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.gitlab-ci.yml
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......@@ -9,12 +9,6 @@ before_script:
test:
script:
# add code quality tests here
# add unit tests here
# add script tests here
#- bash tests/test_scripts_prepare_inputs_table/test.sh
#- bash tests/test_scripts_prepare_inputs_labkey/test.sh
#- bash tests/test_alfa/test.sh
# add integration tests here
- bash tests/test_integration_workflow_with_conda/test.local.sh
- bash tests/test_create_dag_image/test.sh
......
README.md
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......@@ -218,56 +218,6 @@ your run.
bash run.sh
```
### Configuring workflow runs via LabKey tables
Our lab stores metadata for sequencing samples in a locally deployed
[LabKey][labkey] instance. This repository provides two scripts that give
programmatic access to the LabKey data table and convert it to the
corresponding workflow inputs (`samples.tsv` and `config.yaml`), respectively.
As such, these scripts largely automate step 3. of the above instructions.
However, as these scripts were written specifically for the needs of our lab,
they are likely not directly usable or, at least, will require considerable
modification for other setups (e.g., different LabKey table structure).
Nevertheless, they can serve as an example for interfacing between LabKey and
your workflow.
> **NOTE:** All of the below steps assume that your current working directory
> is the repository's root directory.
1. The scripts have additional dependencies that can be installed with:
```bash
pip install -r scripts/requirements.txt
```
2. In order to gain programmatic access to LabKey via its API, a credential
file is required. Create it with the following command after replacing the
placeholder values with your real credentials (talk to your LabKey manager if
you do not have these):
```bash
cat << EOF | ( umask 0377; cat >> ${HOME}/.netrc; )
machine <remote-instance-of-labkey-server>
login <user-email>
password <user-password>
EOF
```
3. Generate the workflow configuration with the following command, after
replacing the placeholders with the appropriate values (check out the
help screen with option '--help' for further options and information):
```bash
python scripts/prepare_inputs.py \
--labkey-domain="my.labkey.service.io"
--labkey-domain="/my/project/path"
--input-to-output-mapping="scripts/prepare_inputs.dict.tsv" \
--resources-dir="/path/to/my/genome/resources" \
--output-table="config/my_run/samples.tsv" \
--config_file="config/my_run/config.yaml" \
<table_name>
```
#### Additional information
The metadata field names in the LabKey instance and those in the parameters
......@@ -328,7 +278,6 @@ Contaminant sequences | contaminant_seqs
[conda]: <https://docs.conda.io/projects/conda/en/latest/index.html>
[profiles]: <https://snakemake.readthedocs.io/en/stable/executing/cli.html#profiles>
[labkey]: <https://www.labkey.com/>
[miniconda-installation]: <https://docs.conda.io/en/latest/miniconda.html>
[rule-graph]: images/rule_graph.svg
[snakemake]: <https://snakemake.readthedocs.io/en/stable/>
......
install/environment.dev.yml
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......@@ -10,5 +10,4 @@ dependencies:
- pip:
- pandas==1.0.1
- biopython==1.76
- labkey==1.2.0
scripts/labkey_api.py deleted 100755 → 0
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# This script targets the client api version 0.4.0 and later
