Move marker plot generation to filter steb for STRs (#69)

lusSTR/scripts/filter_settings.py

@@ -531,6 +531,4 @@ def same_size_filter(df, metadata, datatype):
         else:
             final_df = pd.concat([final_df, df_filt])
     final_df = final_df.reset_index(drop=True)
-    if datatype == "lusplus":
-        final_df = final_df.drop("CE_Allele", axis=1)
     return final_df

lusSTR/scripts/marker.py

@@ -388,12 +388,18 @@ def convert(self):
         else:
             if "GGGCTGCCTA" in self.uas_sequence:
                 break_point = self.uas_sequence.index("GGGCTGCCTA") + 10
-            else:
+                bracketed_form = (
+                    f"{collapse_repeats_by_length(self.uas_sequence[:break_point], 4)} "
+                    f"{collapse_repeats_by_length(self.uas_sequence[break_point:], 4)}"
+                )
+            elif "TTTT" in self.uas_sequence:
                 break_point = self.uas_sequence.index("TTTT") + 14
-            bracketed_form = (
-                f"{collapse_repeats_by_length(self.uas_sequence[:break_point], 4)} "
-                f"{collapse_repeats_by_length(self.uas_sequence[break_point:], 4)}"
-            )
+                bracketed_form = (
+                    f"{collapse_repeats_by_length(self.uas_sequence[:break_point], 4)} "
+                    f"{collapse_repeats_by_length(self.uas_sequence[break_point:], 4)}"
+                )
+            else:
+                bracketed_form = ""
         return bracketed_form
 
 

lusSTR/scripts/repeat.py

@@ -163,6 +163,8 @@ def repeat_copy_number(bf, repeat):
     The input is a sequence string collapsed to bracketed sequence form.
     """
     longest = 0
+    if bf == "":
+        return 0
     for block in bf.split(" "):
         if block == repeat:
             if 1 > longest:

