prepare 0.7.2

CMakeLists.txt

@@ -50,14 +50,14 @@ include_directories(${CMAKE_BINARY_DIR})
 
 
 # Create the first executable dynamont-NT
-# add_library(NT_library src/cpp/utils.cpp src/cpp/NT.cpp)
-# add_executable(dynamont-NT src/cpp/NT_main.cpp)
-# target_link_libraries(dynamont-NT PRIVATE NT_library)
-# if(OpenMP_CXX_FOUND)
-  # target_link_libraries(dynamont-NT PUBLIC OpenMP::OpenMP_CXX)
-# endif()
+add_library(NT_library src/cpp/utils.cpp src/cpp/NT.cpp)
+add_executable(dynamont-NT src/cpp/NT_main.cpp)
+target_link_libraries(dynamont-NT PRIVATE NT_library)
+if(OpenMP_CXX_FOUND)
+  target_link_libraries(dynamont-NT PUBLIC OpenMP::OpenMP_CXX)
+endif()
 # Include header files for NT_library
-# target_include_directories(dynamont-NT PRIVATE include)
+target_include_directories(dynamont-NT PRIVATE include)
 
 # Create the second executable dynamont-NTC
 add_library(NTC_library src/cpp/utils.cpp src/cpp/NTC.cpp)
@@ -101,7 +101,7 @@ target_include_directories(test_dynamont PRIVATE include)
 add_test(NAME DynamontTests COMMAND test_dynamont)
 
 # Specify installation location
-# install(TARGETS dynamont-NT DESTINATION bin)
 install(TARGETS dynamont-NTC DESTINATION bin)
 install(TARGETS dynamont-NT-banded DESTINATION bin)
+install(TARGETS dynamont-NT DESTINATION bin)
 install(TARGETS test_dynamont DESTINATION bin)

README.md

@@ -59,7 +59,7 @@ dynamont-train -r <path/to/pod5/dataset/> -b <basecalls.bam> --mode basic -o <ou
 
 - [rna_r9](models/rna/r9.4.1/rna002_5mer.model) (tested)
 - [rna_rp4](models/rna/rp4/rna004_9mer.model) (tested)
-- dna_r9 not available yet
+- dna_r9 not available
 - [dna_r10.4.1 260 bps](models/dna/r10.4.1/dna_r10.4.1_e8.2_260bps.model) (not tested)
 - [dna_r10.4.1 400 bps](models/dna/r10.4.1/dna_r10.4.1_e8.2_400bps.model) (tested)
 

build.sh

@@ -29,11 +29,12 @@ cd ..
 mkdir -p src/bin
 cp $BUILD_DIR/bin/dynamont-NTC $BIN_DIR
 cp $BUILD_DIR/bin/dynamont-NT-banded $BIN_DIR
-strip "$BIN_DIR/dynamont-NTC" "$BIN_DIR/dynamont-NT-banded"
+cp $BUILD_DIR/bin/dynamont-NT $BIN_DIR
+strip "$BIN_DIR/dynamont-NTC" "$BIN_DIR/dynamont-NT-banded" "$BIN_DIR/dynamont-NT"
 
 # Build Python package (wheel + sdist)
 echo "[INFO] Building Python package..."
-# python -m pip install --user --upgrade setuptools wheel build --ignore-installed -v .
+python -m pip install --user --upgrade setuptools wheel build --ignore-installed -v .
 python -m build
 rm -rf $BUILD_DIR
 # twine upload dist/*

pyproject.toml

@@ -44,7 +44,8 @@ test = [
 dynamont-resquiggle = "python.segmentation.segment:main"
 dynamont-train = "python.segmentation.train:main"
 dynamont-NTC = "python.ntc_entry:main"
-dynamont-NT-banded = "python.nt_entry:main"
+dynamont-NT-banded = "python.nt_entry_banded:main"
+dynamont-NT = "python.nt_entry:main"
 
 [project.urls]
 repository = "https://github.com/rnajena/dynamont"
@@ -77,6 +78,7 @@ packages = ["src/python"]
 "models/rna/rp4/rna004_9mer.model" = "models/rna/rp4/rna004_9mer.model"
 "src/bin/dynamont-NTC" = "src/bin/dynamont-NTC"
 "src/bin/dynamont-NT-banded" = "src/bin/dynamont-NT-banded"
+"src/bin/dynamont-NT" = "src/bin/dynamont-NT"
 
