diff --git a/BALSAMIC/assets/scripts/collect_qc_metrics.py b/BALSAMIC/assets/scripts/collect_qc_metrics.py index 5249dc45d..cb24ce4a8 100755 --- a/BALSAMIC/assets/scripts/collect_qc_metrics.py +++ b/BALSAMIC/assets/scripts/collect_qc_metrics.py @@ -25,10 +25,17 @@ @click.argument("output_path", type=click.Path(exists=False), required=True) @click.argument("multiqc_data_path", type=click.Path(exists=True), required=True) @click.argument("counts_path", nargs=-1, type=click.Path(exists=True), required=False) +@click.option( + "--sex-prediction-path", + type=click.Path(exists=True), + required=False, + help="Path to sex prediction json (optional for balsamic-qc)", +) def collect_qc_metrics( config_path: Path, output_path: Path, multiqc_data_path: Path, + sex_prediction_path: Path, counts_path: List[Path], ): """Extracts the requested metrics from a JSON multiqc file and saves them to a YAML file @@ -37,6 +44,7 @@ def collect_qc_metrics( config_path: Path; case config file path output_path: Path; destination path for the extracted YAML formatted metrics multiqc_data_path: Path; multiqc JSON path from which the metrics will be extracted + sex_prediction_path: Path; sex prediction JSON path from which sex prediction info will be extracted counts_path: Path; list of variant caller specific files containing the number of variants """ @@ -50,6 +58,10 @@ def collect_qc_metrics( for count in counts_path: metrics += get_variant_metrics(count) + # Sex check + if sex_prediction_path: + metrics += get_sex_check_metrics(sex_prediction_path, config) + # Relatedness analysis_type = get_analysis_type(config) if analysis_type == "paired" and "Somalier" in multiqc_data["report_data_sources"]: @@ -108,6 +120,43 @@ def get_multiqc_data_source(multiqc_data: dict, sample: str, tool: str) -> str: ) +def get_sex_check_metrics(sex_prediction_path: str, config: dict) -> list: + """Retrieves the sex check metrics and returns them as a Metric list.""" + metric = "compare_predicted_to_given_sex" + case_id: str = config["analysis"]["case_id"] + sex_prediction = read_json(sex_prediction_path) + + given_sex: str = config["analysis"]["gender"] + tumor_predicted_sex: str = sex_prediction["tumor"]["predicted_sex"] + + tumor_sex_prediction_metrics = Metric( + id=f"{case_id}_tumor", + input=os.path.basename(sex_prediction_path), + name=metric.upper(), + step="sex_check", + value=tumor_predicted_sex, + condition={"norm": "eq", "threshold": given_sex}, + ).model_dump() + + sex_check_metrics = [tumor_sex_prediction_metrics] + + if "normal" in sex_prediction: + normal_predicted_sex: str = sex_prediction["normal"]["predicted_sex"] + + normal_sex_prediction_metrics = Metric( + id=f"{case_id}_normal", + input=os.path.basename(sex_prediction_path), + name=metric.upper(), + step="sex_check", + value=normal_predicted_sex, + condition={"norm": "eq", "threshold": given_sex}, + ).model_dump() + + sex_check_metrics.append(normal_sex_prediction_metrics) + + return sex_check_metrics + + def get_relatedness_metrics(multiqc_data: dict) -> list: """Retrieves the relatedness metrics and returns them as a Metric list.""" source_tool = "Somalier" diff --git a/BALSAMIC/assets/scripts/sex_prediction_tga.py b/BALSAMIC/assets/scripts/sex_prediction_tga.py new file mode 100644 index 000000000..612e69a9b --- /dev/null +++ b/BALSAMIC/assets/scripts/sex_prediction_tga.py @@ -0,0 +1,488 @@ +import click +import pandas as pd +import os +import warnings +from typing import List, Optional, Dict, Any, Tuple + +from BALSAMIC.utils.io import write_json + + +def read_cov(filepath: str) -> List[List[str]]: + """Read a coverage file and return its rows as a list of lists. + + Args: + filepath (str): Path to the coverage file. + + Returns: + List[List[str]]: Parsed rows of the coverage file. + """ + with open(filepath, "r") as rf: + rows = rf.readlines() + return [r.strip("\n").split("\t") for r in rows] + + +def process_data(data_rows: List[List[str]]) -> Optional[pd.DataFrame]: + """Process coverage data rows to filter and structure into a DataFrame. + + Args: + data_rows (List[List[str]]): Rows of coverage data. + + Returns: + Optional[pd.DataFrame]: DataFrame with processed data or None if insufficient data. + """ + filtered_data = [row for row in data_rows if row[0] in {"Y", "X"}] + if len(filtered_data) < 10: + warnings.warn( + "Cannot compute sex due to insufficient data, ignoring", UserWarning + ) + return None + + records: dict = { + f"{row[0]}_{row[1]}_{row[2]}": {"chrom": row[0], "depth": row[4]} + for row in filtered_data + } + + df = pd.DataFrame.from_dict(records, orient="index") + df["depth"] = pd.to_numeric(df["depth"], errors="coerce") + return df + + +def get_stats(df: pd.DataFrame) -> pd.DataFrame: + """Compute statistical summaries of depth grouped by chromosome. + + Args: + df (pd.DataFrame): DataFrame containing coverage data. + + Returns: + pd.DataFrame: DataFrame with statistics such as mean, median, min, max, etc. + """ + return ( + df.groupby("chrom")["depth"] + .agg( + mean="mean", median="median", min="min", max="max", std="std", count="count" + ) + .reset_index() + ) + + +def extract_stats(stats: pd.DataFrame) -> Dict[str, float]: + """Extract statistics from a DataFrame into a dictionary. + + Args: + stats (pd.DataFrame): DataFrame with statistical summaries. + + Returns: + Dict[str, float]: Dictionary containing extracted statistics. + """ + + stats: dict = stats.T.to_dict() + sample_dict = {} + sample_dict["X_mean"] = stats[0]["mean"] + sample_dict["X_median"] = stats[0]["median"] + sample_dict["X_min"] = stats[0]["min"] + sample_dict["X_max"] = stats[0]["min"] + sample_dict["X_std"] = stats[0]["std"] + sample_dict["X_count"] = stats[0]["count"] + sample_dict["Y_mean"] = stats[1]["mean"] + sample_dict["Y_median"] = stats[1]["median"] + sample_dict["Y_min"] = stats[1]["min"] + sample_dict["Y_max"] = stats[1]["max"] + sample_dict["Y_std"] = stats[1]["std"] + sample_dict["Y_count"] = stats[1]["count"] + return sample_dict + + +def retrieve_file_info(filepath: str) -> (str, str): + """Extract sample name and CNN type from file name.""" + filename: str = os.path.basename(filepath) + sample_name: str = filename.split(".")[0] + cnn_type: str = "antitarget" if "antitarget" in filename else "target" + return sample_name, cnn_type + + +def get_predicted_sex(y_x_frac: float) -> Dict[str, str]: + """Predict sex and confidence based on the Y/X ratio. + + Args: + y_x_frac (float): Ratio of Y to X. + + Returns: + Dict[str, str]: Predicted sex and confidence level. + """ + thresholds = { + "male_high": 0.9, + "male_medium": 0.7, + "male_low": 0.2, + "female_high": 0.00001, + "female_medium": 0.05, + "female_low": 0.15, + } + + if y_x_frac > thresholds["male_low"]: + if y_x_frac >= thresholds["male_high"]: + return {"predicted_sex": "male", "confidence": "high"} + elif y_x_frac >= thresholds["male_medium"]: + return {"predicted_sex": "male", "confidence": "medium"} + else: + return {"predicted_sex": "male", "confidence": "low"} + elif y_x_frac <= thresholds["female_low"]: + if y_x_frac >= thresholds["female_medium"]: + return {"predicted_sex": "female", "confidence": "medium"} + elif y_x_frac >= thresholds["female_high"]: + return {"predicted_sex": "female", "confidence": "high"} + else: + return {"predicted_sex": "female", "confidence": "low"} + else: + return {"predicted_sex": "unknown", "confidence": "low"} + + +def predict_sex(cnn_file: str) -> Dict[str, Any]: + """Predict the sex of a sample based on CNN file data. + + Args: + cnn_file (str): Path to the CNN file containing coverage data. + + Returns: + Dict[str, Any]: A dictionary containing sample information, data statistics, + and predicted sex based on mean and median Y/X coverage ratios. + """ + sample_name, cnn_type = retrieve_file_info(cnn_file) + + predicted_sex = { + "sample_name": sample_name, + "cnn_type": cnn_type, + } + + data_rows: list = read_cov(cnn_file) + data_df: pd.DataFrame = process_data(data_rows) + + if not isinstance(data_df, pd.DataFrame): + predicted_sex["sex_prediction"] = "failed" + predicted_sex["data_dict"] = "NA" + return predicted_sex + + stats: pd.DataFrame = get_stats(data_df) + data_dict: dict = extract_stats(stats) + + # Categorize data amount based on X and Y chromosome counts + if data_dict["X_count"] < 10 or data_dict["Y_count"] < 10: + data_dict["data_amount"] = "low" + elif data_dict["X_count"] < 50 or data_dict["Y_count"] < 50: + data_dict["data_amount"] = "medium" + else: + data_dict["data_amount"] = "high" + + # Compute mean and median ratios + data_dict["Y_mean/X_mean"] = round(data_dict["Y_mean"] / data_dict["X_mean"], 5) + data_dict["Y_median/X_median"] = round( + data_dict["Y_median"] / data_dict["X_median"], 5 + ) + + predicted_sex["sex_prediction"] = { + "by_mean": get_predicted_sex(data_dict["Y_mean/X_mean"]), + "by_median": get_predicted_sex(data_dict["Y_median/X_median"]), + } + + predicted_sex["data_dict"] = data_dict + + return predicted_sex + + +def calculate_prediction_score(sex_prediction: dict) -> int: + """Convert sex prediction confidence strings into scores. + + Args: + sex_prediction (dict): Sex prediction dictionary + + Returns: + int: Arbitrary score for sex-prediction confidence. + """ + frac_conf_score = { + "high": 4, + "medium": 3, + "low": 1, + "NA": 0, + } + data_conf_score = { + "high": 6, + "medium": 5, + "low": 1, + "NA": 0, + } + data_type_score = { + "target": 1, + "antitarget": 0, + } + frac_conf: str = sex_prediction["frac_conf"] + data_conf: str = sex_prediction["data_conf"] + data_type: str = sex_prediction["data_type"] + score = ( + frac_conf_score[frac_conf] + + data_conf_score[data_conf] + + data_type_score[data_type] + ) + return score + + +def get_prediction(prediction: Dict[str, Any]) -> Tuple[str, str, str, str]: + """Extract sex predictions and confidence levels from a prediction dictionary. + + Args: + prediction (Dict[str, Any]): A dictionary containing sex prediction details. + + Returns: + Tuple[str, str, str, str]: A tuple containing: + - Predicted sex by mean ratio (or "unknown" if prediction failed). + - Confidence level for the mean ratio prediction (or "NA" if prediction failed). + - Predicted sex by median ratio (or "unknown" if prediction failed). + - Confidence level for the median ratio prediction (or "NA" if prediction failed). + """ + if "failed" in prediction["sex_prediction"]: + return "unknown", "NA", "unknown", "NA" + + return ( + prediction["sex_prediction"]["by_mean"]["predicted_sex"], + prediction["sex_prediction"]["by_mean"]["confidence"], + prediction["sex_prediction"]["by_median"]["predicted_sex"], + prediction["sex_prediction"]["by_median"]["confidence"], + ) + + +def consolidate_sex_predictions( + sex_predictions: List[Dict[str, Any]] +) -> Dict[str, Any]: + """Consolidate multiple sex predictions into a final prediction. + + Args: + sex_predictions (List[Dict[str, Any]]): A list of dictionaries containing sex predictions. + Each dictionary should have: + - "sex" (str): Predicted sex. + - "score" (int): Score for the prediction. + - "data_type" (str): Type of data used for the prediction (e.g., "target"). + + Returns: + Dict[str, Any]: A dictionary containing: + - "sex" (str): The consolidated predicted sex. + - "sex_score" (int): The highest score among the predictions. + - "prediction_confidence" (str): Confidence level based on the score ("low", "medium", "high", or "uncertain"). + """ + final_sex = "unknown" + final_score = 0 + + for sex_prediction in sex_predictions: + sex = sex_prediction["sex"] + score = sex_prediction["score"] + data_type = sex_prediction["data_type"] + + # Set sex based on highest score + if score > final_score: + final_sex = sex + final_score = score + elif score == final_score: + # If score is the same, prioritize target data + if data_type == "target": + final_sex = sex + + score_confidence_levels = { + "high": 9, + "medium": 8, + "low": 