The pipeline for metatranscriptomics analysis
To reproduce the output, you need to use Bioconda.
Please follow the instruction here to install Bioconda.
And then you need to install snakemake and Python package click and pandas:
conda install snakemake=5.5.4
conda install Click=7.0
conda install pandas=0.25.0After this has been done, download the pipeline onto your system:
git clone [email protected]:dawnmy/metat.gitAll the paths must be either relative path to the parent directory of config folder or absolute path.
dataset: mouse # name for the dataset
fq_dir: ../data/seq # dir of the raw FASTQ files
out_dir: ../outputs # dir to put the results
paired: true # is paried end reads?
suffix: # the suffixs of reads; the suffix is the comman suffix for all samples besides the sample name
- _R1.fastq.gz # please keep the hyphen sign
- _R2.fastq.gz
# host_ref: ../ref/mouse.fa
ref: ../ref/mouse_gut_gene_catalog.fa # The gene catalog for quantifying the expression
threads: 20snakemake -s metat.smk -j 20 --use-conda-s to specify the pipeline file, and -j to set the number of threads to use and --use-conda to
let the pipeline install required softwares with specified version. The conda ENVs will be created under
the path of the program by default. The program may take ten minutes to create the ENV for the first time.
If you do not wish to create the conda ENV in the working directory,
please use --conda-prefix parameter to specify the desired path to create the conda ENV.
If you use SGE for the job submission, you can use the following cmd:
snakemake -s metat.smk --latency-wait 30 --use-conda -c "qsub -cwd -q <the job submission queue> \
-pe multislot {threads} -i /dev/null -e <dir for std error logs> -o <dir for std output logs> \
-v PATH" -j 2outputs
└── mouse
├── data
│ ├── bam
│ └── qc_fq
│ |── mrna
│ └── rrna
├── reports
│ ├── benchmarks
│ ├── bwa
│ ├── fastp
│ └── samtools
└── results
└── count