feat: addition of coverage analysis

.gitignore

@@ -188,3 +188,4 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
+data/

notebooks/coverage_eda_lb_20251017.py

@@ -0,0 +1,156 @@
+import marimo
+
+__generated_with = "0.16.4"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import os
+    import json
+    import statistics
+    import marimo as mo
+    import pandas as pd
+    import plotly.express as px
+    return json, mo, os, pd, statistics
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+    # EDA of LB Assay Coverage
+
+    Data was generated by `get_panel_coverage.py` that uses the LB panels LB covered bed (with flankning sequence added, i.e. the variant calling bed) with the deduplicated bam/cram.
+
+    The output json from this analysis for each sample has the following form🥇
+
+    ```json
+    {
+        "chr<#>_<start>_<stop>": [
+            "depth_pos1", 
+            "depth_pos2", 
+            "depth_pos3",
+            ...
+            "depth_posN"
+        ]
+    }
+    ```
+
+    Analysis Plan:
+    - Poorly covered regions (i.e. bad probes/challenging areas to align)
+    - Overly covered regions (i.e. potential areas that are challenging for aligners or have low complexity)
+    - Comparison of Serenomica and Quantgene in overlap areas
+    """
+    )
+    return
+
+
+@app.cell
+def _():
+    # data
+    se_path = "../data/coverage_serenomica_QG/se1_se2/"
+    qg_path = "../data/coverage_serenomica_QG/QG_historical/"
+    return qg_path, se_path
+
+
+@app.cell
+def _(json, os):
+    def parse_data(dpath):
+        files = [f for f in os.listdir(dpath) if f.endswith(".json")]
+        parsed_data, mean_data = {}
+        for file in files:
+            name = file.split("_cov")[0]
+            with open(os.path.join(dpath, file), "r") as handle:
+                parsed_data[name] = json.load(handle)
+        return parsed_data
+    return (parse_data,)
+
+
+@app.cell
+def _(parse_data, qg_path, se_path):
+    se_data = parse_data(se_path)
+    qg_data = parse_data(qg_path)
+    return qg_data, se_data
+
+
+@app.cell
+def _(statistics):
+    def summerize_list(lst):
+        vals = list(map(int, lst))
+        return (
+            statistics.mean(vals),
+            statistics.median(vals),
+            statistics.stdev(vals),
+            min(vals),
+            max(vals),
+            sum(vals),
+            len(vals),
+        )
+
+
+    def get_summary_data(sample_data):
+        out_data = {}
+        sample_sum, sample_pos_count = 0, 0
+        for region, depths in sample_data.items():
+            (mean, median, std, min_val, max_val, sum_vals, count) = (
+                summerize_list(depths)
+            )
+            out_data[region] = {
+                "min": min_val,
+                "max": max_val,
+                "mean": mean,
+                "median": median,
+                "std": std,
+            }
+            sample_sum += sum_vals
+            sample_pos_count += count
+        out_data["overall"] = {"mean": sample_sum / sample_pos_count}
+        return out_data
+    return (get_summary_data,)
+
+
+@app.cell
+def _(get_summary_data, qg_data, se_data):
+    se_summary_data = {}
+    for se_sample, se_sample_data in se_data.items():
+        se_summary_data[se_sample] = get_summary_data(se_sample_data)
+
+    qg_summary_data = {}
+    for qg_sample, qg_sample_data in qg_data.items():
+        qg_summary_data[qg_sample] = get_summary_data(qg_sample_data)
+    return (se_summary_data,)
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell
+def _(pd):
+    def extract_stat_as_df(data_dict, access_key):
+        tmp_dict = {}
+        for name, sample_data in data_dict.items():
+            tmp_dict[name] = {
+                region: data[access_key]
+                for region, data in sample_data.items()
+                if region != "overall"
+            }
+        return pd.DataFrame.from_dict(tmp_dict, orient="index")
+    return (extract_stat_as_df,)
+
+
+@app.cell
+def _(extract_stat_as_df, se_summary_data):
+    extract_stat_as_df(se_summary_data, access_key="std")
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()

notebooks/initial_eda_lb_20250919.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.14.16"
+__generated_with = "0.16.4"
 app = marimo.App(width="medium")
 
