implement regulatory-variant-effect app

Signed-off-by: Zhiyuan Chen <this@zyc.ai>

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README.md

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 ---
 title: Regulatory Variant Effect
-emoji: 📉
-colorFrom: indigo
-colorTo: indigo
+emoji: 🧬
+colorFrom: pink
+colorTo: red
 sdk: gradio
 sdk_version: 6.14.0
-python_version: '3.13'
+python_version: "3.13"
 app_file: app.py
 pinned: false
 license: agpl-3.0
-short_description: Regulatory Variant Effect
+suggested_hardware: t4-small
+models:
+  - multimolecule/a2zchromatin
+  - multimolecule/basset
+  - multimolecule/deepmel
+  - multimolecule/deepsea
+  - multimolecule/deepstarr
+  - multimolecule/malinois
+  - multimolecule/mpradragonn
+  - multimolecule/scbasset
+  - multimolecule/xpresso
+tags:
+  - biology
+  - dna
+  - regulatory-genomics
+  - variant-effect
+  - multimolecule
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Interactive regulatory variant-effect scoring with MultiMolecule.
+
+Enter matched reference and alternative DNA sequences, choose a regulatory variant-effect checkpoint, and inspect the alternative-minus-reference delta table. Sequence-level reference and alternative scores are included when the pipeline returns them. Results can be downloaded as CSV or JSON.
+
+This Space intentionally scores only user-provided sequence windows. It does not perform genome-coordinate lookup, reference-genome retrieval, transcript mapping, or variant normalization.

app.py

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+# MultiMolecule
+# Copyright (C) 2024-Present  MultiMolecule
+
+# This file is part of MultiMolecule.
+
+# MultiMolecule is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# any later version.
+
+# MultiMolecule is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+# For additional terms and clarifications, please refer to our License FAQ at:
+# <https://multimolecule.danling.org/about/license-faq>.
+
+from __future__ import annotations
+
+import csv
+import json
+import re
+import tempfile
+import time
+from functools import lru_cache
+from typing import Any, Mapping
+from urllib.parse import parse_qs, urlparse
+
+import gradio as gr
+import matplotlib
+import numpy as np
+import torch
+from transformers import pipeline
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt  # noqa: E402
+import multimolecule  # noqa: E402, F401 - registers MultiMolecule models and pipelines with Transformers
+
+DEFAULT_REFERENCE_SEQUENCE = "ACGT" * 250
+DEFAULT_ALTERNATIVE_SEQUENCE = "ACGT" * 125 + "TCGA" + "ACGT" * 124
+DEFAULT_MODEL_LABEL = "DeepSEA"
+
+MODEL_OPTIONS = {
+    "A2Z Chromatin": "multimolecule/a2zchromatin",
+    "Basset": "multimolecule/basset",
+    "DeepMEL": "multimolecule/deepmel",
+    "DeepSEA": "multimolecule/deepsea",
+    "DeepSTARR": "multimolecule/deepstarr",
+    "Malinois": "multimolecule/malinois",
+    "MPRA-DragoNN": "multimolecule/mpradragonn",
+    "scBasset": "multimolecule/scbasset",
+    "Xpresso": "multimolecule/xpresso",
+}
+MODEL_LABELS = {model_id: label for label, model_id in MODEL_OPTIONS.items()}
+
+TABLE_HEADERS = ["position", "nucleotide", "channel", "delta_score", "reference_score", "alternative_score"]
+DNA_ALPHABET = set("ACGTN")
+FLOAT_PATTERN = re.compile(r"[-+]?(?:(?:\d*\.\d+)|(?:\d+\.?))(?:[eE][-+]?\d+)?")
+
+
+def _device() -> int:
+    return 0 if torch.cuda.is_available() else -1
+
+
+@lru_cache(maxsize=2)
+def load_predictor(model_id: str):
+    return pipeline("regulatory-variant-effect", model=model_id, device=_device())
+
+
+def clean_sequence(sequence: str, label: str) -> str:
+    sequence = "".join(str(sequence or "").split()).upper().replace("U", "T")
+    if not sequence:
+        raise gr.Error(f"{label} sequence is empty.")
+    invalid = sorted(set(sequence) - DNA_ALPHABET)
+    if invalid:
+        invalid_text = ", ".join(invalid)
+        raise gr.Error(f"{label} sequence contains unsupported symbols: {invalid_text}. Use A, C, G, T, or N.")
