ORFLine

ORFLine identifies and characterizes small open reading frames (smORFs), classifies them by transcript position, and predicts signal peptides to detect candidate secreted micropeptides.

Key Features:

• Stringent Identification: Employs a rigorous computational approach to identify smORFs with high precision, minimizing false positives.
• Classification by Transcript Position: Classifies identified smORFs based on their positional context within transcripts to inform potential functional and regulatory roles.
• Signal Peptide Prediction: Integrates signal peptide prediction to identify smORFs encoding candidate secreted proteins, including known chemokines.
• Novel Micropeptide Discovery: Has identified four novel micropeptides with evidence of secretion as candidate immunoregulatory molecules.

Scientific Applications:

• Functional characterization of smORF-encoded proteins: Provides systematic identification and classification of smORFs to support studies of small protein function.
• Secretome and immunoregulatory micropeptide discovery: Profiles the secretome of lymphocytes, including mouse B and T lymphocytes, to identify candidate secreted chemokines and micropeptides with potential immunoregulatory roles.

Methodology:

Processes genomic datasets from mouse B and T lymphocytes to identify and characterize smORFs and integrates signal peptide prediction to highlight candidate secreted proteins.