 
@@ -168,7 +168,7 @@ def _(mo):
 def _(os, pd, qc_hist_dir):
     # QG historical data
     qg_hist_stats = pd.read_csv(
-        os.path.join(qc_hist_dir, "historical_data_LB.csv"), index_col=0
+        os.path.join(qc_hist_dir, "historical_data_LB-QG.csv"), index_col=0
     )
     qg_hist_stats.columns = [i.replace("|", " / ") for i in qg_hist_stats.columns]
     qg_hist_stats = qg_hist_stats.drop(["batch"], axis=1)
@@ -840,6 +840,270 @@ def _(sc, variants_high_cov_ids, variants_lost_cov_ids, variants_low_cov_ids):
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""# (3) Stats and Metadata""")
+    return
+
+
+@app.cell
+def _(pd):
+    meta = pd.read_excel(
+        "/home/darren/Documents/4_data/3_internal/2_lb/Metadata_LB_20250924.xlsx",
+        index_col="fastq_id",
+    )
+    meta
+    return (meta,)
+
+
+@app.cell
+def _(clinicals, meta, pd, stats):
+    # merge stats and metadata and remove seracares
+    stats_meta = pd.concat([stats, meta], axis=1)
+    stats_meta = stats_meta.loc[clinicals, :]
+    stats_meta = stats_meta.reset_index()
+    stats_meta.head()
+    return (stats_meta,)
+
+
+@app.cell
+def _(pd):
+    meta_qg = pd.read_excel(
+        "/home/darren/Documents/4_data/3_internal/2_lb/QC/lb2_metadata.xlsx",
+        index_col="fastq_id",
+    )
+    meta_qg
+    return (meta_qg,)
+
+
+@app.cell
+def _(meta_qg, pd, qg_hist_stats):
+    # merge stats and metadata and remove seracares
+    stats_meta_qg = pd.concat([qg_hist_stats, meta_qg], axis=1)
+    stats_meta_qg = stats_meta_qg.loc[
+        [
+            i
+            for i in stats_meta_qg.index
+            if "twist" not in i.lower() and "sera" not in i.lower()
+        ],
+        :,
+    ]
+    stats_meta_qg = stats_meta_qg.reset_index()
+    stats_meta_qg.head()
+    return (stats_meta_qg,)
+
+
+@app.cell
+def _(mo, stats_meta):
+    var1 = mo.ui.dropdown(options=stats_meta.columns, label="Variable 1")
+    var2 = mo.ui.dropdown(options=stats_meta.columns, label="Variable 2")
+
+    mo.vstack([var1, var2])
+    return var1, var2
+
+
+@app.cell
+def _(px, stats_meta, var1, var2):
+    # note that all SE1 clinical samples are older QG extracted cfDNAs
+    fig5 = px.scatter(
+        data_frame=stats_meta,
+        x=var1.value,
+        y=var2.value,
+        color="Flow cell #",
+        width=700,
+        height=500,
+        template="simple_white",
+        hover_data=["index"],
+        color_discrete_sequence=px.colors.qualitative.Dark2,
+        category_orders={"Flow cell #": ["SE1", "SE2"]},
+        trendline="ols",
+    )
+    fig5.update_traces(
+        marker=dict(size=9, line=dict(width=1, color="DarkSlateGrey")),
+        selector=dict(mode="markers"),
+    )
+    fig5.add_vline(
+        x=30,
+        line_width=3,
+        line_dash="dot",
+        annotation_text="30ng",
+        annotation_position="top left",
+    )
+    fig5.show()
+    return
+
+
+@app.cell
+def _(stats_meta):
+    stats_meta["ng_group"] = [
+        "< 30ng" if i < 30 else "> 30ng" for i in stats_meta["Input DNA used (ng)"]
+    ]
+    return
+
+
+@app.cell
+def _(pd, stats_meta):
+    stats_meta_melt = pd.melt(
+        stats_meta, id_vars=["index", "sample_type", "ng_group", "Flow cell #"]
+    )
+    stats_meta_melt.head()
+    return (stats_meta_melt,)
+
+
+@app.cell
+def _(mo, stats_meta_melt):
+    box_vars = mo.ui.dropdown(
+        options=stats_meta_melt["variable"].unique(), label="Variables to plot"
+    )
+    mo.hstack([box_vars])
+    return (box_vars,)
+
+
+@app.cell
+def _(box_vars, px, stats_meta_melt):
+    fig6 = px.box(
+        data_frame=stats_meta_melt.loc[
+            (stats_meta_melt["variable"] == box_vars.value)