#
# Check the page: https://github.com/LabKey/labkey-api-python/blob/master/samples/query_examples.py
# for example about filtering in queries.
# A starting point to investigate further is here:
# https://www.labkey.org/download/clientapi_docs/javascript-api/symbols/LABKEY.Query.Filter.html
import labkey
import pandas as pd
import sys
# for convenience, load QueryFilter explicitly (avoids long lines in filter definitions)
from labkey.query import QueryFilter
if __name__ == "__main__":
# These are values of variables for which the script works
# project_name = "TEST_ABOERSCH"
# query_name = "RNA_Seq_data_template"
project_name = sys.argv[1]
query_name = sys.argv[2]
server_context = labkey.utils.create_server_context('labkey.scicore.unibas.ch', '/Zavolan Group/'+project_name, 'labkey', use_ssl=True)
schema_name = "lists"
results = labkey.query.select_rows(server_context,schema_name,query_name)
table_of_data = pd.DataFrame(results["rows"])
print(table_of_data)
scripts/prepare_inputs.dict.tsv deleted 100644 → 0
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labkey snakemake
Entry_Date entry_date
Path_Fastq_Files fastq_path
Condition_Name condition
Sample_Name sample_name
Single_Paired seqmode
Mate1_File fq1
Mate2_File fq2
Mate1_Direction mate1_direction
Mate2_Direction mate2_direction
Mate1_5p_Adapter fq1_5p
Mate1_3p_Adapter fq1_3p
Mate2_5p_Adapter fq2_5p
Mate2_3p_Adapter fq2_3p
Fragment_Length_Mean mean
Fragment_Length_SD sd
Quality_Control_Flag quality_control_flag
Checksum_Raw_FASTQ_Mate1 mate1_checksum
Checksum_Raw_FASTQ_Mate2 mate2_checksum
File_Name_Metadata_File metadata
Name_Quality_Control_File_Mate1 mate1_quality
Name_Quality_Control_File_Mate2 mate2_quality
Organism organism
TaxonID taxon_id
Strain_Isolate_Breed_Ecotype strain_name
Strain_Isolate_Breed_Ecotype_ID strain_id
Biomaterial_Provider biomaterial_provider
Source_Tissue_Name source_name
Tissue_Code tissue_code
Additional_Tissue_Description tissue_description
Genotype_Short_Name genotype_name
Genotype_Description genotype_description
Disease_Short_Name disease_name
Disease_Description disease_description
Treatment_Short_Name treatment
Treatment_Description treatment_description
Gender gender
Age age
Developmental_Stage development_stage
Passage_Number passage_number
Sample_Preparation_Date sample_prep_date
Prepared_By prepared_by
Documentation documentation
Protocol_File protocol_file
Sequencing_Date seq_date
Sequencing_Instrument seq_instrument
Library_preparation_kit library_kit
Cycles cycles
Molecule molecule
Contaminant_Sequences contaminant_seqs
BioAnalyzer_File bioanalyser_file
scripts/prepare_inputs.py deleted 100755 → 0
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#!/usr/bin/env python3
"""Create input table and config for ZARP."""
import argparse
from functools import partial
import gzip
import logging
import math
import os
import sys
from typing import Tuple
from Bio import SeqIO
import labkey
import pandas as pd
logger = logging.getLogger(__name__)
def parse_cli_args() -> argparse.Namespace:
"""
Parses command line arguments.
:returns: parsed CLI arguments
"""
parser = argparse.ArgumentParser(
description=__doc__,
)
parser.add_argument(
"table",
type=str,
default=None,
help="either local file path of input table *or* name of table on "
"LabKey instance (see 'LabKey API' options below)",
metavar="TABLE",
)
api = parser.add_argument_group("LabKey API")
api.add_argument(
"--labkey-domain",
type=str,
default=None,
help="domain of LabKey instance to query; required for obtaining "
"input table via LabKey API",
metavar="STR",
)
api.add_argument(
"--labkey-path",
type=str,
default=None,
help="path to LabKey container that includes specified input table; "
"required for obtaining input table via LabKey API",
metavar="STR",
)
io = parser.add_argument_group("input/output")
io.add_argument(
"--input-to-output-mapping",
type=argparse.FileType('r'),
default=os.path.join(
os.path.dirname(__file__),
'prepare_inputs.dict.tsv',
),
help="lookup table with mappings from input (LabKey or LabKey-like) "
"to output (Snakemake) table; default: '%(default)s'",
metavar="FILE",
)
io.add_argument(
"--resources-dir",
type=str,
default=os.getcwd(),
help="path containing the genome resources for all organisms "
"(default: %(default)s)",
metavar="DIR",
)
io.add_argument(
"--output-table",
type=argparse.FileType('w'),
default="samples.tsv",
help="output sample table for use in ZARP (default: %(default)s)",
metavar="FILE",
)
io.add_argument(
"--config-file",
type=argparse.FileType('w'),
default="config.yaml",
help="output Snakemake configuration file for use in ZARP (default: "
"%(default)s)",
metavar="FILE",
)
io.add_argument(
"--output-dir",
type=str,
default=os.getcwd(),
help="directory to which ZARP results and logs are to be written "
"(default: %(default)s)",
metavar="DIR",
)
parser.add_argument(
"--no-process-paths",
action="store_true",
default=False,
help="do not attempt to create absolute paths in output files",
)
behavior = parser.add_argument_group("workflow behavior")
behavior.add_argument(
"--trim-polya",
type=int,
choices=[True, False],
default=True,
help="cutadapt: trim poly(A) tails option (default: %(default)s)",
)
behavior.add_argument(
"--multimappers",
type=int,
default=100,
help="STAR: number of multimappers to report (default: %(default)s)",
metavar='INT',
)
behavior.add_argument(
"--soft-clip",
type=str,
default="EndToEnd",
help="STAR: soft-clipping option (default: %(default)s)",
choices=['EndToEnd', 'Local'],
)
behavior.add_argument(
"--pass-mode",
type=str,
default="None",
help="STAR: 2-pass mode option (default: %(default)s)",
choices=["None", "Basic"],
)
behavior.add_argument(
"--libtype",
type=str,
default="",
help="Salmon library type (default: %(default)s). Leave empty to infer one of 'SF', 'SR', 'ISF', 'ISR'."
