Source 1primary paper2026MED
Toolkit/ORFannotate
ORFannotate
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file.
Usefulness & Problems
Why this is useful
ORFannotate predicts ORFs from transcript annotations and writes exon-aware CDS and UTR features back into the original GTF/GFF models. It also annotates Kozak strength, non-overlapping uORFs with coding probabilities, UTR features, and predicted NMD susceptibility.; annotating coding sequences in transcriptome assemblies; reinserting CDS and UTR features into GTF/GFF transcript models; supporting long-read and short-read transcriptome analysis; providing transcript-level translational context annotations
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ORFannotate predicts ORFs from transcript annotations and writes exon-aware CDS and UTR features back into the original GTF/GFF models. It also annotates Kozak strength, non-overlapping uORFs with coding probabilities, UTR features, and predicted NMD susceptibility.
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annotating coding sequences in transcriptome assemblies
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reinserting CDS and UTR features into GTF/GFF transcript models
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supporting long-read and short-read transcriptome analysis
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providing transcript-level translational context annotations
Problem solved
It addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.; existing ORF prediction tools often operate on transcript FASTA files and do not reintegrate CDS information back into transcript models; long-read sequencing workflows need GTF/GFF-native CDS annotation rather than FASTA-only ORF calls
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It addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.
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existing ORF prediction tools often operate on transcript FASTA files and do not reintegrate CDS information back into transcript models
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long-read sequencing workflows need GTF/GFF-native CDS annotation rather than FASTA-only ORF calls
Problem links
existing ORF prediction tools often operate on transcript FASTA files and do not reintegrate CDS information back into transcript models
LiteratureIt addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.
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It addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.
long-read sequencing workflows need GTF/GFF-native CDS annotation rather than FASTA-only ORF calls
LiteratureIt addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.
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It addresses the gap left by FASTA-centric ORF callers that do not reintegrate CDS calls into transcript models. This is presented as especially useful for long-read transcriptome workflows where GTF/GFF annotations are the main output.
Published Workflows
Objective: Annotate coding sequences and translational features within transcript models from transcriptome assemblies in a reproducible, GTF/GFF-native manner.
Why it works: The workflow is presented as useful because it starts from transcript annotations rather than FASTA alone and writes CDS/UTR features back into the original transcript models, preserving coordinate-aware annotation needed for downstream long-read and comparative transcript analyses.
ORF prediction from transcript annotationsCDS and UTR reinsertion into transcript modelsKozak context annotationuORF detection with coding probability scoringNMD susceptibility predictionGTF/GFF-native computational annotationPython command-line processingtranscript-level summary generation
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
kozak sequence context annotationnonsense-mediated decay susceptibility predictionorf prediction from transcript annotationsTranslation Controlupstream orf detectionTechniques
Computational DesignTarget processes
translationInput: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: builder
The abstract describes ORFannotate as a Python command-line tool operating on transcript annotation files in GTF/GFF format. It produces updated annotation files plus a transcript-level summary for downstream analysis.; requires transcript annotations as input; implemented as a Python command-line tool; designed around GTF/GFF annotation files
The abstract does not show that ORFannotate solves broader transcriptome annotation tasks beyond coding-sequence and translational-feature annotation. It also does not provide evidence here for experimental validation or gold-standard benchmarking accuracy.; abstract does not report benchmark accuracy metrics or dataset-specific performance
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
ORFannotate is described as fast and scalable and as a practical solution for transcriptome annotation beyond coding potential prediction alone.
ORFannotate is fast, scalable and provides a practical solution for transcriptome annotation beyond coding potential prediction alone.
ORFannotate annotates Kozak sequence strength, detects non-overlapping upstream ORFs with coding probabilities, characterizes 5' and 3' UTRs, and predicts nonsense-mediated decay susceptibility.
In addition, ORFannotate provides biologically informative translational context by annotating Kozak sequence strength, detecting non-overlapping upstream ORFs (uORFs) with coding probabilities, characterising 5' and 3' untranslated regions (UTRs), and predicting nonsense-mediated decay (NMD) susceptibility.
ORFannotate predicts ORFs from transcript annotations and reinserts precise exon-aware CDS and UTR features into the original GTF/GFF file.
We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file.
ORFannotate facilitates reproducible analysis of both long-read and short-read transcriptomes and integrates with visualization tools, genome browsers, and comparative transcript analysis workflows.
By generating GTF files with accurate CDS annotations, ORFannotate facilitates reproducible analysis of both long- and short-read transcriptomes and integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows.
Approval Evidence
1 source4 linked approval claimsfirst-pass slug orfannotate
We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file.
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performance statementsupports
ORFannotate is described as fast and scalable and as a practical solution for transcriptome annotation beyond coding potential prediction alone.
ORFannotate is fast, scalable and provides a practical solution for transcriptome annotation beyond coding potential prediction alone.
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tool capabilitysupports
ORFannotate annotates Kozak sequence strength, detects non-overlapping upstream ORFs with coding probabilities, characterizes 5' and 3' UTRs, and predicts nonsense-mediated decay susceptibility.
In addition, ORFannotate provides biologically informative translational context by annotating Kozak sequence strength, detecting non-overlapping upstream ORFs (uORFs) with coding probabilities, characterising 5' and 3' untranslated regions (UTRs), and predicting nonsense-mediated decay (NMD) susceptibility.
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tool functionsupports
ORFannotate predicts ORFs from transcript annotations and reinserts precise exon-aware CDS and UTR features into the original GTF/GFF file.
We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file.
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workflow fitsupports
ORFannotate facilitates reproducible analysis of both long-read and short-read transcriptomes and integrates with visualization tools, genome browsers, and comparative transcript analysis workflows.
By generating GTF files with accurate CDS annotations, ORFannotate facilitates reproducible analysis of both long- and short-read transcriptomes and integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows.
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Comparisons
Source-stated alternatives
The paper positions ORFannotate against existing ORF prediction tools that operate on transcript FASTA files, and the provided research summary names TransDecoder and ORFanage as explicit nearby comparators.
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The paper positions ORFannotate against existing ORF prediction tools that operate on transcript FASTA files, and the provided research summary names TransDecoder and ORFanage as explicit nearby comparators.
Source-backed strengths
GTF-native workflow; exon-aware CDS and UTR reinsertion; adds Kozak, uORF, UTR, and NMD-related annotations; fast and scalable; integrates with visualization tools, genome browsers, and comparative transcript analysis workflows
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GTF-native workflow
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exon-aware CDS and UTR reinsertion
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adds Kozak, uORF, UTR, and NMD-related annotations
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fast and scalable
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integrates with visualization tools, genome browsers, and comparative transcript analysis workflows
Compared with 4pLRE-cPAOX1
ORFannotate and 4pLRE-cPAOX1 address a similar problem space because they share translation.
Shared frame: shared target processes: translation; shared mechanisms: translation_control; same primary input modality: light
Compared with blue-light-activated DNA template ON switch
ORFannotate and blue-light-activated DNA template ON switch address a similar problem space because they share translation.
Shared frame: shared target processes: translation; shared mechanisms: translation_control; same primary input modality: light
Compared with computational/AI-assisted protein design
ORFannotate and computational/AI-assisted protein design address a similar problem space because they share translation.
Shared frame: same top-level item type; shared target processes: translation; shared mechanisms: translation_control; same primary input modality: light
Strengths here: looks easier to implement in practice.
Ranked Citations
- 1.