Analysis pipeline for bulk data. From fastq to anndata containing count matrix.
conda create -n bulktools -c bioconda -c defaults star subread python=3.8 -y
conda activate bulktools
pip install git+https://github.com/LouisFaure/bulktools-py.git
To work by default, the data structure should look like this:
├── fastq/ <-- the first element of the filename before '_' is the sample name
│ ├── sample1_S1_R1_L001.fastq.gz
│ └── sample2_S2_R1_L001.fastq.gz
├── genes.gtf <-- genome annotation
├── star_index/ <-- index folder created using STAR tool
│ ├── ...
bt -s star_index -g genes.gtf
usage: bt [-h] [--fq_path FQ_PATH] [--bam_path BAM_PATH] [--star_ref STAR_REF] [--gtf GTF]
[--n_threads N_THREADS] [--adata_out ADATA_OUT]
[cleanup]
Full bulk pipeline, from fastq to adata count matrix!
Performs the following: fastq -STAR-> bam -featureCounts-> anndata.h5ad
positional arguments:
cleanup remove temporary folders and files.
optional arguments:
-h, --help show this help message and exit
--fq_path FQ_PATH, -f FQ_PATH
Path for input fastq files (relative, default: fastq).
--bam_path BAM_PATH, -b BAM_PATH
Path for aligned BAMs (default: aligned).
--star_ref STAR_REF, -s STAR_REF
STAR index path.
--gtf GTF, -g GTF GTF file path for featureCounts.
--n_threads N_THREADS, -n N_THREADS
Total number of threads to use for both STAR and featureCounts.
--adata_out ADATA_OUT, -o ADATA_OUT
Path for the adata output (relative, default: adata_bulk_star.h5ad).