"Warning: If value is provided by user, it will be applied to ALL samples. If the table contains samples from different sequencing modes this might cause errors in zarp.",
metavar="STR",
choices=["", "SF", "SR", "ISF", "ISR", "OSF", "OSR", "MSF", "MSR"]
)
report = parser.add_argument_group("report")
report.add_argument(
"--description",
type=str,
default="N/A",
help="short description to be added to the report (default: "
"%(default)s)",
metavar="STR",
)
report.add_argument(
"--logo",
type=argparse.FileType('r'),
default=None,
help="path to image file to be added to the report (default: "
"%(default)s)",
metavar="FILE",
)
report.add_argument(
"--url",
type=str,
default="N/A",
help="contact URL to be added to the report (default: %(default)s)",
metavar="STR",
)
parser.add_argument(
"-v", "--verbose",
action="store_true",
default=False,
help="print log messages to STDERR",
)
parser.add_argument(
"--debug",
action="store_true",
default=False,
help="print log and debug messages to STDERR",
)
args = parser.parse_args()
if args.logo:
args.logo.close()
args.logo = args.logo.name
else:
args.logo = ""
if (args.labkey_domain and not args.labkey_path) or \
(args.labkey_path and not args.labkey_domain):
parser.print_help()
sys.exit(
"\n[ERROR] Either none or both of '--labkey-domain' and "
"'--labkey-path' are required."
)
return args
def setup_logging(
logger: logging.Logger,
verbose: bool = False,
debug: bool = False,
) -> None:
"""
Configure logger.
:param logger: the `logging.Logger` object to configure
:param verbose: whether `logging.INFO` messages shall be logged
:param debug: whether `logging.DEBUG` messages shall be logged
:returns: None
:raises ?: TODO
"""
if debug:
logger.setLevel(logging.DEBUG)
elif verbose:
logger.setLevel(logging.INFO)
else:
logger.setLevel(logging.WARNING)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter(
"[%(asctime)-15s: %(levelname)-8s @ %(funcName)s] %(message)s"
))
logger.addHandler(handler)
def fetch_labkey_table(
domain: str,
container_path: str,
query_name: str,
context_path: str = "labkey",
schema_name: str = "lists",
) -> pd.DataFrame:
"""
Export LabKey table as Pandas data frame.
:param domain: domain of LabKey instance
:param container_path: path to LabKey container that includes the table of
interest
:param query_name: name of LabKey table to export
:context_path: required by API; usage unclear TODO
:schema_name: required by API; usage unclear TODO
:returns: Pandas data frame
:raises ?: TODO
"""
server_context = labkey.utils.create_server_context(
domain=domain,
container_path=container_path,
context_path=context_path,
use_ssl=True,
)
results = labkey.query.select_rows(
server_context=server_context,
schema_name=schema_name,
query_name=query_name,
)
input_table = pd.DataFrame(results["rows"])
return input_table
def get_read_length(file: str) -> int:
"""
Returns read length of first entry of gzipped FASTQ file.
:param file: path to gzipped FASTQ file
:returns: read length
:raises FileNotFoundError: file does not exist
:raises IsADirectoryError: file is a directory
:raises OSError: file is not gzipped
:raises PermissionError: file cannot be read
:raises ValueError: not a valid FASTQ file
"""
with gzip.open(file, "rt") as handle:
return len(next(SeqIO.parse(handle, "fastq")))
def kmer_from_read_length(
length: int,
k_max: int = 31,
k_min: int = 11,
) -> int:
"""
Given a read length, returns appropriate kmer parameter size for Salmon
(https://salmon.readthedocs.io/) or similar k-mer-based quantification
tools.
References for implementation:
https://salmon.readthedocs.io/en/latest/salmon.html#preparing-transcriptome-indices-mapping-based-mode
https://groups.google.com/d/msg/sailfish-users/fphjX7OIGzY/bMBwlCaZAgAJ
:param length: length of read in nucleotides
:param k_max: maximum allowed k-mer size
:param k_min: minimum allowed k-mer size
:returns: k_max for l > 2 * k_max, or else the maximum of k and k_min,
where k is biggest odd integer that fulfills k < l / 2
"""
k = k_max
if length < 2 * k_max + 1:
# ensure kmer is smaller than half of read length
k = math.floor((length - 1) / 2)
# ensure kmer is odd
if not k % 2:
k -= 1
if k < k_min:
k = k_min
return k
def get_libtype(directionality: str, seqmode: str) -> str:
"""
Returns libtype (https://salmon.readthedocs.io/en/latest/library_type.html) given strings indicating the
"directionality", and sequencing mode of a sequencing library, respectively.
:param directionality: direction in which library was sequenced (one of
"SENSE" and "ANTISENSE")
:param seqmode: sequencing mode(one of
"pe" and "se")
:returns: salmon code (one of 'SF', 'SR', 'ISF', 'ISR') for specified directionality;
"""
if seqmode == "pe":
option = "I"
else:
option = ""
if directionality == "SENSE":
option += "SF"
elif directionality == "ANTISENSE":
option += "SR"
else:
logger.error(
f"[ERROR] Can't infer library type."
f"Make sure directionality and sequencing mode are specified correctly."
)
sys.exit("Execution aborted.")
return option
def get_polya_adapter_seqs(directionality: str) -> Tuple[str, str]:
"""
Returns repeat oligomers for detecting and trimming of poly(A) signals from
a sequencing library, given a string indicating the library's
"directionality".
:param directionality: direction in which library was sequenced (one of
"SENSE" and "ANTISENSE")
:returns: tuple of two 15-mers to be used to detect and trim poly(A)
signals from the 3' and 5' ends of the reads of sequencing library,
respectively
"""
if directionality == 'SENSE':
three = 'AAAAAAAAAAAAAAA'
five = 'XXXXXXXXXXXXXXX'
elif directionality == 'ANTISENSE':
three = 'XXXXXXXXXXXXXXX'
five = 'TTTTTTTTTTTTTTT'
else:
three = 'XXXXXXXXXXXXXXX'
five = 'XXXXXXXXXXXXXXX'
return (three, five)
def expand_path(
*args: str,
anchor: str = os.getcwd(),
expand: bool = True,
no_abs: bool = False,
) -> str:
"""
Constructs absolute path.
Not tested with symbolic links.
:param args: path fragments which will be joined to the anchor from left
to right
:param anchor: path relative to which the path fragments in *args shall
be interpreted; can be absolute or relative; in the latter case, it is
interpreted relative to the current working directory; if path
fragments evaluate to absolute path (either before or after expansion),
the path will be returned without considering the anchor
:param expand: whether environment variables and user directories (e.g,
`~`) shall be expanded
:param join_only: path fragments in args are joined, but no further
processing is done
:returns: absolute path
"""
suffix = os.path.join(*args)
if no_abs:
return suffix
if os.path.isabs(suffix):
return os.path.normpath(suffix)
if expand:
suffix = os.path.expanduser(
os.path.expandvars(
suffix
)
)
if os.path.isabs(suffix):
return os.path.normpath(suffix)
anchor = os.path.expanduser(
os.path.expandvars(
anchor
)
)
path = os.path.join(anchor, suffix)
return os.path.normpath(path)
def main(args):
"""
Create input table and config for ZARP.
"""
setup_logging(
logger=logger,
verbose=args.verbose,
debug=args.debug,
)
# get input table from LabKey or CLI
if args.labkey_domain:
logger.info(
f"Fetching input table from LabKey instance "
"'{args.labkey_domain}'..."
)
input_table = fetch_labkey_table(
domain=args.labkey_domain,
container_path=args.labkey_path,
query_name=args.table,
)
labkey_table = expand_path(
'.'.join([args.output_table.name, "labkey"])
)
input_table.to_csv(
labkey_table,
sep='\t',
index=False,
)
from_api = True
else:
logger.info(f"Reading input table from file '{args.table}'...")
input_table = pd.read_csv(
args.table,
header=0,
sep='\t',
index_col=None,
comment='#',
engine='python',
)
from_api = False
# get LabKey to Snakemake sample table field mappings
input_dict = pd.read_csv(
args.input_to_output_mapping,
header=0,
sep='\t',
index_col=None,
comment='#',
engine='python',
)
args.input_to_output_mapping.close()
input_dict.set_index('snakemake', inplace=True, drop=True)
# create Snakemake table
logger.info("Creating Snakemake input table...")
snakemake_table = pd.DataFrame()
for index, row in input_table.iterrows():
# extract data from LabKey-like table
lk_sample_name = row[input_dict.loc['sample_name', 'labkey']]
lk_condition = row[input_dict.loc['condition', 'labkey']]
lk_seqmode = row[input_dict.loc['seqmode', 'labkey']]
lk_fastq_path = row[input_dict.loc['fastq_path', 'labkey']]
lk_fq1 = row[input_dict.loc['fq1', 'labkey']]
lk_fq2 = row[input_dict.loc['fq2', 'labkey']]
lk_fq1_3p = row[input_dict.loc['fq1_3p', 'labkey']]
lk_fq1_5p = row[input_dict.loc['fq1_5p', 'labkey']]
lk_fq2_3p = row[input_dict.loc['fq2_3p', 'labkey']]
lk_fq2_5p = row[input_dict.loc['fq2_5p', 'labkey']]
lk_organism = row[input_dict.loc['organism', 'labkey']]
lk_sd = row[input_dict.loc['sd', 'labkey']]
lk_mean = row[input_dict.loc['mean', 'labkey']]
lk_mate1_direction = row[input_dict.loc['mate1_direction', 'labkey']]
lk_mate2_direction = row[input_dict.loc['mate2_direction', 'labkey']]
# extract, infer or convert to Snakemake input format
if from_api and not os.path.isabs(lk_fastq_path):
anchor = os.getcwd()
logger.warning(
f"[WARNING] Don't know how to interpret relative paths "
"inside LabKey table. Trying with current working directory "
f"'{anchor}' as an anchor, but it may be better to use"
"absolute paths wherever possible..."
)
else:
anchor = os.path.abspath(os.path.dirname(args.table))
sample = "_".join([lk_sample_name, lk_condition])
if lk_seqmode == 'PAIRED':
seqmode = 'pe'
fq2 = expand_path(
lk_fastq_path,
lk_fq2,
anchor=anchor,
)
elif lk_seqmode == 'SINGLE':
seqmode = 'se'
fq2 = "XXXXXXXXXXXXXXX"
else:
logger.error(
f"[ERROR] Illegal sequencing mode '{lk_seqmode}' in row "
f"{index+1}."
)
sys.exit("Execution aborted.")
