---
license: cc-by-4.0
task_categories:
  - other
tags:
  - splicing
  - genomics
  - sqtl
  - variant-effect-prediction
---

# SPLAIRE scores for HAEC185 sQTL credible sets

SPLAIRE-ref and SPLAIRE-var variant effect predictions for all variants in the HAEC185 sQTL credible sets.

## Files

- `HAEC185_sQTL_credible_sets_splaire_scores.tsv.gz` — per-credible-set-row summary (146,362 rows × 80 columns, 29 MB)
- `h5/cs_all.splaire.ref.h5` — full ref/alt per-position scores from SPLAIRE-ref (86,549 unique variants, 26 GB)
- `h5/cs_all.splaire.var.h5` — full ref/alt per-position scores from SPLAIRE-var (same variants, 26 GB)

The H5s hold the raw per-position score arrays the TSV is derived from.

## Source data

146,362 variant-phenotype rows in the HAEC185 sQTL credible sets, deduplicated to 86,549 unique (variant, strand) pairs. Each pair scored with both SPLAIRE models. Input window 20,001 bp centered on each variant, 10,001 output positions covering ±5 kb.

## Indel handling

Two-stage alignment so `alt − ref` comparisons stay position-aligned at the same genomic coordinate.

- **Input window** — the alt sequence is built by substituting at center, then padding (deletions) or trimming (insertions) the downstream end of the 20,001 bp window to keep total length fixed.
- **Output realignment** — the alt prediction track is realigned to ref coordinates. Extra positions from insertions are collapsed at center via element-wise max. Deleted positions are filled with zeros. Same convention as SpliceAI and Pangolin.

## TSV columns

Shape: (146362, 80). First 8 columns from the original credible sets file:

`gene`, `phenotype`, `ensembl_id`, `variant_id`, `credible_set_number`, `posterior_inclusion_probability`, `chr`, `strand`

The remaining 72 are score columns templated by model (`splaire`, `splaireVar`) and head (`don`, `acc`, `ssu`) — 12 columns per (model, head):

| column | meaning |
|---|---|
| `{model}_{head}_max_inc` | largest positive delta (alt − ref) in the 10 kb window |
| `{model}_{head}_max_inc_off` | offset from the variant (bp) |
| `{model}_{head}_max_inc_pos` | genomic coordinate of the max increase |
| `{model}_{head}_max_dec` | largest negative delta (alt − ref) |
| `{model}_{head}_max_dec_off` | offset from the variant (bp) |
| `{model}_{head}_max_dec_pos` | genomic coordinate of the max decrease |
| `{model}_{head}_{coord}_ref` | ref score at leafcutter junction boundary `coord` (`istart` = intron start, `iend` = intron end) |
| `{model}_{head}_{coord}_alt` | alt score at the same boundary |
| `{model}_{head}_{coord}_delta` | alt − ref at the same boundary |

Intron-coordinate columns are NaN when the boundary falls outside the ±5 kb scored window (~87% of rows).

## H5 contents

Each H5 contains five datasets (variant at center index 5000 of the 10,001-position window):

| dataset | shape | dtype | meaning |
|---|---|---|---|
| `cls_ref` | (86549, 10001, 3) | float32 | classifier softmax (neither, acceptor, donor), ref allele |
| `cls_alt` | (86549, 10001, 3) | float32 | same, alt allele |
| `reg_ref` | (86549, 10001) | float32 | regression SSU (sigmoid, 0-1), ref allele |
| `reg_alt` | (86549, 10001) | float32 | same, alt allele |
| `var_key` | (86549,) | vlen UTF-8 string | variant identifier (`chr:pos:ref:alt`), sorted by (chrom, var_pos) |

Use `var_key` to align H5 rows back to `variant_id` in the TSV.

## Models

- `splaire` — SPLAIRE-ref, trained on reference genome sequences
- `splaireVar` — SPLAIRE-var, trained on sequences containing personal genetic variants

Each is a 5-fold ensemble, predictions are averaged across folds.

## Output heads

- `don` — donor splice site probability (classifier softmax index 2)
- `acc` — acceptor splice site probability (classifier softmax index 1)
- `ssu` — splice site usage (regression sigmoid, 0-1)

## Usage

```python
import pandas as pd
import h5py
from huggingface_hub import hf_hub_download

tsv = hf_hub_download(
    repo_id="mrunyan1/splaire-haec-sqtl-credible-sets",
    filename="HAEC185_sQTL_credible_sets_splaire_scores.tsv.gz",
    repo_type="dataset",
)
df = pd.read_csv(tsv, sep="\t")
print(df.shape)  # (146362, 80)

# load full per-position scores for one model
h5_path = hf_hub_download(
    repo_id="mrunyan1/splaire-haec-sqtl-credible-sets",
    filename="h5/cs_all.splaire.ref.h5",
    repo_type="dataset",
)
with h5py.File(h5_path, "r") as f:
    var_keys = f["var_key"][:]
    cls = f["cls_ref"][0]   # (10001, 3) softmax track for first variant
    ssu = f["reg_ref"][0]   # (10001,) SSU track for first variant
```

## Download

```bash
pip install huggingface_hub
hf download --repo-type dataset mrunyan1/splaire-haec-sqtl-credible-sets
```