4, + } + + final_confidence = "uncertain" + if final_score > score_confidence_levels["low"]: + final_confidence = "low" + if final_score > score_confidence_levels["medium"]: + final_confidence = "medium" + if final_score > score_confidence_levels["high"]: + final_confidence = "high" + + return { + "predicted_sex": final_sex, + "predicted_sex_score": final_score, + "prediction_confidence": final_confidence, + } + + +def summarise_sample_sex_prediction( + target_predicted_sex: Dict[str, Any], + antitarget_predicted_sex: Dict[str, Any], + sample_type: str, +) -> Dict[str, Any]: + """Summarize sex predictions for a sample based on target and antitarget data. + + Args: + target_predicted_sex (Dict[str, Any]): Predicted sex information from target data. + antitarget_predicted_sex (Dict[str, Any]): Predicted sex information from antitarget data. + sample_type (str): Type of the sample (e.g., "tumor" or "normal"). + + Returns: + Dict[str, Any]: A dictionary summarizing the sex predictions, including individual + predictions, confidence scores, and final consolidated prediction. + """ + sample_predicted_sex = {sample_type: {}} + + if "failed" in target_predicted_sex["sex_prediction"]: + target_data_amount = "NA" + else: + target_data_amount = target_predicted_sex["data_dict"]["data_amount"] + + ( + mean_target_predicted_sex, + mean_target_predicted_sex_conf, + median_target_predicted_sex, + median_target_predicted_sex_conf, + ) = get_prediction(target_predicted_sex) + + if "failed" in antitarget_predicted_sex["sex_prediction"]: + antitarget_data_amount = "NA" + else: + antitarget_data_amount = antitarget_predicted_sex["data_dict"]["data_amount"] + + ( + mean_antitarget_predicted_sex, + mean_antitarget_predicted_sex_conf, + median_antitarget_predicted_sex, + median_antitarget_predicted_sex_conf, + ) = get_prediction(antitarget_predicted_sex) + + sample_predicted_sex[sample_type]["predicted_sex"] = [ + { + "sex": mean_target_predicted_sex, + "frac_conf": mean_target_predicted_sex_conf, + "data_conf": target_data_amount, + "data_type": "target", + }, + { + "sex": median_target_predicted_sex, + "frac_conf": median_target_predicted_sex_conf, + "data_conf": target_data_amount, + "data_type": "target", + }, + { + "sex": mean_antitarget_predicted_sex, + "frac_conf": mean_antitarget_predicted_sex_conf, + "data_conf": antitarget_data_amount, + "data_type": "antitarget", + }, + { + "sex": median_antitarget_predicted_sex, + "frac_conf": median_antitarget_predicted_sex_conf, + "data_conf": antitarget_data_amount, + "data_type": "antitarget", + }, + ] + + for idx, sex_prediction in enumerate( + sample_predicted_sex[sample_type]["predicted_sex"] + ): + # Convert to score from sex prediction confidence strings + score = calculate_prediction_score(sex_prediction) + sample_predicted_sex[sample_type]["predicted_sex"][idx].update({"score": score}) + + sample_predicted_sex[sample_type] = consolidate_sex_predictions( + sample_predicted_sex[sample_type]["predicted_sex"] + ) + + # Add raw data + sample_predicted_sex[sample_type]["target_predicted_sex"] = target_predicted_sex + sample_predicted_sex[sample_type][ + "antitarget_predicted_sex" + ] = antitarget_predicted_sex + + return sample_predicted_sex + + +def case_sex_prediction(predicted_sex: Dict) -> Dict: + """Add case level sex prediction, setting sex to conflicting if tumor and normal doesn't match.""" + predicted_sex["case_sex"] = {} + tumor_sex: str = predicted_sex["tumor"]["predicted_sex"] + if "normal" in predicted_sex: + normal_sex: str = predicted_sex["normal"]["predicted_sex"] + if tumor_sex != normal_sex: + case_sex = "conflicting" + warnings.warn( + f"Conflicting sex prediction found, tumor sex: {tumor_sex}, is not the same as normal sex: {normal_sex}", + UserWarning, + ) + else: + case_sex: str = tumor_sex + else: + case_sex = tumor_sex + + predicted_sex["case_sex"] = case_sex + return predicted_sex + + +@click.command() +@click.option( + "--target-cnn-tumor", + type=click.Path(exists=True), + required=True, + help="Path to the target CNN tumor file.", +) +@click.option( + "--antitarget-cnn-tumor", + type=click.Path(exists=True), + required=True, + help="Path to the antitarget CNN tumor file.", +) +@click.option( + "--output", + type=click.Path(writable=True), + required=True, + help="Path to the output file to be created.", +) +@click.option( + "--target-cnn-normal", + type=click.Path(exists=True), + default=None, + help="Optional path to the target CNN normal file.", +) +@click.option( + "--antitarget-cnn-normal", + type=click.Path(exists=True), + default=None, + help="Optional path to the antitarget CNN normal file.", +) +def predict_sex_main( + target_cnn_tumor: str, + antitarget_cnn_tumor: str, + output: str, + target_cnn_normal: Optional[str], + antitarget_cnn_normal: Optional[str], +) -> None: + """Process tumor and optional normal CNN files to predict sex and output JSON results. + + Args: + target_cnn_tumor (str): Path to the target CNN tumor file. + antitarget_cnn_tumor (str): Path to the antitarget CNN tumor file. + output (str): Path to the output JSON file. + target_cnn_normal (Optional[str]): Path to the target CNN normal file. + antitarget_cnn_normal (Optional[str]): Path to the antitarget CNN normal file. + """ + # Predict tumor sex based on CNVkit output + tumor_target_predicted_sex: dict = predict_sex(target_cnn_tumor) + tumor_antitarget_predicted_sex: dict = predict_sex(antitarget_cnn_tumor) + predicted_sex: dict = summarise_sample_sex_prediction( + tumor_target_predicted_sex, tumor_antitarget_predicted_sex, "tumor" + ) + + if target_cnn_normal: + # Predict normal sex based on CNVkit output + normal_target_predicted_sex: dict = predict_sex(target_cnn_normal) + normal_antitarget_predicted_sex: dict = predict_sex(antitarget_cnn_normal) + normal_predicted_sex: dict = summarise_sample_sex_prediction( + normal_target_predicted_sex, normal_antitarget_predicted_sex, "normal" + ) + predicted_sex.update(normal_predicted_sex) + + # Create case-level prediction (compare tumor and normal sex) + predicted_sex: dict = case_sex_prediction(predicted_sex) + + # Write json report + write_json(predicted_sex, output) + + +if __name__ == "__main__": + predict_sex_main() diff --git a/BALSAMIC/assets/scripts/sex_prediction_wgs.py b/BALSAMIC/assets/scripts/sex_prediction_wgs.py new file mode 100644 index 000000000..7f72cf376 --- /dev/null +++ b/BALSAMIC/assets/scripts/sex_prediction_wgs.py @@ -0,0 +1,123 @@ +import click +import json +from typing import List, Dict +import numpy as np + + +def predict_sex_from_y_and_x_cov( + tumor_y_coverage_path: str, tumor_x_coverage_path: str, sample_type: str +) -> Dict[str, float]: + """ + Predicts the sex of a sample based on the median coverage of Y and X chromosomes. + + Args: + tumor_y_coverage_path (str): Path to the file containing Y chromosome coverage values. + tumor_x_coverage_path (str): Path to the file containing X chromosome coverage values. + + Returns: + Dict[str, float]: A dictionary containing predicted sex, median coverages, and their ratio. + """ + tumor_y_cov = np.loadtxt(tumor_y_coverage_path) + tumor_x_cov = np.loadtxt(tumor_x_coverage_path) + + tumor_median_y_cov = np.median(tumor_y_cov) + tumor_median_x_cov = np.median(tumor_x_cov) + tumor_y_x_median_frac = round(tumor_median_y_cov / tumor_median_x_cov, 5) + + if tumor_y_x_median_frac > 0.1: + predicted_sex = "male" + elif tumor_y_x_median_frac < 0.08: + predicted_sex = "female" + else: + predicted_sex = "unknown" + + return { + sample_type: { + "predicted_sex": predicted_sex, + "tumor_median_x_coverage": tumor_median_x_cov, + "tumor_median_y_coverage": tumor_median_y_cov, + "tumor_y_x_median_frac": tumor_y_x_median_frac, + } + } + + +def write_json(json_obj: Dict, path: str) -> None: + """ + Writes a dictionary to a JSON file. + + Args: + json_obj (Dict): The data to be written as JSON. + path (str): Path to the output file. + + Raises: + OSError: If there is an error writing the file. + """ + try: + with open(path, "w") as fn: + json.dump(json_obj, fn, indent=4) + except OSError as error: + raise OSError(f"Error while writing JSON file: {path}, error: {error}") + + +@click.command() +@click.option( + "--tumor-y-coverage", + type=click.Path(exists=True), + required=True, + help="Optional path to file with coverage per base in Y chromosome (for T only cases)", +) +@click.option( + "--tumor-x-coverage", + type=click.Path(exists=True), + required=True, + help="Optional path to file with coverage per base in X chromosome (for T only cases)", +) +@click.option( + "--normal-y-coverage", + type=click.Path(exists=True), + required=False, + help="Optional path to file with coverage per base in Y chromosome (for T only cases)", +) +@click.option( + "--normal-x-coverage", + type=click.Path(exists=True), + required=False, + help="Optional path to file with coverage per base in X chromosome (for T only cases)", +) +@click.option( + "--output", + type=click.Path(writable=True), + required=True, + help="Path to the output file to be created.", +) +def predict_sex_wgs( + tumor_y_coverage: str, + tumor_x_coverage: str, + normal_y_coverage: str, + normal_x_coverage: str, + output: str, +) -> None: + """ + Determines the sex of a sample based on ASCAT statistics or chromosome coverage. + + Args: + case_ascat_statistics (str): Path to the ASCAT statistics file. + sample_y_coverage (str): Path to the Y chromosome coverage file. + sample_x_coverage (str): Path to the X chromosome coverage file. + output (str): Path to the output file for the results. + """ + predicted_sex = predict_sex_from_y_and_x_cov( + tumor_y_coverage, tumor_x_coverage, "tumor" + ) + + if normal_y_coverage and normal_x_coverage: + predicted_normal_sex = predict_sex_from_y_and_x_cov( + normal_y_coverage, normal_x_coverage, "normal" + ) + predicted_sex.update(predicted_normal_sex) + + write_json(predicted_sex, output) + + +if __name__ == "__main__": + predict_sex_wgs() diff --git a/BALSAMIC/constants/cluster_analysis.json b/BALSAMIC/constants/cluster_analysis.json index a87210f52..f9b63198c 100644 --- a/BALSAMIC/constants/cluster_analysis.json +++ b/BALSAMIC/constants/cluster_analysis.json @@ -188,6 +188,10 @@ "time": "24:00:00", "n": 12 }, + "sex_prediction": { + "time": "01:00:00", + "n": 2 + }, "vardict_merge": { "time": "01:30:00", "n": 5 diff --git a/BALSAMIC/constants/rules.py b/BALSAMIC/constants/rules.py index b30034aff..4c49e8f6c 100644 --- a/BALSAMIC/constants/rules.py +++ b/BALSAMIC/constants/rules.py @@ -58,6 +58,7 @@ "snakemake_rules/annotation/vcf2cytosure_convert.rule", "snakemake_rules/annotation/final_vcf_reheader.rule", "snakemake_rules/annotation/rankscore.rule", + "snakemake_rules/quality_control/sex_check.rule", ], }, "single_targeted": { @@ -160,6 +161,7 @@ "snakemake_rules/quality_control/picard_wgs.rule", "snakemake_rules/quality_control/somalier.rule", "snakemake_rules/quality_control/samtools_qc_wgs.rule", + "snakemake_rules/quality_control/sex_check.rule", ], "align": [ "snakemake_rules/align/wgs_bam_postprocess.rule", diff --git a/BALSAMIC/models/metrics.py b/BALSAMIC/models/metrics.py index 55f4877a1..b6d157882 100644 --- a/BALSAMIC/models/metrics.py +++ b/BALSAMIC/models/metrics.py @@ -13,12 +13,12 @@ class MetricCondition(BaseModel): """Defines the metric condition model. Attributes: - norm (string, optional) : Validation condition. - threshold (float, optional) : Validation cut off. + norm (str, optional) : Validation condition (e.g., "eq", "lt"). + threshold (Any, optional) : Validation cutoff or expected value. """ norm: Optional[str] = None - threshold: Optional[float] = None + threshold: Optional[Any] = None class Metric(BaseModel): @@ -30,7 +30,7 @@ class Metric(BaseModel): input (str, required) : Input file. name (str, required) : Metric name. step (str, required) : Step that generated the metric. - value (Any, required) : Metric value. + value (Any, required) : Metric value (can be float or str). condition (MetricCondition, required) : Metric validation condition. """ @@ -45,13 +45,28 @@ class Metric(BaseModel): def validate_metric(metric: Metric): """Checks if a metric meets its filtering condition.""" - if metric.condition and not VALID_OPS[metric.condition.norm]( - metric.value, metric.condition.threshold - ): - raise ValueError( - f"QC metric {metric.name}: {metric.value} validation has failed. " - f"(Condition: {metric.condition.norm} {metric.condition.threshold}, ID: {metric.id})." - ) + if metric.condition: + norm: Optional[str] = metric.condition.norm + threshold: Optional[Any] = metric.condition.threshold + value: Any = metric.value + + # Validate the norm operator + if norm not in VALID_OPS: + raise ValueError(f"Unsupported operation: {norm}") + + # Attempt validation using the operator + try: + if not VALID_OPS[norm](value, threshold): + raise ValueError( + f"QC metric {metric.name}: {value} validation has failed. " + f"(Condition: {norm} {threshold}, ID: {metric.id})." + ) + except TypeError: + raise ValueError( + f"Type mismatch in QC metric {metric.name}: {value} and {threshold} " + f"are not compatible with operator {norm}. (ID: {metric.id})." + ) + LOG.info(f"QC metric {metric.name}: {metric.value} meets its condition.") return metric diff --git a/BALSAMIC/snakemake_rules/quality_control/multiqc.rule b/BALSAMIC/snakemake_rules/quality_control/multiqc.rule index 03cd40543..b822116c7 100644 --- a/BALSAMIC/snakemake_rules/quality_control/multiqc.rule +++ b/BALSAMIC/snakemake_rules/quality_control/multiqc.rule @@ -58,11 +58,9 @@ if config["analysis"]["sequencing_type"] == 'wgs': # Sentieon metrics multiqc_input.extend(expand(qc_dir + "tumor.