For better handle different pipelines with vsearch/usearch, qiime2-deblur or qiime2-dada2. We present a unified pipelines which you could perforom different analysis with single data_input.tab
red arrow means: combine with multiple down stream tasks.
Because this pipelines are embed with qiime2 pipelines, it is better to install a qiime2 environment.
Just follow the official instruction qiime2 like that :
wget https://data.qiime2.org/distro/core/qiime2-2019.4-py36-linux-conda.yml
conda env create -n qiime2-2019.4 --file qiime2-2019.4-py36-linux-conda.yml
Just follow the official instruction vsearch like that :
wget https://github.com/torognes/vsearch/archive/v2.6.2.tar.gz
tar xzf v2.6.2.tar.gz
cd vsearch-2.6.2
./autogen.sh
./configure
make
make install # (optional) # as root or sudo make install
Requirements could follow requirements.txt
Using pip install -r requirements.txt
or install its inside the environment of qiime2
source active qiime2-2019.4
pip install -r requirements.txt
there are multiple config file need to be adjusted.
- conf of pipelines:
config/soft_db_path.py
- conf of fastq_screen db:
dir_of_fastq_screen/fastq_screen.conf
Within environment of qiime2 (if you don't want to perform qiime2 relative analysis pipelines), you could ignore this.
Just type:
source activate qiime2-2019.4
python3 main.py test -o ~/temp/16s_testdata --local-scheduler
It will run all three pipelines with testing data contained at testset directory.
Because there have --local-scheduler
, it will not monitor by luigid
. More detailed about the scheduler and luigid, please follow luigi Central Scheduler doc
After installation, you need to run testdata first to validate all software and required database is installed.
When everything is ready, you may have your own pair-end sequencing data.
Following the header and separator of config.data_input.template
, fulfill a new data_input.tab
.
With this tab, you could run:
python3 main.py run -- workflow --tab data_input.tab --odir output_dir --analysis-type otu --workers 4 --log-path output_dir/cmd_log.txt
Besides the params --tab
, --odir
, --analysis-type
, --log-path
, other params are luigi implemented.
Here describe a little bit about these params. For more detailed, you should check the documentation of luigi at luigi doc
--tab
: given a path(could be relative/absolute) ofinput_data.tab
--odir
: jus the path of output directory. a little be need to say is that, different pipelines likeotu, deblur, dada2
, it will separately located the final output of different pipelines. So don't worry using same output dir will confuse the result.--analysis-type
: for now, three options including otu, deblur, dada2 could be selected, if you want to perform all at once. You could passall
param to it. Because there are a lot of overlapped tasks among three different pipelines, it would save a lot of time than running these separately with differentodir
.--log-path
: it just record the cmd history.(optional)--workers
: it could control how many tasks could be parallel.
If you look at the config.data_input.template
, there are only three header.
Tab
is taken as separator of this input_data.tab
for better handle some weird filename.
Inside this iniput_data.tab
, you could append more columns besides the necessary three columns(sample ID R1 R2)
. This pipelines only check you have these three instead of only have these three.
If you have a multiplexed data and you want to use this pipelines. First, you need to use a demux
tool to demutiplex your data, and fulfill a data_input.tab
with generated/demutiplexed data.
If you are not sure which demux tool to use, you could use our demux pipelines instead of main pipelines.
please following README.md
at static
directory.
All parameter have been embedd into a unify directory called config
. If you want to change the path of software/database, you could see config/soft_db_path.py
. If you want to change the parameter of otu/deblur/dada2, you could see config/default_params.py
.
config/default_file_structures.py
is not yet used at pipelines, so changing it would not change the result.
Maybe because the version of r-base
in global environment is higher than the installed r-base
version inside the conda environment. So please united the version of R in global and env.
Please file questions, bugs or ideas to the Issue Tracker
Tianhua Liao email: [email protected]