+            & (stats_meta_melt["Flow cell #"] == "SE2")
+        ],
+        color="ng_group",
+        y="value",
+        x="variable",
+        template="simple_white",
+        points="all",
+        labels={
+            "ng_group": "cfDNA Input for Library",
+            "value": box_vars.value,
+            "variable": "",
+        },
+        color_discrete_sequence=px.colors.qualitative.Dark2,
+        category_orders={"ng_group": ["< 30ng", "> 30ng"]},
+        hover_data=["index"],
+        width=800,
+    )
+    fig6.update_traces(
+        marker=dict(size=10, line=dict(width=1, color="DarkSlateGrey")),
+        selector=dict(type="points"),
+    )
+    fig6.update_xaxes(ticks="", showticklabels=False)
+    fig6.update_yaxes(showgrid=True, tickfont_size=14)
+    fig6.update_legends(font_size=14)
+
+    for trace in fig6.select_traces():
+        trace.marker.update(size=10, line=dict(width=1, color="DarkSlateGrey"))
+
+    fig6.show()
+    return
+
+
+@app.cell
+def _(stats_meta):
+    stats_meta["ng_group"].value_counts()
+    return
+
+
+@app.cell
+def _(stats_meta_melt):
+    stats_meta_melt["Flow cell #"].unique()
+    return
+
+
+@app.cell
+def _(qg_hist_stats):
+    qg_hist_stats
+    return
+
+
+@app.cell
+def _(pd, stats_meta, stats_meta_qg):
+    stats_meta["lab"] = "Serenomica"
+    stats_meta_qg["lab"] = "Quantgene"
+    all_stats_meta = pd.concat([stats_meta, stats_meta_qg], axis=0)
+    all_stats_meta.shape
+    return (all_stats_meta,)
+
+
+@app.cell
+def _(all_stats_meta):
+    all_stats_meta["ng_group"] = [
+        "< 30ng" if i < 30 else "> 30ng"
+        for i in all_stats_meta["Input DNA used (ng)"]
+    ]
+    all_stats_meta[["lab", "ng_group"]].value_counts()
+    return
+
+
+@app.cell
+def _(all_stats_meta, pd):
+    all_stats_meta_melt = pd.melt(
+        all_stats_meta,
+        id_vars=["index", "sample_type", "ng_group", "Flow cell #", "lab"],
+    )
+    all_stats_meta_melt
+    return (all_stats_meta_melt,)
+
+
+@app.cell
+def _(all_stats_meta_melt, mo):
+    box_vars2 = mo.ui.dropdown(
+        options=all_stats_meta_melt["variable"].unique(), label="Variables to plot"
+    )
+    mo.hstack([box_vars2])
+    return (box_vars2,)
+
+
+@app.cell
+def _(all_stats_meta_melt, box_vars2, px):
+    fig7 = px.box(
+        data_frame=all_stats_meta_melt.loc[
+            (all_stats_meta_melt["variable"] == box_vars2.value)
+        ],
+        color="ng_group",
+        y="value",
+        x="lab",
+        template="simple_white",
+        points="all",
+        labels={
+            "ng_group": "cfDNA Input for Library",
+            "value": box_vars2.value,
+            "lab": "Laboratory",
+        },
+        color_discrete_sequence=px.colors.qualitative.Dark2,
+        category_orders={"ng_group": ["< 30ng", "> 30ng"]},
+        hover_data=["index"],
+        width=800,
+    )
+    fig7.update_traces(
+        marker=dict(size=10, line=dict(width=1, color="DarkSlateGrey")),
+        selector=dict(type="points"),
+    )
+    fig7.update_xaxes(tickfont_size=14)
+    fig7.update_yaxes(showgrid=True, tickfont_size=14)
+    fig7.update_legends(font_size=14)
+
+    for trace2 in fig7.select_traces():
+        trace2.marker.update(
+            size=7, line=dict(width=1, color="DarkSlateGrey"), opacity=0.8
+        )
+
+    fig7.show()
+    return
+
+
+@app.cell
+def _(all_stats_meta):
+    all_stats_meta[["lab", "ng_group"]].value_counts()
+    return
+
+
+@app.cell
+def _(stats_meta):
+    stats_meta.sort_values("Input DNA used (ng)")[
+        ["index", "Input DNA used (ng)", "Flow cell #"]
+    ]
+    return
+
+
 @app.cell
 def _():
     return