 [tool.hatch.build.targets.sdist]
 include = [
@@ -93,6 +95,7 @@ include = [
 "models/rna/rp4/rna004_9mer.model" = "models/rna/rp4/rna004_9mer.model"
 "src/bin/dynamont-NTC" = "src/bin/dynamont-NTC"
 "src/bin/dynamont-NT-banded" = "src/bin/dynamont-NT-banded"
+"src/bin/dynamont-NT" = "src/bin/dynamont-NT"
 
 [tool.hatch.version]
 source = "vcs"

src/python/misc/collectMetrics.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python
+# author: Jannes Spangenberg
+# e-mail: [email protected]
+# github: https://github.com/JannesSP
+# website: https://jannessp.github.io
+
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, Namespace
+import pandas as pd
+import json
+
+def parse() -> Namespace:
+    parser = ArgumentParser(
+        formatter_class=ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("scores", type=str, help="Path to the scores txt file")
+    # read metrics after segmentation
+    parser.add_argument("dynamont", type=str, help="Path to the reads metrics json file")
+    parser.add_argument("uncalled4", type=str, help="Path to the reads metrics json file")
+    parser.add_argument("f5c_eventalign", type=str, help="Path to the reads metrics json file")
+    parser.add_argument("f5c_resquiggle", type=str, help="Path to the reads metrics json file")
+    parser.add_argument("control", type=str, help="Path to the reads metrics json file")
+    parser.add_argument("outfile", type=str, help="Path to the output file")
+    parser.add_argument("time_dynamont", type=str, help="Path to the tools time file")
+    parser.add_argument("time_uncalled4", type=str, help="Path to the tools time file")
+    parser.add_argument("time_f5c_eventalign", type=str, help="Path to the tools time file")
+    parser.add_argument("time_f5c_resquiggle", type=str, help="Path to the tools time file")
+    parser.add_argument("subtools_dorado", type=str, help="Path to the downstream tool metrics file")
+    parser.add_argument("subtools_dynamont", type=str, help="Path to the downstream tool metrics file")
+    parser.add_argument("subtools_uncalled4", type=str, help="Path to the downstream tool metrics file")
+    parser.add_argument("subtools_f5c_eventalign", type=str, help="Path to the downstream tool metrics file")
+    parser.add_argument("subtools_f5c_resquiggle", type=str, help="Path to the downstream tool metrics file")
+    parser.add_argument("--tombo", type=str, default=None, help="Path to the reads metrics json file")
+    parser.add_argument("--time_tombo", type=str, default=None, help="Path to the tools time file")
+    parser.add_argument("--subtools_tombo", type=str, default=None, help="Path to the downstream tool metrics time file")
+    return parser.parse_args()
+
+def main() -> None:
+    args = parse()
+    scores = pd.read_csv(args.scores, sep="\t")
+    scores.rename(columns={"Median Score": "Value", "Segment Quality" : "Metric"}, inplace=True)
+
+    jsons = {
+        "dynamont": args.dynamont,
+        "uncalled4": args.uncalled4,
+        "f5c_eventalign": args.f5c_eventalign,
+        "f5c_resquiggle": args.f5c_resquiggle
+    }
+
+    times = {
+        "dynamont": args.time_dynamont,
+        "uncalled4": args.time_uncalled4,
+        "f5c_eventalign": args.time_f5c_eventalign,
+        "f5c_resquiggle": args.time_f5c_resquiggle
+    }
+
+    downstream_tools = {
+        "dorado" : args.subtools_dorado,
+        "dynamont": args.subtools_dynamont,
+        "uncalled4": args.subtools_uncalled4,
+        "f5c_eventalign": args.subtools_f5c_eventalign,
+        "f5c_resquiggle": args.subtools_f5c_resquiggle
+    }
+
+    # NA rna004
+    if args.tombo and args.tombo != '':
+        jsons["tombo"] = args.tombo
+
+    if args.time_tombo and args.time_tombo != '':
+        times["tombo"] = args.time_tombo
+
+    if args.subtools_tombo and args.subtools_tombo != '':
+        downstream_tools["tombo"] = args.subtools_tombo
+
+    for name, json_path in jsons.items():
+        with open(json_path, "r") as json_file:
+            json_data = json.load(json_file)
+            for metric, value in json_data.items():
+                if metric == "lengths":
+                    continue
+                # scores.loc[name, metric] = value
+                new_entry = pd.DataFrame({
+                    "Tool": [name],
+                    "Value": [value],
+                    "Metric": [metric.lower().replace('n50', 'n50_length')]
+                })
+                scores = pd.concat([scores, new_entry], ignore_index=True)
+
+    for name, time_path in times.items():
+        with open(time_path, "r") as time_file:
+            time = time_file.readline()[14:22]
+            memory = time_file.readline().strip()[13:].split(" MB")[0]
+            new_entry = pd.DataFrame({
+                "Tool": [name, name],
+                "Value": [time, memory],
+                "Metric": ["Time in hh:mm:ss", "Memory in MB"]
+            })  
