ORFannotate: reproducible coding sequence annotation of transcriptome assemblies

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate annotation of coding sequences (CDS) and translational features is crucial for interpreting assembled transcriptomes.
• Existing open reading frame (ORF) prediction tools often fail to reintegrate CDS information back into transcript models, limiting their utility, especially in long-read sequencing workflows.

Purpose of the Study:

• To develop a novel, GTF-native tool for predicting ORFs and reinserting precise, exon-aware CDS and untranslated region (UTR) features into transcript annotations.
• To provide comprehensive translational context, including Kozak sequence strength, upstream ORFs (uORFs), and nonsense-mediated decay (NMD) susceptibility.

Main Methods:

• ORFannotate, a lightweight Python command-line tool, predicts ORFs directly from transcript annotations in GTF/GFF files.
• The tool reinserts predicted CDS and UTR features, annotates translational elements like Kozak sequences and uORFs, and predicts NMD susceptibility.
• Annotations are consolidated into transcript-level summaries for downstream analysis.

Main Results:

• ORFannotate successfully predicts ORFs and annotates translational features, generating GTF files with accurate CDS annotations.
• The tool facilitates reproducible analysis of both long- and short-read transcriptomes.
• ORFannotate integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows.

Conclusions:

• ORFannotate offers a practical and scalable solution for transcriptome annotation, extending beyond simple coding potential prediction.
• The tool enhances the interpretation of assembled transcriptomes by providing accurate CDS and translational feature annotations.
• By generating comprehensive GTF annotations, ORFannotate supports reproducible bioinformatics analyses and downstream applications.