Spaces:
Running on Zero
Running on Zero
EmmaScharfmann HF Staff
Claude Opus 5
Report the sites the cutoff rejected, instead of nothing
c357a0a | title: Translatome to Epitope | |
| emoji: 𧬠| |
| colorFrom: indigo | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.31.0 | |
| python_version: '3.12' | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Predicted initiation sites to candidate HLA epitopes | |
| # Translatome β microprotein β candidate epitope | |
| An end-to-end demo of what can be assembled **today** from existing, openly | |
| licensed tools: | |
| ``` | |
| transcript sequence | |
| β TIS Transformer P(translation initiation) per nucleotide [MIT] | |
| β ORF typing uORF / intORF / N-ext / CDS ... [this repo] | |
| β MHCflurry HLA class I presentation [Apache-2.0] | |
| β candidate peptides | |
| ``` | |
| No new models. The contribution is the join, and what the join reveals about | |
| where sequence-only prediction stops being useful. | |
| ## The result that matters | |
| Sequence-only initiation prediction is **not** blind to non-canonical | |
| translation. It finds some hidden ORFs confidently. What it misses is the | |
| short ones, and microproteins are short by definition. | |
| Scored against validated ORFs, each on the model fold that excluded its | |
| chromosome from training: | |
| | ORF | length | score | found? | | |
| | --- | ---: | ---: | :---: | | |
| | MIEF1 upstream ORF | 70 aa | **0.674** | yes | | |
| | ATF4 uORF2 | 59 aa | **0.303** | yes | | |
| | HOXB-AS3 peptide | 53 aa | 0.000892 | no | | |
| | TYK2 uORF | 8 aa | 0.000038 | no | | |
| | ATF4 uORF1 | 3 aa | 0.000044 | no | | |
| The MIEF1 ORF scores **above** the canonical start in its own transcript | |
| (0.674 vs 0.349). ATF4 is the cleanest case because both uORFs sit in the | |
| same 282 nt leader, 99 nt apart, differing mainly in length: the 59 aa one is | |
| ranked second in the transcript, the 3 aa one is invisible despite a textbook | |
| Kozak context (`GCCACC[ATG]G`). | |
| So the useful claim is not "sequence models cannot find non-canonical starts". | |
| It is narrower and more actionable: **sensitivity falls away in the short-ORF | |
| regime, which is exactly where microproteins live.** Ribosome profiling is the | |
| evidence that does not degrade with length. | |
| HOXB-AS3 is the informative exception β 53 aa but still missed, and the only | |
| case with no annotated CDS anywhere in the transcript. The model may also | |
| lean on surrounding protein-coding context that a lncRNA does not provide. | |
| One data point is not enough to claim this. | |
| ### Caveat on these matches | |
| ORFs are matched to published ones by **position and length only**. That is | |
| inference, not a cited coordinate, and no paper here publishes transcript | |
| coordinates. The ATF4 assignments are strong: uORF1 is 3 codons and uORF2 is | |
| 59 codons overlapping the CDS out of frame, both exactly as described, and | |
| the 87 nt inter-uORF spacing falls out of the human sequence independently | |
| and matches the published figure. The MIEF1 match rests on length (70 aa) | |
| and position alone. Confirm before quoting. | |
| ## The examples, and what each shows | |
| The Space ships four example sets, each preselecting the fold that held out | |
| its chromosome. The order is deliberate: the tool gets credit before any | |
| limitation is shown. | |
| **1. MIEF1 + ATF4 (chr22, fold 0)** β the model finds a hidden microprotein | |
| and misses a shorter one, in the same run. | |
| ``` | |
| MIEF1 @176 0.674 70 aa uORF found, outranks the canonical start | |
| MIEF1 @487 0.349 463 aa CDS | |
| ATF4 @185 0.303 59 aa uoORF uORF2, found | |
| ATF4 @282 0.565 351 aa CDS | |
| ATF4 @86 0.000044 3 aa uORF1, missed | |
| ``` | |
| At the default cutoff this yields 2 non-canonical ORFs and **12 novel | |
| candidate peptides** β peptides absent from any canonical protein in the | |
| input. | |
| **2. TYK2 (chr19, fold 3)** β a type 1 diabetes risk gene carrying a | |
| validated primate-specific uORF that the model scores at essentially zero. | |
| ``` | |
| TYK2 per-nucleotide initiation probability (chr19 held out) | |
| @186 published uORF (validated) 0.000038 | |
| @214 unvalidated 36 aa ORF 0.007686 | |
| @378 canonical CDS 0.984922 ~26,000x higher | |