{sample}_sentieon_wgs_metrics.txt", sample=tumor_sample)) - multiqc_input.extend(expand(qc_dir + "tumor.{sample}_coverage.gz", sample=tumor_sample)) if config['analysis']['analysis_type'] == "paired": multiqc_input.extend(expand(qc_dir + "normal.{sample}_sentieon_wgs_metrics.txt", sample=normal_sample)) - multiqc_input.extend(expand(qc_dir + "normal.{sample}_coverage.gz", sample=normal_sample)) if config["analysis"]["analysis_workflow"] != "balsamic-qc": diff --git a/BALSAMIC/snakemake_rules/quality_control/qc_metrics.rule b/BALSAMIC/snakemake_rules/quality_control/qc_metrics.rule index d1a3dc988..267568f36 100644 --- a/BALSAMIC/snakemake_rules/quality_control/qc_metrics.rule +++ b/BALSAMIC/snakemake_rules/quality_control/qc_metrics.rule @@ -14,21 +14,43 @@ if config["analysis"]["analysis_workflow"] == "balsamic": if config["analysis"]["analysis_workflow"] == "balsamic-umi": bcftools_counts_input.append(vep_dir + "SNV.somatic." + config["analysis"]["case_id"] + ".tnscope_umi.clinical.filtered.pass.stats") -rule collect_custom_qc_metrics: - input: - bcftools_counts = bcftools_counts_input, - json = qc_dir + "multiqc_data/multiqc_data.json" - output: - yaml = qc_dir + config["analysis"]["case_id"] + "_metrics_deliverables.yaml" - params: - config_path = f"{analysis_dir_home}/{case_id}/{case_id}.json", - collect_qc_metrics_script = get_script_path("collect_qc_metrics.py"), - housekeeper_id = {"id": config["analysis"]["case_id"], "tags": "qc-metrics"} - threads: - get_threads(cluster_config, "collect_custom_qc_metrics") - message: - "Extract the manually specified QC metric for validation and delivery" - shell: - """ -python {params.collect_qc_metrics_script} {params.config_path} {output.yaml} {input.json} {input.bcftools_counts} - """ + +if config["analysis"]["analysis_workflow"] != "balsamic-qc": + rule collect_custom_qc_metrics: + input: + bcftools_counts = bcftools_counts_input, + sex_prediction_json = qc_dir + "sex_prediction.json", + json = qc_dir + "multiqc_data/multiqc_data.json" + output: + yaml = qc_dir + config["analysis"]["case_id"] + "_metrics_deliverables.yaml" + params: + config_path = f"{analysis_dir_home}/{case_id}/{case_id}.json", + collect_qc_metrics_script = get_script_path("collect_qc_metrics.py"), + housekeeper_id = {"id": config["analysis"]["case_id"], "tags": "qc-metrics"} + threads: + get_threads(cluster_config, "collect_custom_qc_metrics") + message: + "Extract the manually specified QC metric for validation and delivery" + shell: + """ + python {params.collect_qc_metrics_script} --sex-prediction-path {input.sex_prediction_json} {params.config_path} {output.yaml} {input.json} {input.bcftools_counts} + """ +else: + rule collect_custom_qc_metrics: + input: + bcftools_counts = bcftools_counts_input, + json = qc_dir + "multiqc_data/multiqc_data.json" + output: + yaml = qc_dir + config["analysis"]["case_id"] + "_metrics_deliverables.yaml" + params: + config_path = f"{analysis_dir_home}/{case_id}/{case_id}.json", + collect_qc_metrics_script = get_script_path("collect_qc_metrics.py"), + housekeeper_id = {"id": config["analysis"]["case_id"], "tags": "qc-metrics"} + threads: + get_threads(cluster_config, "collect_custom_qc_metrics") + message: + "Extract the manually specified QC metric for validation and delivery" + shell: + """ + python {params.collect_qc_metrics_script} {params.config_path} {output.yaml} {input.json} {input.bcftools_counts} + """ diff --git a/BALSAMIC/snakemake_rules/quality_control/sentieon_qc_metrics.rule b/BALSAMIC/snakemake_rules/quality_control/sentieon_qc_metrics.rule index 96df58f60..82ef28394 100644 --- a/BALSAMIC/snakemake_rules/quality_control/sentieon_qc_metrics.rule +++ b/BALSAMIC/snakemake_rules/quality_control/sentieon_qc_metrics.rule @@ -8,7 +8,8 @@ if config["analysis"]["sequencing_type"] == 'wgs': reference = config["reference"]["reference_genome"] output: wgs_metrics = qc_dir + "{sample_type}.{sample}_sentieon_wgs_metrics.txt", - coverage_metrics = qc_dir + "{sample_type}.{sample}_coverage.gz" + coverage_metrics_y = qc_dir + "{sample_type}.{sample}_Y_cov_per_base.txt", + coverage_metrics_x = qc_dir + "{sample_type}.{sample}_X_cov_per_base.txt" benchmark: Path(benchmark_dir,'sentieon_wgs_metrics_' + "{sample_type}_{sample}.tsv").as_posix() params: @@ -41,10 +42,11 @@ if config["analysis"]["sequencing_type"] == 'wgs': -r {input.reference} \ --algo CoverageMetrics \ --gene_list {params.gene_list} {params.cov_threshold} \ - {output.coverage_metrics}_tmp; + {params.tmpdir}/coverage_per_base_wgs.txt ; - gzip -c {output.coverage_metrics}_tmp > {output.coverage_metrics} ; - rm {output.coverage_metrics}_tmp ; + grep ^Y {params.tmpdir}/coverage_per_base_wgs.txt | cut -f2 > {output.coverage_metrics_y} ; + grep ^X {params.tmpdir}/coverage_per_base_wgs.txt | cut -f2 > {output.coverage_metrics_x} ; + rm -rf {params.tmpdir} ; """ diff --git a/BALSAMIC/snakemake_rules/quality_control/sex_check.rule b/BALSAMIC/snakemake_rules/quality_control/sex_check.rule new file mode 100644 index 000000000..a33fafd1d --- /dev/null +++ b/BALSAMIC/snakemake_rules/quality_control/sex_check.rule @@ -0,0 +1,104 @@ +# vim: syntax=python tabstop=4 expandtab +# coding: utf-8 + + +if config["analysis"]["sequencing_type"] == 'wgs' and config["analysis"]["analysis_type"] == 'paired': + rule sex_prediction_wgs_tumor_normal: + input: + tumor_coverage_metrics_y = expand(qc_dir + "tumor.{sample}_Y_cov_per_base.txt", sample = tumor_sample), + tumor_coverage_metrics_x = expand(qc_dir + "tumor.{sample}_X_cov_per_base.txt", sample = tumor_sample), + normal_coverage_metrics_y = expand(qc_dir + "normal.{sample}_Y_cov_per_base.txt",sample = normal_sample), + normal_coverage_metrics_x = expand(qc_dir + "normal.{sample}_X_cov_per_base.txt", sample = normal_sample) + output: + sex_prediction_json = qc_dir + "sex_prediction.json" + benchmark: + Path(benchmark_dir, 'sex_prediction.tsv').as_posix() + params: + sex_check_script = get_script_path("sex_prediction_wgs.py"), + case_name = config["analysis"]["case_id"], + threads: + get_threads(cluster_config, "sex_prediction") + message: + "Running sex_prediction on case {params.case_name}" + shell: + """ + python {params.sex_check_script} \ + --tumor-x-coverage {input.tumor_coverage_metrics_x} \ + --tumor-y-coverage {input.tumor_coverage_metrics_y} \ + --normal-x-coverage {input.normal_coverage_metrics_x} \ + --normal-y-coverage {input.normal_coverage_metrics_y} \ + --output {output.sex_prediction_json} ; + """ +elif config["analysis"]["sequencing_type"] == 'wgs' and config["analysis"]["analysis_type"] == 'single': + rule sex_prediction_wgs_tumor_only: + input: + coverage_metrics_y = expand(qc_dir + "tumor.{sample}_Y_cov_per_base.txt", sample = tumor_sample), + coverage_metrics_x = expand(qc_dir + "tumor.{sample}_X_cov_per_base.txt", sample = tumor_sample) + output: + sex_prediction_json = qc_dir + "sex_prediction.json" + benchmark: + Path(benchmark_dir, 'sex_prediction.tsv').as_posix() + params: + sex_check_script = get_script_path("sex_prediction_wgs.py"), + case_name = config["analysis"]["case_id"], + threads: + get_threads(cluster_config, "sex_prediction") + message: + "Running sex_prediction on case {params.case_name}" + shell: + """ + python {params.sex_check_script} \ + --tumor-x-coverage {input.coverage_metrics_x} \ + --tumor-y-coverage {input.coverage_metrics_y} \ + --output {output.sex_prediction_json} ; + """ +elif config["analysis"]["sequencing_type"] == 'targeted' and config["analysis"]["analysis_type"] == 'paired': + rule sex_prediction_tga_tumor_normal: + input: + target_cnn_tumor = expand(cnv_dir + "{sample}.targetcoverage.cnn", sample = tumor_sample), + antitarget_cnn_tumor = expand(cnv_dir + "{sample}.antitargetcoverage.cnn", sample = tumor_sample), + target_cnn_normal = expand(cnv_dir + "{sample}.targetcoverage.cnn",sample = normal_sample), + antitarget_cnn_normal = expand(cnv_dir + "{sample}.antitargetcoverage.cnn",sample = normal_sample) + output: + sex_prediction_json = qc_dir + "sex_prediction.json" + benchmark: + Path(benchmark_dir, 'sex_prediction.tsv').as_posix() + params: + sex_check_script = get_script_path("sex_prediction_tga.py"), + case_name = config["analysis"]["case_id"], + threads: + get_threads(cluster_config, "sex_prediction") + message: + "Running sex_prediction on tumor and normal sample of {params.case_name}" + shell: + """ + python {params.sex_check_script} \ + --target-cnn-tumor {input.target_cnn_tumor} \ + --antitarget-cnn-tumor {input.antitarget_cnn_tumor} \ + --target-cnn-normal {input.target_cnn_normal} \ + --antitarget-cnn-normal {input.antitarget_cnn_normal} \ + --output {output.sex_prediction_json} ; + """ +elif config["analysis"]["sequencing_type"] == 'targeted' and config["analysis"]["analysis_type"] == 'single': + rule sex_prediction_tga_tumor_only: + input: + target_cnn_tumor = expand(cnv_dir + "{sample}.targetcoverage.cnn", sample = tumor_sample), + antitarget_cnn_tumor = expand(cnv_dir + "{sample}.antitargetcoverage.cnn", sample = tumor_sample), + output: + sex_prediction_json = qc_dir + "sex_prediction.json" + benchmark: + Path(benchmark_dir, 'sex_prediction.tsv').as_posix() + params: + sex_check_script = get_script_path("sex_prediction_tga.py"), + case_name = config["analysis"]["case_id"], + threads: + get_threads(cluster_config, "sex_prediction") + message: + "Running sex_prediction on tumor sample of {params.case_name}" + shell: + """ + python {params.sex_check_script} \ + --target-cnn-tumor {input.target_cnn_tumor} \ + --antitarget-cnn-tumor {input.antitarget_cnn_tumor} \ + --output {output.sex_prediction_json} ; + """ diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a79aa97fd..b049afe31 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,3 +1,20 @@ +[X.X.X] +------- + +Added: +^^^^^^ +* check to verify sample sex for all workflows https://github.com/Clinical-Genomics/BALSAMIC/pull/1516 + +Changed: +^^^^^^^^ + +Removed: +^^^^^^^^ + + +Fixed: +^^^^^^ + [16.0.0] ------- diff --git a/tests/conftest.py b/tests/conftest.py index 0f60f36f4..612b3ae52 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,6 +13,7 @@ from pydantic_core import Url from BALSAMIC import __version__ as