+    return sequence
+
+
+def parse_features(features_text: str) -> Any | None:
+    text = str(features_text or "").strip()
+    if not text:
+        return None
+
+    try:
+        parsed = json.loads(text)
+    except json.JSONDecodeError:
+        values = FLOAT_PATTERN.findall(text)
+        if not values:
+            raise gr.Error("Features must be JSON or comma/space-separated numbers.")
+        return [float(value) for value in values]
+
+    if isinstance(parsed, Mapping):
+        for key in ("features", "values", "reference_features", "alternative_features"):
+            if key in parsed:
+                return parsed[key]
+        if all(isinstance(value, int | float) for value in parsed.values()):
+            return list(parsed.values())
+        raise gr.Error("Feature JSON objects must contain a features/values list or only numeric values.")
+    if isinstance(parsed, str):
+        return parse_features(parsed)
+    return parsed
+
+
+def feature_summary(features: Any | None) -> dict[str, Any]:
+    if features is None:
+        return {"provided": False}
+    try:
+        array = np.asarray(features, dtype=float)
+    except (TypeError, ValueError):
+        return {"provided": True, "shape": None}
+    return {"provided": True, "shape": list(array.shape)}
+
+
+def unpack_prediction_result(result: Any) -> dict[str, Any]:
+    if isinstance(result, list):
+        if len(result) != 1:
+            raise gr.Error(f"Expected one prediction result, got {len(result)}.")
+        result = result[0]
+    if not isinstance(result, dict):
+        raise gr.Error(f"Expected a prediction dictionary, got {type(result).__name__}.")
+    return result
+
+
+def build_delta_rows(result: Mapping[str, Any]) -> list[dict[str, Any]]:
+    if "delta_score" in result:
+        return [
+            {
+                "position": "",
+                "nucleotide": "",
+                "channel": "score",
+                "delta_score": result.get("delta_score"),
+                "reference_score": result.get("reference_score", ""),
+                "alternative_score": result.get("alternative_score", ""),
+            }
+        ]
+
+    delta_scores = result.get("delta_scores")
+    if isinstance(delta_scores, Mapping):
+        reference_scores = result.get("reference_scores") if isinstance(result.get("reference_scores"), Mapping) else {}
+        alternative_scores = (
+            result.get("alternative_scores") if isinstance(result.get("alternative_scores"), Mapping) else {}
+        )
+        return [
+            {
+                "position": "",
+                "nucleotide": "",
+                "channel": str(channel),
+                "delta_score": value,
+                "reference_score": reference_scores.get(channel, ""),
+                "alternative_score": alternative_scores.get(channel, ""),
+            }
+            for channel, value in delta_scores.items()
+        ]
+
+    if isinstance(delta_scores, list):
+        return build_axis_delta_rows(result, delta_scores)
+
+    raise gr.Error("The selected model did not return delta scores.")
+
+
+def build_axis_delta_rows(result: Mapping[str, Any], delta_scores: list[Any]) -> list[dict[str, Any]]:
+    channels = [str(channel) for channel in result.get("channels", [])]
+    reference_scores = _index_axis_rows(result.get("reference_scores"))
+    alternative_scores = _index_axis_rows(result.get("alternative_scores"))
+    output_rows: list[dict[str, Any]] = []
+
+    for row_index, row in enumerate(delta_scores):
+        if not isinstance(row, Mapping):
+            continue
+        position = row.get("position", row.get("bin", row_index))
+        channel_names = channels or [
+            str(key) for key in row if key not in {"position", "bin", "nucleotide"} and _is_number(row[key])
+        ]
+        ref_row = reference_scores.get(position, {})
+        alt_row = alternative_scores.get(position, {})
+        for channel in channel_names:
+            if channel not in row:
+                continue
+            output_rows.append(
+                {
+                    "position": position,
+                    "nucleotide": row.get("nucleotide", ""),