| All six upstream ATGs in the 378 nt leader: | |
| {154: 0.000387, 186: 0.000038, 205: 0.003272, | |
| 214: 0.007686, 251: 0.000004, 283: 0.000272} | |
| ``` | |
| Walters et al. ([doi:10.2337/db24-0527](https://doi.org/10.2337/db24-0527)) | |
| characterise an 8 aa primate-specific uORF, `MPGSVLNV`, spanning the first | |
| two exons of the TYK2 5β² leader, and validate it by mutating its start codon. | |
| It maps to position 186. The ORF the model does surface at 214 is a | |
| different, longer, unvalidated one, out of frame with both the TYK2 CDS and | |
| theirs. | |
| We have not read their supplementary nuORF tables, so whether the ORF at 214 | |
| appears there as a separate entry is open. | |
| **3. INS (chr11, fold 4)** β insulin, the reference point. Canonical start | |
| only, 0.912, nothing else above threshold. | |
| **4. IAPP (chr12, fold 3)** β islet amyloid polypeptide, the other gene a | |
| Ξ²-cell lab reaches for first. Same shape as insulin, lower confidence in the | |
| annotated start. | |
| ``` | |
| INS @59 0.912 110 aa CDS | |
| INS @71 0.003660 106 aa N-trunc below cutoff | |
| IAPP @118 0.180 89 aa CDS | |
| IAPP @737 0.000812 59 aa dORF below cutoff | |
| ``` | |
| Scored right down to 0.0005, those four sites are *everything* the model | |
| finds in the two transcripts. Nothing in an alternative frame registers on | |
| either gene β including the alternative-frame insulin product described by | |
| Kracht et al. ([doi:10.1038/nm.4289](https://doi.org/10.1038/nm.4289)). | |
| That absence is a property of the model, not of the missing ribosome-profiling | |
| track. The sequence was scored and came back near zero. Ribo-seq conditioning | |
| is a reason to expect a *different* answer; it is not the reason this one is | |
| empty. | |
| ## Reporting what the cutoff rejected | |
| Every run scores down to `NEAR_MISS_FLOOR` (0.0005) whatever cutoff is | |
| selected, and the sites below the cutoff are listed in their own tab rather | |
| than dropped. This costs nothing β the model scores every nucleotide either | |
| way β and it changes what an empty result means. "No non-canonical ORFs" and | |
| "the best non-canonical candidate scored 0.0008, three orders of magnitude | |
| under the annotated start" are the same table with and without the near | |
| misses, and only the second one says whether the gene is worth a Ribo-seq | |
| experiment. | |
| ## Choosing a fold | |
| The model ships as five versions, each trained with certain chromosomes held | |
| out. Running a sequence on a fold that was trained on its chromosome inflates | |
| the score. The tool prints a warning when this happens, and it is easy to | |
| miss in the log noise. | |
| | gene | chromosome | fold | | |
| | --- | --- | --- | | |
| | MIEF1, ATF4 | 22 | 0 | | |
| | TYK2 | 19 | 3 | | |
| | IAPP | 12 | 3 | | |
| | INS | 11 | 4 | | |
| Fold 0 has an empty test set, meaning any contig not in its train or | |
| validation split lands there. Chromosome 22 is absent from both, so fold 0 is | |
| genuinely correct for ATF4 and MIEF1 β but it will also happily score a | |
| chromosome it trained on, so it is the wrong default for anything else. | |
| Leakage is worth roughly Β±0.06 here: canonical starts score 0.889β0.985 on | |
| held-out folds versus 0.944β0.965 on fold 0. The headline findings are | |
| unaffected. | |
| The bundled examples set their own fold. Anything pasted in does not, so | |
| pick one. | |
| ## Verified facts about TIS Transformer | |
| Checked by running it, not inferred from docs: | |
| - CLI is `tis_transformer` (not `transcript_transformer`, which is deprecated). | |
| `--fasta --model {human,mouse} --fold {0..4} --accelerator --out_prefix | |
| --prob_cutoff --start_codons --min_ORF_len` are all valid on the FASTA path. | |
| - **The FASTA-path CSV schema differs from the documented one.** It is | |
| `transcript_id, transcript_length, TIS_pos, output, start_codon, TTS_pos, | |
| stop_codon, TTS_on_transcript, protein_length, protein_sequence`. The ~40 | |
| column table in the docs belongs to the h5/GTF pipeline. | |
| - **The score column is `output`**, not `tis_transformer_score`. It is a real | |
| probability, a 2-class softmax per nucleotide. Positions are scored | |