notebooks/requirements.txt

@@ -0,0 +1,9 @@
+marimo==0.16.4
+matplotlib==3.10.3
+matplotlib-inline==0.1.7
+matplotlib-venn==1.1.2
+openpyxl==3.1.5
+pandas==2.3.1
+plotly==6.2.0
+pyyaml==6.0.3
+seaborn==0.13.2

src/get_panel_coverage.py

@@ -0,0 +1,140 @@
+import os
+from subprocess import run
+import logging
+import sys
+
+logging.basicConfig(
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    level=logging.INFO,
+    stream=sys.stderr,
+)
+
+
+def main(
+    s3_paths: list[str], bed_path: str, ref_path: str, cli_bin: str, tars: bool
+) -> None:
+    logging.info("Script started...")
+    logging.info(f"Using reference: {ref_path}")
+    logging.info(f"Using bed file: {bed_path}")
+    logging.info(f"Number of BAM/CRAMs to process: {len(s3_paths)}")
+    logging.info(f"BAM/CRAMs to process: {s3_paths}")
+
+    for idx, file in enumerate(s3_paths):
+        name = file.split("/")[-1]
+        file_num = f"{idx+1}/{len(s3_paths)}"
+
+        logging.info(f"({file_num}) Downloading file: {name}")
+        if tars:
+            down_unpack_cmd = (
+                f"{cli_bin} s3 cp {file} . --profile=se"
+                if cli_bin == "aws"
+                else f"{cli_bin} get {file} . && tar -xf {name} --strip-components=4 --wildcards --no-anchored '*.cram' && rm {name}"
+            )
+            run(down_unpack_cmd, shell=True, capture_output=False)
+            name = name.split(".")[0] + ".collapsed.cram"
+        else:
+            down_cmd = (
+                f"{cli_bin} s3 cp {file} . --profile=se"
+                if cli_bin == "aws"
+                else f"{cli_bin} get {file} ."
+            )
+            run(down_cmd, shell=True, capture_output=False)
+
+        sort_name = f"{name.split('.')[0]}.sorted.cram"
+        sort_cmd = f"samtools sort -@ 18 {name} --reference {ref_path} > {sort_name}"
+        run(sort_cmd, shell=True, capture_output=False)
+
+        logging.info(f"({file_num}) Getting coverage for '{name}'")
+        cov_cmd = (
+            f"bedtools coverage -d -sorted -a {bed_path} -b {sort_name} | "
+            f"python parse_cov.py > {name.split(".")[0]}_cov.json"
+        )
+        run(cov_cmd, shell=True, capture_output=False)
+        os.remove(name)
+        os.remove(sort_name)
+
+        logging.info(f"({file_num}) Processing of '{name}' completed!")
+
+
+if __name__ == "__main__":
+    s3_paths = [
+        # "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0087-pa-13Jun2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0089-pa-13Jun2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0090-pa-13Jun2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0091-pa-13Jun2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0092-pa-13Jun2023_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0096-pa-13Jun2023_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0098-pa-29Jun2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0099-pa-29Jun2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0100-pa-29Jun2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0103-pa-29Jun2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0105-pa-29Jun2023_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0107-pa-29Jun2023_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0112-pa-19Jul2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0113-pa-19Jul2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0119-pa-19Jul2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0122-pa-19Jul2023_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0123-pa-19Jul2023_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0124-pa-19Jul2023_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0125-pa-19Jul2023_S8.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0126-pa-19Jul2023_S9.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0128-pa-19Jul2023_S10.