNA-based biomarkers and therapeutic targets linked to modification patterns.
• Discovery of novel mRNA modifications: Enables detection of previously unknown mRNA modifications from standard RNA-seq data.

Methodology:

Applies a statistical framework to analyze deletion and mis-incorporation signals in standard RNA-sequencing reads to identify nucleotide-resolution RNA modifications, including N^1-methyladenosine and N^2,N^2-dimethylguanosine, and is designed to handle large datasets.