def alignment_blat_oldMaster(FASTA, reference, fileName, outDir):
'''
Align a set of sequence into a reference with blat
Input:
1. FASTA: Path to FASTA file with sequences to align
2. reference: Path to the reference genome in fasta format
3. fileName: output file will be named accordingly
4. outDir: Output directory
Output:
1. SAM: Path to SAM file containing input sequences alignments or 'None' if alignment failed
'''
## Align the sequences into the reference
PSL = outDir + '/' + fileName + '.psl'
err = open(outDir + '/align.err', 'w')
command = 'blat -stepSize=5 -repMatch=2253 -minScore=20 -minIdentity=0 -noHead -out=psl ' + reference + ' ' + FASTA + ' ' + PSL
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN'
msg = 'Alignment failed'
log.step(step, msg)
return PSL
def alignment_minimap2(FASTA, index, fileName, processes, outDir):
'''
Align a set of sequence into a reference with minimap2
Input:
1. FASTA: Path to FASTA file with sequences to align
2. index: Path to the the index of the reference in .mmi format (generated with minimap2)
3. fileName: output file will be named accordingly
4. processes: Number of processes used by minimap2
5. outDir: Output directory
Output:
1. PAF: Path to PAF file containing input sequences alignments or 'None' if alignment failed
'''
## Align the sequences into the reference
# Note, condider to use -Y to get soft clippings for supplementary alignments
PAF = outDir + '/' + fileName + '.paf'
err = open(outDir + '/align.err', 'w')
command = 'minimap2 -t ' + str(
processes) + ' ' + index + ' ' + FASTA + ' > ' + PAF
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN'
msg = 'Local alignment failed'
log.step(step, msg)
return PAF
def alignment_bwa(FASTA, reference, fileName, processes, outDir):
'''
Align a set of sequence into a reference with bwa mem
Input:
1. FASTA: Path to FASTA file with sequences to align
2. reference: Path to the reference genome in fasta format (bwa mem index must be located in the same folder)
3. fileName: output file will be named accordingly
4. processes: Number of processes used by bwa mem
5. outDir: Output directory
Output:
1. SAM: Path to SAM file containing input sequences alignments or 'None' if alignment failed
'''
## Align the sequences into the reference
SAM = outDir + '/' + fileName + '.sam'
err = open(outDir + '/align.err', 'w')
command = 'bwa mem -Y -t ' + str(
processes) + ' ' + reference + ' ' + FASTA + ' > ' + SAM
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN'
msg = 'Alignment failed'
log.step(step, msg)
return SAM
def komplexityFilter(komplexityThreshold, inFasta, outFasta, outDir):
'''
Filter fasta file using komplexity tool
Input:
1. komplexityThreshold: Complexity threshold filter.
2. inFasta: input FASTA file name
3. outFasta: output FASTA file name
4. outDir: input AND output directory (it must be the same)
Output:
1. allFastas: Filteres FASTA file complete path.
'''
# Set input an output files
allFastas_all = outDir + '/' + inFasta
allFastas = outDir + '/' + outFasta
logDir = outDir + '/Logs'
unix.mkdir(logDir)
command = 'kz --filter --threshold ' + str(
komplexityThreshold
) + ' --fasta < ' + allFastas_all + ' > ' + allFastas
err = open(logDir + '/komplexity.err', 'w')
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'KOMPLEXITY'
msg = 'Komplexity filter failed. PID: ' + str(os.getpid())
log.step(step, msg)
return allFastas
def BAM2BED(BAM, outDir):
'''
Convert BAM file into BED using bedtools
Input:
1. BAM: Path to BAM file
2. outDir: Output directory
Output:
1. BED: Path to BED file
'''
## 0. Create logs directory
logDir = outDir + '/Logs'
unix.mkdir(logDir)
## 1. Convert BAM into BED
BED_path = outDir + '/alignments.bed'
err = open(logDir + '/BAM2BED.err', 'w')
command = 'bedtools bamtobed -split -i ' + BAM + ' > ' + BED_path
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'BAM2BED'
msg = 'BAM to BED conversion failed'
log.step(step, msg)
## 2. Add header to BED file
header = "#ref \t beg \t end \t name \t score \t strand \n"
with open(BED_path, 'r') as original:
data = original.read()
with open(BED_path, 'w') as modified:
modified.write(header + data)
return BED_path
def aligmentMaxNbMatches(FASTA_file, db, PAF_file, outDir):
'''
'''