lusSTR/tests/data/LUSPlus_stutter_test/LUSPlus_sequence_info.csv

@@ -1,24 +1,24 @@
-SampleID,Locus,LUS_Plus,Reads,allele_type,parent_allele1,parent_allele2,allele1_ref_reads,allele2_ref_reads,perc_noise,perc_stutter,CE_Allele
-Sample1,D4S2408,10_10_0,1022,real_allele,,,,,,,
-Sample1,D4S2408,9_9_0,116,-1_stutter/+1_stutter,10_10_0,8_8_0,1022.0,1050.0,,,
-Sample1,D4S2408,8_8_0,1050,real_allele,,,,,,,
-Sample1,D8S1179,14_12_1_0,869,real_allele,,,,,,,
-Sample1,D8S1179,13_11_1_0,184,-1_stutter,14_12_1_0,,869.0,,,0.212,
-Sample1,D8S1179,12_10_1_0,37,-2_stutter,14_12_1_0,,869.0,,,0.201,
-Sample1,D9S1122,13_11,948,real_allele,,,,,,,
-Sample1,D9S1122,12_10,108,-1_stutter,13_11,,948.0,,,0.114,
-Sample1,D9S1122,11_11,991,real_allele,,,,,,,
-Sample1,D9S1122,10_10,87,-1_stutter,11_11,,991.0,,,0.088,
-Sample1,FGA,23_15_3_0,1436,real_allele,,,,,,,
-Sample1,FGA,22_14_3_0,262,-1_stutter,23_15_3_0,,1436.0,,,0.182,
-Sample1,FGA,21_13_3_0,48,BelowAT,,,,,0.013,,
-Sample1,FGA,20_12_3_0,1750,real_allele,,,,,,,
-Sample1,FGA,18_10_3_0,181,real_allele,,,,,,,
-Sample1,FGA,17_9_3_0,15,BelowAT,,,,,0.004,,
-Sample1,PENTA D,15_15,50,real_allele,,,,,,,
-Sample1,PENTA D,13_13,1000,real_allele,,,,,,,
-Sample1,PENTA E,7_7,505,real_allele,,,,,,,7.0
-Sample1,TH01,7_7,2197,real_allele,,,,,,,
-Sample1,TH01,6_6,1632,real_allele,,,,,,,
-Sample1,TH01,5_5,66,BelowAT,,,,,0.017,,
-Sample1,TPOX,11_11,15,BelowAT,,,,,1.0,,11.0
+SampleID,Locus,CE_Allele,LUS_Plus,Reads,allele_type,parent_allele1,parent_allele2,allele1_ref_reads,allele2_ref_reads,perc_noise,perc_stutter
+Sample1,D4S2408,10.0,10_10_0,1022,real_allele,,,,,,
+Sample1,D4S2408,9.0,9_9_0,116,-1_stutter/+1_stutter,10_10_0,8_8_0,1022.0,1050.0,,
+Sample1,D4S2408,8.0,8_8_0,1050,real_allele,,,,,,
+Sample1,D8S1179,14.0,14_12_1_0,869,real_allele,,,,,,
+Sample1,D8S1179,13.0,13_11_1_0,184,-1_stutter,14_12_1_0,,869.0,,,0.212
+Sample1,D8S1179,12.0,12_10_1_0,37,-2_stutter,14_12_1_0,,869.0,,,0.201
+Sample1,D9S1122,13.0,13_11,948,real_allele,,,,,,
+Sample1,D9S1122,12.0,12_10,108,-1_stutter,13_11,,948.0,,,0.114
+Sample1,D9S1122,11.0,11_11,991,real_allele,,,,,,
+Sample1,D9S1122,10.0,10_10,87,-1_stutter,11_11,,991.0,,,0.088
+Sample1,FGA,23.0,23_15_3_0,1436,real_allele,,,,,,
+Sample1,FGA,22.0,22_14_3_0,262,-1_stutter,23_15_3_0,,1436.0,,,0.182
+Sample1,FGA,21.0,21_13_3_0,48,BelowAT,,,,,0.013,
+Sample1,FGA,20.0,20_12_3_0,1750,real_allele,,,,,,
+Sample1,FGA,18.0,18_10_3_0,181,real_allele,,,,,,
+Sample1,FGA,17.0,17_9_3_0,15,BelowAT,,,,,0.004,
+Sample1,PENTA D,15.0,15_15,50,real_allele,,,,,,
+Sample1,PENTA D,13.0,13_13,1000,real_allele,,,,,,
+Sample1,PENTA E,7.0,7_7,505,real_allele,,,,,,
+Sample1,TH01,7.0,7_7,2197,real_allele,,,,,,
+Sample1,TH01,6.0,6_6,1632,real_allele,,,,,,
+Sample1,TH01,5.0,5_5,66,BelowAT,,,,,0.017,
+Sample1,TPOX,11.0,11_11,15,BelowAT,,,,,1.0,

lusSTR/tests/data/NGS_stutter_test/Sample1_nofilter.csv

@@ -1,28 +1,28 @@
 Locus,CE Allele,Allele Seq,Reads
-D4S2408,8,ATCTATCTATCTATCTATCTATCTATCTATCT,1000
-D4S2408,9,ATCTATCTATCTATCTATCTATCTATCTATCTATCT,1357
-D4S2408,10,ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT,900
-D8S1179,12,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTA,26
-D8S1179,12,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,11
-D8S1179,13,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,95
-D8S1179,13,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,89
-D8S1179,14,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,739
-D8S1179,14,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,130
-D9S1122,10,TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,87
-D9S1122,11,TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,991
-D9S1122,12,TAGATCGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,108
-D9S1122,13,TAGATCGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,948
-FGA,17,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,15
-FGA,18,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,181
-FGA,20,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,1750
-FGA,21,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,48
-FGA,22,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,262
-FGA,23,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,1436
-PentaD,13,AAAAGAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,1000
-PentaD,15,AAAAGAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,50
-PentaE,7,AAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,505
-TH01,5,AATGAATGAATGAATGAATG,66
-TH01,6,AATGAATGAATGAATGAATGAATG,1632
-TH01,7,AATGAATGAATGAATGAATGAATGAATG,2197
-TPOX,11,AATGAATGAATGAATGAATGAATGAATGAATGAATGAATGAATG,15
-vWA,16,TCTATCTGTCTGTCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCCATCTA,6
+D4S2408,8.0,ATCTATCTATCTATCTATCTATCTATCTATCT,1000
+D4S2408,9.0,ATCTATCTATCTATCTATCTATCTATCTATCTATCT,1357
+D4S2408,10.0,ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT,900
+D8S1179,12.0,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTA,26
+D8S1179,12.0,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,11
+D8S1179,13.0,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,95
+D8S1179,13.0,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,89
+D8S1179,14.0,TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,739
+D8S1179,14.0,TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA,130
+D9S1122,10.0,TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,87
+D9S1122,11.0,TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,991
+D9S1122,12.0,TAGATCGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,108
+D9S1122,13.0,TAGATCGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGA,948
+FGA,17.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,15
+FGA,18.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,181
+FGA,20.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,1750
+FGA,21.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,48
+FGA,22.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,262
+FGA,23.0,TTTCTTTCTTTCTTTTTTCTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTCCTTCCTTCC,1436
+PentaD,13.0,AAAAGAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,1000
+PentaD,15.0,AAAAGAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,50
+PentaE,7.0,AAAGAAAAGAAAAGAAAAGAAAAGAAAAGAAAAGA,505
+TH01,5.0,AATGAATGAATGAATGAATG,66
+TH01,6.0,AATGAATGAATGAATGAATGAATG,1632
+TH01,7.0,AATGAATGAATGAATGAATGAATGAATG,2197
+TPOX,11.0,AATGAATGAATGAATGAATGAATGAATGAATGAATGAATGAATG,15
+vWA,16.0,TCTATCTGTCTGTCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCCATCTA,6

