feat: add patient or environmental mode flag

.tests/config/config.yaml

@@ -1,5 +1,8 @@
 pepfile: config/pep/config.yaml
 
+# execution mode. Can be either "patient" or "environment"
+mode: patient
+
 # for testing, uncomment to limit to n strain genomes
 testing:
   limit-strain-genomes: 5

config/config.yaml

@@ -1,5 +1,8 @@
 pepfile: config/pep/config.yaml
 
+# execution mode. Can be either "patient" or "environment"
+mode: patient
+
 # virus genome to use as reference. Must be a NCBI accession
 virus-reference-genome:
   - NC_045512.2

docs/user-guide/configuration.md

@@ -1,5 +1,20 @@
 # Configuration
 
+## Execution Mode
+
+Accepted values: `patient`, `environment`. Defaults to `patient`.
+
+Defines the execution mode of UnCoVar.
+
+When the mode is set to `patient`, the sample is assumed come be from a single
+host organism and contains only one strain of SARS-CoV-2. The parts of the
+workflow for reconstructing the SARS-CoV-2 strain genome are activated.
+
+If the mode is set to `environment`, the sample is assumed to be from the
+environment (e.g. wastewater) and to contain different SARS-CoV-2 strains.
+The parts of the workflow responsible for creating and analysing individual
+genomes (e.g. assembly, lineage calling via Pangolin) are disabled.
+
 ## Adapters
 
 There are three ways to transfer adapter sequences to UnCoVar to remove them

workflow/Snakefile

@@ -79,25 +79,7 @@ rule save_latest_run:
 
 checkpoint all:
     input:
-        expand(
-            [
-                "results/{date}/qc/multiqc.html",
-                "results/reports/{date}.zip",
-                "results/{date}/tables/assembly_comparison.tsv",
-            ],
-            date=get_all_run_dates(),
-        ),
-        expand(
-            "results/{date}/plots/all.{mode}-strain.strains.kallisto.svg",
-            date=get_all_run_dates(),
-            mode=["major", "any"],
-        ),
-        zip_expand(
-            "results/{zip1}/filtered-calls/ref~main/{zip2}.subclonal.{{exp}}.bcf",
-            zip_wildcard_1=get_dates(),
-            zip_wildcard_2=get_samples(),
-            expand_wildcard=config["variant-calling"]["filters"],
-        ),
+        get_input_by_mode,
     output:
         touch(
             expand(

workflow/rules/common.smk

@@ -1543,6 +1543,48 @@ def get_aggregated_pangolin_calls(wildcards, return_list="paths"):
     return expanded_patterns
 
 
+def get_checked_mode():
+    mode = config["mode"]
+    if mode == "patient" or mode == "environment":
+        return mode
+
+    raise TypeError(f'Mode {mode} not recognized. Can be "patient" or "environment".')
+
+
+def get_input_by_mode(wildcard):
+    paths = [
+        expand(
+            "results/{mode}-reports/{date}.zip",
+            mode=config["mode"],
+            date=get_all_run_dates(),
+        ),
+        expand(
+            "results/{date}/plots/all.{mode}-strain.strains.kallisto.svg",
+            date=get_all_run_dates(),
+            mode=["major", "any"],
+        ),
+        zip_expand(
+            "results/{zip1}/filtered-calls/ref~main/{zip2}.subclonal.{{exp}}.bcf",
+            zip_wildcard_1=get_dates(),
+            zip_wildcard_2=get_samples(),
+            expand_wildcard=config["variant-calling"]["filters"],
+        ),
+    ]
+
+    if config["mode"] == "patient":
+        paths += [
+            expand(
+                [
+                    "results/{date}/qc/multiqc.html",
+                    "results/{date}/tables/assembly_comparison.tsv",
+                ],
+                date=get_all_run_dates(),
+            ),
+        ]
+
+    return sum(paths, [])
+
+
 def get_pangolin_for_report(wildcards):
     paths = []
 

workflow/rules/generate_output.smk

@@ -118,7 +118,7 @@ rule high_quality_genomes_report:
         "../scripts/generate-high-quality-report.py"
 