# DESILENCIAAAAAR!!!
# TODO: append en el error!
err = open(outDir + '/identifyMate.err', 'w')
command = 'minimap2 ' + db + ' ' + FASTA_file + ' > ' + PAF_file
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'IDENTIFY MATE SEQ'
msg = 'Identify mate sequence failed'
log.step(step, msg)
# If PAF file is not empty
if not os.stat(PAF_file).st_size == 0:
PAFObj = formats.PAF()
PAFObj.read(PAF_file)
# Pick the identity of the aligment with highest number of matches
aligmentMaxNbMatches = PAFObj.sortNbMatches()[0]
else:
aligmentMaxNbMatches = None
return aligmentMaxNbMatches
def targeted_alignment_minimap2(FASTA, targetInterval, reference, outDir,
outFormat):
# NOTE 2020: In 2020:
# def targeted_alignment_minimap2(FASTA, targetInterval, reference, outDir):
'''
Align a set of sequences into a reference target region.
Useful for doing local realignment of reads around SV breakpoints. Much faster than whole genome realignment
Input:
1. FASTA: Path to FASTA file with sequences to align
2. targetInterval: Reference genome interval where sequences will be aligned. The interval must be provided as chr:beg-end.
3. reference: Path to the reference sequences in fasta format. An index of the reference generated with samtools faidx must be located in the same directory
4. outDir: Output directory
5. outFormat: BAM or SAM
Output:
1. BAM: Path to sorted BAM file containing input sequences alignments or 'None' if realignment failed
'''
## 0. Create logs directory
logDir = outDir + '/Logs'
unix.mkdir(logDir)
## 1. Extract the reference target region prior alignment
target = outDir + '/target.fa'
err = open(logDir + '/target.err', 'w')
command = 'samtools faidx ' + reference + ' ' + targetInterval + ' > ' + target
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'TARGET'
msg = 'Extraction of reference target region failed'
log.step(step, msg)
return None
## 2. Align the sequences into the target region
# Use -Y to get soft clippings for supplementary alignments
SAM = outDir + '/alignments.sam'
err = open(logDir + '/align.err', 'w')
command = 'minimap2 -Y -a ' + target + ' ' + FASTA + ' > ' + SAM
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN'
msg = 'Local alignment failed'
log.step(step, msg)
return None
# NOTE 2020: In 2020 these 2 lines were deleted:
if outFormat == "SAM":
return SAM
## 3. Convert SAM to sorted BAM
BAM = bamtools.SAM2BAM(SAM, outDir)
## 4. Do cleanup
unix.rm([target, SAM])
return BAM
def index_bwa(fastaPath, outDir):
'''
Wrapper to generate BWA index for fasta file
'''
err = open(outDir + '/index.err', 'w')
command = 'bwa index ' + fastaPath
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'INDEX'
msg = 'BWA indexing failed'
log.step(step, msg)
def run(self):
log.print_action_header("up")
log.step("Parsing dependencies")
# Read all dependencies and validate
all_deps = self.__parse_dependencies()
# Check to see which differ
log.step("Diffing dependencies to current")
deps_to_sync = self.__diff_to_current(all_deps)
# Sync dependencies
results = []
if len(deps_to_sync) > 0:
log.step(
"Syncing {count} dependencies".format(count=len(deps_to_sync)))
for dep in deps_to_sync:
self.download_and_apply(dep)
# Output results
log.step("Collating results")
print("")
self.__output_results(results)