fq1 = expand_path(
lk_fastq_path,
lk_fq1,
anchor=anchor,
)
read_length = get_read_length(fq1)
index_size = read_length - 1
kmer = kmer_from_read_length(read_length)
fq1_3p = lk_fq1_3p
fq1_5p = lk_fq1_5p
fq2_3p = lk_fq2_3p
fq2_5p = lk_fq2_5p
organism = lk_organism.replace(' ', '_').lower()
gtf = expand_path(
args.resources_dir,
organism,
'annotation.gtf',
)
genome = expand_path(
args.resources_dir,
organism,
'genome.fa',
)
sd = lk_sd
mean = lk_mean
fq1_polya_3p, fq1_polya_5p = get_polya_adapter_seqs(lk_mate1_direction)
fq2_polya_3p, fq2_polya_5p = get_polya_adapter_seqs(lk_mate2_direction)
# construct row in Snakemake input table
snakemake_table.loc[index, 'sample'] = sample
snakemake_table.loc[index, 'seqmode'] = seqmode
snakemake_table.loc[index, 'fq1'] = fq1
snakemake_table.loc[index, 'fq2'] = fq2
snakemake_table.loc[index, 'index_size'] = index_size
snakemake_table.loc[index, 'kmer'] = kmer
snakemake_table.loc[index, 'fq1_3p'] = fq1_3p
snakemake_table.loc[index, 'fq1_5p'] = fq1_5p
snakemake_table.loc[index, 'fq2_3p'] = fq2_3p
snakemake_table.loc[index, 'fq2_5p'] = fq2_5p
snakemake_table.loc[index, 'organism'] = organism
snakemake_table.loc[index, 'gtf'] = gtf
snakemake_table.loc[index, 'genome'] = genome
snakemake_table.loc[index, 'sd'] = sd
snakemake_table.loc[index, 'mean'] = mean
# add CLI argument-dependent parameters
snakemake_table.loc[index, 'multimappers'] = args.multimappers
snakemake_table.loc[index, 'soft_clip'] = args.soft_clip
snakemake_table.loc[index, 'pass_mode'] = args.pass_mode
if not args.libtype:
snakemake_table.loc[index, 'libtype'] = get_libtype(lk_mate1_direction, seqmode)
elif args.libtype in ['SF', 'SR', 'ISF', 'ISR', 'OSF', 'OSR', 'MSF', 'MSR']:
snakemake_table.loc[index, 'libtype'] = args.libtype
logger.warning(
f"Library type {args.libtype} set for sample {sample}."
)
if args.trim_polya is True:
snakemake_table.loc[index, 'fq1_polya_3p'] = fq1_polya_3p
snakemake_table.loc[index, 'fq1_polya_5p'] = fq1_polya_5p
snakemake_table.loc[index, 'fq2_polya_3p'] = fq2_polya_3p
snakemake_table.loc[index, 'fq2_polya_5p'] = fq2_polya_5p
# adjust sample table format
snakemake_table.fillna('XXXXXXXXXXXXXXX', inplace=True)
snakemake_table = snakemake_table.astype(
{
"sd": int,
"mean": int,
"multimappers": int,
"kmer": int,
"index_size": int,
}
)
# write Snakemake sample table
logger.info("Writing Snakemake input table...")
snakemake_table.to_csv(
args.output_table,
sep='\t',
header=True,
index=False)
args.output_table.close()
# compile entries for Snakemake config file
logger.info("Creating Snakemake config file...")
results_dir = expand_path(
args.output_dir,
"results",
)
log_dir = expand_path(
args.output_dir,
"logs",
)
kallisto_indexes = expand_path(
results_dir,
"kallisto_indexes",
)
salmon_indexes = expand_path(
results_dir,
"salmon_indexes",
)
star_indexes = expand_path(
results_dir,
"star_indexes",
)
alfa_indexes = expand_path(
results_dir,
"alfa_indexes",
)
# write Snakemake config file
logger.info("Writing Snakemake config file...")
config_file_content = f'''---
samples: "{expand_path(args.output_table.name)}"
output_dir: "{results_dir}"
log_dir: "{log_dir}"
kallisto_indexes: "{kallisto_indexes}"
salmon_indexes: "{salmon_indexes}"
star_indexes: "{star_indexes}"
alfa_indexes: "{alfa_indexes}"
report_description: "{args.description}"
report_logo: "{args.logo}"
report_url: "{args.url}"
...
'''
args.config_file.write(config_file_content)
args.config_file.close()
if __name__ == '__main__':
args = parse_cli_args()
# Set default according to CLI arg
expand_path = partial(expand_path, no_abs=args.no_process_paths) # type: ignore
main(args)
logger.info("Program completed successfully.")
sys.exit(0)
scripts/requirements.txt deleted 100644 → 0
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biopython==1.76
labkey==1.2.0
pandas==0.25.3
tests/test_alfa/expected_output.md5 deleted 100644 → 0
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90e42aa46890e9cd0a47800428699fbf results/alfa_indexes/homo_sapiens/75/ALFA/sorted_genes.stranded.ALFA_index
ccc3cf5a57fddb0d469e597d4376b1bf results/alfa_indexes/homo_sapiens/75/ALFA/sorted_genes.unstranded.ALFA_index
e5959524a2daf35da9249fb313920315 results/samples/paired_end_R1_on_minus_antisense/ALFA/Unique/paired_end_R1_on_minus_antisense.ALFA_feature_counts.tsv
c406c1800d3690dd774aaa7e3c190523 results/samples/paired_end_R1_on_minus_sense/ALFA/Unique/paired_end_R1_on_minus_sense.ALFA_feature_counts.tsv
c406c1800d3690dd774aaa7e3c190523 results/samples/paired_end_R1_on_plus_antisense/ALFA/Unique/paired_end_R1_on_plus_antisense.ALFA_feature_counts.tsv
e5959524a2daf35da9249fb313920315 results/samples/paired_end_R1_on_plus_sense/ALFA/Unique/paired_end_R1_on_plus_sense.ALFA_feature_counts.tsv
e5959524a2daf35da9249fb313920315 results/samples/paired_end_R1_on_minus_antisense/ALFA/UniqueMultiple/paired_end_R1_on_minus_antisense.ALFA_feature_counts.tsv
c406c1800d3690dd774aaa7e3c190523 results/samples/paired_end_R1_on_minus_sense/ALFA/UniqueMultiple/paired_end_R1_on_minus_sense.ALFA_feature_counts.tsv
c406c1800d3690dd774aaa7e3c190523 results/samples/paired_end_R1_on_plus_antisense/ALFA/UniqueMultiple/paired_end_R1_on_plus_antisense.ALFA_feature_counts.tsv
e5959524a2daf35da9249fb313920315 results/samples/paired_end_R1_on_plus_sense/ALFA/UniqueMultiple/paired_end_R1_on_plus_sense.ALFA_feature_counts.tsv
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_antisense/ALFA/UniqueMultiple/paired_end_R1_on_minus_antisense.UniqueMultiple.minus.bg
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5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_antisense/ALFA/UniqueMultiple/paired_end_R1_on_plus_antisense.UniqueMultiple.plus.bg
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8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_minus_antisense/ALFA/Unique/paired_end_R1_on_minus_antisense.Unique.plus.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_minus_sense/ALFA/Unique/paired_end_R1_on_minus_sense.Unique.minus.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_sense/ALFA/Unique/paired_end_R1_on_minus_sense.Unique.plus.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_antisense/ALFA/Unique/paired_end_R1_on_plus_antisense.Unique.minus.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_antisense/ALFA/Unique/paired_end_R1_on_plus_antisense.Unique.plus.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_sense/ALFA/Unique/paired_end_R1_on_plus_sense.Unique.minus.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_sense/ALFA/Unique/paired_end_R1_on_plus_sense.Unique.plus.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_antisense/STAR_coverage/paired_end_R1_on_minus_antisense_Signal.UniqueMultiple.str1.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_minus_antisense/STAR_coverage/paired_end_R1_on_minus_antisense_Signal.UniqueMultiple.str2.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_antisense/STAR_coverage/paired_end_R1_on_minus_antisense_Signal.Unique.str1.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_minus_antisense/STAR_coverage/paired_end_R1_on_minus_antisense_Signal.Unique.str2.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_sense/STAR_coverage/paired_end_R1_on_minus_sense_Signal.UniqueMultiple.str1.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_minus_sense/STAR_coverage/paired_end_R1_on_minus_sense_Signal.UniqueMultiple.str2.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_minus_sense/STAR_coverage/paired_end_R1_on_minus_sense_Signal.Unique.str1.out.bg
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8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_antisense/STAR_coverage/paired_end_R1_on_plus_antisense_Signal.UniqueMultiple.str1.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_antisense/STAR_coverage/paired_end_R1_on_plus_antisense_Signal.UniqueMultiple.str2.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_antisense/STAR_coverage/paired_end_R1_on_plus_antisense_Signal.Unique.str1.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_antisense/STAR_coverage/paired_end_R1_on_plus_antisense_Signal.Unique.str2.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_sense/STAR_coverage/paired_end_R1_on_plus_sense_Signal.UniqueMultiple.str1.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_sense/STAR_coverage/paired_end_R1_on_plus_sense_Signal.UniqueMultiple.str2.out.bg
8e23d52d7f635d927e292174f33168eb results/samples/paired_end_R1_on_plus_sense/STAR_coverage/paired_end_R1_on_plus_sense_Signal.Unique.str1.out.bg
5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_sense/STAR_coverage/paired_end_R1_on_plus_sense_Signal.Unique.str2.out.bg
tests/test_alfa/test.sh deleted 100755 → 0
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#!/bin/bash
# Tear down test environment
cleanup () {
rc=$?