lusSTR/tests/test_suite.py

@@ -323,24 +323,15 @@ def test_snakemake(command, output, format_out, convert_out, all_out, tmp_path):
     assert filecmp.cmp(exp_output, obs_output) is True
 
 
-@pytest.mark.parametrize(
-    "sex",
-    [True, False],
-)
-def test_marker_plots(sex, tmp_path):
+def test_marker_plots(tmp_path):
     inputfile = data_file("UAS_bulk_input/Positive Control Sample Details Report 2315.xlsx")
     exp_output = str(tmp_path / "MarkerPlots/lusstr_output_Positive_Control_marker_plots.pdf")
     sex_exp = str(tmp_path / "MarkerPlots/lusstr_output_Positive_Control_sexchr_marker_plots.pdf")
-    if sex:
-        arglist = ["config", "-w", str(tmp_path), "--input", str(inputfile), "--sex"]
-    else:
-        arglist = ["config", "-w", str(tmp_path), "--input", str(inputfile)]
+    arglist = ["config", "-w", str(tmp_path), "--input", str(inputfile)]
     lusSTR.cli.main(lusSTR.cli.get_parser().parse_args(arglist))
-    snakemake_arglist = ["strs", "convert", "-w", str(tmp_path)]
+    snakemake_arglist = ["strs", "all", "-w", str(tmp_path)]
     lusSTR.cli.main(lusSTR.cli.get_parser().parse_args(snakemake_arglist))
     assert os.path.exists(exp_output) is True
-    if sex:
-        assert os.path.exists(sex_exp) is True
 
 
 def test_genemarker(tmp_path):

lusSTR/wrappers/convert.py

@@ -12,8 +12,6 @@
 
 import csv
 import json
-import math
-import matplotlib.pyplot as plt
 import numpy as np
 import os
 import pandas as pd
@@ -23,7 +21,6 @@
 from lusSTR.scripts.repeat import collapse_all_repeats, collapse_repeats_by_length
 from lusSTR.scripts.repeat import sequence_to_bracketed_form, split_by_n
 from lusSTR.scripts.repeat import reverse_complement, reverse_complement_bracketed
-from matplotlib.backends.backend_pdf import PdfPages
 from pathlib import Path
 
 
@@ -101,9 +98,8 @@ def format_table(input, software, kit="forenseq"):
             ]
             flanks_list.append(flank_summary)
             continue
-
         marker = STRMarkerObject(locus, sequence, software, kit=kit)
-        if locus == "D12S391" and kit == "powerseq":
+        if locus == "D12S391" and kit == "powerseq" and software == "straitrazor":
             if "." in str(marker.canonical):
                 check_sr += 1
                 if check_sr > 10:
@@ -185,48 +181,6 @@ def sort_table(table):
     return sorted_table
 
