Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Transcriptome assembly methods often lack robust open reading frame (ORF) identification capabilities.
• Accurate ORF annotation is crucial for understanding gene function and identifying novel protein variants.

Purpose of the Study:

• To introduce ORFanage, a novel system for assigning ORFs to gene transcripts.
• To demonstrate ORFanage's utility in identifying novel protein variants and improving existing ORF annotations in large-scale RNA sequencing datasets.

Main Methods:

• Development of the ORFanage system utilizing a highly accurate and efficient pseudo-alignment algorithm.
• Application of ORFanage to analyze RNA sequencing data and human annotation databases.

Main Results:

• ORFanage successfully identified novel protein variants within RNA-seq datasets.
• The system improved ORF annotations for tens of thousands of transcript models in human databases.
• ORFanage demonstrated significantly faster performance compared to existing ORF annotation methods.

Conclusions:

• ORFanage provides a fast and accurate solution for ORF identification in transcriptome assemblies.
• The system aids in distinguishing biological signal from transcriptional noise, facilitating the discovery of functional transcript variants.
• ORFanage has the potential to advance biological and medical research through improved transcript annotation.