TransCirc

TransCirc predicts the translation potential and putative peptides of human circular RNAs by integrating multi-omics and experimental evidence.

Key Features:

• Integration of Multi-Omics Evidence: Consolidates seven types of evidence to evaluate circRNA translation potential.
• Ribosome/Polysome Binding Evidence: Reports ribosome and polysome occupancy supporting translation of circRNAs.
• Experimentally Mapped Translation Initiation Sites: Includes mapped initiation sites that indicate where translation begins on circRNAs.
• Internal Ribosome Entry Sites (IRES): Identifies IRES elements within circRNAs that can facilitate cap-independent initiation.
• N6-Methyladenosine (m6A) Modification Data: Incorporates published m6A modification data associated with promotion of translation initiation.
• Open Reading Frame (ORF) Lengths: Evaluates lengths of circRNA-specific ORFs relevant to coding potential.
• Sequence Composition Scores (machine learning): Provides sequence composition scores derived from machine learning predictions to assess potential ORFs.
• Mass Spectrometry Evidence across Back-Splice Junctions: Supplies MS data that directly support peptides encoded by circRNAs spanning back-splice junctions.
• Coding Potential Prediction: Predicts coding potential and putative translation products for each human circRNA based on integrated evidence.

Scientific Applications:

• Identification of circRNA-Encoded Peptides: Enables discovery and prioritization of peptides encoded by circRNAs using mass spectrometry and ORF evidence.
• Investigation of Translation Mechanisms: Supports study of mechanisms such as ribosome binding, IRES-driven initiation, mapped initiation sites, and m6A-mediated initiation.
• Extension to Additional Evidence or Species: Facilitates incorporation of new evidence types or expansion beyond human circRNAs for comparative analyses.

Methodology:

Integrates seven explicit evidence types—ribosome/polysome binding, experimentally mapped translation initiation sites, IRES elements, N6-methyladenosine (m6A) modification data, ORF length evaluation, sequence composition scores from machine learning, and mass spectrometry across back-splice junctions—to predict coding potential and putative translation products of human circRNAs.