 
-rule overview_table_csv:
+rule overview_table_patient_csv:
     input:
         reads_raw=get_raw_reads_counts,
         reads_trimmed=get_trimmed_reads_counts,
@@ -142,27 +142,49 @@ rule overview_table_csv:
         # as input file for the rule. Please add the output of the respective checkpoint to the rule inputs.
         _=get_checkpoints_for_overview_table,
     output:
-        qc_data="results/{date}/tables/overview.csv",
+        qc_data="results/{date}/tables/patient-overview.csv",
     params:
         assembly_used=lambda wildcards: get_assemblies_for_submission(
             wildcards, "all samples"
         ),
         mth=config.get("mth"),
         samples=lambda wildcards: get_samples_for_date(wildcards.date),
+        mode=config["mode"],
     log:
-        "logs/{date}/overview-table.log",
+        "logs/{date}/patient-overview-table.log",
     conda:
         "../envs/pysam.yaml"
     script:
         "../scripts/generate-overview-table.py"
 
 
+use rule overview_table_patient_csv as overview_table_environment_csv with:
+    input:
+        reads_raw=get_raw_reads_counts,
+        reads_trimmed=get_trimmed_reads_counts,
+        kraken=get_kraken_output,
+        reads_used_for_assembly=expand_samples_for_date(
+            "results/{{date}}/tables/read_pair_counts/{sample}.txt",
+        ),
+        bcf=expand_samples_for_date(
+            "results/{{date}}/filtered-calls/ref~main/{sample}.subclonal.high+moderate-impact.bcf",
+        ),
+    output:
+        qc_data="results/{date}/tables/environment-overview.csv",
+    params:
+        mth=config.get("mth"),
+        samples=lambda wildcards: get_samples_for_date(wildcards.date),
+        mode=config["mode"],
+    log:
+        "logs/{date}/environment-overview-table.log",
+
+
 rule overview_table_html:
     input:
-        "results/{date}/tables/overview.csv",
+        "results/{date}/tables/{execution_mode}-overview.csv",
     output:
         report(
-            directory("results/{date}/overview/"),
+            directory("results/{date}/{execution_mode}/overview/"),
             htmlindex="index.html",
             caption="../report/qc-report.rst",
             category="1. Overview",
@@ -172,7 +194,7 @@ rule overview_table_html:
         formatter=get_resource("report-table-formatter.js"),
         pin_until="Sample",
     log:
-        "logs/{date}/qc_report_html.log",
+        "logs/{date}/{execution_mode}-qc-report-html.log",
     conda:
         "../envs/rbt.yaml"
     shell:
@@ -323,10 +345,13 @@ rule pangolin_call_overview_html:
         "rbt csv-report {input} --pin-until {params.pin_until} {output} > {log} 2>&1"
 