| independently, so they do not compete for probability mass. | |
| - **`TIS_pos` is 1-based.** Everything in `orf.py` is 0-based; the conversion | |
| lives in one named function so an off-by-one cannot hide. | |
| - **Weights ship inside the wheel** (~128 MB, 5 folds Γ 2 species). No runtime | |
| download. MHCflurry's weights are *not* bundled; see `MHCFLURRY_BOOTSTRAP`. | |
| - `--start_codons` defaults to `.*TG$` β near-cognate (ATG, CTG, GTG, TTG). | |
| - `--prob_cutoff` effectively defaults to 0.03 via `configs/defaults.tt.yml`. | |
| - pyfaidx writes a `.fai` beside the input, so that directory must be writable. | |
| - Licence: MIT. | |
| ## Known limitations | |
| Also listed in the app's "What this does not show" tab: | |
| - **Sensitivity tracks ORF length**, as above. This is the finding, not a | |
| footnote. | |
| - **Sequence alone cannot be cell-type specific.** The same answer comes back | |
| for a Ξ²-cell, a hepatocyte and a neuron. | |
| - **One start per transcript is a learned prior.** Training labels come from | |
| annotation, which records exactly one CDS start per transcript, so the model | |
| reproduces that shape. | |
| - **Novelty here is local** β a peptide is compared only against the canonical | |
| CDSs of the submitted sequences, not the human proteome. | |
| - **Class I only.** The strongest genetic risk in T1D is class II (DQ8/DQ2), | |
| which MHCflurry does not model. | |
| - **Binding β immunogenicity.** | |
| - **No proteomic evidence is used.** | |
| ### A trap worth knowing | |
| lncRNAs famous for encoding a validated microprotein tend to get reclassified | |
| as protein-coding, precisely because of that discovery. LINC00961 is now | |
| SPAAR, LINC00948 is MRLN, LINC01420 is NBDY β all `protein_coding` in current | |
| Ensembl. Any benchmark that defines its classes by biotype is built on a | |
| moving target, and the best-characterised examples migrate out of the | |
| "non-coding" class first. Pin an annotation release, or define classes by | |
| evidence rather than label. | |
| ## Deliberately out of scope | |
| RiboTIE, the ribosome-profiling model from the same package. It takes mapped | |
| BAM files that can need >100 GB of RAM. That is not a Space β it is the part | |
| that would need real infrastructure, and it is the natural next step. | |
| ## Running locally | |
| ```bash | |
| pip install -r requirements.txt | |
| mhcflurry-downloads fetch models_class1_presentation | |
| TRISTAN_ACCELERATOR=cpu MHCFLURRY_BOOTSTRAP=0 python app.py | |
| pytest -q | |
| ``` | |
| ## ZeroGPU notes | |
| - TIS Transformer runs **in-process** on the GPU path, because a subprocess | |
| may not inherit the ZeroGPU allocation. Any failure falls back to the CPU | |
| subprocess route, which is the tested one. | |
| - **MHCflurry is pinned to CPU** via `configure_pytorch(backend="cpu")`. It is | |
| a PyTorch model whose device defaults to `auto`, so on a GPU box it calls | |
| `network.to('cuda')` during prediction β in the Gradio worker thread, | |
| outside any `@spaces.GPU` function, which ZeroGPU rejects. Its sibling | |
| option `gpu_device_nums=[]` must be avoided: it sets `CUDA_VISIBLE_DEVICES` | |
| process-wide and breaks the TIS stage. | |
| ## Citing | |
| - TIS Transformer: Clauwaert et al., *NAR Genomics and Bioinformatics* (2023), | |
| [doi:10.1093/nargab/lqad021](https://doi.org/10.1093/nargab/lqad021) | |
| - MHCflurry 2.0: O'Donnell et al., *Cell Systems* (2020), | |
| [doi:10.1016/j.cels.2020.06.010](https://doi.org/10.1016/j.cels.2020.06.010) | |
| - TYK2 uORF: Walters et al., *Diabetes* (2025), | |
| [doi:10.2337/db24-0527](https://doi.org/10.2337/db24-0527) | |
| - ATF4 uORFs: Lu, Harding & Ron, *J Cell Biol* (2004), | |
| [doi:10.1083/jcb.200408003](https://doi.org/10.1083/jcb.200408003); Vattem & | |
| Wek, *PNAS* (2004), | |
| [doi:10.1073/pnas.0400541101](https://doi.org/10.1073/pnas.0400541101) | |
| - Transcript sequences: Ensembl GRCh38, MANE Select | |
| ## Before making this public | |
| Email the TRISTAN authors. Their repo invites contributions, so an issue | |
| offering to mirror weights to the Hub and host a demo is the right opening. | |
| The MIEF1 result is a good thing to lead with: their model finds a validated | |
| microprotein and ranks it above the canonical start. | |