balsamic_version +from BALSAMIC.assets.scripts.sex_prediction_tga import predict_sex_main from BALSAMIC.assets.scripts.preprocess_gens import cli as gens_preprocessing_cli from BALSAMIC.commands.base import cli from BALSAMIC.constants.analysis import ( @@ -420,6 +421,86 @@ def config_dict_w_singularity( return modify_dict +@pytest.fixture(scope="session") +def male_target_cnn_file(test_data_dir: str) -> str: + """Return path to test cnvkit target cnn file from male sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "male.targetcoverage.cnn" + ).as_posix() + + +@pytest.fixture(scope="session") +def male_antitarget_cnn_file(test_data_dir: str) -> str: + """Return path to test cnvkit antitarget cnn file from male sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "male.antitargetcoverage.cnn" + ).as_posix() + + +@pytest.fixture(scope="session") +def female_target_cnn_file(test_data_dir: str) -> str: + """Return path to test cnvkit target cnn file from mfeale sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "female.targetcoverage.cnn" + ).as_posix() + + +@pytest.fixture(scope="session") +def female_antitarget_cnn_file(test_data_dir: str) -> str: + """Return path to test cnvkit antitarget cnn file from female sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "female.antitargetcoverage.cnn" + ).as_posix() + + +@pytest.fixture(scope="session") +def male_200k_x_coverage(test_data_dir: str) -> str: + """Return path to 200k lines of x coverage from male sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "male_200k_x_bases.txt" + ).as_posix() + + +@pytest.fixture(scope="session") +def male_200k_y_coverage(test_data_dir: str) -> str: + """Return path to 200k lines of y coverage from male sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "male_200k_y_bases.txt" + ).as_posix() + + +@pytest.fixture(scope="session") +def female_200k_x_coverage(test_data_dir: str) -> str: + """Return path to 200k lines of x coverage from female sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "female_200k_x_bases.txt" + ).as_posix() + + +@pytest.fixture(scope="session") +def female_200k_y_coverage(test_data_dir: str) -> str: + """Return path to 200k lines of y coverage from female sample.""" + return Path( + test_data_dir, "qc_files", "sex_check", "female_200k_y_bases.txt" + ).as_posix() + + +@pytest.fixture(scope="session") +def tga_female_sex_prediction(test_data_dir: str) -> str: + """Return path sex prediction json for female.""" + return Path( + test_data_dir, "qc_files", "sex_check", "female_sex_prediction.json" + ).as_posix() + + +@pytest.fixture(scope="session") +def tga_male_sex_prediction(test_data_dir: str) -> str: + """Return path sex prediction json for male.""" + return Path( + test_data_dir, "qc_files", "sex_check", "male_sex_prediction.json" + ).as_posix() + + @pytest.fixture(scope="session") def pon_config_path(test_data_dir: str) -> str: """Created path for PON config json file.""" diff --git a/tests/models/test_metric_models.py b/tests/models/test_metric_models.py index a93b6f105..eba2fd0cf 100644 --- a/tests/models/test_metric_models.py +++ b/tests/models/test_metric_models.py @@ -1,10 +1,11 @@ """Tests for the QC metrics related methods.""" import copy from typing import Any, Dict - +from pathlib import Path import pytest from BALSAMIC.models.metrics import MetricValidation, Metric, MetricCondition +from BALSAMIC.assets.scripts.collect_qc_metrics import get_sex_check_metrics def test_metric_condition(): @@ -96,6 +97,44 @@ def test_multiple_metric_validation_fail(qc_extracted_metrics: dict): assert metrics[8]["name"] in str(val_exc.value) +def test_wrong_sex_check_metrics(tga_female_sex_prediction: str, config_dict: dict): + """Test MetricValidation for capturing conflicting predicted to given sex.""" + + # GIVEN female sex prediction JSON and male config dictionary + sex_metrics: list = get_sex_check_metrics(tga_female_sex_prediction, config_dict) + + # WHEN comparing predicted female sex and supplied male gender + # THEN check that the model filters the metrics according to its norm + with pytest.raises(ValueError) as val_exc: + MetricValidation(metrics=sex_metrics) + + # THEN a ValueError should be triggered containing this text + assert ( + "QC metric COMPARE_PREDICTED_TO_GIVEN_SEX: female validation has failed. " + "(Condition: eq male, ID: id1_tumor)." in str(val_exc.value) + ) + + +def test_validate_metric_type_error(): + """Test MetricValidation TypeError for incompatible variable-type comparisons.""" + + # Create a Metric object where value and threshold are incompatible types + metrics = [ + { + "name": "Test Metric", + "input": "fake_input_file", + "step": "fake_step", + "value": "string-value", # Incompatible with numerical comparisons + "condition": {"norm": "lt", "threshold": 10}, + "id": "caseid", + } + ] + + # Validate that the TypeError handling raises the correct ValueError + with pytest.raises(ValueError, match=r"Type mismatch in QC metric Test Metric: .*"): + MetricValidation(metrics=metrics) + + def test_metric_validation_norm_fail(qc_extracted_metrics: dict): """Test MetricValidation ValueError raising for an operator that it is not accepted.""" @@ -106,5 +145,5 @@ def test_metric_validation_norm_fail(qc_extracted_metrics: dict): # THEN model raises an error due to a non accepted norm try: MetricValidation(metrics=metrics) - except KeyError as key_exc: + except ValueError as key_exc: assert metrics[4]["condition"]["norm"] in str(key_exc) diff --git a/tests/scripts/test_collect_qc_metrics.py b/tests/scripts/test_collect_qc_metrics.py index bc66661fc..d2d134857 100644 --- a/tests/scripts/test_collect_qc_metrics.py +++ b/tests/scripts/test_collect_qc_metrics.py @@ -1,6 +1,7 @@ import copy import json import os.path +import pytest from pathlib import Path import yaml @@ -15,6 +16,7 @@ get_variant_metrics, get_metric_condition, get_relatedness_metrics, + get_sex_check_metrics, get_sample_id, ) @@ -237,7 +239,7 @@ def test_get_variant_metrics(bcftools_counts_path): def test_collect_qc_metrics_targeted( - tmp_path, config_path, multiqc_data_path, cli_runner + tmp_path, config_path, multiqc_data_path, cli_runner, tga_male_sex_prediction ): """tests qc metrics yaml file generation for targeted analysis""" @@ -246,10 +248,16 @@ def test_collect_qc_metrics_targeted( # GIVEN a config path - # WHEN invoking the python script + # WHEN invoking the CLI script result = cli_runner.invoke( collect_qc_metrics, - [config_path, output_path, multiqc_data_path], + [ + str(config_path), + str(output_path), + str(multiqc_data_path), + "--sex-prediction-path", + str(tga_male_sex_prediction), + ], ) # THEN check if the YAML is correctly created and there are no errors @@ -296,6 +304,30 @@ def test_get_sample_id(tumor_sample_name): assert get_sample_id(multiqc_key) == tumor_sample_name +def test_get_sex_check_metrics(tga_male_sex_prediction, config_dict): + """Tests sex check metric retrieval.""" + # GIVEN male sex prediction JSON and male config dictionary + + # GIVEN an expected MetricsModel dictionary + expected_sex_check_metric = [ + { + "header": None, + "id": "id1_tumor", + "input": "male_sex_prediction.json", + "name": "COMPARE_PREDICTED_TO_GIVEN_SEX", + "step": "sex_check", + "value": "male", + "condition": {"norm": "eq", "threshold": "male"}, + } + ] + + # WHEN comparing predicted male sex and supplied male gender + sex_metrics: list = get_sex_check_metrics(tga_male_sex_prediction, config_dict) + + # THEN check that the relatedness metrics has been correctly shaped + assert sex_metrics == expected_sex_check_metric + + def test_get_relatedness_metrics(multiqc_data_dict): """Tests relatedness metrics retrieval.""" diff --git a/tests/scripts/test_sex_prediction.py b/tests/scripts/test_sex_prediction.py new file mode 100644 index 000000000..eaaa13374 --- /dev/null +++ b/tests/scripts/test_sex_prediction.py @@ -0,0 +1,158 @@ +from pathlib import Path +from BALSAMIC.assets.scripts.sex_prediction_tga import predict_sex_main +from BALSAMIC.assets.scripts.sex_prediction_wgs import predict_sex_wgs +from BALSAMIC.utils.io import read_json + + +def test_tga_male_sex_prediction( + male_target_cnn_file: str, male_antitarget_cnn_file: str, tmp_path, cli_runner +): + """Ensure that TGA sex prediction is working using cnvkit cnn files for male sample.""" + # GIVEN the output path, and input cnn files + output_path = str(tmp_path / "male_sex_prediction.json") + + # WHEN invoking the python script + result = cli_runner.invoke( + predict_sex_main, + [ + "--target-cnn-tumor", + male_target_cnn_file, + "--antitarget-cnn-tumor", + male_antitarget_cnn_file, + "--output", + output_path, + ], + ) + + # THEN check if the JSON is correctly created and there are no errors + assert result.exit_code == 0 + assert Path(output_path).exists() + + # THEN check that male sex has been correctly predicted + male_sex_prediction: dict = read_json(output_path) + assert male_sex_prediction["case_sex"] == "male" + + +def test_tga_female_sex_prediction( + female_target_cnn_file: str, female_antitarget_cnn_file: str, tmp_path, cli_runner +): + """Ensure that TGA sex prediction is working using cnvkit cnn files for female sample.""" + # GIVEN the output path, and input cnn files + output_path = str(tmp_path / "female_sex_prediction.json") + + # WHEN invoking the python script + result = cli_runner.invoke( + predict_sex_main, + [ + "--target-cnn-tumor", + female_target_cnn_file, + "--antitarget-cnn-tumor", + female_antitarget_cnn_file, + "--output", + output_path, + ], + ) + + # THEN check if the JSON is correctly created and there are no errors + assert result.exit_code == 0 + assert Path(output_path).exists() + + # THEN check that female sex has been correctly predicted + female_sex_prediction: dict = read_json(output_path) + assert female_sex_prediction["case_sex"] == "female" + + +def test_tga_conflicting_sex_prediction( + male_target_cnn_file: str, + male_antitarget_cnn_file: str, + female_target_cnn_file: str, + female_antitarget_cnn_file: str, + tmp_path, + cli_runner, +): + """Ensure that TGA sex is reported as conflicting when using mixed up samples of opposite sexes.""" + # GIVEN the output path, and input cnn files + output_path = str(tmp_path / "conflicting_sex_prediction.json") + + # WHEN invoking the python script + result = cli_runner.invoke( + predict_sex_main, + [ + "--target-cnn-tumor", + male_target_cnn_file, + "--antitarget-cnn-tumor", + male_antitarget_cnn_file, + "--target-cnn-normal", + female_target_cnn_file, + "--antitarget-cnn-normal", + female_antitarget_cnn_file, + "--output", + output_path, + ], + ) + + # THEN check if the JSON is correctly created and there are no errors + assert result.exit_code == 0 + assert Path(output_path).exists() + + # THEN check that conflicting sex has been correctly predicted + conflicting_sex_prediction: dict = read_json(output_path) + assert conflicting_sex_prediction["case_sex"] == "conflicting" + + +def test_wgs_tumor_only_male_sex_prediction( + male_200k_x_coverage: str, male_200k_y_coverage: str, tmp_path, cli_runner +): + """Ensure that WGS sex prediction is working for tumor only using per base X and Y coverages for male sample.""" + # GIVEN the output path, and input cnn files + output_path = str(tmp_path / "male_wgs_sex_prediction.json") + + # WHEN invoking the python script + result = cli_runner.invoke( + predict_sex_wgs, + [ + "--tumor-x-coverage", + male_200k_x_coverage, + "--tumor-y-coverage", + male_200k_y_coverage, + "--output", + output_path, + ], + ) + + # THEN check if the JSON is correctly created and there are no errors + assert result.exit_code == 0 + assert Path(output_path).exists() + + # THEN check that male sex has been correctly predicted + sex_prediction: dict = read_json(output_path) + assert sex_prediction["tumor"]["predicted_sex"] == "male" + + +def test_wgs_tumor_only_female_sex_prediction( + female_200k_x_coverage: str, female_200k_y_coverage: str, tmp_path, cli_runner +): + """Ensure that WGS sex prediction is working for tumor only using per base X and Y coverages for female sample.""" + # GIVEN the output path, and input cnn files + output_path = str(tmp_path / "female_wgs_sex_prediction.json") + + # WHEN invoking the python script + result = cli_runner.invoke( + predict_sex_wgs, + [ + "--tumor-x-coverage", + female_200k_x_coverage, + "--tumor-y-coverage", + female_200k_y_coverage, + "--output", + output_path, + ], + ) + + # THEN check if the JSON is correctly created and there are no errors + assert result.exit_code == 0 + assert Path(output_path).exists() + + # THEN check that female sex has been correctly predicted + sex_prediction: dict = read_json(output_path) + assert sex_prediction["tumor"]["predicted_sex"] == "female" diff --git a/tests/test_data/qc_files/sex_check/female.antitargetcoverage.cnn b/tests/test_data/qc_files/sex_check/female.antitargetcoverage.cnn new file mode 100644 index 000000000..076fc9d8f --- /dev/null +++ b/tests/test_data/qc_files/sex_check/female.antitargetcoverage.cnn @@ -0,0 +1,1263 @@ +chromosome start end gene depth log2 +X 60500 94321 Antitarget 0.0768162 -3.70245 +X 145321 230884 Antitarget 0.377453 -1.40563 +X 281884 434918 Antitarget 0.589608 -0.762173 +X 434918 587953 Antitarget 0.340674 -1.55354 +X 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Antitarget 0.0011253 -9.79548 +Y 28470821 28627225 Antitarget 0.0140533 -6.15294 +Y 28627225 28783629 Antitarget 0.00238485 -8.71189 +Y 58968156 59099792 Antitarget 0.647103 -0.627934 +Y 59099792 59231429 Antitarget 0 -20 +Y 59231429 59363066 Antitarget 0 -20 diff --git a/tests/test_data/qc_files/sex_check/female.targetcoverage.cnn b/tests/test_data/qc_files/sex_check/female.targetcoverage.cnn new file mode 100644 index 000000000..d26d7af1a --- /dev/null +++ b/tests/test_data/qc_files/sex_check/female.targetcoverage.cnn @@ -0,0 +1,904 @@ +chromosome start end gene depth log2 +X 1321321 1321421 CRLF2 621.92 9.28059 +X 2703582 2703682 XG 56.23 5.81327 +X 4119591 4119691 - 42.1 5.39575 +X 4702536 4702636 - 107.67 6.75047 +X 5955865 5955965 NLGN4X 39.66 5.30961 +X 7092351 7092451 STS 69.69 6.12288 +X 8405526 8405626 - 96.64 6.59455 +X 8866042 8866142 - 57.94 5.85649 +X 10301899 10301999 - 90.94 6.50684 +X 12214397 12214497 FRMPD4 40.58 5.3427 +X 12910568 12910668 - 46.13 5.52763 +X 13681587 13681687 TCEANC 64.54 6.01212 +X 14861688 14861895 FANCB 539.681 9.07596 +X 14861895 14862103 FANCB 490.389 8.93778 +X 14862624 14862862 FANCB 544.718 9.08937 +X 14862977 14863192 FANCB 541.967 9.08206 +X 14863192 14863408 FANCB 553.199 9.11165 +X 14868626 14868796 FANCB 429.553 8.74669 +X 14871160 14871289 FANCB 342.364 8.41939 +X 14875979 14876079 FANCB 474.11 8.88908 +X 14877303 14877456 FANCB 461.608 8.85052 +X 14882681 14882918 FANCB 492.629 8.94436 +X 14882918 14883156 FANCB 497.025 8.95718 +X 14883156 14883394 FANCB 555.269 9.11704 +X 14883394 14883632 FANCB 542.395 9.0832 +X 15364113 15364213 PIR-FIGF,VEGFD 99.13 6.63125 +X 15407010 15407110 PIR-FIGF,PIR 71.08 6.15137 +X 15808588 15808688 ZRSR2 651.22 9.347 +X 15809046 15809146 ZRSR2 655.7 9.35689 +X 15817985 15818085 ZRSR2 538.02 9.07152 +X 15821810 15821919 ZRSR2 546.11 9.09305 +X 15822226 15822326 ZRSR2 544.59 9.08903 +X 15826324 15826424 ZRSR2 577.55 9.1738 +X 15827322 15827441 ZRSR2 616.042 9.26688 +X 15833799 15834013 ZRSR2 541.757 9.0815 +X 15836687 15836787 ZRSR2 548.22 9.09861 +X 15838329 15838439 ZRSR2 589.4 9.2031 +X 15840853 15841109 ZRSR2 605.781 9.24265 +X 15841109 15841365 ZRSR2 605.637 9.24231 +X 15886834 15886934 - 66.74 6.06048 +X 16564701 16564801 - 141.62 7.14588 +X 18156387 18156487 - 97.9 6.61324 +X 18712975 18713075 PPEF1 107.06 6.74228 +X 20146376 20146476 EIF1AX 463.94 8.85779 +X 20148629 20148729 EIF1AX 352.76 8.46254 +X 20150290 20150390 EIF1AX 467.73 8.86953 +X 20152049 20152149 EIF1AX 344.23 8.42723 +X 20153855 20153959 EIF1AX 538.625 9.07314 +X 20156648 20156748 EIF1AX 404.76 8.66092 +X 20159700 20159800 EIF1AX 672.08 9.39249 +X 20594042 20594142 - 97.89 6.61309 +X 21835901 21836001 - 76.05 6.24888 +X 23373754 23373854 PTCHD1 68.01 6.08767 +X 23626422 23626522 - 70.15 6.13237 +X 25314671 25314771 - 93.52 6.5472 +X 25805464 25805564 - 78.35 6.29186 +X 26633832 26633932 - 58.5 5.87036 +X 27654703 27654803 DCAF8L2 71.89 6.16772 +X 28753062 28753162 IL1RAPL1 72.91 6.18804 +X 30191448 30191548 - 79.7 6.31651 +X 30804715 30804815 - 59.4 5.89239 +X 31139948 31140048 DMD 460.91 8.84834 +X 31144724 31144824 DMD 465.2 8.86171 +X 31152214 31152314 DMD 510.29 8.99517 +X 31164407 31164531 DMD 448.371 8.80855 +X 31165391 31165635 DMD 497.639 8.95896 +X 31187559 31187718 DMD 498.113 8.96033 +X 31190447 31190547 DMD 397.35 8.63427 +X 31191638 31191738 DMD 416.79 8.70318 +X 31196017 31196117 DMD 457.26 8.83687 +X 31196781 31196922 DMD 446.894 8.80379 +X 31198486 31198598 DMD 455.161 8.83023 +X 31200854 31201021 DMD 351.784 8.45855 +X 31222077 31222235 DMD 533.487 9.05931 +X 31224691 31224791 DMD 561.47 9.13307 +X 31227614 31227816 DMD 517.99 9.01678 +X 31241150 31241250 DMD 396.03 8.62947 +X 31279052 31279152 DMD 546.4 9.09381 +X 31284886 31284986 DMD 638.25 9.31798 +X 31341694 31341794 DMD 427.09 8.7384 +X 31366661 31366761 DMD 485.24 8.92255 +X 31462597 31462744 DMD 497.068 8.9573 +X 31496222 31496491 DMD 378.212 8.56305 +X 31497099 31497220 DMD 484.678 8.92088 +X 31514904 31515061 DMD 446.701 8.80316 +X 31525397 31525570 DMD 528.231 9.04503 +X 31526289 31526389 DMD 478.63 8.90277 +X 31645789 31645979 DMD 477.489 8.89933 +X 31676106 31676261 DMD 452.058 8.82036 +X 31697491 31697703 DMD 461.462 8.85007 +X 31747747 31747865 DMD 599.898 9.22857 +X 31792076 31792309 DMD 442.33 8.78898 +X 31838091 31838200 DMD 570.44 9.15593 +X 31854834 31854939 DMD 410.048 8.67965 +X 31893307 31893490 DMD 519.393 9.02068 +X 31947712 31947862 DMD 449.2 8.81121 +X 31950196 31950344 DMD 469.649 8.87544 +X 31986455 31986631 DMD 453.159 8.82387 +X 32149349 32149449 DMD 31.31 4.96855 +X 32235032 32235180 DMD 387.777 8.59908 +X 32305645 32305818 DMD 401.63 8.64972 +X 32328198 32328393 DMD 407.062 8.6691 +X 32360216 32360399 DMD 499.541 8.96446 +X 32361250 32361403 DMD 397.621 8.63525 +X 32364059 32364197 DMD 476.29 8.8957 +X 32366522 32366645 DMD 394.667 8.62449 +X 32380904 32381075 DMD 467.322 8.86827 +X 32382698 32382827 DMD 446.186 8.8015 +X 32383136 32383316 DMD 445.156 8.79817 +X 32398626 32398797 DMD 449.316 8.81159 +X 32404426 32404582 DMD 368.769 8.52657 +X 32407617 32407791 DMD 447.299 8.8051 +X 32408187 32408298 DMD 478.793 8.90326 +X 32429868 32430030 DMD 538.599 9.07307 +X 32430104 32430204 DMD 550.91 9.10567 +X 32430252 32430352 DMD 540.78 9.0789 +X 32456357 32456507 DMD 502.287 8.97237 +X 32459296 32459431 DMD 355.719 8.47459 +X 32466572 32466755 DMD 412.579 8.68853 +X 32472778 32472949 DMD 325.099 8.34474 +X 32481555 32481711 DMD 321.333 8.32793 +X 32482702 32482816 DMD 468.921 8.8732 +X 32486614 32486827 DMD 439.765 8.78059 +X 32490280 32490426 DMD 346.007 8.43466 +X 32503035 32503216 DMD 477.796 8.90025 +X 32509393 32509635 DMD 499.442 8.96417 +X 32519865 32519965 DMD 504.37 8.97834 +X 32536124 32536248 DMD 507.96 8.98857 +X 32563275 32563451 DMD 507.193 8.98639 +X 32583818 32583998 DMD 514.194 9.00617 +X 32591646 32591754 DMD 543.657 9.08655 +X 32591861 32591963 DMD 524.108 9.03372 +X 32613873 32613993 DMD 429.892 8.74783 +X 32632419 32632570 DMD 430.278 8.74913 +X 32662248 32662430 DMD 448.423 8.80872 +X 32663080 32663269 DMD 468.354 8.87146 +X 32715986 32716115 DMD 366.178 8.5164 +X 32717228 32717410 DMD 419.176 8.71141 +X 32827609 32827728 DMD 408.966 8.67584 +X 32834584 32834757 DMD 525.948 9.03878 +X 32841407 32841507 DMD 427.53 8.73988 +X 32862888 32862988 DMD 380.6 8.57213 +X 32867840 32867940 DMD 422.2 8.72178 +X 32991607 32991707 DMD 77.25 6.27146 +X 33038236 33038336 DMD 266.96 8.06048 +X 33146222 33146322 DMD 571.42 9.15841 +X 33229363 33229463 DMD 388.09 8.60025 +X 33357328 33357428 DMD 384.09 8.5853 +X 33550987 33551087 - 80.97 6.33932 +X 34637327 34637427 - 70.29 6.13525 +X 35659387 35659487 - 51.03 5.67327 +X 37312564 37312664 PRRG1 136.09 7.08842 +X 38171710 38171810 RPGR 57.6 5.848 +X 38664774 38664874 MID1IP1 63.61 5.99118 +X 38692454 38692554 - 56.85 5.82909 +X 39911361 39911653 BCOR 434.894 8.76452 +X 39913138 39913295 BCOR 599.006 9.22643 +X 39913497 39913597 BCOR 480.45 8.90824 +X 39914620 39914766 BCOR 480.884 8.90954 +X 39916407 39916574 BCOR 492.485 8.94394 +X 39921391 39921646 BCOR 494.89 8.95096 +X 39921998 39922324 BCOR 474.721 8.89094 +X 39922860 39923205 BCOR 487.516 8.92931 +X 39923588 39923852 BCOR 428.095 8.74179 +X 39930225 39930412 BCOR 602.396 9.23457 +X 39930866 39930966 BCOR 524.64 9.03518 +X 39931601 39931858 BCOR 523.401 9.03177 +X 39931858 39932115 BCOR 470.786 8.87893 +X 39932115 39932373 BCOR 520.62 9.02409 +X 39932373 39932630 BCOR 584.564 9.19122 +X 39932630 39932888 BCOR 651.178 9.34691 +X 39932888 39933145 BCOR 621.984 9.28073 +X 39933145 39933403 BCOR 567.547 9.14859 +X 39933403 39933660 BCOR 667.518 9.38266 +X 39933660 39933918 BCOR 576.702 9.17168 +X 39933918 39934175 BCOR 406.323 8.66648 +X 39934175 39934433 BCOR 488.271 8.93154 +X 39935695 39935795 BCOR 551.82 9.10805 +X 39937089 39937189 BCOR 646.72 9.337 +X 40345060 40345160 - 75.85 6.24508 +X 40836104 40836204 - 0 -20 +X 40982879 40982979 USP9X 548.17 9.09848 +X 40988252 40988398 USP9X 518.699 9.01875 +X 40990699 40990799 USP9X 493.93 8.94816 +X 40993977 40994090 USP9X 490.602 8.93841 +X 40996056 40996275 USP9X 415.676 8.69931 +X 40999908 41000024 USP9X 500.397 8.96693 +X 41000218 41000470 USP9X 416.976 8.70382 +X 41000545 41000684 USP9X 446.432 8.8023 +X 41002543 41002696 USP9X 394.418 8.62358 +X 41003774 41003879 USP9X 529.219 9.04772 +X 41007621 41007828 USP9X 463.696 8.85703 +X 41010173 41010310 USP9X 507.27 8.98661 +X 41012200 41012334 USP9X 504.806 8.97959 +X 41022036 41022136 USP9X 567.28 9.14792 +X 41025124 41025467 USP9X 495.965 8.95409 +X 41026732 41026832 USP9X 458.72 8.84147 +X 41027259 41027471 USP9X 522.986 9.03063 +X 41029247 41029488 USP9X 432.369 8.75612 +X 41029722 41029872 USP9X 554.973 9.11627 +X 41031090 41031211 USP9X 550.81 9.10541 +X 41043250 41043381 USP9X 561.809 9.13394 +X 41043649 41043928 USP9X 473.018 8.88575 +X 41045769 41045895 USP9X 557.992 9.1241 +X 41047244 41047370 USP9X 487.556 8.92942 +X 41048561 41048728 USP9X 578.563 9.17633 +X 41055503 41055612 USP9X 632.073 9.30395 +X 41055844 41055991 USP9X 525.068 9.03636 +X 41056616 41056763 USP9X 443.728 8.79353 +X 41057780 41058003 USP9X 457.404 8.83732 +X 41060312 41060533 USP9X 584.199 9.19032 +X 41064555 41064746 USP9X 534.204 9.06125 +X 41069761 41069935 USP9X 592.621 9.21097 +X 41073820 41073962 USP9X 481.092 8.91017 +X 41075151 41075402 USP9X 619.355 9.27462 +X 41075402 41075653 USP9X 597.625 9.2231 +X 41075653 41075905 USP9X 555.135 9.11669 +X 41076472 41076596 USP9X 526.218 9.03952 +X 41077624 41077850 USP9X 541.044 9.0796 +X 41078354 41078484 USP9X 564.8 9.1416 +X 41082469 41082655 USP9X 600.022 9.22887 +X 41083994 41084215 USP9X 623.48 9.2842 +X 41084295 41084395 USP9X 625.01 9.28774 +X 41088505 41088662 USP9X 594.159 9.21471 +X 41088819 41089080 USP9X 528.414 9.04552 +X 41089751 41089851 USP9X 586.25 9.19537 +X 41091639 41091777 USP9X 656.246 9.35809 +X 41193477 41193577 DDX3X 701.69 9.45469 +X 41196639 41196739 DDX3X 599.74 9.22819 +X 41198262 41198362 DDX3X 254.76 7.99299 +X 41200736 41200869 DDX3X 472.188 8.88322 +X 41201747 41201906 DDX3X 546.415 9.09385 +X 41201989 41202089 DDX3X 584.28 9.19052 +X 41202468 41202604 DDX3X 521.191 9.02567 +X 41202982 41203082 DDX3X 546.01 9.09278 +X 41203281 41203381 DDX3X 501.3 8.96953 +X 41203491 41203652 DDX3X 524.981 9.03612 +X 41204432 41204577 DDX3X 339.179 8.4059 +X 41204656 41204801 DDX3X 509.4 8.99266 +X 41205481 41205663 DDX3X 552.725 9.11042 +X 41205757 41205875 DDX3X 477.161 8.89833 +X 41206111 41206265 DDX3X 487.208 8.92839 +X 41206564 41206704 DDX3X 540.686 9.07865 +X 41206882 41206982 DDX3X 519.12 9.01992 +X 41974751 41974851 - 88.66 6.47021 +X 43324573 43324673 - 70.67 6.14303 +X 43612657 43612757 - 61.15 5.93428 +X 44654904 44655004 - 101.42 6.6642 +X 44732797 44732958 KDM6A 425.478 8.73294 +X 44733151 44733251 KDM6A 607.14 9.24589 +X 44820528 44820637 KDM6A 530.385 9.0509 +X 44833885 44833985 KDM6A 456.55 8.83463 +X 44870184 44870284 KDM6A 458.35 8.84031 +X 44879854 44879975 KDM6A 398.322 8.63779 +X 44894152 44894252 KDM6A 560.24 9.1299 +X 44896866 44896966 KDM6A 513.04 9.00293 +X 44910950 44911050 KDM6A 359.08 8.48816 +X 44913073 44913200 KDM6A 543.772 9.08686 +X 44918249 44918349 KDM6A 461.81 8.85116 +X 44918491 44918711 KDM6A 512.814 9.00229 +X 44919266 44919401 KDM6A 353.437 8.46531 +X 44919853 44920009 KDM6A 481.128 8.91028 +X 44920566 44920666 KDM6A 339.83 8.40867 +X 44921891 44921993 KDM6A 417.225 8.70468 +X 44922666 44923062 KDM6A 534.066 9.06087 +X 44928823 44929082 KDM6A 522 9.02791 +X 44929082 44929342 KDM6A 531.112 9.05287 +X 44929342 44929602 KDM6A 563.204 9.13751 +X 44935834 44935934 KDM6A 279.57 8.12707 +X 44935941 44936071 KDM6A 480.115 8.90724 +X 44937644 44937750 KDM6A 389.783 8.60653 +X 44938390 44938596 KDM6A 465.447 8.86247 +X 44941802 44941902 KDM6A 470.53 8.87814 +X 44941946 44942046 KDM6A 540.65 9.07855 +X 44942704 44942853 KDM6A 350.141 8.45179 +X 44945109 44945224 KDM6A 612.122 9.25767 +X 44948987 44949175 KDM6A 551.59 9.10745 +X 44949967 44950109 KDM6A 435.021 8.76494 +X 44966654 44966781 KDM6A 504.543 8.97883 +X 44969323 44969494 KDM6A 542.766 9.08419 +X 44970591 44970691 KDM6A 439.44 8.77952 +X 45671943 45672043 - 84.74 6.40497 +X 47004830 47004930 RBM10 680.58 9.41062 +X 47006755 47006897 RBM10 674.683 9.39807 +X 47028713 47028897 RBM10 655.864 9.35725 +X 47030426 47030657 RBM10 669.013 9.38589 +X 47032511 47032611 RBM10 700.45 9.45214 +X 47034404 47034504 RBM10 396.53 8.63129 +X 47035891 47035991 RBM10 479.03 8.90397 +X 47038481 47038581 RBM10 719.7 9.49125 +X 47038717 47038894 RBM10 816.503 9.67331 +X 47039278 47039439 RBM10 559.348 9.1276 +X 47039609 47039709 RBM10 692.17 9.43498 +X 47039811 47039911 RBM10 394.6 8.62425 +X 47040613 47040800 RBM10 536.797 9.06823 +X 47040905 47041045 RBM10 630.371 9.30006 +X 47041147 47041265 RBM10 645.958 9.3353 +X 47041345 47041445 RBM10 749.29 9.54938 +X 47041560 47041725 RBM10 524.394 9.03451 +X 47044453 47044603 RBM10 800.66 9.64505 +X 47044683 47044783 RBM10 705.32 9.46213 +X 47044840 47045029 RBM10 841.894 9.7175 +X 47045101 47045201 RBM10 674.79 9.39829 +X 47045463 47045570 RBM10 680.963 9.41143 +X 47045656 47045786 RBM10 822.5 9.68387 +X 47045872 47045998 RBM10 695.421 9.44174 +X 47393701 47393801 - 90.06 6.49281 +X 47422364 47422464 ARAF 692.89 9.43648 +X 47422624 47422728 ARAF 701.663 9.45464 +X 47424195 47424298 ARAF 597.806 9.22353 +X 47424383 47424538 ARAF 678.019 9.40518 +X 47424641 47424753 ARAF 720.196 9.49225 +X 47426037 47426179 ARAF 658.451 9.36293 +X 47426246 47426346 ARAF 582.67 9.18654 +X 47426384 47426530 ARAF 704.747 9.46096 +X 47426628 47426831 ARAF 626.911 9.29212 +X 47428116 47428293 ARAF 571.271 9.15803 +X 47428358 47428458 ARAF 523.84 9.03298 +X 47428937 47429056 ARAF 685.613 9.42125 +X 47429291 47429423 ARAF 667.182 9.38194 +X 47430276 47430411 ARAF 636.904 9.31493 +X 47430721 47430856 ARAF 829.481 9.69607 +X 47709164 47709264 ZNF81 111.27 6.79792 +X 48891766 48892035 TFE3 749.167 9.54914 +X 48892035 48892304 TFE3 765.61 9.58047 +X 48892304 48892573 TFE3 589.866 9.20424 +X 48892573 48892842 TFE3 73.4647 6.19898 +X 48892842 48893111 TFE3 34.1264 5.09282 +X 48893111 48893380 TFE3 427.981 8.7414 +X 48893380 48893650 TFE3 228.459 7.83579 +X 48893650 48893919 TFE3 11.3606 3.50597 +X 48893919 48894188 TFE3 16.9777 4.08557 +X 48894188 48894457 TFE3 273.58 8.09582 +X 48894457 48894726 TFE3 358.182 8.48455 +X 48894726 48894995 TFE3 517.067 9.01421 +X 48894995 48895264 TFE3 284.342 8.15148 +X 48895264 48895534 TFE3 663.922 9.37487 +X 48895630 48895730 TFE3 797.94 9.64014 +X 50359037 50359137 SHROOM4 107.29 6.74537 +X 53221922 53222022 KDM5C 620.57 9.27745 +X 53222148 53222514 KDM5C 1128.09 10.1397 +X 53222618 53222818 KDM5C 1070.49 10.0641 +X 53222943 53223043 KDM5C 923.43 9.85086 +X 53223320 53223620 KDM5C 887.193 9.7931 +X 53223620 53223920 KDM5C 1014.62 9.98672 +X 53224112 53224250 KDM5C 904.13 9.82039 +X 53224412 53224592 KDM5C,MIR6895 956.667 9.90187 +X 53225097 53225236 KDM5C 1075.96 10.0714 +X 53225867 53226226 KDM5C 976.34 9.93124 +X 53226952 53227058 KDM5C 728.557 9.5089 +X 53227671 53227819 KDM5C 862.973 9.75317 +X 53227945 53228070 KDM5C,MIR6894 933.472 9.86646 +X 53228158 53228340 KDM5C 735.83 9.52323 +X 53230731 53230926 KDM5C 751.6 9.55382 +X 53231035 53231155 KDM5C 670.708 9.38954 +X 53239595 53239758 KDM5C 919.19 9.84422 +X 53239857 53240039 KDM5C 821.214 9.68161 +X 53240678 53240837 KDM5C 935.151 9.86906 +X 53240968 53241088 KDM5C 839.767 9.71384 +X 53243870 53244029 KDM5C 862.428 9.75226 +X 53244976 53245158 KDM5C 884.214 9.78825 +X 53245255 53245379 KDM5C 722.323 9.4965 +X 53246324 53246459 KDM5C 657.43 9.36069 +X 53246977 53247148 KDM5C 673.526 9.39559 +X 53247457 53247580 KDM5C 917.959 9.84229 +X 53250009 53250109 KDM5C 914.06 9.83615 +X 53253921 53254071 KDM5C 804.2 9.65141 +X 53407015 53407115 SMC1A 1074.4 10.0693 +X 53407540 53407651 SMC1A 913.495 9.83525 +X 53407923 53408023 SMC1A 684.7 9.41933 +X 53409152 53409304 SMC1A 927.461 9.85714 +X 53409426 53409581 SMC1A 1105.23 10.1101 +X 53410017 53410174 SMC1A 852.484 9.73553 +X 53421697 53421808 SMC1A 697.82 9.44671 +X 53423146 53423300 SMC1A 921.87 9.84842 +X 53423391 53423537 SMC1A 937.623 9.87286 +X 53426510 53426652 SMC1A 685.275 9.42054 +X 53430497 53430604 SMC1A 884.299 9.78839 +X 53430708 53430825 SMC1A 943.068 9.88122 +X 53431943 53432081 SMC1A 929.703 9.86063 +X 53432176 53432323 SMC1A 886.884 9.7926 +X 53432424 53432604 SMC1A,MIR6857 993.161 9.95588 +X 53432702 53432888 SMC1A 967.478 9.91809 +X 53435992 53436200 SMC1A 925.37 9.85389 +X 53436342 53436442 SMC1A 975.91 9.9306 +X 53438710 53438851 SMC1A 1107.04 10.1125 +X 53438944 53439203 SMC1A 1066.93 10.0593 +X 53439849 53440088 SMC1A 979.623 9.93608 +X 53440181 53440385 SMC1A 950.23 9.89213 +X 53441706 53441819 SMC1A 905.903 9.82321 +X 53441929 53442118 SMC1A 924.402 9.85238 +X 53448815 53448915 SMC1A 689.76 9.42995 +X 53449440 53449549 SMC1A 815.45 9.67145 +X 53560269 53560372 HUWE1 663.291 9.3735 +X 53560967 53561158 HUWE1 656.524 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6.37521 +X 62974244 62974344 ARHGEF9 118.65 6.89057 +X 62993695 62993795 ARHGEF9 138.78 7.11666 +X 63409758 63410020 AMER1 728.737 9.50925 +X 63410020 63410282 AMER1 847.061 9.72632 +X 63410282 63410544 AMER1 828.511 9.69438 +X 63410544 63410806 AMER1 785.115 9.61676 +X 63410806 63411068 AMER1 915.553 9.8385 +X 63411068 63411330 AMER1 917.08 9.8409 +X 63411330 63411593 AMER1 972.589 9.92569 +X 63411593 63411855 AMER1 892.679 9.802 +X 63411855 63412117 AMER1 797.767 9.63982 +X 63412117 63412379 AMER1 825.279 9.68874 +X 63412379 63412641 AMER1 843.111 9.71958 +X 63412641 63412903 AMER1 597.782 9.22348 +X 63412903 63413166 AMER1 661.008 9.36852 +X 66100452 66100552 - 50.51 5.6585 +X 66104412 66104512 - 90.53 6.50032 +X 66104555 66104655 - 108.26 6.75836 +X 66108276 66108376 - 76.15 6.25077 +X 66111579 66111679 - 130.39 7.02669 +X 66115072 66115172 - 112.7 6.81634 +X 66119522 66119622 - 108.21 6.75769 +X 66120065 66120165 - 126.06 6.97797 +X 66124865 66124965 - 128.1 7.00113 +X 66131644 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6.1815 +X 69916031 69916131 TEX11 134.65 7.07307 +X 70077656 70077756 TEX11 3.66 1.87184 +X 70234119 70234219 - 77.88 6.28318 +X 70241654 70241754 - 212.39 7.73057 +X 70338603 70338703 MED12 672.9 9.39425 +X 70339222 70339327 MED12 805.952 9.65455 +X 70339535 70339727 MED12 838.203 9.71116 +X 70339863 70340020 MED12 922.408 9.84926 +X 70340820 70341002 MED12 843.599 9.72041 +X 70341176 70341287 MED12 886.072 9.79128 +X 70341411 70341666 MED12 776.204 9.60029 +X 70342049 70342196 MED12 810.122 9.662 +X 70342357 70342457 MED12 949.79 9.89146 +X 70342587 70342724 MED12 758.613 9.56722 +X 70342944 70343076 MED12 620.591 9.2775 +X 70343443 70343570 MED12 798.126 9.64047 +X 70344008 70344238 MED12 668.626 9.38506 +X 70344603 70344703 MED12 878.45 9.77882 +X 70344825 70344996 MED12 845.047 9.72289 +X 70345200 70345345 MED12 828.745 9.69478 +X 70345487 70345587 MED12 416.8 8.70321 +X 70345885 70346004 MED12 602.756 9.23543 +X 70346190 70346334 MED12 869.84 9.76461 +X 70346818 70346982 MED12 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610.39 9.25359 +X 70471010 70471110 ZMYM3 691.2 9.43296 +X 70471379 70471479 ZMYM3 953.39 9.89692 +X 70472438 70472660 ZMYM3 945.018 9.8842 +X 70472660 70472882 ZMYM3 1023.33 9.99906 +X 70472882 70473105 ZMYM3 1090.08 10.0902 +X 70543304 70543404 - 123.48 6.94813 +X 70586164 70586344 TAF1 516.533 9.01272 +X 70587348 70587463 TAF1 893.713 9.80367 +X 70587903 70588020 TAF1 815.342 9.67126 +X 70595016 70595136 TAF1 703.992 9.45941 +X 70596799 70597041 TAF1 725.207 9.50225 +X 70597452 70597671 TAF1 720.986 9.49383 +X 70598084 70598303 TAF1 773.155 9.59461 +X 70598673 70598881 TAF1 783.683 9.61413 +X 70601592 70601769 TAF1 814.373 9.66955 +X 70602385 70602513 TAF1 842.594 9.71869 +X 70602610 70602718 TAF1 832.287 9.70094 +X 70602840 70603014 TAF1 919.638 9.84492 +X 70603811 70603985 TAF1 735.195 9.52198 +X 70604794 70604899 TAF1 816.286 9.67293 +X 70607110 70607311 TAF1 825.945 9.6899 +X 70608086 70608228 TAF1 712.683 9.47712 +X 70608587 70608718 TAF1 749.817 9.55039 +X 70609424 70609524 TAF1 852.78 9.73603 +X 70612418 70612568 TAF1 716.433 9.48469 +X 70612724 70612844 TAF1 889.267 9.79647 +X 70613150 70613326 TAF1 982.693 9.9406 +X 70613916 70614095 TAF1 798.464 9.64108 +X 70617102 70617316 TAF1 699.299 9.44977 +X 70618421 70618587 TAF1 996.771 9.96112 +X 70621377 70621589 TAF1 877.283 9.7769 +X 70626487 70626596 TAF1 764.211 9.57783 +X 70627422 70627522 TAF1 786.63 9.61954 +X 70627823 70628001 TAF1 900.09 9.81393 +X 70641142 70641242 TAF1 620.64 9.27761 +X 70642966 70643089 TAF1 918.455 9.84307 +X 70643819 70643919 TAF1 670.33 9.38873 +X 70643995 70644095 TAF1 725.11 9.50206 +X 70674003 70674103 TAF1 970.52 9.92261 +X 70674590 70674711 TAF1 754.719 9.5598 +X 70678088 70678216 TAF1 675.062 9.39888 +X 70679401 70679558 TAF1 810.49 9.66265 +X 70680475 70680653 TAF1 909.41 9.82879 +X 70683673 70683896 TAF1 930.251 9.86148 +X 70745272 70745372 TAF1 100.66 6.65335 +X 70899390 70899490 - 85.74 6.4219 +X 71051013 71051113 - 0 -20 +X 71222436 71222536 NHSL2 86.77 6.43912 +X 71305263 71305363 NHSL2 132.6 7.05094 +X 71385966 71386066 - 90.57 6.50096 +X 71547319 71547419 - 140.41 7.1335 +X 71699799 71699899 HDAC8 92.57 6.53247 +X 71867535 71867635 PHKA1 153.21 7.25937 +X 72101692 72101792 - 145.32 7.18309 +X 72208547 72208647 - 100.23 6.64717 +X 76433236 76433336 - 144.49 7.17483 +X 76763828 76764107 ATRX 738.366 9.52819 +X 76776265 76776394 ATRX 644.248 9.33147 +X 76776878 76776978 ATRX 653.58 9.35222 +X 76777740 76777866 ATRX 788.833 9.62358 +X 76778729 76778879 ATRX 641.307 9.32487 +X 76812921 76813116 ATRX 575.026 9.16748 +X 76814139 76814317 ATRX 454.635 8.82856 +X 76829714 76829823 ATRX 598.936 9.22626 +X 76845303 76845410 ATRX 668.794 9.38542 +X 76849165 76849319 ATRX 845.325 9.72336 +X 76854879 76855049 ATRX 875.818 9.77449 +X 76855194 76855294 ATRX 447.32 8.80516 +X 76855902 76856033 ATRX 681.13 9.41179 +X 76872080 76872198 ATRX 610.254 9.25327 +X 76874273 76874449 ATRX 645.489 9.33425 +X 76875862 76876000 ATRX 698.094 9.44728 +X 76888694 76888872 ATRX 547.213 9.09596 +X 76889053 76889200 ATRX 670.395 9.38887 +X 76890084 76890194 ATRX 670.664 9.38945 +X 76891405 76891547 ATRX 717.521 9.48688 +X 76907603 76907843 ATRX 868.983 9.76318 +X 76909587 76909690 ATRX 577.35 9.1733 +X 76912046 76912146 ATRX 656.73 9.35916 +X 76918870 76919047 ATRX 