 
-rule snakemake_reports:
+rule snakemake_reports_patient:
     input:
         # 1. Overview
-        "results/{date}/overview/",
+        expand(
+            "results/{{date}}/{execution_mode}/overview/",
+            execution_mode=get_checked_mode(),
+        ),
         "results/{date}/plots/lineages-over-time.svg",
         expand(
             "results/{{date}}/tables/variants-{ORFNAME}-over-time.csv",
@@ -368,7 +393,7 @@ rule snakemake_reports:
         "results/high-quality-genomes/{date}.fasta",
         "results/high-quality-genomes/{date}.csv",
     output:
-        "results/reports/{date}.zip",
+        "results/patient-reports/{date}.zip",
     params:
         for_testing=get_if_testing("--snakefile ../workflow/Snakefile"),
     conda:
@@ -379,3 +404,29 @@ rule snakemake_reports:
         "snakemake --nolock --report-stylesheet resources/custom-stylesheet.css {input} "
         "--report {output} {params.for_testing} "
         "> {log} 2>&1"
+
+
+use rule snakemake_reports_patient as snakemake_reports_environment with:
+    input:
+        # 1. Overview
+        expand(
+            "results/{{date}}/{execution_mode}/overview/",
+            execution_mode=get_checked_mode(),
+        ),
+        "results/{date}/plots/all.major-strain.strains.kallisto.svg",
+        expand_samples_for_date(
+            ["results/{{date}}/plots/strain-calls/{sample}.strains.kallisto.svg"]
+        ),
+        # 2. Variant Call Details
+        lambda wildcards: expand(
+            "results/{{date}}/vcf-report/{target}.{filter}",
+            target=get_samples_for_date(wildcards.date) + ["all"],
+            filter=config["variant-calling"]["filters"],
+        ),
+        # 3. Sequencing Details
+        "results/{date}/qc/laboratory/multiqc.html",
+        "results/{date}/plots/coverage-reference-genome.svg",
+    output:
+        "results/environment-reports/{date}.zip",
+    log:
+        "logs/snakemake_reports/{date}.log",

workflow/scripts/generate-overview-table.py

@@ -19,6 +19,10 @@ def iter_with_samples(inputfiles):
     return zip(snakemake.params.samples, inputfiles)
 
 
+def is_patient_report():
+    return snakemake.params.mode == "patient"
+
+
 data = pd.DataFrame(index=snakemake.params.samples)
 
 
@@ -104,63 +108,65 @@ def register_contig_lengths(assemblies, name):
                 data.loc[sample, name] = max(len(contig.sequence) for contig in infile)
 