 
-def marker_plots(df, output_name, sex=False):
-    Path("MarkerPlots").mkdir(parents=True, exist_ok=True)
-    df["CE_Allele"] = df["CE_Allele"].astype(float)
-    for id in df["SampleID"].unique():
-        sample_id = f"{id}_sexchr" if sex else id
-        with PdfPages(f"MarkerPlots/{output_name}_{sample_id}_marker_plots.pdf") as pdf:
-            make_plot(df, id, sex, sameyaxis=False)
-            pdf.savefig()
-            make_plot(df, id, sex)
-            pdf.savefig()
-
-
-def make_plot(df, id, sex=False, sameyaxis=True):
-    sample_df = df[df["SampleID"] == id]
-    sample_id = f"{id}_sexchr" if sex else id
-    max_reads = max(sample_df["Reads"])
-    n = 100 if max_reads > 1000 else 10
-    max_yvalue = int(math.ceil(max_reads / n)) * n
-    increase_value = int(math.ceil((max_yvalue / 5)) / n) * n
-    fig = plt.figure(figsize=(31, 31)) if sex is True else plt.figure(figsize=(30, 30))
-    n = 0
-    for marker in sample_df["Locus"].unique():
-        n += 1
-        marker_df = sample_df[sample_df["Locus"] == marker].sort_values(by="CE_Allele")
-        ax = fig.add_subplot(6, 6, n) if sex is True else fig.add_subplot(6, 5, n)
-        ax.bar(marker_df["CE_Allele"], marker_df["Reads"])
-        if sameyaxis:
-            ax.set_yticks(np.arange(0, max_yvalue, increase_value))
-        ax.set_xticks(
-            np.arange(min(marker_df["CE_Allele"]) - 1, max(marker_df["CE_Allele"]) + 2, 1.0)
-        )
-        ax.title.set_text(marker)
-    if sameyaxis:
-        plt.text(
-            0.4, 0.95, "Marker Plots With Same Y-Axis Scale", transform=fig.transFigure, size=24
-        )
-    else:
-        plt.text(
-            0.4, 0.95, "Marker Plots With Custom Y-Axis Scale", transform=fig.transFigure, size=24
-        )
-
-
 def main(input, out, kit, software, sex, nocombine):
     input = str(input)
     out = str(out)
@@ -248,7 +202,6 @@ def main(input, out, kit, software, sex, nocombine):
                 sex_final_table.to_csv(f"{output_name}_sexloci.txt", sep="\t", index=False)
         else:
             sex_final_table.to_csv(f"{output_name}_sexloci.txt", sep="\t", index=False)
-        marker_plots(sex_final_table, output_name, sex=True)
     if software != "uas":
         if not autosomal_final_table.empty:
             autosomal_flank_table.to_csv(f"{output_name}_flanks.txt", sep="\t", index=False)
@@ -260,7 +213,6 @@ def main(input, out, kit, software, sex, nocombine):
             autosomal_final_table.to_csv(out, sep="\t", index=False)
     else:
         autosomal_final_table.to_csv(out, sep="\t", index=False)
-    marker_plots(autosomal_final_table, output_name)
 
 
 if __name__ == "__main__":

lusSTR/wrappers/filter.py

@@ -16,6 +16,9 @@
 import json
 import lusSTR
 from lusSTR.scripts.filter_settings import filters, flags
+import math
+from matplotlib.backends.backend_pdf import PdfPages
+import matplotlib.pyplot as plt
 import numpy as np
 import os
 import pandas as pd
@@ -322,6 +325,98 @@ def format_ref_table(new_rows, sample_data, datatype):
     return sort_df
 
