Analyze Reported Results With Sources

Bifunctional ORF prioritization — concise plan

I propose a reproducible, multi-layer ranking pipeline that (1) aggregates transcript/ORF evidence (transcript abundance, Kozak, start-codon type, ORF length, BLAST/OpenProt/MS/Ribo-seq support, circRNA/BT evidence, miRNA-ceRNA potential), (2) scores ORFs with interpretable weights, and (3) outputs ranked candidate lists for experimental validation plus interactive visualizations and database links. Key data and methods derive from a recent genome-wide bifunctional gene atlas and large circRNA/proteogenomics resources cited below.

Key sources: bifunctional genes reannotation (4387 genes; 125,696 predicted ORFs) Bifunctional Genes Atlas (2026); circRNA/coding evidence and proteomics support circRNADb (2016); large-scale circRNA BT proteogenomics demonstrating widespread circRNA-derived peptides BawaCirc BT proteogenomics (2025).

Bifunctional ORF Prioritization — Visual Plan

Analysis & Prioritization Plan (stepwise)

1. Data ingestion & canonical sources — acquire the reannotation tables and scripts from the authors (bifunctional genes database and GitHub) and circRNA / proteomics resources to crosslink evidence: Bifunctional Genes DB and code (bifunc_database, GitHub link in source) Bifunctional Genes Atlas (2026).
2. Raw evidence layers to collect per candidate ORF — build a per-ORF table with these columns (minimum): gene_id, transcript_id, transcript_tpm (GTEx/CHESS), ORF_id, start_codon, start_pos, ORF_length_aa, Kozak_score, hydrophobicity, BLASTP_hit (bits/evalue), OpenProt_support (Ribo/MS), Ribo-seq reads (BSJ-spanning if circRNA), MS peptide evidence (PRIDE/DIA-NN reanalysis), circRNA_association (circAtlas/CIRCpedia), BT-evidence (BawaCirc Ribo+MS flags), miRNA_seed_sites (LiftOver/TargetScan), conservation (orthogene ortholog fraction), expression specificity (tissue entropy), predicted disorder/alpha-helical content, subcellular localization prediction (signal peptides), NMD flag for transcript, and number_of_transcripts_from_locus. Key resources: circRNADb (coding/circRNA proteomics) circRNADb (2016) and BawaCirc BT proteome results BawaCirc BT proteogenomics (2025).
3. Scoring rubric (interpretable, weighted):
   • Transcript abundance (TPM, log10 scaled) — weight 15%
   • Translation support (Ribo-seq reads or OpenProt/MS peptide) — weight 25%
   • Kozak strength & start codon type (ATG > near-cognate) — weight 10%
   • ORF length & canonical features (>=20 aa preferred for microproteins) — weight 8%
   • Proteomics evidence (unique peptide spanning ORF / BSJ) — weight 20%
   • Conservation (ortholog presence / syntenic conservation) — weight 8%
   • Regulatory potential (miRNA seed sink, circRNA abundance, ceRNA potential) — weight 6%
   • Structural plausibility (hydrophobicity, predicted disorder, signal peptide) — weight 4%
   • Experimental tractability flags (tissue expression match to available models, uniqueness vs host CDS to avoid confusion) — binary bonus +4%

   Score normalization: each metric scaled 0–1 across dataset, then weighted sum yields final "Prioritization Score" (0–1). Provide per-ORF breakdown so biologists can see why an ORF ranks where it does.