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0129-pa-19Jul2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0130-pa-09Aug2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0132-pa-09Aug2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0133-pa-09Aug2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0134-pa-09Aug2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0135-pa-09Aug2023_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0136-pa-09Aug2023_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0137-pa-09Aug2023_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0138-pa-30Aug2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0140-pa-30Aug2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0141-pa-30Aug2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0142-pa-30Aug2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0144-pa-30Aug2023_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0145-pa-30Aug2023_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0146-pa-30Aug2023_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0147-pa-30Aug2023_S8.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0153-pa-25Oct2023_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0156-pa-25Oct2023_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0159-pa-25Oct2023_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0160-pa-25Oct2023_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-23-0186-pa-14Feb2024_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0001-pa-14Feb2024_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0015-pa-14Feb2024_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0019-pa-14Feb2024_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0023-pa-14Feb2024_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0024-pa-14Feb2024_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0027-pa-14Feb2024_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0037-pa-28Mar2024_S16.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0038-pa-28Mar2024_S17.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0044-pa-13May2024_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0046-pa-13May2024_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0047-pa-13May2024_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0047-pa-23May2024_S1.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0048-pa-13May2024_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0049-pa-13May2024_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0049-pa-23May2024_S2.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0050-pa-13May2024_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0050-pa-23May2024_S3.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0053-pa-13May2024_S8.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0053-pa-23May2024_S4.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0054-pa-13May2024_S9.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0055-pa-13May2024_S10.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0056-pa-13May2024_S11.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0057-pa-13May2024_S12.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0058-pa-13May2024_S13.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0059-pa-23May2024_S5.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0060-pa-23May2024_S6.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0063-pa-23May2024_S7.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0064-pa-23May2024_S8.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-24-0065-pa-23May2024_S9.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-GVA-HC-246-13Jun2023_S10.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-GVA-ME-339-13Jun2023_S9.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-GVA-PA-307-13Jun2023_S8.bams.tar",
+        "s3://serenomica-pipeline-archive/historical-qg-qc-samples/LB-GVA-PA-308-13Jun2023_S7.bams.tar",
+    ]
+    # "/home/darren/Documents/4_data/3_internal/1_panels/TWIST/twist_LB_probes_plus_30bp_merged.sorted.bed"
+    bed_path = "/home/darren/Documents/4_data/3_internal/1_panels/Aglient/LB_3413441_Covered_50ext.sorted.bed"
+    ref_path = "/home/darren/Documents/4_data/1_genomes/human/hg38/Homo_sapiens_assembly38.fasta"
+    main(s3_paths, bed_path, ref_path, cli_bin="s3cmd", tars=True)

src/parse_cov.py

@@ -0,0 +1,11 @@
+import sys
+from collections import defaultdict
+import json
+
+data = defaultdict(lambda: [])
+
+for line in sys.stdin:
+    chrom, start, stop, _, depth = line.rstrip().split("\t")
+    data[f"{chrom}_{start}_{stop}"].append(depth)
+
+print(json.dumps(dict(data)))