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Mtase linker column fixes #141

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Merged
SorenHeidelbach merged 2 commits into from
Nov 12, 2025
Merged

Mtase linker column fixes #141

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dabf5e4
Input column update motif_assignment.py
JSBoejer Aug 11, 2025
1a8d98b
column compatibility with new bin-motifs output
JSBoejer Nov 12, 2025
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8 changes: 4 additions & 4 deletions nanomotif/datasets/e_coli_bin-motifs.tsv
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
bin mod_type motif mod_position n_mod_bin n_nomod_bin motif_type motif_complement mod_position_complement n_mod_complement n_nomod_complement
e_coli_k12 a GATC 1 38207 42 palindrome GATC 1 38207 42
e_coli_k12 a GCACNNNNNNGTT 2 591 5 bipartite AACNNNNNNGTGC 1 586 10
e_coli_k12 m CCWGG 1 23336 759 palindrome CCWGG 1 23336 759
reference motif mod_position mod_type n_mod n_nomod motif_type motif_complement mod_position_complement n_mod_complement n_nomod_complement
e_coli_k12 GATC 1 a 38207 42 palindrome GATC 1 38207 42
e_coli_k12 GCACNNNNNNGTT 2 a 591 5 bipartite AACNNNNNNGTGC 1 586 10
e_coli_k12 CCWGG 1 m 23336 759 palindrome CCWGG 1 23336 759
46 changes: 23 additions & 23 deletions nanomotif/mtase_linker/src/motif_assignment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,13 +44,13 @@
#%% Importing bin_contig table from mmlong2
contig_bin_tsv_path = snakemake.input['contig_bin']
contig_bin_df = pd.read_csv(contig_bin_tsv_path , sep = "\t", header = None)
contig_bin_df.columns = ['contig', 'bin name']
contig_bin_df.columns = ['contig', 'reference']


#%% Merging dataframes
# Merging bin origin information into DefenseFinder based on contig number.
bin_DF_blastp_df = DefenseFinder_blastp_df.merge(contig_bin_df[['bin name', 'contig']], on = 'contig', how = 'left')
bin_DF_blastp_df_filtered = bin_DF_blastp_df.dropna(subset=['bin name'])
bin_DF_blastp_df = DefenseFinder_blastp_df.merge(contig_bin_df[['reference', 'contig']], on = 'contig', how = 'left')
bin_DF_blastp_df_filtered = bin_DF_blastp_df.dropna(subset=['reference'])

# Merging modtype table into Defensefinder table
MTase_table = bin_DF_blastp_df_filtered.merge(gene_mod_df[['gene_id', 'mod_type']], on = 'gene_id', how = 'left')
Expand Down Expand Up @@ -101,17 +101,17 @@
MTase_table_assigned.loc[:,'linked'] = False


#Group by 'bin name' and 'mod type'
Cgrouped_MTase = combined_MTase_df.groupby(['bin name', 'mod_type'], dropna=True)
grouped_nanomotif = nanomotif_table_mm50.groupby(['bin', 'assign_mod_type'])
#Group by 'reference' and 'mod type'
Cgrouped_MTase = combined_MTase_df.groupby(['reference', 'mod_type'], dropna=True)
grouped_nanomotif = nanomotif_table_mm50.groupby(['reference', 'assign_mod_type'])


#%% Assigment rule system

# Step 1: Priority 1 Assignment based on similarity to motif guess
for (bin_name, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (bin_name, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((bin_name, assign_mod_type))
for (reference, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (reference, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((reference, assign_mod_type))
for idx, row in nanomotif_group.iterrows():
# Calculate reverse complement of the motif
rev_comp_motif = reverse_complement(row['motif'])
Expand Down Expand Up @@ -158,17 +158,17 @@
MTase_table_assigned['detected_motif'] = MTase_table_assigned['detected_motif'].replace('nan', np.nan)
#%%
#%% Re-group to exclude assigned entries
grouped_nanomotif = nanomotif_table_mm50[nanomotif_table_mm50['linked'] != True].groupby(['bin', 'assign_mod_type'])
grouped_nanomotif = nanomotif_table_mm50[nanomotif_table_mm50['linked'] != True].groupby(['reference', 'assign_mod_type'])
idx_MTase_table_assigned = MTase_table_assigned[MTase_table_assigned['linked'] != True].index #Extract indicies of non assigned entries
#%%
combined_MTase_df = combined_MTase_df.loc[idx_MTase_table_assigned] #FIlter combined_MTase based on extracted indicies
Cgrouped_MTase = combined_MTase_df.groupby(['bin name', 'mod_type'], dropna=True)
Cgrouped_MTase = combined_MTase_df.groupby(['reference', 'mod_type'], dropna=True)
#%%