+            scores = pd.concat([scores, new_entry], ignore_index=True)
+
+    for name, downstream_path in downstream_tools.items():
+        with open(downstream_path, "r") as downstream_file:
+            total_assembly_length = int(downstream_file.readline().strip().split(': ')[1])
+            n50 = int(downstream_file.readline().strip().split(': ')[1])
+            mean_cov = float(downstream_file.readline().strip().split(': ')[1])
+            struct_vars = int(downstream_file.readline().strip().split(': ')[1])
+
+            new_entry = pd.DataFrame({
+                "Tool": [name, name, name, name],
+                "Value": [total_assembly_length, n50, mean_cov, struct_vars],
+                "Metric": ["flye total length", "flye n50", "flye mean coverage", "SVIM structural variants"]
+            })
+            scores = pd.concat([scores, new_entry], ignore_index=True)
+
+    #! add default control values to dorado
+    control = pd.read_csv(args.control, sep="\t")
+    for _, row in control.iterrows():
+        new_entry = pd.DataFrame({
+            "Tool": ["Dorado"],
+            "Value": [row["Value"]],
+            "Metric": [row["Metric"].lower() + '_length']
+        })
+        scores = pd.concat([scores, new_entry], ignore_index=True)
+
+    # print(scores.loc[scores["Metric"] == "total", "Value"].values)
+    total_reads = scores.loc[scores["Metric"] == "total", "Value"].values[0]
+    new_entry = pd.DataFrame({
+        "Tool": ["Dorado", "Dorado", "Dorado", "Dorado", "Dorado", "Dorado"],
+        "Metric": ["total", "present", "missing", "truncated", "identical", "nt changed"],
+        "Value": [total_reads, total_reads, 0, 0, total_reads, 0],
+    })
+    scores = pd.concat([scores, new_entry], ignore_index=True)
+
+    #! remove controls and dorado
+    scores = scores[scores["Tool"] != "Control Random"]
+    scores = scores[scores["Tool"] != "Control Uniform"]
+    # scores = scores[scores["Tool"] != "Dorado"]
+
+    # fix names
+    scores["Tool"] = scores["Tool"].replace(
+        {
+            "dynamont": "Dynamont",
+            "Dynamont NT": "Dynamont",
+            "f5c_eventalign": "f5c Eventalign",
+            "f5c_resquiggle": "f5c Resquiggle",
+            "uncalled4": "Uncalled4",
+            "tombo": "Tombo",
+            "dorado": "Dorado",
+        }
+    )
+
+    # Remove unwanted metrics
+    # Exclude specific metrics (e.g., "Time in hh:mm:ss") from the Metric Score calculation
+    excluded_metrics = ["missing reads", "identical reads", "Time in hh:mm:ss", "Memory in MB"]
+    numeric_scores = scores[~scores["Metric"].isin(excluded_metrics)]
+    numeric_scores["Value"] = pd.to_numeric(numeric_scores["Value"], errors="coerce")
+
+    # Calculate Metric Score only for numeric values
+    scores["Metric Score"] = numeric_scores.groupby("Metric")["Value"].transform(
+        lambda x: x / x.max() if x.max() > 0 else 0
+    )
+    # print("GROUP: ", scores["Metric Score"])
+    # exit(1)
+
+    # Fill non-numeric rows with NaN for "Metric Score"
+    scores["Metric Score"] = scores["Metric Score"].fillna(0)
+
+    # calculate metric score
+    # scores["Metric Score"] = scores.groupby("Metric")["Value"].transform(lambda x: x / x.max() if x.max() > 0 else 0)
+
+    # Adjust Metric Score for specific metrics
+    scores.loc[scores["Metric"].isin(["Homogeneity", "missing", "truncated", "nt_changed", "min_length"]), "Metric Score"] = 1 - scores["Metric Score"]
+
+    # Calculate Metric Score only for numeric values
+    # def metric_score(series, lower_is_better=False):
+    #     if series.max() == series.min():
+    #         return pd.Series([1.0] * len(series), index=series.index)
+    #     if lower_is_better:
+    #         return (series.max() - series) / (series.max() - series.min())
+    #     else:
+    #         return (series - series.min()) / (series.max() - series.min())
+
+    # # Define which metrics are "lower is better"
+    # lower_is_better_metrics = ["Homogeneity", "missing", "truncated", "nt_changed", "min_length"]
+
+    # # Calculate scores for each metric
+    # scores["Metric Score"] = 0.0
+    # for metric in numeric_scores["Metric"].unique():
+    #     mask = scores["Metric"] == metric
+    #     lower_is_better = metric in lower_is_better_metrics
+    #     values = pd.to_numeric(scores.loc[mask, "Value"], errors="coerce")
+    #     scores.loc[mask, "Metric Score"] = metric_score(values, lower_is_better=lower_is_better)
+
+    # # Fill non-numeric rows with NaN for "Metric Score"
+    # scores["Metric Score"] = scores["Metric Score"].fillna(0)
+
+    # Finalize the DataFrame
+    scores = scores[["Tool", "Metric", "Value", "Metric Score"]]
+    scores = scores.sort_values(by=["Metric", "Tool"])
+    scores.reset_index(drop=True, inplace=True)
+
+    print("\nWriting to", args.outfile, "\n")
+    scores.to_csv(args.outfile, sep="\t", index=False)
+
+if __name__ == '__main__':
+    main()

src/python/edgeDetection/compareDatasets.py → src/python/misc/compareDatasets.py

@@ -71,6 +71,32 @@
       "Read Lengths" : "/data/fass5/projects/js_dynamont/benchmark/comparison/rna004/ivt/comparison_w0_readLengths.csv",
       "Read Quality" : "/data/fass5/projects/js_dynamont/benchmark/comparison/rna004/ivt/comparison_w0_readQuality.csv",
     },
+  },
+  "DNA R10.4.1 5kHz" : {
+    "H. Sapiens" : {
+      "Score" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/h_sapiens/comparison_w0_score.txt",
+      "Reads Segmentation Ratio" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/h_sapiens/comparison_w0_segmentedReadsRatio.csv",
+      "Read Lengths" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/h_sapiens/comparison_w0_readLengths.csv",
+      "Read Quality" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/h_sapiens/comparison_w0_readQuality.csv",
+    },
+    "Zymo hmw" : {
+      "Score" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/zymo_hmw/comparison_w0_score.txt",
+      "Reads Segmentation Ratio" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/zymo_hmw/comparison_w0_segmentedReadsRatio.csv",
+      "Read Lengths" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/zymo_hmw/comparison_w0_readLengths.csv",
+      "Read Quality" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/zymo_hmw/comparison_w0_readQuality.csv",
+    },
+    "S. Aureus" : {
+      "Score" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/p_anserina/comparison_w0_score.txt",
+      "Reads Segmentation Ratio" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/p_anserina/comparison_w0_segmentedReadsRatio.csv",
+      "Read Lengths" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/p_anserina/comparison_w0_readLengths.csv",
+      "Read Quality" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/p_anserina/comparison_w0_readQuality.csv",
+    },
+    "P. Anserina" : {
+      "Score" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/s_aureus/comparison_w0_score.txt",
+      "Reads Segmentation Ratio" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/s_aureus/comparison_w0_segmentedReadsRatio.csv",
+      "Read Lengths" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/s_aureus/comparison_w0_readLengths.csv",
+      "Read Quality" : "/data/fass5/projects/js_dynamont/benchmark/comparison/dna_r10.4.1_5kHz/s_aureus/comparison_w0_readQuality.csv",
+    }
   }
 }