+                    "channel": channel,
+                    "delta_score": row[channel],
+                    "reference_score": ref_row.get(channel, ""),
+                    "alternative_score": alt_row.get(channel, ""),
+                }
+            )
+    return output_rows
+
+
+def _index_axis_rows(rows: Any) -> dict[Any, Mapping[str, Any]]:
+    if not isinstance(rows, list):
+        return {}
+    indexed = {}
+    for row_index, row in enumerate(rows):
+        if isinstance(row, Mapping):
+            indexed[row.get("position", row.get("bin", row_index))] = row
+    return indexed
+
+
+def _is_number(value: Any) -> bool:
+    return isinstance(value, int | float | np.number)
+
+
+def table_values(rows: list[Mapping[str, Any]]) -> list[list[Any]]:
+    return [[row.get(header, "") for header in TABLE_HEADERS] for row in rows]
+
+
+def plot_delta_rows(rows: list[Mapping[str, Any]], max_bars: int = 24):
+    numeric_rows = [row for row in rows if _is_number(row.get("delta_score"))]
+    fig, ax = plt.subplots(figsize=(7.0, 2.4))
+    if not numeric_rows:
+        ax.text(0.5, 0.5, "No numeric delta scores", ha="center", va="center", transform=ax.transAxes)
+        ax.set_axis_off()
+        fig.tight_layout()
+        return fig
+
+    top_rows = sorted(numeric_rows, key=lambda row: abs(float(row["delta_score"])), reverse=True)[:max_bars]
+    labels = [_row_label(row) for row in top_rows]
+    values = [float(row["delta_score"]) for row in top_rows]
+    colors = ["#1b9e77" if value >= 0 else "#d95f02" for value in values]
+
+    height = min(7.0, max(2.4, 0.28 * len(top_rows) + 1.2))
+    fig.set_size_inches(7.0, height, forward=True)
+    ax.barh(range(len(top_rows)), values, color=colors)
+    ax.axvline(0, color="#333333", linewidth=0.8)
+    ax.set_yticks(range(len(top_rows)), labels)
+    ax.invert_yaxis()
+    ax.set_xlabel("Alternative - reference")
+    ax.set_title("Largest absolute delta scores")
+    ax.tick_params(axis="y", labelsize=8)
+    fig.tight_layout()
+    return fig
+
+
+def _row_label(row: Mapping[str, Any]) -> str:
+    channel = str(row.get("channel", "score"))
+    position = row.get("position")
+    if position not in ("", None):
+        nucleotide = row.get("nucleotide")
+        suffix = f" {nucleotide}" if nucleotide not in ("", None) else ""
+        return f"{position}{suffix} {channel}"
+    return channel
+
+
+def write_result_files(
+    model_id: str,
+    result: Mapping[str, Any],
+    rows: list[Mapping[str, Any]],
+    metadata: Mapping[str, Any],
+) -> tuple[str, str]:
+    csv_file = tempfile.NamedTemporaryFile("w", suffix=".csv", newline="", delete=False)
+    writer = csv.DictWriter(csv_file, fieldnames=TABLE_HEADERS)
+    writer.writeheader()
+    writer.writerows({header: row.get(header, "") for header in TABLE_HEADERS} for row in rows)
+    csv_file.close()
+
+    json_file = tempfile.NamedTemporaryFile("w", suffix=".json", delete=False)
+    json.dump(
+        {
+            "metadata": dict(metadata),
+            "model": model_id,
+            "result": result,
+            "delta_table": [{header: row.get(header, "") for header in TABLE_HEADERS} for row in rows],
+        },
+        json_file,
+        indent=2,
+        default=_json_default,
+    )
+    json_file.close()
+    return csv_file.name, json_file.name
+
+
+def _json_default(value: Any):
+    if isinstance(value, np.generic):
+        return value.item()
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    raise TypeError(f"Object of type {type(value).__name__} is not JSON serializable")
+
+
+def predict(
+    model_label: str,
+    reference_sequence: str,
+    alternative_sequence: str,
+    reference_features_text: str,
+    alternative_features_text: str,
+):
+    model_id = MODEL_OPTIONS[model_label]
+    reference_sequence = clean_sequence(reference_sequence, "Reference")
+    alternative_sequence = clean_sequence(alternative_sequence, "Alternative")
+    if len(reference_sequence) != len(alternative_sequence):
+        raise gr.Error(
+            f"Reference and alternative sequences must have the same length. "
+            f"Got {len(reference_sequence)} and {len(alternative_sequence)}."
