Snakemake Workflow for Assigning New MAGs to Predefined Groups Based on Whole-Genome Similarity

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assign-mags
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A Snakemake workflow for assigning new MAGs to already-defined groups based on whole-genome similarity.

I suggest using this filtering workflow before running this one.

Code Snippets

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import os, glob
from turtle import distance
import pandas as pd
from collections import defaultdict
from multiprocessing import Pool


from snakemake.shell import shell
import logging, traceback

logging.basicConfig(
    filename=snakemake.log[0],
    level=logging.INFO,
    format="%(asctime)s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logging.captureWarnings(True)


def handle_exception(exc_type, exc_value, exc_traceback):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return

    logging.error(
        "".join(
            [
                "Uncaught exception: ",
                *traceback.format_exception(exc_type, exc_value, exc_traceback),
            ]
        )
    )


# Install exception handler
sys.excepthook = handle_exception

# Start script

logger.info('Starting preparing input data!')

distance_df = pd.read_csv(snakemake.input.distance,sep='\t')

distance_df['hash_found'] = distance_df['hash_ratio'].str.split("/",expand=True)[0].astype('int')
distance_df['hash_total'] = distance_df['hash_ratio'].str.split("/",expand=True)[1].astype('int')
distance_df['hash_ratio'] = distance_df['hash_found']/distance_df['hash_total']

group_def = pd.read_csv(snakemake.config['group_def'],sep='\t')
group_def.subspecies = group_def.subspecies.astype('str').str.rjust(10,'0')

distance_df['target_genome'] = distance_df['target'].str.rsplit("/",1,expand=True)[1].str.rsplit(".",2,expand=True)[0]

distance_df = distance_df.merge(group_def,left_on='target_genome',right_on='genome',how='left')
logger.info('Finished preparing input data!')

output_df = pd.DataFrame()
short_df = pd.DataFrame()

def assign_group(query_MAG):
    short_output_dict = defaultdict()
    temp_df = distance_df.loc[distance_df['query'] == query_MAG]  
    subsp_dist = defaultdict()
    for subsp in temp_df.subspecies.unique():
        df = temp_df.loc[temp_df['subspecies'] == subsp]
        df = df.loc[df['hash_found'] > int(snakemake.config['min_hash'])]
        if df.shape[0] > 0:
            subsp_dist[subsp] = df['dist'].median()
    dist_df = pd.DataFrame.from_dict(subsp_dist,orient='index')
    dist_df.columns = ['median_dist']
    dist_df = dist_df.reset_index()
    dist_df = dist_df.rename(columns={'index':'subsp'})
    dist_df['query'] = query_MAG

    chosen_subsp = dist_df.sort_values(by='median_dist',ascending=True).iloc[0]['subsp']
    short_output_dict[query_MAG] = chosen_subsp
    short_output_df = pd.DataFrame.from_dict(short_output_dict,orient='index')
    short_output_df.columns = ['subsp']
    short_output_df = short_output_df.reset_index()
    short_output_df = short_output_df.rename(columns={'index':'query'})
    short_output_df['median_dist'] = dist_df.loc[dist_df['subsp'] == chosen_subsp]['median_dist'].iloc[0]

    logger.info(f"Finished assigning group for {query_MAG}")

    return dist_df, short_output_df

all_queries = distance_df['query'].unique().tolist()
logger.info('Starting assigning groups!')

for query in all_queries:
    dist_df, short_output_df = assign_group(query)
    output_df = pd.concat([output_df,dist_df])
    short_df = pd.concat([short_df, short_output_df])

cols = list(output_df.columns)
cols = [cols[-1]] + cols[:-1]
output_df = output_df[cols]

output_df.to_csv(snakemake.output.full_output,sep='\t',index=False)

short_df.to_csv(snakemake.output.short_output, sep='\t', index=False)

logger.info('Finished assigning groups!')
Python Snakemake Pandas From line 1 of scripts/assign_group.py 
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import os, glob
import pandas as pd
from collections import defaultdict

from snakemake.shell import shell
import logging, traceback

logging.basicConfig(
    filename=snakemake.log[0],
    level=logging.INFO,
    format="%(asctime)s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logging.captureWarnings(True)


def handle_exception(exc_type, exc_value, exc_traceback):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return

    logging.error(
        "".join(
            [
                "Uncaught exception: ",
                *traceback.format_exception(exc_type, exc_value, exc_traceback),
            ]
        )
    )


# Install exception handler
sys.excepthook = handle_exception

# Start script
output_df = pd.DataFrame()

for distance_df in snakemake.input.all_input:
    MAG = distance_df.split("/")[-1].split(".")[0]
    if os.path.getsize(distance_df) > 0:
        df = pd.read_csv(distance_df,sep='\t',names=['query','target','dist','p_value','hash_ratio'],header=None)
        output_df = pd.concat([output_df,df],axis=0)
    else:
        logger.warning(f"Table for MAG {MAG} is empty!")

