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analyzer-nebius.py
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analyzer-nebius.py
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# Name: GenomeSuite Analyzer
# Author: Richard Casey
# Date: 09-02-2024 (DD-MM-YYYY)
# Purpose: Human genome structural variant caller.
# Version: v.1.2.0
# Notes: In production mode, this application requires Nvidia GPU's (H100, A100, V100).
# Standard library imports
import argparse
import os
import subprocess
import sys
# Third-party imports
import boto3
from botocore.exceptions import ClientError
from botocore.exceptions import NoCredentialsError
from botocore.exceptions import PartialCredentialsError
# Local application/library specific imports
from utilities.fileUtil import clear_files
from utilities.loggerUtil import logger
def run_command(command: str) -> None:
"""Run a shell command and print the output."""
try:
result = subprocess.run(command, shell=True, check=True, capture_output=True, text=True)
logger.info(f"{result.stdout}")
logger.info(f"{result.stderr}")
except subprocess.CalledProcessError as e:
logger.error(f"ERROR: run_command failed with error: {e}")
logger.error(f"STDOUT: {e.stdout}")
logger.error(f"STDERR: {e.stderr}")
sys.exit(1)
def set_environment_variables(sample_name: str) -> None:
"""Set environment variables."""
try:
# check which server is hosting the code
home_dir = os.path.expanduser("~") # get home directory
if home_dir == '/home/seqcenter':
os.environ['BASE_DIR'] = '/home/seqcenter/analyzer' # for development ubuntu
elif home_dir == '/home/ubuntu':
os.environ['BASE_DIR'] = '/home/ubuntu/analyzer' # for production ubuntu on AWS
else:
logger.error("ERROR: unknown home directory setting")
sys.exit(1)
except ValueError as e:
logger.error(f"Error: an unexpected error occured{e}")
sys.exit(1)
# MAIN DIRS
os.environ['INPUT_DIR'] = f"{os.environ['BASE_DIR']}/data/INPUTS"
os.environ['OUTPUT_DIR'] = f"{os.environ['BASE_DIR']}/data/OUTPUTS"
# POD5
os.environ['POD5_FILES_DIR'] = f"{os.environ['INPUT_DIR']}/POD5_FILES"
# FAST5
os.environ['FAST5_FILES_DIR'] = f"{os.environ['INPUT_DIR']}/FAST5_FILES"
# # BAM
os.environ['BAM_FILES_DIR'] = f"{os.environ['INPUT_DIR']}/BAM_FILES"
os.environ['BAM_FILE'] = f"{os.environ['INPUT_DIR']}/BAM_FILES/{sample_name}.bam"
os.environ['BAM_SORTED_FILE'] = f"{os.environ['INPUT_DIR']}/BAM_FILES/{sample_name}.sorted.bam"
# REFERENCE
os.environ['REF_FILES_DIR'] = f"{os.environ['INPUT_DIR']}/REF_FILES"
os.environ['REF_FILE'] = f"{os.environ['INPUT_DIR']}/REF_FILES/GCA_009914755.4_T2T-CHM13v2.0_genomic.fna.gz"
# # MODELS
os.environ['DORADO_MODELS'] = f"{os.environ['BASE_DIR']}/dorado_models"
# ANALYZER
os.environ['ANALYZER_FILES_DIR'] = f"{os.environ['OUTPUT_DIR']}/analyzer_output"
os.environ['ANALYZER_VCF_FILE'] = f"{os.environ['ANALYZER_FILES_DIR']}/{sample_name}.vcf.gz"
# THREADS
os.environ['THREADS'] = '14'
def check_s3_bucket_exists(bucket_name: str) -> bool:
"""Check if the specified S3 bucket exists."""
s3 = boto3.client('s3')
try:
s3.head_bucket(Bucket=bucket_name)
return True
except ClientError as e:
if e.response['Error']['Code'] == '404':
return False
else:
raise
except NoCredentialsError:
logger.error("ERROR: AWS credentials not found.")
sys.exit(1)
except PartialCredentialsError:
logger.error("ERROR: Incomplete AWS credentials.")