+        )
+
+    reference_features = parse_features(reference_features_text)
+    alternative_features = parse_features(alternative_features_text)
+    started = time.perf_counter()
+
+    predictor = load_predictor(model_id)
+    try:
+        result = predictor(
+            reference_sequence,
+            alternative=alternative_sequence,
+            features=reference_features,
+            alternative_features=alternative_features,
+        )
+    except Exception as error:
+        raise gr.Error(f"Prediction failed for {model_id}: {error}") from error
+
+    result = unpack_prediction_result(result)
+    rows = build_delta_rows(result)
+    if not rows:
+        raise gr.Error("The selected model returned no tabular delta scores.")
+
+    metadata = {
+        "task": "regulatory-variant-effect",
+        "model": model_id,
+        "device": "cuda" if torch.cuda.is_available() else "cpu",
+        "reference_length": len(reference_sequence),
+        "alternative_length": len(alternative_sequence),
+        "reference_features": feature_summary(reference_features),
+        "alternative_features": feature_summary(alternative_features),
+        "alternative_features_inherit_reference": alternative_features is None and reference_features is not None,
+        "score_definition": "alternative_minus_reference",
+        "num_delta_rows": len(rows),
+        "has_reference_scores": any(row.get("reference_score") not in ("", None) for row in rows),
+        "has_alternative_scores": any(row.get("alternative_score") not in ("", None) for row in rows),
+        "elapsed_seconds": round(time.perf_counter() - started, 3),
+    }
+    csv_path, json_path = write_result_files(model_id, result, rows, metadata)
+
+    return (
+        table_values(rows),
+        metadata,
+        plot_delta_rows(rows),
+        csv_path,
+        json_path,
+    )
+
+
+def initial_model(request: gr.Request):
+    if request is None:
+        return DEFAULT_MODEL_LABEL
+
+    query_params = getattr(request, "query_params", None)
+    model_id = None
+    if query_params is not None:
+        model_id = query_params.get("model")
+    if not model_id and getattr(request, "url", None):
+        parsed = parse_qs(urlparse(str(request.url)).query)
+        model_values = parsed.get("model")
+        model_id = model_values[0] if model_values else None
+
+    return MODEL_LABELS.get(model_id, DEFAULT_MODEL_LABEL)
+
+
+with gr.Blocks(title="Regulatory Variant Effect") as demo:
+    gr.Markdown(
+        "# Regulatory Variant Effect\n"
+        "Score matched reference and alternative DNA windows with MultiMolecule regulatory variant-effect models."
+    )
+
+    model = gr.Dropdown(
+        choices=list(MODEL_OPTIONS.keys()),
+        value=DEFAULT_MODEL_LABEL,
+        label="Checkpoint",
+    )
+
+    with gr.Row():
+        reference_sequence = gr.Textbox(label="Reference DNA sequence", value=DEFAULT_REFERENCE_SEQUENCE, lines=5)
+        alternative_sequence = gr.Textbox(label="Alternative DNA sequence", value=DEFAULT_ALTERNATIVE_SEQUENCE, lines=5)
+
+    with gr.Accordion("Optional numeric features", open=False), gr.Row():
+        reference_features = gr.Textbox(
+            label="Reference features JSON/text",
+            placeholder='[0.1, 0.2, 0.3] or {"features": [0.1, 0.2, 0.3]}',
+            lines=3,
+        )
+        alternative_features = gr.Textbox(
+            label="Alternative features JSON/text",
+            placeholder="Leave blank to reuse reference features when provided.",
+            lines=3,
+        )
+
+    run = gr.Button("Run prediction", variant="primary")
+
+    delta_table = gr.Dataframe(headers=TABLE_HEADERS, label="Delta scores", interactive=False, wrap=True)
+    with gr.Row():
+        metadata = gr.JSON(label="Run metadata")
+        delta_plot = gr.Plot(label="Delta plot")
+
+    with gr.Row():
+        csv_download = gr.File(label="Download CSV")
+        json_download = gr.File(label="Download JSON")
+
+    run.click(
+        predict,
+        inputs=[model, reference_sequence, alternative_sequence, reference_features, alternative_features],
+        outputs=[delta_table, metadata, delta_plot, csv_download, json_download],
+    )
+    demo.load(initial_model, outputs=model)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+matplotlib
+multimolecule @ git+https://github.com/DLS5-Omics/multimolecule.git@master
+numpy
+torch
+transformers