4. Computational pipeline & reproducibility
   1. Data retrieval: download provided tables from the bifunctional GitHub; pull circRNA sets from CIRCpedia v3 / circRNADb; fetch public MS raw files from PRIDE/ProteomeXchange for peptides that map to candidate ORFs (CIRCpedia v3 (2025)).
   2. ORF evidence: re-run ORFipy or ORFfinder across transcripts to ensure coordinate consistency (start codon types as in source). Add Kozak context scoring (position -6..+4) with same threshold used in paper (0.64) to flag strong contexts Bifunctional ORF predictions.
   3. Proteogenomics: build an expanded search database containing (a) canonical proteome, (b) predicted ORFs (6-frame where necessary), (c) circRNA-derived ORFs (linearized for MS), and (d) BT candidate peptides from BawaCirc to re-search relevant PRIDE/DIA datasets with DIA-NN/MaxQuant or recommended pipelines; apply strict FDR and require unique peptide mapping when possible (N-terminal COFRADIC / BSJ-spanning peptides prioritized) Proteogenomics review (2014).
   4. Ribo-seq cross-check: collect public Ribo-seq / RiboWaltz processed metrics where available; compute P-site density across the ORF, BSJ-spanning reads for circ candidates, and periodicity; flag ORFs with ≥2 BSJ-spanning Ribo reads (as in BawaCirc) as high confidence BawaCirc Ribo-seq criteria.
   5. Evolutionary filtering: run eggNOG-mapper or orthogene to detect orthologs and derive a conservation score (useful because the original study reported 47% chimp, 45% mouse, 40% zebrafish ortholog fractions for bifunctional genes) eggNOG-mapper v2 (2021).
5. Downstream prioritization & output
   • Produce ranked tables (CSV & interactive DataTable) with per-metric scores and an explanation column; include sequence FASTA exports per candidate, peptide-spectrum matches (PSMs) if available, Ribo-seq pileups (small bigWig tracks), conservation alignments, and predicted structure snapshots (AlphaFold/OmegaFold where sequence length >25 aa).
   • Generate subsets: (A) high-confidence translated microproteins (Ribo/MS), (B) likely regulatory noncoding transcripts with high miRNA-binding capacity (ceRNA candidates), (C) circRNA-associated ORFs with BT evidence, (D) tissue-specific low-abundance ORFs suitable for specialized assays.
   • Provide lab-ready designs: top 50 candidates with suggested assays (antibody epitope selection, targeted PRM/DIA peptides, primers for divergent PCR for circRNA, CRISPR guides to delete ORF without affecting host CDS), using CIRCpedia primer/sgRNA modules as guidance CIRCpedia experimental tools.
6. QC, validation & false-positive control
   • Require orthogonal support for top-priority ORFs: at least two independent evidence types (Ribo-seq + unique MS peptide; or MS peptide + conservation; or Ribo-seq + transcript TPM > threshold).
   • Filter likely artifacts: peptides mapping to homologous regions in canonical proteins, misassigned peptides from shared tryptic peptides, or low-complexity regions flagged by SEG.
   • Estimate FDR empirically by searching decoy expanded ORF databases and by using PSM-level FDR controls; for Ribo-seq use framing/periodicity tests and examine upstream ribosome occupancy to exclude scanning artifacts.
7. Deliverables & interactive outputs
   • Ranked CSVs and an interactive HTML report with DataTables (initialized with DataTable('#myTable')) and Plotly visualizations for summary metrics.
   • Genome browser tracks (bigWig for RNA/Ribo coverage, BED for ORFs), searchable by gene/transcript/ORF.
   • Shiny-like prioritization dashboard (or static HTML) replicating the bifunctional DB approach for exploration and export.
8. Software stack & recommended tools

   Python3/R pipelines orchestrated by Snakemake or Nextflow; key packages: ORFipy/ORFfinder, ORF annotation with eggNOG-mapper/MMseqs2, Ribo-seq processing with RiboWaltz/P-site tools, proteomics search with DIA-NN / MaxQuant / MSFragger and downstream validation (Percolator), sequence handling with Biopython/pandas, plotting with Plotly, DataTables for interactive tables, and AlphaFold/OmegaFold for structure where helpful. Use GitHub repo and containerized environments (Docker/Singularity).

9. Limitations, blindspots & how to falsify results
   • The prioritization depends on available annotations and public MS/Ribo datasets; low-expression ORFs may be missed and ORF predictions can overcall near-cognate starts. The 2026 reannotation authors warned about annotation biases and limited experimental validation Bifunctional paper limitations.
   • Falsification criteria: consistent failure to detect orthogonal evidence (no MS peptides in deep targeted PRM after prioritization; no Ribo-seq periodicity in matched tissues; lack of phenotype upon ORF deletion where predicted functional) would reduce confidence and indicate false positives.

Quick example prioritization workflow (commands & pseudocode)

# 1) load reannotation table (bifunctional ORF table)
# 2) compute normalized scores per metric (TPM, Kozak, Ribo, MS, conservation)
# 3) produce weighted sum and rank
# 4) export CSV, bigWig, BED, interactive HTML

# Pseudocode: read -> scale_metrics -> score = sum(weights * scaled_metrics) -> rank -> export

Next actions / deliverables I can run for you

• Reproduce the prioritization pipeline and produce top-100 candidate table with per-ORF evidence breakdown and downloadable FASTA/PSM lists.
• Perform targeted MS re-search (PRIDE datasets listed in circRNADb/BawaCirc) to validate top candidates with strict FDR.
• Generate browser tracks and a Shiny/HTML dashboard for interactive exploration.

Primary citations used for plan (examples)

• Bifunctional Genes Atlas (2026)
• circRNADb (2016)
• BawaCirc BT proteogenomics (2025)
• CIRCpedia v3 (2025)
• eggNOG-mapper v2 (2021)

Caveats & epistemic humility

All prioritization outputs will reflect the content and biases of source datasets (transcript catalogs, circRNA databases, MS/Ribo dataset selection). Predicted ORFs without orthogonal empirical support should be treated as hypotheses. The plan above emphasizes orthogonal validation (Ribo + MS + conservation) before asserting translation or function. If you want, I can run the full pipeline on the provided data and return the ranked candidate lists and interactive report.