output_df.to_csv(snakemake.output.distance_df,sep='\t',index=False)
Python Snakemake Pandas From line 1 of scripts/concat_queries.py 
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import os
import pandas as pd

from snakemake.shell import shell
import logging, traceback

logging.basicConfig(
    filename=snakemake.log[0],
    level=logging.INFO,
    format="%(asctime)s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logging.captureWarnings(True)


def handle_exception(exc_type, exc_value, exc_traceback):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return

    logging.error(
        "".join(
            [
                "Uncaught exception: ",
                *traceback.format_exception(exc_type, exc_value, exc_traceback),
            ]
        )
    )


# Install exception handler
sys.excepthook = handle_exception

# Start script
os.makedirs(snakemake.output.batch_dir, exist_ok=True)

paths = snakemake.config["MAGS_path_table"]

paths_df = pd.read_csv(paths, sep="\t")[['path','group']]


# Split based on batch_size in random batches

groups = paths_df.group.unique()
for group in groups:
    family_df = paths_df.loc[paths_df["group"] == group]
    names = (
        family_df.path.str.rsplit("/", 1, expand=True)[1]
        .str.rsplit(".", 2, expand=True)[0]
        .values.tolist()
    )
    output_df = pd.DataFrame()
    output_df["F"] = family_df["path"].values.tolist()
    group = group.lstrip("f__")
    output_df.to_csv(f"{snakemake.output.batch_dir}/{group}.tsv", sep='\t',index=False,header=False)
    logging.info(f"Created batch file {snakemake.output.batch_dir}/{group}.tsv")
Python Snakemake Pandas From line 1 of scripts/create_batch_file.py 
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import os
import pandas as pd

from snakemake.shell import shell
import logging, traceback

logging.basicConfig(
    filename=snakemake.log[0],
    level=logging.INFO,
    format="%(asctime)s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logging.captureWarnings(True)


def handle_exception(exc_type, exc_value, exc_traceback):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return

    logging.error(
        "".join(
            [
                "Uncaught exception: ",
                *traceback.format_exception(exc_type, exc_value, exc_traceback),
            ]
        )
    )


# Install exception handler
sys.excepthook = handle_exception

# Start script
os.makedirs(snakemake.output.query_dir, exist_ok=True)

df_path = snakemake.config['query_paths']
df = pd.read_csv(df_path,sep='\t')

for MAG in df.Genome:
    temp_df = df.query("Genome == @MAG")
    path = temp_df['genome_file'].iloc[0]
    if os.path.exists(path):
        temp_df.to_csv(f"{snakemake.output.query_dir}/{MAG}.tsv", sep='\t',index=False)
    else:
        logger.warning(f"Path not found for {path}")
Python Snakemake Pandas From line 1 of scripts/query_MAGs.py 
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import os, glob
import pandas as pd

from snakemake.shell import shell
import logging, traceback

logging.basicConfig(
    filename=snakemake.log[0],
    level=logging.INFO,
    format="%(asctime)s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logging.captureWarnings(True)


def handle_exception(exc_type, exc_value, exc_traceback):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return

    logging.error(
        "".join(
            [
                "Uncaught exception: ",
                *traceback.format_exception(exc_type, exc_value, exc_traceback),
            ]
        )
    )


# Install exception handler
sys.excepthook = handle_exception

# Start script

df= pd.read_csv(snakemake.input.query_MAG,sep='\t')

path = df.iloc[0]['genome_file']
group = df.iloc[0]['group']


if f"{group}.sketch" in os.listdir("output/reference_sketches"):

    shell(f"bindash sketch --nthreads={snakemake.threads} --kmerlen={snakemake.params.kmer_len} --outfname={snakemake.resources.tmpdir}/{snakemake.wildcards.MAG}.sketch {path} &> {snakemake.log}")
    shell(f"bindash dist --nthreads={snakemake.threads} --outfname={snakemake.output.result} {snakemake.resources.tmpdir}/{snakemake.wildcards.MAG}.sketch output/reference_sketches/{group}.sketch 2>> {snakemake.log}")

else:
    logger.warning("Group was not found in reference! Skipping it . . .")
    shell(f"touch {snakemake.output.result}")
Python Snakemake Pandas From line 1 of scripts/sketch_query.py 
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script:
    "scripts/create_batch_file.py"
SnakeMake From line 18 of workflow/Snakefile 
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shell:
    "bindash sketch --nthreads={threads} --kmerlen={params.kmer_len} --listfname={input.MAG_list} --outfname={output.sketch} &> {log}"
SnakeMake From line 37 of workflow/Snakefile 
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script:
    "scripts/query_MAGs.py"
SnakeMake From line 63 of workflow/Snakefile 
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script:
    "scripts/sketch_query.py"
SnakeMake From line 82 of workflow/Snakefile 
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script:
    "scripts/concat_queries.py"
SnakeMake From line 104 of workflow/Snakefile 
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script:
    "scripts/assign_group.py"
SnakeMake From line 122 of workflow/Snakefile
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Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/trickovicmatija/assign-MAGs
Name: assign-mags
Version: 1
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Copyright: Public Domain
License: MIT License
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