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# SignalP 6.0 — Usage

Two ways to run it: the **web server** (no install, good for a handful of
sequences) or the **standalone Python package** (for pipelines / large datasets).

## Web server

- URL: https://services.healthtech.dtu.dk/services/SignalP-6.0/
- Paste sequences or upload a FASTA file.
- **Limits:** proteins must be **≥10 aa**; **max 1000 proteins** per job; long
  output "might timeout for more than 100 entries."
- **Organism:** `Eukarya` (predicts **Sec/SPI only**) or `Other` (all five types).
- **Output format:** *Long* (with per-residue probability figures) or *Short* (no
  figures).
- **Model mode:** *Fast* or *Slow* (slow ≈ 6× slower, better region borders).
- **Overloaded?** Use the BioLib mirror: https://dtu.biolib.com/SignalP-6

## Standalone install

Download the package from the DTU "Downloads" tab (free for academics; commercial
needs a license), then:

```bash
# in a fresh Python env (Python ≤ 3.10)
pip install signalp-6-package/
pip install "numpy<2"          # needed for the 6.0h build

# copy the bundled model weights into the installed package
SIGNALP_DIR=$(python3 -c "import signalp, os; print(os.path.dirname(signalp.__file__))")
cp -r signalp-6-package/models/* $SIGNALP_DIR/model_weights/
```

This exposes a `signalp6` command inside that environment.
(Source & instructions: https://github.com/fteufel/signalp-6.0)

## Command line

```bash
signalp6 \
  --fastafile input.fasta \
  --organism other \
  --output_dir results/ \
  --format txt \
  --mode fast
```

| Argument | Short | Values | Default | Notes |
|----------|-------|--------|---------|-------|
| `--fastafile` | `-ff` | path | — | **required** input FASTA |
| `--output_dir` | `-od` | path | — | **required** output folder |
| `--organism` | `-org` | `other`, `eukarya` | `other` | `eukarya` → Sec/SPI only |
| `--format` | `-fmt` | `txt`, `png`, `eps`, `all`, `none` | `txt` | plot/image outputs |
| `--mode` | `-m` | `fast`, `slow`, `slow-sequential` | `fast` | speed vs. region accuracy / memory |
| `--bsize` | `-bs` | int | 10 | batch size |
| `--torch_num_threads` | `-tt` | int | 8 | CPU threads |
| `--write_procs` | `-wp` | int | ≤ CPU count | parallel output writers |
| `--model_dir` | `-md` | path | install default | custom weights |

Run `signalp6 -h` for the full list.

## Output files

| File | Contents |
|------|----------|
| **`prediction_results.txt`** | Tab-delimited, one row per protein: ID, predicted type, per-type probabilities, cleavage-site position + probability |
| **`processed_entries.fasta`** | Mature sequences (signal peptide removed) |
| **`output.gff3`** | SP start/end coordinates |
| **`region_output.gff3`** | n/h/c region coordinates |
| **`output.json`** | Run parameters + paths to all outputs — the hook for pipeline integration |
| **`*_plot.txt` / `*_plot.png` / `*_plot.eps`** | Per-position probabilities and figures (when `--format` requests them) |

### Reading `prediction_results.txt`

The **`Prediction`** column is one of these labels (note it uses short codes, not
the "Sec/SPI" names):

| Label | Meaning |
|-------|---------|
| `OTHER`   | No signal peptide |
| `SP`      | Sec/SPI — standard secretory SP |
| `LIPO`    | Sec/SPII — lipoprotein SP |
| `TAT`     | Tat/SPI |
| `TATLIPO` | Tat/SPII |
| `PILIN`   | Sec/SPIII — pilin |

Then there is one **probability column per class** (they sum to ~1) and a
**`CS Position`** column giving the predicted cleavage site, e.g.
`CS pos: 21-22. Pr: 0.87` — cut between residues 21 and 22 with probability 0.87.
The mature protein in `processed_entries.fasta` starts at that position.

Next: [04-benchmarks-and-context.md](04-benchmarks-and-context.md).

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