 
+def marker_plots(df, output_name):
+    Path("MarkerPlots").mkdir(parents=True, exist_ok=True)
+    df["CE_Allele"] = df["CE_Allele"].astype(float)
+    filt_df = df[df["allele_type"] == "real_allele"]
+    for sample_id in df["SampleID"].unique():
+        # sample_id = f"{id}_sexchr" if sex else id
+        with PdfPages(f"MarkerPlots/{output_name}_{sample_id}_marker_plots.pdf") as pdf:
+            make_plot(filt_df, sample_id, filters=True, at=False)
+            pdf.savefig()
+            make_plot(df, sample_id)
+            pdf.savefig()
+            make_plot(df, sample_id, sameyaxis=True)
+            pdf.savefig()
+
+
+def make_plot(df, sample_id, sameyaxis=False, filters=False, at=True):
+    sample_df = df[df["SampleID"] == sample_id].copy()
+    # sample_id = f"{id}_sexchr" if sex else id
+    conditions = [
+        sample_df["allele_type"].str.contains("real"),
+        sample_df["allele_type"].str.contains("BelowAT"),
+        sample_df["allele_type"].str.contains("stutter"),
+    ]
+    values = ["Typed", "BelowAT", "Stutter"]
+    sample_df.loc[:, "Type"] = np.select(conditions, values)
+    max_reads = max(sample_df["Reads"])
+    n = 100 if max_reads > 1000 else 10
+    max_yvalue = int(math.ceil(max_reads / n)) * n
+    increase_value = int(math.ceil((max_yvalue / 5)) / n) * n
+    fig = plt.figure(figsize=(30, 30))
+    n = 0
+    for marker in sample_df["Locus"].unique():
+        n += 1
+        colors = {"Typed": "g", "Stutter": "b", "BelowAT": "r"}
+        marker_df = sample_df[sample_df["Locus"] == marker].sort_values(by="CE_Allele")
+        ax = fig.add_subplot(6, 5, n)
+        p = ax.bar(
+            marker_df["CE_Allele"],
+            marker_df["Reads"],
+            edgecolor="k",
+            color=[colors[i] for i in marker_df["Type"]],
+        )
+        if at:
+            at = get_at(marker_df, marker)
+            ax.axhline(at, linestyle="--", color="k")
+            ax.text(round(min(marker_df["CE_Allele"])) - 0.9, at + (at * 0.1), f"AT", size=12)
+        labels = marker_df["Type"].unique()
+        handles = [plt.Rectangle((0, 0), 1, 1, color=colors[l]) for l in labels]
+        if not filters:
+            plt.legend(handles, labels, title="Allele Type")
+        else:
+            for i, row in marker_df.iterrows():
+                marker_df.loc[i, "Label"] = (
+                    str(int(marker_df.loc[i, "CE_Allele"]))
+                    if ".0" in str(marker_df.loc[i, "CE_Allele"])
+                    else str(marker_df.loc[i, "CE_Allele"])
+                )
+            ax.bar_label(p, labels=marker_df["Label"])
+        if sameyaxis:
+            ax.set_yticks(np.arange(0, max_yvalue, increase_value))
+        ax.set_xticks(
+            np.arange(
+                round(min(marker_df["CE_Allele"]) - 1), round(max(marker_df["CE_Allele"])) + 2, 1.0
+            )
+        )
+        ax.title.set_text(marker)
+    if sameyaxis:
+        title = "Marker Plots for All Alleles With Same Y-Axis Scale"
+    elif filters:
+        title = "Marker Plots for Typed Alleles With Custom Y-Axis Scale"
+    else:
+        title = "Marker Plots for All Alleles With Custom Y-Axis Scale"
+    plt.text(0.4, 0.95, title, transform=fig.transFigure, size=24)
+
+
+def get_at(df, locus):
+    metadata = filter_marker_data[locus]
+    thresh_use = metadata["AnalyticalThresholdUse"]
+    at_st = float(metadata["AnalyticalThresholdStaticCount"])
+    at_dy = metadata["AnalyticalThresholdDynamicPercent"]
+    at_dy_num = df["Reads"].sum() * float(at_dy)
+    if thresh_use.lower() == "both":
+        at = at_st if at_st > at_dy_num else at_dy_num
+    elif thresh_use.lower() == "static":
+        at = at_st
+    elif thresh_use.lower() == "dynamic":
+        at = at_dy_num
+    else:
+        raise ValueError("Incorrect AT specified in filters.json. Please check and re-run.")
+    return at
+
+
 def main(
     input, output_type, profile_type, data_type, output_dir, info, separate, nofilters, strand
 ):
@@ -343,13 +438,15 @@ def main(
     )
     if nofilters:
         full_df["allele_type"] = "real_allele"
+        marker_plots(full_df, outpath)
         if output_type == "efm" or output_type == "mpsproto":
             EFM_output(full_df, outpath, profile_type, data_type, brack_col, separate)
         else:
             STRmix_output(full_df, outpath, profile_type, data_type, seq_col)
     else:
         dict_loc = {k: v for k, v in full_df.groupby(["SampleID", "Locus"])}
         final_df, flags_df = process_strs(dict_loc, data_type, seq_col)
+        marker_plots(final_df, outpath)
         if output_type == "efm" or output_type == "mpsproto":
             EFM_output(final_df, outpath, profile_type, data_type, brack_col, separate)
         else: