Covid19

bio_hansel/main.py

@@ -12,15 +12,15 @@
 import attr
 import pandas as pd
 
-from . import program_desc, __version__, program_name
-from .const import SUBTYPE_SUMMARY_COLS, REGEX_FASTQ, REGEX_FASTA, JSON_EXT_TMPL
-from .subtype import Subtype
-from .subtype_stats import subtype_counts
-from .subtyper import \
+from bio_hansel import program_desc, __version__, program_name
+from bio_hansel.const import SUBTYPE_SUMMARY_COLS, REGEX_FASTQ, REGEX_FASTA, JSON_EXT_TMPL
+from bio_hansel.subtype import Subtype
+from bio_hansel.subtype_stats import subtype_counts
+from bio_hansel.subtyper import \
     subtype_contigs_samples, \
     subtype_reads_samples
-from .metadata import read_metadata_table, merge_results_with_metadata
-from .utils import (genome_name_from_fasta_path, get_scheme_fasta, does_file_exist, collect_fastq_from_dir,
+from bio_hansel.metadata import read_metadata_table, merge_results_with_metadata
+from bio_hansel.utils import (genome_name_from_fasta_path, get_scheme_fasta, does_file_exist, collect_fastq_from_dir,
                     group_fastqs, collect_fasta_from_dir, init_subtyping_params, df_field_fillna)
 
 SCRIPT_NAME = 'hansel'
@@ -81,6 +81,9 @@ def init_parser():
     parser.add_argument('--min-kmer-freq',
                         type=int,
                         help='Min k-mer freq/coverage')
+    parser.add_argument('--min-kmer-frac',
+                        type=float,
+                        help='Proportion of k-mer required for detection (0.0 - 1)')
     parser.add_argument('--max-kmer-freq',
                         type=int,
                         help='Max k-mer freq/coverage')
@@ -258,6 +261,8 @@ def main():
     if output_kmer_results:
         if len(dfs) > 0:
             dfall: pd.DataFrame = pd.concat([df.sort_values('is_pos_kmer', ascending=False) for df in dfs], sort=False)
+            #Error message is redundant accross each of the k-mers
+            dfall = dfall.drop(columns=['qc_message'])
             dfall = df_field_fillna(dfall)
             dfall.to_csv(output_kmer_results, **kwargs_for_pd_to_table)
             logging.info('Kmer results written to "{}".'.format(output_kmer_results))

bio_hansel/subtyper.py

@@ -228,6 +228,32 @@ def parallel_query_reads(reads: List[Tuple[List[str], str]],
     outputs = [x.get() for x in res]
     return outputs
 
+def get_kmer_fraction(row):
+    """Calculate the percentage frequency of a given position
+
+    Args:
+        df: BioHansel k-mer frequence pandas df row
+    Returns:
+        - float of percentage abundance
+    """
+    total_freq = row.total_refposition_kmer_frequency
+    return row.freq / total_freq if total_freq > 0 else 0.0
+
+def calc_kmer_fraction(df):
+    """Calculate the percentage each k-mer frequency represents for a given position
+
+    Args:
+        df: BioHansel k-mer frequence pandas df
+
+    Returns:
+        - pd.DataFrame with k-mers with kmer_fraction and total_refposition_kmer_frequency columns added
+    """
+    position_frequencies = df[['refposition','freq']].groupby(['refposition']).sum().to_dict()
+    df['total_refposition_kmer_frequency'] =  df.apply(lambda row: position_frequencies['freq'].get(row.refposition, 0), axis=1)
+    df['kmer_fraction'] = df.apply(get_kmer_fraction, axis=1)
+
+    return df
+
 
 def subtype_reads(reads: Union[str, List[str]],
                   genome_name: str,
@@ -285,9 +311,12 @@ def subtype_reads(reads: Union[str, List[str]],
     df['subtype'] = subtypes
     df['is_pos_kmer'] = ~df.kmername.str.contains('negative')
     df['is_kmer_freq_okay'] = (df.freq >= subtyping_params.min_kmer_freq) & (df.freq <= subtyping_params.max_kmer_freq)
+    #apply a scaled approach for filtering of k-mers required for high coverage amplicon data
+    df = calc_kmer_fraction(df)
+    df['is_kmer_fraction_okay'] = df.kmer_fraction >= subtyping_params.min_kmer_frac
     st.avg_kmer_coverage = df['freq'].mean()
-    st, df = process_subtyping_results(st, df[df.is_kmer_freq_okay], scheme_subtype_counts)
-    st.qc_status, st.qc_message = perform_quality_check(st, df, subtyping_params)
+    st, filtered_df = process_subtyping_results(st, df[(df.is_kmer_freq_okay & df.is_kmer_fraction_okay)], scheme_subtype_counts)
+    st.qc_status, st.qc_message = perform_quality_check(st, filtered_df, subtyping_params)
     df['file_path'] = str(st.file_path)
     df['sample'] = genome_name
     df['scheme'] = scheme_name or scheme

bio_hansel/subtyping_params.py

@@ -8,9 +8,10 @@ class SubtypingParams(object):
     min_ambiguous_kmers = attr.ib(default=3, validator=attr.validators.instance_of(int))
     max_perc_intermediate_kmers = attr.ib(default=0.05, validator=attr.validators.instance_of(float))
     min_kmer_freq = attr.ib(default=8, validator=attr.validators.instance_of((float, int)))
-    max_kmer_freq = attr.ib(default=10000, validator=attr.validators.instance_of((float, int)))
+    min_kmer_frac = attr.ib(default=0.05, validator=attr.validators.instance_of(float))
+    max_kmer_freq = attr.ib(default=1000000, validator=attr.validators.instance_of((float, int)))
     min_coverage_warning = attr.ib(default=20, validator=attr.validators.instance_of((float, int)))
-    max_degenerate_kmers = attr.ib(default=100000, validator=attr.validators.instance_of(int))
+    max_degenerate_kmers = attr.ib(default=10000000, validator=attr.validators.instance_of(int))
 
     @max_perc_missing_kmers.validator
     def _validate_max_perc_missing_kmers(self, attribute, value):

bio_hansel/utils.py

@@ -200,6 +200,8 @@ def init_subtyping_params(args: Optional[Any] = None,
             subtyping_params.min_coverage_warning = args.low_cov_warning
         if args.min_kmer_freq:
             subtyping_params.min_kmer_freq = args.min_kmer_freq
+        if args.min_kmer_frac:
+            subtyping_params.min_kmer_frac = args.min_kmer_frac
         if args.max_kmer_freq:
             subtyping_params.max_kmer_freq = args.max_kmer_freq
         if args.max_degenerate_kmers:

tests/__init__.py

@@ -19,6 +19,7 @@
                   'is_pos_kmer', 'sample', 'file_path', 'scheme', 'scheme_version', 'qc_status', 'qc_message']
 
 exp_fastq_cols = ['kmername', 'refposition', 'subtype', 'seq', 'freq', 'is_pos_kmer', 'is_kmer_freq_okay',
+                  'kmer_fraction','total_refposition_kmer_frequency','is_kmer_fraction_okay',
                   'sample', 'file_path', 'scheme', 'scheme_version', 'qc_status', 'qc_message']