 
-# add lengths of Initial contigs
-register_contig_lengths(snakemake.input.initial_contigs, "Largest Contig (bp)")
-
-# add lengths of polished contigs
-register_contig_lengths(snakemake.input.polished_contigs, "De Novo Sequence (bp)")
-
-# add lengths of pseudo assembly
-register_contig_lengths(snakemake.input.pseudo_contigs, "Pseudo Sequence (bp)")
-
-# add lengths of Consensus assembly
-register_contig_lengths(snakemake.input.consensus_contigs, "Consensus Sequence (bp)")
-
-
-# add type of assembly use:
-for ele in snakemake.params.assembly_used:
-    sample, used = ele.split(",")
-    if "pseudo" == used:
-        data.loc[sample, "Best Quality"] = "Pseudo"
-    elif "normal" == used:
-        data.loc[sample, "Best Quality"] = "De Novo"
-    elif "consensus" == used:
-        data.loc[sample, "Best Quality"] = "Consensus"
-    elif "not-accepted" == used:
-        data.loc[sample, "Best Quality"] = "-"
-
-# add pangolin results
-for sample, file in iter_with_samples(snakemake.input.pangolin):
-    pangolin_results = pd.read_csv(file)
-    assert (
-        pangolin_results.shape[0] == 1
-    ), "unexpected number of rows (>1) in pangolin results"
-    lineage = pangolin_results.loc[0, "lineage"]
-    scorpio = pangolin_results.loc[0, "scorpio_call"]
-    if lineage == "None":
-        pangolin_call = "no strain called"
-    else:
-        # TODO parse scorpio output
-        #     match = re.match(
-        #         "((?P<varcount>\d+/\d+) .+ SNPs$)|(seq_len:\d+)$|($)",
-        #         pangolin_results.fillna("").loc[0, "note"].strip(),
-        #     )
-        #     assert (
-        #         match is not None
-        #     ), "unexpected pangolin note, please update above regular expression"
-        #     varcount = match.group("varcount") or ""
-        #     if varcount:
-        #         varcount = f" ({varcount})"
-        # pangolin_call = f"{lineage}{varcount}"
-        pangolin_call = f"{lineage}"
-    data.loc[sample, "Pango Lineage"] = pangolin_call
-    if scorpio == "None":
-        scorpio_call = "-"
-    else:
-        scorpio_call = f"{scorpio}"
-    data.loc[sample, "WHO Label"] = scorpio_call
-    data["WHO Label"].fillna("-", inplace=True)
-    data["WHO Label"].replace({"nan": "-"}, inplace=True)
+if is_patient_report():
+    # add lengths of Initial contigs
+    register_contig_lengths(snakemake.input.initial_contigs, "Largest Contig (bp)")
+
+    # add lengths of polished contigs
+    register_contig_lengths(snakemake.input.polished_contigs, "De Novo Sequence (bp)")
+
+    # add lengths of pseudo assembly
+    register_contig_lengths(snakemake.input.pseudo_contigs, "Pseudo Sequence (bp)")
+
+    # add lengths of Consensus assembly
+    register_contig_lengths(
+        snakemake.input.consensus_contigs, "Consensus Sequence (bp)"
+    )
+
+    # add type of assembly use:
+    for ele in snakemake.params.assembly_used:
+        sample, used = ele.split(",")
+        if "pseudo" == used:
+            data.loc[sample, "Best Quality"] = "Pseudo"
+        elif "normal" == used:
+            data.loc[sample, "Best Quality"] = "De Novo"
+        elif "consensus" == used:
+            data.loc[sample, "Best Quality"] = "Consensus"
+        elif "not-accepted" == used:
+            data.loc[sample, "Best Quality"] = "-"
+
+    # add pangolin results
+    for sample, file in iter_with_samples(snakemake.input.pangolin):
+        pangolin_results = pd.read_csv(file)
+        assert (
+            pangolin_results.shape[0] == 1
+        ), "unexpected number of rows (>1) in pangolin results"
+        lineage = pangolin_results.loc[0, "lineage"]
+        scorpio = pangolin_results.loc[0, "scorpio_call"]
+        if lineage == "None":
+            pangolin_call = "no strain called"
+        else:
+            # TODO parse scorpio output
+            #     match = re.match(
+            #         "((?P<varcount>\d+/\d+) .+ SNPs$)|(seq_len:\d+)$|($)",
+            #         pangolin_results.fillna("").loc[0, "note"].strip(),
+            #     )
+            #     assert (
+            #         match is not None
+            #     ), "unexpected pangolin note, please update above regular expression"
+            #     varcount = match.group("varcount") or ""
+            #     if varcount:
+            #         varcount = f" ({varcount})"
+            # pangolin_call = f"{lineage}{varcount}"
+            pangolin_call = f"{lineage}"
+        data.loc[sample, "Pango Lineage"] = pangolin_call
+        if scorpio == "None":
+            scorpio_call = "-"
+        else:
+            scorpio_call = f"{scorpio}"
+        data.loc[sample, "WHO Label"] = scorpio_call
+        data["WHO Label"].fillna("-", inplace=True)
+        data["WHO Label"].replace({"nan": "-"}, inplace=True)
 
 
 # add variant calls
@@ -240,12 +246,17 @@ def fmt_variants(variants):
     "Raw Reads (#)",
     "Trimmed Reads (#)",
     "Filtered Reads (#)",
-    "Largest Contig (bp)",
-    "De Novo Sequence (bp)",
-    "Pseudo Sequence (bp)",
-    "Consensus Sequence (bp)",
 ]
 
+if is_patient_report():
+    int_cols += [
+        "Largest Contig (bp)",
+        "De Novo Sequence (bp)",
+        "Pseudo Sequence (bp)",
+        "Consensus Sequence (bp)",
+    ]
+
+
 data[int_cols] = data[int_cols].fillna("0").applymap(lambda x: "{0:,}".format(int(x)))
 data = data.loc[:, (data != "0").any(axis=0)]
 data.index.name = "Sample"