sys.exit(1)
def dorado_models():
"""List of available dorado models"""
models = [
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mC_5hmC@v1",
"[email protected]_5mCG_5hmCG@v1",
"[email protected]_6mA@v1",
"[email protected]_6mA@v2",
"[email protected]_4mC_5mC@v1",
"[email protected]_5mC_5hmC@v1",
"[email protected]_5mCG_5hmCG@v1",
"[email protected]_6mA@v1",
"[email protected]_5mCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mC_5hmC@v1",
"[email protected]_5mCG_5hmCG@v2",
"[email protected]_5mC@v2",
"[email protected]_6mA@v2",
"[email protected]_6mA@v3",
"[email protected]_5mC_5hmC@v1",
"[email protected]_5mCG_5hmCG@v1",
"[email protected]_6mA@v1",
"[email protected]_6mA@v2",
"[email protected]_4mC_5mC@v1",
"[email protected]_5mC_5hmC@v1",
"[email protected]_5mCG_5hmCG@v1",
"[email protected]_6mA@v1",
"[email protected]_5mCG_5hmCG@v0",
"[email protected]_5mCG_5hmCG@v0",
"[email protected]_5mCG_5hmCG@v0",
"rna002_70bps_fast@v3",
"rna002_70bps_hac@v3",
"[email protected]_m6A@v1",
"[email protected]_pseU@v1",
"[email protected]_m6A_DRACH@v1",
"[email protected]_m6A@v1",
"[email protected]_pseU@v1",
]
return models
def print_dorado_models():
"""Print the list of available dorado models"""
models = dorado_models()
print("Available dorado model names:")
for model in models:
print(model)
def copy_pod5_sample_files_S3_to_EC2(bucket_name: str, sample_name: str) -> None:
"""Copy pod5 sample files from S3 to EC2.
Parameters:
--recursive : Copy files recursively. This is a required parameter.
"""
command = (
f"aws s3 cp s3://{bucket_name}/ "
f"{os.environ['POD5_FILES_DIR']} "
"--recursive"
)
run_command(command)
def copy_fast5_sample_files_S3_to_EC2(bucket_name: str, sample_name: str) -> None:
"""Copy fast5 sample files from S3 to EC2.
Parameters:
--recursive : Copy files recursively. This is a required parameter.
"""
command = (
f"aws s3 cp s3://{bucket_name}/ "
f"{os.environ['FAST5_FILES_DIR']} "
"--recursive"
)
run_command(command)
def convert_fast5_to_pod5(sample_name: str) -> None:
"""Convert FAST5 file to POD5 file.
In pod5 output directory, the default output filename is output.pod5. Rename this file using the "-s" parameter.
Parameters:
-o : POD5 output directory.
-t : Number of threads.
sample_name : The name to use for renaming the output.pod5 file.
"""
command = (
"pod5 convert fast5 "
f" {os.environ['FAST5_FILES_DIR']} "
f"-o {os.environ['POD5_FILES_DIR']} "
f"-t {os.environ['THREADS']}"
)
run_command(command)
# Rename output.pod5 file using the "-s" parameter
try:
original_file = os.path.join(os.environ['POD5_FILES_DIR'], "output.pod5")
new_file = os.path.join(os.environ['POD5_FILES_DIR'], f"{sample_name}.pod5")
os.rename(original_file, new_file)
logger.info(f"Renamed {original_file} to {new_file}")
except Exception as e:
logger.error(f"ERROR: Failed to rename output.pod5 to {sample_name}.pod5: {e}")
sys.exit(1)
def get_gpu_architecture() -> str:
"""Determine if a Nvidia GPU with architecture Volta, Ampere or Hopper is available"""
try:
# Use nvidia-smi to get GPU info
result = subprocess.run(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if result.returncode != 0:
return None # No Nvidia GPU detected
gpu_name: str = result.stdout.strip()
# Check for architectures of interest
if any(arch in gpu_name for arch in ['Volta', 'Ampere', 'Hopper', 'H100']):
return 'cuda'
else:
return 'cpu'
except FileNotFoundError:
return 'cpu' # nvidia-smi not found, so assume no GPU is present
def convert_pod5_to_bam(modelname: str) -> None:
"""Convert POD5 file to BAM file using dorado basecaller.
