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Capfinder: Advanced RNA Cap Type Prediction Framework

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Capfinder is a cutting-edge deep learning framework designed for accurate prediction of RNA cap types in mRNAs sequenced using Oxford Nanopore Technologies (ONT) SQK-RNA004 chemistry. By leveraging the power of native RNA sequencing data, Capfinder predicts the cap type on individual transcript molecules with high accuracy.

Key Features

  • Pre-trained Model: Ready-to-use classifier for immediate cap type prediction on ONT RNA-seq data.
  • Extensible Architecture: Advanced users can train the classifier on additional cap classes, allowing for customization and expansion.
  • Comprehensive ML Pipeline: Includes data preparation, hyperparameter tuning, and model training.
  • High Accuracy: State-of-the-art performance in distinguishing between various cap types.
  • Flexibility: Supports both CNN-LSTM, CNN-LSTML-Attention, ResNet, and Transformer-based Encoder model architectures.
  • Scalability: Designed to efficiently handle large-scale RNA sequencing datasets.

Supported Cap Types

Capfinder's pre-trained model offers accurate out-of-the-box predictions for the following RNA cap structures:

  1. Cap0: Unmodified cap structure
  2. Cap1: Methylated at the 2'-O position of the first nucleotide
  3. Cap2: Methylated at the 2'-O position of the first and second nucleotides
  4. Cap2,-1: Methylated at the 2'-O position of the first and second nucleotides, with an additional methylation at the -1 position

These cap types represent the most common modifications found in eukaryotic mRNAs. Capfinder's ability to distinguish between these structures enables researchers to gain valuable insights into RNA processing and regulation.

For advanced users, Capfinder's extensible architecture allows for training on additional cap types, expanding its capabilities to meet specific research needs.