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Commit 25546e95 authored by BIOPZ-Bak Maciej's avatar BIOPZ-Bak Maciej
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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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scripts/requirements.txt deleted 100644 → 0
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biopython==1.76
labkey==1.2.0
pandas==0.25.3
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90e42aa46890e9cd0a47800428699fbf results/alfa_indexes/homo_sapiens/75/ALFA/sorted_genes.stranded.ALFA_index
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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_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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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
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5e90c760710980f4f4866dbe9aa32c6c results/samples/paired_end_R1_on_plus_sense/STAR_coverage/paired_end_R1_on_plus_sense_Signal.Unique.str2.out.bg
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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 $(dirname {})' \;
md5sum --check "expected_output.md5"
tests/test_scripts_prepare_inputs_labkey/expected_output.md5 deleted 100644 → 0
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aa583b9bad45eeb520d9d624cca0af78 samples.tsv
c4cda83b069eb7ccb16547e1a9cdb34a config.yaml
\ No newline at end of file
tests/test_scripts_prepare_inputs_labkey/test.sh deleted 100755 → 0
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#!/bin/bash
# Scripts requires environment variables 'LABKEY_HOST', 'LABKEY_USER' and
# 'LABKEY_PASS' to be set with the appropriate values
# Tear down test environment
cleanup () {
rc=$?
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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