Note: In production mode, set "-x" to "cuda:all" so it uses Nvidia GPU's (H100, A100, V100).
Parameters:
-x : use cpu only or use gpu's. device string in format "cuda:0,...,N", "cuda:all", "metal", "cpu", etc.., [default: "cuda:all"]
"""
# Determine whether to use CPU or GPU (GPU architecture must be Volta, Ampere or Hopper)
gpu_arch = get_gpu_architecture()
# Construct appropriate dorado command for CPU or GPU
if gpu_arch == 'cuda':
command = (
"dorado basecaller "
"-x cuda:all "
f"--reference {os.environ['REF_FILE']} "
f" {os.environ['DORADO_MODELS']}/{modelname} "
f" {os.environ['POD5_FILES_DIR']} > " # this parameter is either a single file name or a directory name (confusing)
f" {os.environ['BAM_FILE']}"
)
else:
command = (
"dorado basecaller "
"-x cpu "
f"--reference {os.environ['REF_FILE']} "
f" {os.environ['DORADO_MODELS']}/{modelname} "
f" {os.environ['POD5_FILES_DIR']} > " # this parameter is either a single file name or a directory name (confusing)
f" {os.environ['BAM_FILE']}"
)
run_command(command)
def create_bam_index_file() -> None:
"""Create bam index file."""
command = (
"samtools index "
f"-@ {os.environ['THREADS']} "
f" {os.environ['BAM_FILE']}"
)
run_command(command)
def sort_bam_file() -> None:
"""Sort bam file."""
command = (
"samtools sort "
f"--threads {os.environ['THREADS']} "
f" {os.environ['BAM_FILE']} "
f"-o {os.environ['BAM_SORTED_FILE']}"
)
run_command(command)
def create_sorted_bam_index_file() -> None:
"""Create sorted bam index file."""
command = (
"samtools index "
f"-@ {os.environ['THREADS']} "
f" {os.environ['BAM_SORTED_FILE']}"
)
run_command(command)
def run_analyzer() -> None:
"""
Perform sturctural variant calling.
Parameters:
-i : Input sorted BAM file.
-v : Output analyzer VCF file
-t : Number of threads.
"""
command = (
"sniffles "
f"-i {os.environ['BAM_SORTED_FILE']} "
f"-v {os.environ['ANALYZER_VCF_FILE']} "
f"-t {os.environ['THREADS']}"
)
run_command(command)
def copy_vcf_file_to_s3(bucket_name: str, sample_name: str) -> None:
"""Copy the VCF file to the specified S3 bucket."""
try:
output_dir = os.environ['ANALYZER_FILES_DIR']
vcf_file = f"{output_dir}/{sample_name}.vcf.gz"
command = f"aws s3 cp {vcf_file} s3://{bucket_name}/"
run_command(command)
except Exception as e:
logger.error(f"ERROR: Failed to copy VCF file to S3: {e}")
sys.exit(1)
# main entry point
if __name__ == "__main__":
try:
# set S3 bucket name and sample name
parser = argparse.ArgumentParser()
parser.add_argument("-b", "--bucketname", help="Enter a bucket name for this run.")
parser.add_argument("-s", "--samplename", help="Enter a sample name for this run.")
parser.add_argument('-f', '--filetype', help='Specify the type of input file: pod5 or fast5', choices=['pod5', 'fast5'])
parser.add_argument('-m', '--modelname', help="Specify the model name to use or omit to see available models. If not specified, default model will be used.", nargs='?')
# Parse arguments
args, unknown = parser.parse_known_args()
# Check if only `-m` is provided
if args.modelname is None and not args.bucketname and not args.samplename and not args.filetype:
print_dorado_models()
sys.exit(0)
# If other required arguments are missing, show an error
if not args.bucketname or not args.samplename or not args.filetype:
logger.error("ERROR: the following arguments are required: -b/--bucketname, -s/--samplename, -f/--filetype")
sys.exit(1)
# Use default dorado model if one is not provided on command line
modelname = args.modelname if args.modelname else "[email protected]"
# Validate user supplied dorado model name against the list in dorado_models
if modelname not in dorado_models():
logger.error(f"ERROR: Model name '{modelname}' not found in the list of available models. Check your spelling for the model name.")
print_dorado_models()
sys.exit(1)
# Start the run
logger.info("Start Analyzer...")
# set env vars
try:
logger.info("Set environment variables...")
set_environment_variables(args.samplename)
except Exception as e:
logger.error(f"ERROR: Could not set environment variables {e}")
sys.exit(1)
# # delete all files in subdirectories to set initial state for application
# try:
# logger.info("Clear files...")
# clear_files()
# except Exception as e:
# logger.error(f"ERROR: Failed to clear files: {e}")
# # Check if S3 bucket exists
# try:
# if not check_s3_bucket_exists(args.bucketname):
# logger.error(f"ERROR: AWS S3 bucket '{args.bucketname}' does not exist.")
# sys.exit(1)
# except NoCredentialsError:
# logger.error("ERROR: AWS credentials not found.")
# sys.exit(1)
# except PartialCredentialsError:
# logger.error("ERROR: Incomplete AWS credentials.")
# sys.exit(1)
# except Exception as e:
# logger.error(f"ERROR: An error occurred while checking the AWS S3 bucket: {e}")
# sys.exit(1)
# # 1. copy pod5 or fast5 sample files from S3 seqcenter-samples bucket to EC2 POD5 or FAST5 directory
# if args.filetype == "pod5":
# try:
# logger.info("Copy POD5 sample files from S3 to EC2...")
# copy_pod5_sample_files_S3_to_EC2(args.bucketname, args.samplename)
# except Exception as e:
# logger.error(f"ERROR: Failed to copy POD5 sample files from S3 to EC2: {e}")
# sys.exit(1)
# elif args.filetype == "fast5":
# try:
# logger.info("Copy FAST5 sample files from S3 to EC2...")
# copy_fast5_sample_files_S3_to_EC2(args.bucketname, args.samplename)
# except Exception as e:
# logger.error(f"ERROR: Failed to copy FAST5 sample files from S3 to EC2: {e}")
# sys.exit(1)
# # 2. convert fast5 file to pod5 file
# if args.filetype == "fast5":
# try:
# logger.info("Convert fast5 to pod5...")
# convert_fast5_to_pod5(args.samplename)
# except Exception as e:
# logger.error(f"ERROR: Failed to convert fast5 to pod5: {e}")
# sys.exit(1)
# 2. convert pod5 file to bam file
try:
logger.info("Convert pod5 to bam...")
convert_pod5_to_bam(modelname)
except Exception as e:
logger.error(f"ERROR: Failed to convert pod5 to bam: {e}")
sys.exit(1)
# 3. sort bam file
try:
logger.info("Sort bam file...")
sort_bam_file()
except Exception as e:
logger.error(f"ERROR: Failed to sort bam file: {e}")
sys.exit(1)
# 4. create sorted bam index file
try:
logger.info("Create sorted bam index file...")
create_sorted_bam_index_file()
except Exception as e:
logger.error(f"ERROR: Failed to create sorted bam index file: {e}")
sys.exit(1)
# 5. perform structural variant calling
try:
logger.info("Perform structural variant calling...")
run_analyzer()
except Exception as e:
logger.error(f"ERROR: Failed to perform structural variant calling: {e}")
sys.exit(1)
# # 6. copy VCF file to S3 bucket
# try:
# logger.info("Copy VCF file to S3 bucket...")
# copy_vcf_file_to_s3(args.bucketname, args.samplename)
# except Exception as e:
# logger.error(f"ERROR: Failed to copy VCF file to S3 bucket: {e}")
# sys.exit(1)
# End the run
logger.info("Finish Analyzer...")
except Exception as e:
logger.error(f"ERROR: an error occurred in main: {e}")
sys.exit(1)