rm -rf .java/
rm -rf .snakemake/
rm -rf logs/
rm -rfv results/alfa_indexes/
rm -rfv results/*/*/ALFA/
rm -rfv results/*/*/STAR_coverage/
rm -rfv results/ALFA/
cd $user_dir
echo "Exit status: $rc"
}
trap cleanup EXIT
# Set up test environment
set -eo pipefail # ensures that script exits at first command that exits with non-zero status
set -u # ensures that script exits when unset variables are used
set -x # facilitates debugging by printing out executed commands
user_dir=$PWD
script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)"
cd $script_dir
# Run tests
snakemake \
--snakefile="../../Snakefile" \
--configfile="../input_files/config_alfa.yaml" \
--cores=4 \
--printshellcmds \
--rerun-incomplete \
--use-singularity \
--singularity-args="--bind ${PWD}/../input_files,${PWD}/../../images" \
--verbose \
--notemp \
--no-hooks \
results/ALFA/ALFA_plots_mqc.png
# Check md5 sum of some output files
find results/ -type f -name \*\.gz -exec gunzip '{}' \;
find results/ -type f -name \*\.zip -exec sh -c 'unzip -o {} -d $(dirname {})' \;
md5sum --check "expected_output.md5"
tests/test_scripts_prepare_inputs_labkey/expected_output.md5 deleted 100644 → 0
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− 2
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aa583b9bad45eeb520d9d624cca0af78 samples.tsv
c4cda83b069eb7ccb16547e1a9cdb34a config.yaml
\ No newline at end of file
tests/test_scripts_prepare_inputs_labkey/test.sh deleted 100755 → 0
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− 55
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#!/bin/bash
# Scripts requires environment variables 'LABKEY_HOST', 'LABKEY_USER' and
# 'LABKEY_PASS' to be set with the appropriate values
# Tear down test environment
cleanup () {
rc=$?
rm -rf ${HOME}/.netrc
rm -rf .snakemake/
rm -rf config.yaml
rm -rf samples.tsv.labkey
rm -rf samples.tsv
cd $user_dir
echo "Exit status: $rc"
}
trap cleanup EXIT
# Set up test environment
set -eo pipefail # ensures that script exits at first command that exits with non-zero status
set -u # ensures that script exits when unset variables are used
set -x # facilitates debugging by printing out executed commands
user_dir=$PWD
script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)"
cd $script_dir
cat << EOF | ( umask 0377; cat >> ${HOME}/.netrc; )
machine ${LABKEY_HOST}
login ${LABKEY_USER}
password ${LABKEY_PASS}
EOF
# Run tests
python "../../scripts/prepare_inputs.py" \
--labkey-domain="${LABKEY_HOST}" \
--labkey-path="/Zavolan Group/TEST_LABKEY" \
--input-to-output-mapping="../../scripts/prepare_inputs.dict.tsv" \
--resources-dir="../input_files" \
--output-table="samples.tsv" \
--config-file="config.yaml" \
--multimappers='10' \
--logo="../../images/logo.128px.png" \
--debug \
"RNA_Seq_data_template_raw"
# Check if dry run completes
snakemake \
--snakefile="../../Snakefile" \
--configfile="config.yaml" \
--dryrun \
--verbose
#md5sum --check "expected_output.md5"
# MD5 sums obtained with command:
# md5sum config.yaml samples.tsv > expected_output.md5
md5sum config.yaml samples.tsv
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