# Step 2: Priority 2 Assignment: based single subtypes in group
for (bin_name, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (bin_name, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((bin_name, assign_mod_type))
for (reference, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (reference, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((reference, assign_mod_type))

# Count unique motif types in nanomotif and MTase table
for idx, row in nanomotif_group.iterrows():
Expand Down Expand Up @@ -209,15 +209,15 @@
MTase_table_assigned['detected_motif'] = MTase_table_assigned['detected_motif'].replace('nan', np.nan)

#%% Re-group to exclude assigned entries
grouped_nanomotif = nanomotif_table_mm50[nanomotif_table_mm50['linked'] != True].groupby(['bin', 'assign_mod_type'])
grouped_nanomotif = nanomotif_table_mm50[nanomotif_table_mm50['linked'] != True].groupby(['reference', 'assign_mod_type'])
idx_MTase_table_assigned = MTase_table_assigned[MTase_table_assigned['linked'] != True].index #Extract indicies of non assigned entries
combined_MTase_df = combined_MTase_df.loc[idx_MTase_table_assigned] #Filter combined_MTase based on extracted indicies
Cgrouped_MTase = combined_MTase_df.groupby(['bin name', 'mod_type'], dropna=True)
Cgrouped_MTase = combined_MTase_df.groupby(['reference', 'mod_type'], dropna=True)

#%% Step 3: Assign candidate genes
for (bin_name, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (bin_name, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((bin_name, assign_mod_type))
for (reference, assign_mod_type), nanomotif_group in grouped_nanomotif:
if (reference, assign_mod_type) in Cgrouped_MTase.groups:
mtase_group = Cgrouped_MTase.get_group((reference, assign_mod_type))

# Count unique motif types in nanomotif and MTase table
for idx, row in nanomotif_group.iterrows():
Expand Down Expand Up @@ -264,11 +264,11 @@


# %%
MTase_table_assigned = MTase_table_assigned[['bin name', 'gene_id', 'contig', 'mod_type', 'sub_type', 'RM_system', 'sys_id', 'motif_type', 'REbase_ID', 'motif_guess', 'linked', 'detected_motif']]
MTase_table_assigned.columns = ['bin', 'gene_id', 'contig', 'mod_type_pred', 'sub_type_pred', 'RM_system', 'DF_system_ID', 'motif_type_pred', 'REbase_ID', 'motif_pred', 'linked', 'detected_motif']
MTase_table_assigned_cl = MTase_table_assigned.dropna(subset=['bin'])
MTase_table_assigned = MTase_table_assigned[['reference', 'gene_id', 'contig', 'mod_type', 'sub_type', 'RM_system', 'sys_id', 'motif_type', 'REbase_ID', 'motif_guess', 'linked', 'detected_motif']]
MTase_table_assigned.columns = ['reference', 'gene_id', 'contig', 'mod_type_pred', 'sub_type_pred', 'RM_system', 'DF_system_ID', 'motif_type_pred', 'REbase_ID', 'motif_pred', 'linked', 'detected_motif']
MTase_table_assigned_cl = MTase_table_assigned.dropna(subset=['reference'])

nanomotif_table_mm50 = nanomotif_table_mm50[['bin', 'mod_type', 'motif', 'mod_position', 'n_mod', 'n_nomod', 'motif_type', 'motif_complement', 'mod_position_complement', 'n_mod_complement', 'n_nomod_complement', 'linked', 'candidate_genes']]
nanomotif_table_mm50 = nanomotif_table_mm50[['reference', 'motif', 'mod_position', 'mod_type', 'n_mod', 'n_nomod', 'motif_type', 'motif_complement', 'mod_position_complement', 'n_mod_complement', 'n_nomod_complement', 'linked', 'candidate_genes']]
#%%
MTase_table_assigned_cl.to_csv(snakemake.output['MTase_assignment_table'] , sep='\t', index=False)
nanomotif_table_mm50.to_csv(snakemake.output['nanomotif_assignment_table'] , sep='\t', index=False)
Expand Down