601.062 9.23137 +X 76920133 76920267 ATRX 672.172 9.39269 +X 76931706 76931806 ATRX 613.69 9.26137 +X 76937011 76937267 ATRX 713.383 9.47853 +X 76937267 76937523 ATRX 768.094 9.58514 +X 76937523 76937779 ATRX 706.902 9.46537 +X 76937779 76938035 ATRX 793.727 9.6325 +X 76938035 76938291 ATRX 814.898 9.67048 +X 76938291 76938548 ATRX 770.825 9.59026 +X 76938548 76938804 ATRX 748.621 9.54809 +X 76938804 76939060 ATRX 776.156 9.6002 +X 76939060 76939316 ATRX 716.543 9.48491 +X 76939316 76939572 ATRX 807.262 9.65689 +X 76939572 76939828 ATRX 790.41 9.62646 +X 76939828 76940085 ATRX 636.817 9.31474 +X 76940414 76940514 ATRX 537.62 9.07044 +X 76944310 76944420 ATRX 691.182 9.43292 +X 76949312 76949426 ATRX 662.711 9.37224 +X 76952064 76952192 ATRX 494.875 8.95092 +X 76953046 76953146 ATRX 612.02 9.25743 +X 76954039 76954139 ATRX 428.75 8.74399 +X 76972607 76972720 ATRX 632.221 9.30429 +X 77041427 77041527 ATRX 456.52 8.83453 +X 78426937 78427037 GPR174 123.7 6.9507 +X 78766101 78766201 - 117.98 6.8824 +X 80619147 80619247 - 115.15 6.84737 +X 82149303 82149403 - 123.08 6.94345 +X 82562223 82562323 - 127.81 6.99786 +X 83776739 83776839 - 72.5 6.17991 +X 85106546 85106646 - 116.94 6.86962 +X 86107395 86107495 - 72.65 6.18289 +X 86878694 86878794 KLHL4 72.24 6.17473 +X 87821572 87821672 - 122.82 6.9404 +X 89372854 89372954 - 117.53 6.87689 +X 90331481 90331581 - 125.47 6.9712 +X 90707684 90707784 - 138.56 7.11437 +X 92215304 92215404 - 165.31 7.36903 +X 93108963 93109063 - 105.96 6.72738 +X 94414220 94414320 - 74.94 6.22766 +X 95140544 95140644 - 77.99 6.28522 +X 95823911 95824011 - 70.48 6.13914 +X 95940057 95940189 DIAPH2 527.364 9.04265 +X 95990722 95990822 DIAPH2 538.98 9.07409 +X 95993584 95993761 DIAPH2 582.401 9.18587 +X 96013152 96013257 DIAPH2 755 9.56033 +X 96136577 96136717 DIAPH2 712.971 9.4777 +X 96139309 96139571 DIAPH2,RPA4 767.969 9.58491 +X 96139571 96139833 DIAPH2,RPA4 703.611 9.45863 +X 96139833 96140095 DIAPH2,RPA4 759.966 9.56979 +X 96167313 96167413 DIAPH2 462.1 8.85206 +X 96167466 96167566 DIAPH2 514.55 9.00717 +X 96171436 96171573 DIAPH2 586.307 9.19551 +X 96173507 96173616 DIAPH2 727.661 9.50712 +X 96185731 96185842 DIAPH2 569.162 9.1527 +X 96192231 96192350 DIAPH2 605.773 9.24263 +X 96194264 96194381 DIAPH2 404.692 8.66068 +X 96197016 96197135 DIAPH2 645.681 9.33468 +X 96200507 96200607 DIAPH2 581.8 9.18438 +X 96203933 96204038 DIAPH2 721.705 9.49526 +X 96212826 96213147 DIAPH2 742.031 9.53534 +X 96220091 96220206 DIAPH2 615.13 9.26475 +X 96327939 96328041 DIAPH2 791.559 9.62855 +X 96330162 96330262 DIAPH2 700.51 9.45226 +X 96354692 96354794 DIAPH2 612.765 9.25919 +X 96369724 96369964 DIAPH2 780.546 9.60834 +X 96396663 96396793 DIAPH2 547.408 9.09647 +X 96502713 96502838 DIAPH2 753.168 9.55683 +X 96603114 96603279 DIAPH2 789.806 9.62535 +X 96638907 96639043 DIAPH2 838.926 9.7124 +X 96684646 96684746 DIAPH2 535.27 9.06412 +X 96724667 96724767 DIAPH2 719.83 9.49151 +X 96854233 96854333 DIAPH2 866.92 9.75976 +X 97003798 97003898 - 105.67 6.72342 +X 97893670 97893770 - 110.68 6.79025 +X 99935806 99935906 SYTL4 103.58 6.6946 +X 100668889 100668989 RPL36A-HNRNPH2,HNRNPH2 113.58 6.82757 +X 100708629 100708729 ARMCX4 105.58 6.72219 +X 101138394 101138494 ZMAT1 80.4 6.32912 +X 102444156 102444256 - 108.62 6.76315 +X 102964238 102964338 GLRA4 139.62 7.12536 +X 103706622 103706722 - 211.35 7.72349 +X 105937209 105937309 RNF128 108.69 6.76408 +X 107235122 107235222 - 76.08 6.24945 +X 108083495 108083595 - 101.65 6.66747 +X 109197352 109197452 - 145.88 7.18864 +X 109416645 109416745 TMEM164 98.83 6.62688 +X 111854248 111854348 - 134.19 7.06813 +X 112760931 112761031 LOC101928437 65.86 6.04133 +X 113725670 113725770 - 118.87 6.89324 +X 114766295 114766395 PLS3-AS1 407.78 8.67165 +X 115654186 115654286 - 105.02 6.71452 +X 116852028 116852128 - 104.39 6.70584 +X 118273677 118273777 KIAA1210 142.13 7.15107 +X 119390600 119390700 ZBTB33 111.97 6.80697 +X 119401711 119401811 TMEM255A 153.38 7.26097 +X 119871555 119871655 - 82.13 6.35984 +X 120603656 120603756 - 103.13 6.68832 +X 121995770 121995870 - 93.85 6.55228 +X 122588082 122588182 GRIA3 88.18 6.46238 +X 123156449 123156549 STAG2 511.43 8.99839 +X 123159678 123159778 STAG2 646.06 9.33552 +X 123164810 123164975 STAG2 891.448 9.80001 +X 123171374 123171474 STAG2 503.45 8.9757 +X 123176406 123176506 STAG2 646 9.33539 +X 123179013 123179218 STAG2 661.888 9.37044 +X 123181203 123181355 STAG2 644.309 9.33161 +X 123182841 123182941 STAG2 604.55 9.23972 +X 123184035 123184159 STAG2 732.065 9.51583 +X 123184969 123185069 STAG2 629.67 9.29845 +X 123185154 123185254 STAG2 565.72 9.14394 +X 123189977 123190085 STAG2 684.287 9.41846 +X 123191715 123191827 STAG2 782.812 9.61252 +X 123195073 123195191 STAG2 604.915 9.24059 +X 123195620 123195724 STAG2 672.356 9.39308 +X 123196747 123196847 STAG2 899.47 9.81293 +X 123196960 123197060 STAG2 693.53 9.43781 +X 123197697 123197901 STAG2 729.358 9.51048 +X 123199710 123199810 STAG2 618.27 9.27209 +X 123200018 123200118 STAG2 523.84 9.03298 +X 123200195 123200295 STAG2 556.94 9.12138 +X 123202409 123202509 STAG2 552.49 9.1098 +X 123204998 123205173 STAG2 678.309 9.4058 +X 123210181 123210321 STAG2 787.157 9.62051 +X 123211806 123211908 STAG2 657.324 9.36046 +X 123215229 123215378 STAG2 727.671 9.50714 +X 123217270 123217399 STAG2 582.085 9.18509 +X 123220396 123220620 STAG2 719.915 9.49168 +X 123224424 123224614 STAG2 828.247 9.69392 +X 123224703 123224814 STAG2 817.784 9.67558 +X 123227867 123227994 STAG2 616.291 9.26747 +X 123229210 123229310 STAG2 708.44 9.4685 +X 123234385 123234485 STAG2 598.38 9.22492 +X 124653575 124653675 - 96.47 6.59201 +X 128436704 128436804 - 87.21 6.44642 +X 128581124 128581224 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9.69794 +X 129171341 129171508 BCORL1 657.928 9.36179 +X 129173111 129173257 BCORL1 770.301 9.58928 +X 129184680 129184780 BCORL1 836.58 9.70836 +X 129185834 129185991 BCORL1 841.338 9.71654 +X 129189828 129190111 BCORL1 836.693 9.70855 +X 129359140 129359240 ZNF280C 59.37 5.89166 +X 130430028 130430128 - 81.16 6.3427 +X 130685352 130685452 - 130.96 7.03298 +X 131874351 131874451 HS6ST2 121.86 6.92908 +X 133429909 133430009 - 132.75 7.05257 +X 134324098 134324198 - 135.35 7.08055 +X 134680270 134680370 INTS6L 75.02 6.2292 +X 135279653 135279753 FHL1 98.65 6.62425 +X 135883837 135883937 - 90.19 6.4949 +X 137086013 137086113 - 143.1 7.16088 +X 138447361 138447461 - 140.92 7.13873 +X 138655254 138655354 - 40.86 5.35262 +X 140254185 140254285 - 104.18 6.70293 +X 140856802 140856902 - 69.64 6.12184 +X 142185109 142185209 - 96.72 6.59574 +X 142777578 142777678 - 111.01 6.79455 +X 143738331 143738431 - 95.69 6.5803 +X 145350101 145350201 - 90.08 6.49313 +X 145980396 145980496 - 98.52 6.62234 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724.707 9.50125 +X 41057780 41058003 USP9X 721.43 9.49472 +X 41060312 41060533 USP9X 919.824 9.84521 +X 41064555 41064746 USP9X 740.859 9.53305 +X 41069761 41069935 USP9X 810.333 9.66237 +X 41073820 41073962 USP9X 625.542 9.28896 +X 41075151 41075402 USP9X 879.944 9.78127 +X 41075402 41075653 USP9X 941.45 9.87874 +X 41075653 41075905 USP9X 869.841 9.76461 +X 41076472 41076596 USP9X 723.903 9.49965 +X 41077624 41077850 USP9X 813.102 9.66729 +X 41078354 41078484 USP9X 762.254 9.57413 +X 41082469 41082655 USP9X 843.161 9.71966 +X 41083994 41084215 USP9X 891.131 9.79949 +X 41084295 41084395 USP9X 982.96 9.94099 +X 41088505 41088662 USP9X 817.465 9.67501 +X 41088819 41089080 USP9X 802.134 9.6477 +X 41089751 41089851 USP9X 902.41 9.81764 +X 41091639 41091777 USP9X 958.217 9.90421 +X 41193477 41193577 DDX3X 727.69 9.50718 +X 41196639 41196739 DDX3X 731.33 9.51438 +X 41198262 41198362 DDX3X 432.56 8.75676 +X 41200736 41200869 DDX3X 720.406 9.49267 +X 41201747 41201906 DDX3X 807.755 9.65777 +X 41201989 41202089 DDX3X 850.86 9.73278 +X 41202468 41202604 DDX3X 723.353 9.49856 +X 41202982 41203082 DDX3X 828.73 9.69476 +X 41203281 41203381 DDX3X 812.15 9.6656 +X 41203491 41203652 DDX3X 759.068 9.56809 +X 41204432 41204577 DDX3X 605.055 9.24092 +X 41204656 41204801 DDX3X 788.097 9.62223 +X 41205481 41205663 DDX3X 839.676 9.71369 +X 41205757 41205875 DDX3X 671.203 9.39061 +X 41206111 41206265 DDX3X 743.662 9.5385 +X 41206564 41206704 DDX3X 920.986 9.84703 +X 41206882 41206982 DDX3X 766.83 9.58276 +X 44732797 44732958 KDM6A 613.745 9.2615 +X 44733151 44733251 KDM6A 724.73 9.5013 +X 44820528 44820637 KDM6A 717.266 9.48636 +X 44833885 44833985 KDM6A 613.38 9.26064 +X 44870184 44870284 KDM6A 644.69 9.33246 +X 44879854 44879975 KDM6A 795.099 9.63499 +X 44894152 44894252 KDM6A 789.4 9.62461 +X 44896866 44896966 KDM6A 601.79 9.23312 +X 44910950 44911050 KDM6A 516.4 9.01235 +X 44913073 44913200 KDM6A 776.606 9.60104 +X 44918249 44918349 KDM6A 642.2 9.32688 +X 44918491 44918711 KDM6A 880.295 9.78184 +X 44919266 44919401 KDM6A 666.852 9.38122 +X 44920566 44920666 KDM6A 571.07 9.15752 +X 44921891 44921993 KDM6A 716.157 9.48413 +X 44922666 44923062 KDM6A 957.664 9.90338 +X 44928823 44929082 KDM6A 923.124 9.85038 +X 44929082 44929342 KDM6A 828.154 9.69375 +X 44929342 44929602 KDM6A 894.112 9.80431 +X 44935941 44936071 KDM6A 695.185 9.44125 +X 44937644 44937750 KDM6A 742.396 9.53605 +X 44938390 44938596 KDM6A 736.684 9.5249 +X 44941802 44941902 KDM6A 625.58 9.28905 +X 44941946 44942046 KDM6A 806.72 9.65592 +X 44942704 44942853 KDM6A 655.872 9.35727 +X 44945109 44945224 KDM6A 826.27 9.69047 +X 44948987 44949175 KDM6A 822.516 9.6839 +X 44949967 44950109 KDM6A 716.718 9.48526 +X 44966654 44966781 KDM6A 883.52 9.78712 +X 44969323 44969494 KDM6A 918.047 9.84242 +X 44970591 44970691 KDM6A 742.12 9.53551 +X 48649516 48649736 GATA1 995.505 9.95928 +X 48650251 48650628 GATA1 1060.36 10.0503 +X 48650730 48650875 GATA1 868.366 9.76216 +X 48651579 48651704 GATA1 980.72 9.9377 +X 48652200 48652572 GATA1 983.952 9.94244 +X 49070763 49070863 CACNA1F 1027.68 10.0052 +X 53221922 53222022 KDM5C 745.38 9.54183 +X 53222148 53222514 KDM5C 1339.25 10.3872 +X 53222619 53222818 KDM5C 1118.93 10.1279 +X 53222944 53223044 KDM5C 964.37 9.91344 +X 53223321 53223620 KDM5C 874.569 9.77243 +X 53223620 53223920 KDM5C 1092.5 10.0934 +X 53224113 53224250 KDM5C 911.036 9.83137 +X 53224413 53224592 KDM5C,MIR6895 959.313 9.90586 +X 53225098 53225236 KDM5C 970.384 9.92241 +X 53225868 53226226 KDM5C 1030.53 10.0092 +X 53226953 53227058 KDM5C 937.943 9.87336 +X 53227672 53227819 KDM5C 888.286 9.79488 +X 53227946 53228070 KDM5C,MIR6894 938.806 9.87468 +X 53228159 53228340 KDM5C 843.762 9.72069 +X 53230732 53230926 KDM5C 844.387 9.72176 +X 53231036 53231155 KDM5C 906.269 9.8238 +X 53239596 53239758 KDM5C 1100.35 10.1037 +X 53239858 53240039 KDM5C 1024.81 10.0011 +X 53240679 53240837 KDM5C 995.551 9.95935 +X 53240969 53241088 KDM5C 978.916 9.93504 +X 53243871 53244029 KDM5C 961.013 9.90841 +X 53244977 53245158 KDM5C 1001.36 9.96775 +X 53245256 53245379 KDM5C 745.78 9.54261 +X 53246325 53246459 KDM5C 830.619 9.69804 +X 53246978 53247148 KDM5C 815.718 9.67193 +X 53247458 53247580 KDM5C 1054.95 10.043 +X 53250009 53250109 KDM5C 953.56 9.89718 +X 53253922 53254072 KDM5C 945.587 9.88507 +X 53407015 53407115 SMC1A 1151.75 10.1696 +X 53407541 53407651 SMC1A 936.4 9.87098 +X 53407923 53408023 SMC1A 928.41 9.85862 +X 53409153 53409304 SMC1A 998.325 9.96337 +X 53409427 53409581 SMC1A 1050.35 10.0367 +X 53410018 53410174 SMC1A 834.699 9.70511 +X 53421698 53421808 SMC1A 955.309 9.89982 +X 53423147 53423300 SMC1A 1009.9 9.97999 +X 53423392 53423537 SMC1A 1046.28 10.0311 +X 53426511 53426652 SMC1A 821.688 9.68245 +X 53430498 53430604 SMC1A 1098.09 10.1008 +X 53430709 53430825 SMC1A 1055.06 10.0431 +X 53431944 53432081 SMC1A 1027.43 10.0048 +X 53432177 53432323 SMC1A 975.295 9.92969 +X 53432425 53432604 SMC1A,MIR6857 1112.33 10.1194 +X 53432703 53432888 SMC1A 1167.92 10.1897 +X 53435993 53436200 SMC1A 985.531 9.94476 +X 53436343 53436443 SMC1A 1020.88 9.9956 +X 53438711 53438851 SMC1A 1190.72 10.2176 +X 53438945 53439203 SMC1A 1153.6 10.1719 +X 53439850 53440088 SMC1A 1002.87 9.96991 +X 53440182 53440385 SMC1A 961.054 9.90847 +X 53441707 53441819 SMC1A 957.786 9.90356 +X 53441930 53442118 SMC1A 972.202 9.92511 +X 53449441 53449550 SMC1A 827.257 9.69219 +X 69254710 69254810 EDA 906.68 9.82445 +X 70338603 70338703 MED12 798.42 9.641 +X 70339222 70339327 MED12 968.4 9.91946 +X 70339535 70339727 MED12 904.99 9.82176 +X 70339863 70340020 MED12 966.529 9.91667 +X 70340820 70341002 MED12 917.148 9.84101 +X 70341176 70341287 MED12 1012.33 9.98347 +X 70341411 70341666 MED12 1088.29 10.0879 +X 70342049 70342196 MED12 914.361 9.83662 +X 70342357 70342457 MED12 1166.84 10.1884 +X 70342587 70342724 MED12 936.839 9.87166 +X 70342944 70343076 MED12 855.75 9.74105 +X 70343443 70343570 MED12 1012.54 9.98376 +X 70344008 70344238 MED12 884.639 9.78895 +X 70344603 70344703 MED12 924.98 9.85328 +X 70344825 70344996 MED12 1039.04 10.021 +X 70345200 70345345 MED12 941.6 9.87897 +X 70345487 70345587 MED12 857.86 9.7446 +X 70345885 70346004 MED12 889.756 9.79727 +X 70346190 70346334 MED12 1007.41 9.97643 +X 70346818 70346982 MED12 891.604 9.80026 +X 70347185 70347317 MED12 822.621 9.68408 +X 70347742 70347970 MED12 919.711 9.84504 +X 70348145 70348290 MED12 977.848 9.93347 +X 70348447 70348568 MED12 991.835 9.95396 +X 70348963 70349065 MED12 897.48 9.80974 +X 70349165 70349279 MED12 1185.42 10.2112 +X 70349529 70349705 MED12 1051.5 10.0382 +X 70349884 70350064 MED12 796.544 9.63761 +X 70351385 70351485 MED12 1078.09 10.0743 +X 70351922 70352056 MED12 913.246 9.83486 +X 70352226 70352388 MED12 1043 10.0265 +X 70352694 70352806 MED12 867.589 9.76087 +X 70352967 70353067 MED12 863.07 9.75333 +X 70354206 70354316 MED12 852.027 9.73476 +X 70354562 70354698 MED12 900.404 9.81443 +X 70354941 70355103 MED12 890.698 9.79879 +X 70356130 70356505 MED12 969.131 9.92055 +X 70356728 70356879 MED12 953.278 9.89675 +X 70357036 70357233 MED12 898.036 9.81063 +X 70357396 70357496 MED12 846.95 9.72613 +X 70357575 70357793 MED12 1059.22 10.0488 +X 70360484 70360707 MED12 919.031 9.84397 +X 70361079 70361220 MED12 1085.89 10.0847 +X 70361723 70361823 MED12 1006.3 9.97484 +X 70361996 70362096 MED12 1010.54 9.98091 +X 70460765 70460958 ZMYM3 914.171 9.83632 +X 70461077 70461194 ZMYM3 1010.98 9.98154 +X 70462020 70462274 ZMYM3 986.906 9.94677 +X 70462820 70462934 ZMYM3 938.588 9.87435 +X 70463679 70463830 ZMYM3 999.709 9.96536 +X 70464152 70464320 ZMYM3 881.298 9.78349 +X 70464640 70464743 ZMYM3 780.427 9.60812 +X 70465189 70465335 ZMYM3 931.082 9.86276 +X 70465518 70465692 ZMYM3 969.649 9.92132 +X 70465836 70465948 ZMYM3 1144.94 10.1611 +X 70466203 70466362 ZMYM3 1003.9 9.9714 +X 70466443 70466543 ZMYM3 1191.45 10.2185 +X 70467195 70467360 ZMYM3 847.297 9.72672 +X 70467584 70467756 ZMYM3 875.68 9.77426 +X 70468012 70468162 ZMYM3 998.487 9.9636 +X 70468281 70468381 ZMYM3 1076.48 10.0721 +X 70468536 70468651 ZMYM3 915.487 9.8384 +X 70468869 70469019 ZMYM3 925.107 9.85348 +X 70469292 70469529 ZMYM3 937.966 9.87339 +X 70469876 70470053 ZMYM3 869.723 9.76441 +X 70470282 70470576 ZMYM3 940.122 9.8767 +X 70471011 70471111 ZMYM3 877.6 9.77742 +X 70471379 70471479 ZMYM3 954.86 9.89915 +X 70472439 70472661 ZMYM3 1070.21 10.0637 +X 70472661 70472883 ZMYM3 1074.32 10.0692 +X 70472883 70473106 ZMYM3 1120.57 10.13 +X 71788820 71788920 HDAC8 918.79 9.84359 +X 73434897 73434997 FTX 809.01 9.66001 +X 76763828 76764107 ATRX 870.556 9.76579 +X 76776266 76776394 ATRX 686.406 9.42292 +X 76776878 76776978 ATRX 771.36 9.59126 +X 76777741 76777866 ATRX 869.536 9.7641 +X 76778730 76778879 ATRX 739.295 9.53001 +X 76812922 76813116 ATRX 792.866 9.63093 +X 76814140 76814317 ATRX 745.113 9.54132 +X 76829715 76829823 ATRX 690.074 9.43061 +X 76845304 76845410 ATRX 649.377 9.34291 +X 76849166 76849319 ATRX 916.137 9.83942 +X 76854880 76855049 ATRX 1066.72 10.059 +X 76855195 76855295 ATRX 554.23 9.11434 +X 76855903 76856033 ATRX 730.631 9.513 +X 76872081 76872198 ATRX 604.35 9.23924 +X 76874274 76874449 ATRX 654.114 9.3534 +X 76875863 76876000 ATRX 749.577 9.54993 +X 76888695 76888872 ATRX 785.667 9.61777 +X 76889054 76889200 ATRX 817.781 9.67557 +X 76890085 76890194 ATRX 929.046 9.85961 +X 76891406 76891547 ATRX 790.518 9.62665 +X 76907604 76907843 ATRX 964.762 9.91403 +X 76909588 76909690 ATRX 764.676 9.57871 +X 76912046 76912146 ATRX 704.15 9.45974 +X 76918871 76919047 ATRX 835.449 9.70641 +X 76920134 76920267 ATRX 821.241 9.68166 +X 76931707 76931807 ATRX 781.54 9.61018 +X 76937012 76937268 ATRX 838.242 9.71122 +X 76937268 76937524 ATRX 792.922 9.63103 +X 76937524 76937780 ATRX 705.672 9.46285 +X 76937780 76938036 ATRX 860.527 9.74908 +X 76938036 76938292 ATRX 939.512 9.87577 +X 76938292 76938548 ATRX 864.258 9.75532 +X 76938548 76938804 ATRX 862.859 9.75298 +X 76938804 76939060 ATRX 914.078 9.83617 +X 76939060 76939316 ATRX 870.105 9.76505 +X 76939316 76939572 ATRX 925.746 9.85447 +X 76939572 76939828 ATRX 876.641 9.77584 +X 76939828 76940085 ATRX 788.276 9.62256 +X 76940414 76940514 ATRX 553.12 9.11145 +X 76944311 76944420 ATRX 787.688 9.62148 +X 76949313 76949426 ATRX 762.168 9.57397 +X 76952065 76952192 ATRX 511.701 8.99916 +X 76953047 76953147 ATRX 709.9 9.47147 +X 76954039 76954139 ATRX 424.75 8.73047 +X 76972608 76972720 ATRX 776.036 9.59998 +X 77041428 77041528 ATRX 489.75 8.9359 +X 86830795 86830895 KLHL4 687.6 9.42543 +X 92181857 92181957 - 561.77 9.13384 +X 93324930 93325030 - 547.4 9.09645 +X 93434385 93434485 - 650.37 9.34512 +X 97019151 97019251 - 860.72 9.7494 +X 100604858 100604958 BTK 803.56 9.65026 +X 100608181 100608339 BTK 789.709 9.62518 +X 100608857 100608976 BTK 665.084 9.37739 +X 100609599 100609699 BTK 1012.8 9.98413 +X 100611039 100611256 BTK 637.622 9.31656 +X 100611771 100611943 BTK 941.39 9.87865 +X 100612483 100612583 BTK 928.15 9.85821 +X 100613297 100613425 BTK 816.281 9.67292 +X 100613594 100613694 BTK 662.85 9.37254 +X 100614257 100614357 BTK 878.02 9.77811 +X 100615056 100615156 BTK 638.19 9.31784 +X 100615555 100615743 BTK 964.638 9.91384 +X 100617144 100617244 BTK 1080.18 10.0771 +X 100617548 100617677 BTK 885.837 9.7909 +X 100624976 100625076 BTK 953.94 9.89775 +X 100626604 100626704 BTK 786.85 9.61994 +X 100629522 100629622 BTK 789.84 9.62542 +X 100630131 100630272 BTK 894.589 9.80508 +X 106444159 106444259 NUP62CL 433.68 8.76049 +X 115503868 115503968 - 792.66 9.63056 +X 119387270 119387522 ZBTB33 920.726 9.84663 +X 119387522 119387774 ZBTB33 935.139 9.86904 +X 119387774 119388027 ZBTB33 1030.55 10.0092 +X 119388027 119388279 ZBTB33 1007.16 9.97608 +X 119388279 119388531 ZBTB33 864.329 9.75544 +X 119388531 119388784 ZBTB33 796.862 9.63819 +X 119388784 119389036 ZBTB33 832.433 9.70119 +X 119389036 119389289 ZBTB33 913.261 9.83488 +X 122696513 122696613 - 706.1 9.46373 +X 123156449 123156549 STAG2 432.84 8.75769 +X 123159679 123159779 STAG2 668.23 9.3842 +X 123164811 123164975 STAG2 858.073 9.74496 +X 123171375 123171475 STAG2 554.17 9.11418 +X 123176407 123176507 STAG2 666.06 9.37951 +X 123179014 123179218 STAG2 664.657 9.37647 +X 123181204 123181355 STAG2 764.185 9.57778 +X 123182841 123182941 STAG2 483.79 8.91824 +X 123184036 123184159 STAG2 724.366 9.50057 +X 123184970 123185070 STAG2 568.35 9.15064 +X 123185154 123185254 STAG2 483.12 8.91624 +X 123189978 123190085 STAG2 781.981 9.61099 +X 123191716 123191827 STAG2 756.775 9.56372 +X 123195074 123195191 STAG2 764.017 9.57746 +X 123195621 123195724 STAG2 697.087 9.4452 +X 123196748 123196848 STAG2 893.6 9.80349 +X 123196960 123197060 STAG2 621.5 9.27961 +X 123197698 123197901 STAG2 733.113 9.51789 +X 123199711 123199811 STAG2 594 9.21432 +X 123200018 123200118 STAG2 597.13 9.2219 +X 123200196 123200296 STAG2 590.36 9.20545 +X 123202410 123202510 STAG2 493.74 8.94761 +X 123204999 123205173 STAG2 698.885 9.44891 +X 123210182 123210321 STAG2 722.626 9.49711 +X 123211807 123211908 STAG2 623.752 9.28483 +X 123215230 123215378 STAG2 863.534 9.75411 +X 123217271 123217399 STAG2 701.555 9.45441 +X 123220397 123220620 STAG2 828.632 9.69459 +X 123224425 123224614 STAG2 966.011 9.9159 +X 123224704 123224814 STAG2 941.464 9.87876 +X 123227868 123227994 STAG2 725.659 9.50315 +X 123229210 123229310 STAG2 854.39 9.73875 +X 123234386 123234486 STAG2 611.23 9.25557 +X 125318891 125318991 - 610.48 9.2538 +X 125499286 125499386 - 278.5 8.12153 +X 129139200 129139300 BCORL1 811.17 9.66386 +X 129146549 129146649 BCORL1 887.59 9.79375 +X 129146926 129147197 BCORL1 974.269 9.92818 +X 129147197 129147469 BCORL1 1236.99 10.2726 +X 129147469 129147741 BCORL1 1488.19 10.5393 +X 129147741 129148013 BCORL1 1356.19 10.4053 +X 129148013 129148285 BCORL1 1074.58 10.0696 +X 129148285 129148557 BCORL1 1273.7 10.3148 +X 129148557 129148829 BCORL1 1269.29 10.3098 +X 129148829 129149101 BCORL1 1242.11 10.2786 +X 129149101 129149373 BCORL1 1184.88 10.2105 +X 129149373 129149645 BCORL1 1149.11 10.1663 +X 129149645 129149917 BCORL1 1227.6 10.2616 +X 129149917 129150189 BCORL1 1036.32 10.0172 +X 129154960 129155125 BCORL1 891.794 9.80057 +X 129156862 129156962 BCORL1 974.83 9.92901 +X 129158965 129159354 BCORL1 1041.72 10.0248 +X 129162610 129162836 BCORL1 1091.89 10.0926 +X 129171342 129171508 BCORL1 972.855 9.92608 +X 129173112 129173257 BCORL1 895.924 9.80723 +X 129184680 129184780 BCORL1 992.87 9.95546 +X 129185835 129185991 BCORL1 961.34 9.9089 +X 129189829 129190112 BCORL1 1020.65 9.99528 +X 132127795 132127895 - 869.73 9.76442 +X 132467673 132467773 GPC4 789.97 9.62565 +X 133511647 133511785 PHF6 761.572 9.57284 +X 133512035 133512136 PHF6 607.941 9.24779 +X 133527531 133527664 PHF6 655.368 9.35616 +X 133527910 133528010 PHF6 742.94 9.5371 +X 133547518 133547687 PHF6 1023.17 9.99882 +X 133547853 133547996 PHF6 761.168 9.57207 +X 133549046 133549253 PHF6 696.005 9.44295 +X 133551199 133551332 PHF6 696.18 9.44332 +X 133559231 133559361 PHF6 795.477 9.63568 +X 140519317 140519417 - 654.43 9.3541 +X 145042054 145042154 - 343.82 8.42551 +X 145109292 145109410 MIR891A 783.195 9.61323 +X 145961839 145961939 - 882.39 9.78527 +X 147332630 147332730 - 358.64 8.48639 +X 149924688 149924788 MTMR1 899.03 9.81223 +X 153626821 153626921 RPL10 737 9.52552 +X 153627657 153627757 RPL10 736.02 9.5236 +X 153627827 153627935 RPL10 834.889 9.70544 +X 153628143 153628282 RPL10 1091.56 10.0922 +X 153628804 153628967 RPL10 725.018 9.50187 +X 153629042 153629195 RPL10 1247.16 10.2844 +X 153991017 153991256 DKC1 1194.16 10.2218 +X 154299802 154299925 BRCC3 1036.57 10.0176 +X 154300560 154300660 BRCC3 605.33 9.24158 +X 154301627 154301727 BRCC3 724.86 9.50156 +X 154305445 154305564 BRCC3 839.521 9.71342 +X 154306884 154306984 BRCC3 940.98 9.87802 +X 154317532 154317632 BRCC3 796.23 9.63704 +X 154319036 154319136 BRCC3 750.23 9.55119 +X 154327577 154327677 BRCC3 845.42 9.72352 +X 154344332 154344463 BRCC3 830.649 9.69809 +X 154344957 154345057 BRCC3 794.96 9.63474 +X 154348274 154348426 BRCC3 781.316 9.60976 +Y 2655251 2655351 SRY 982.6 9.94046 +Y 2829451 2829551 ZFY 749.25 9.5493 +Y 4971363 4971463 PCDH11Y 349.85 8.45059 +Y 5258332 5258432 PCDH11Y 686.87 9.42389 +Y 6111347 6111447 TTTY23,TTTY23B 723.71 9.49927 +Y 6736151 6736251 AMELY 1077.03 10.0728 +Y 7194051 7194151 PRKY 672.14 9.39262 +Y 9238609 9238709 TSPY3,TSPY10,TSPY4 2093.51 11.0317 +Y 14891451 14891551 USP9Y 593.45 9.21298 +Y 19370866 19370966 - 466.47 8.86564 +Y 21897249 21897349 KDM5D 583.86 9.18948 +Y 22930702 22930802 RPS4Y2 598.45 9.22509 +Y 25310901 25311001 DAZ1,DAZ4 372.54 8.54125 diff --git a/tests/test_data/qc_files/sex_check/male_200k_x_bases.txt b/tests/test_data/qc_files/sex_check/male_200k_x_bases.txt new file mode 100644 index 000000000..7d9c0a605 --- /dev/null +++ b/tests/test_data/qc_files/sex_check/male_200k_x_bases.txt @@ -0,0 +1,200001 @@ +87 +86 +88 +90 +91 +94 +95 +97 +97 +97 +96 +97 +96 +96 +95 +95 +95 +95 +95 +94 +93 +91 +91 +91 +91 +92 +89 +91 +91 +92 +91 +92 +93 +95 +96 +96 +95 +95 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