def index_minimap2(fastaPath, fileName, outDir):
'''
Wrapper to generate minimap2 index for fasta file
'''
indexPath = outDir + '/' + fileName + '.mmi'
err = open(outDir + '/index.err', 'w')
command = 'minimap2 -k 11 -w 1 -d ' + indexPath + ' ' + fastaPath
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'INDEX'
msg = 'minimap2 indexing failed'
log.step(step, msg)
return indexPath
def getPAFAlign(FASTA_file, indexDb, outDir):
# Alineo el fasta consenso
# TODO ponerlo bien
PAF_file = FASTA_file.replace(".fa", "_alignments.paf")
#err = open(logDir + '/align.err', 'w')
command = 'minimap2 ' + indexDb + ' ' + FASTA_file + ' > ' + PAF_file
err = open(outDir + '/minimap2.err', 'w')
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN-INSERT'
msg = 'minimap2 alignment failed'
log.step(step, msg)
return PAF_file
def rm(paths):
'''
Delete set of files/directories. Directories are deleted recursively
Input:
1. files: list containing file/directory paths to be deleted
'''
for path in paths:
exist = os.path.exists(path)
# Only attempt to delete file if it does exists
if exist:
try:
command = ['rm', '-r', path]
subprocess.call(command)
except OSError:
step = 'ERROR'
msg = "Deletion of the file %s failed" % filePath
log.step(step, msg)
def mkdir(path):
'''
Create directory
Note: improve function to be able to create lists of directories
Input:
1. path: directory to be created
'''
exist = os.path.isdir(path)
# Only attempt to create directory if it does not exists
if not exist:
try:
command = ['mkdir', '-p', path]
subprocess.call(command)
except OSError:
step = 'ERROR'
msg = "Creation of the directory %s failed" % path
log.step(step, msg)
def alignment_blat(FASTA, reference, args, fileName, outDir):
'''
Align a set of sequence into a reference with blat
Input:
1. FASTA: Path to FASTA file with sequences to align
2. reference: Path to the reference genome in fasta format (bwa mem index must be located in the same folder)
3. args: dictionary containing blat arguments
4. fileName: output file will be named accordingly
5. outDir: Output directory
Output:
1. SAM: Path to SAM file containing input sequences alignments or 'None' if alignment failed
'''
## Align the sequences into the reference
PSL = outDir + '/' + fileName + '.psl'
err = open(outDir + '/align.err', 'w')
# Set blat arguments
blatArgs = []
if 'stepSize' in args.keys():
blatArgs.append('-stepSize=' + str(args['stepSize']))
if 'tileSize' in args.keys():
blatArgs.append('-tileSize=' + str(args['tileSize']))
command = 'blat ' + ' '.join(
blatArgs
) + ' -repMatch=2253 -minScore=20 -minIdentity=0 -noHead -out=psl ' + reference + ' ' + FASTA + ' ' + PSL
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'ALIGN'
msg = 'Alignment failed'
log.step(step, msg)
return PSL
def create_targeted_fasta(targetIntervalList, reference, outDir):
'''
Extract regions of interest from a fasta file.
Input:
1. targetIntervalList: Reference genome list of intervals to be extracted. The intervals must be provided as chr:beg-end.
2. reference: Path to fasta file. An index of the reference generated with samtools faidx must be located in the same directory
3. outDir: Output directory
Output:
1. target: Path to fasta file with sequences extarcted from intervals.
'''
## 0. Create logs directory
logDir = outDir + '/Logs'
unix.mkdir(logDir)
## 1. Extract the reference target regions
target = outDir + '/targetRegions.fa'
err = open(logDir + '/target.err', 'w')
targetRegionsPath = outDir + '/targetRegions.txt'
targetRegions = open(targetRegionsPath, 'w')
for targetInterval in targetIntervalList:
targetRegions.write(targetInterval + '\n')
targetRegions.close()
command = 'samtools faidx ' + reference + ' -r ' + targetRegionsPath + ' -o ' + target
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'TARGET'
msg = 'Extraction of reference target region failed'
log.step(step, msg)
return None
# TODO: remove targetRegionsPath file
return target
def SAM2BAM(SAM, outDir):
'''
Convert SAM file into sorted BAM and make BAM index
Input:
1. SAM: File containing alignments in SAM format
Output:
1. BAM_sorted: Sorted and indexed BAM file. BAM index located in the same directory with the extension '.bai'
'''
## 0. Create logs directory
logDir = outDir + '/Logs'
unix.mkdir(logDir)
## 1. Convert SAM into BAM
BAM = outDir + '/alignments.bam'
err = open(logDir + '/SAM2BAM.err', 'w')
command = 'samtools view -Sb ' + SAM + ' > ' + BAM
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'SAM2BAM'
msg = 'SAM to BAM conversion failed'
log.step(step, msg)
## 2. Sort bam
BAM_sorted = outDir + '/alignments.sorted.bam'
err = open(logDir + '/sort.err', 'w')
command = 'samtools sort ' + BAM + ' > ' + BAM_sorted
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'SORT'
msg = 'BAM sorting failed'
log.step(step, msg)
## 3. Index bam
BAM_index = outDir + '/alignments.sorted.bam.bai'
err = open(logDir + '/index.err', 'w')
command = 'samtools index ' + BAM_sorted + ' > ' + BAM_index
status = subprocess.call(command, stderr=err, shell=True)
if status != 0:
step = 'INDEX'
msg = 'BAM indexing failed'
log.step(step, msg)
return BAM_sorted
if ssh_key is None:
ssh_key = digitalocean.SSHKey(token=token)
ssh_key.name = key_name
ssh_key.public_key = public_key
ssh_key.create()
droplet_name = env('AZK_MID', 'azk-deploy')
droplet_name = env('BOX_NAME', droplet_name)
droplet_region = env('BOX_REGION', 'nyc3')
droplet_image = env('BOX_IMAGE', 'ubuntu-14-04-x64')
droplet_size = env('BOX_SIZE', '1gb')
droplet_backup = True if env('BOX_BACKUP', 'false') == 'true' else None
droplet_private_networking = True if env('BOX_PRIVATE_NETWORKING', 'false') == 'true' else None
log.step('Logging into DigitalOcean')
manager = digitalocean.Manager(token=token)
log.step_done()
log.step('Getting existing droplets')
droplets = manager.get_all_droplets()
log.step_done()
droplet = None
for a_droplet in droplets:
if a_droplet.name == droplet_name:
droplet = a_droplet
break
if not droplet is None and (
droplet.region['slug'] != droplet_region or
ssh_key = digitalocean.SSHKey(token=token)
ssh_key.name = key_name
ssh_key.public_key = public_key
ssh_key.create()
droplet_name = env('AZK_MID', 'azk-deploy')
droplet_name = env('BOX_NAME', droplet_name)
droplet_region = env('BOX_REGION', 'nyc3')
droplet_image = env('BOX_IMAGE', 'ubuntu-14-04-x64')
droplet_size = env('BOX_SIZE', '1gb')
droplet_backup = True if env('BOX_BACKUP', 'false') == 'true' else None
droplet_private_networking = True if env('BOX_PRIVATE_NETWORKING',
'false') == 'true' else None
log.step('Logging into DigitalOcean')
manager = digitalocean.Manager(token=token)
log.step_done()
log.step('Getting existing droplets')
droplets = manager.get_all_droplets()
log.step_done()
droplet = None
for a_droplet in droplets:
if a_droplet.name == droplet_name:
droplet = a_droplet
break
if not droplet is None and (
droplet.region['slug'] != droplet_region
