About Biosnaker

Biosnaker simplifies bioinformatic process by building, versioning, and hosting of your docs, automatically.

Why use this tool？

├── data
├── results
└── scripts
    ├── bowtie2.sh
    ├── cluster.json
    ├── cluster.yaml
    ├── config
    │   ├── bioinfo.yaml
    │   ├── cluster.json
    │   ├── cluster.yaml
    │   ├── config.yaml
    │   └── jobscript.sh
    ├── config.yaml
    ├── Count_matrix.py
    ├── Count_matrix.sh
    ├── debug1.sh
    ├── deseq2.R
    ├── envs
    │   ├── bioinfo.yaml
    │   ├── py.yaml
    │   └── sc.yaml
    ├── generate_sample_info.py
    ├── jobscript.sh
    ├── logs
    │   └── debug.log
    ├── mamba.sh
    ├── merge_counts.yaml
    ├── modify_files.sh
    ├── py.yaml
    ├── README.md
    ├── refer
    │   ├── genetic_screen_human_metabolism_genes.txt
    │   ├── human_metabolic_genes.csv
    │   └── pathway_class_information.csv
    ├── remove_rRNA.sh
    ├── Rscripts
    │   ├── heatmap.r
    │   ├── multi_group_DEA.R
    │   ├── PLSDA.R
    │   ├── README.md
    │   └── RNA-seq-DEA-function-repos.R
    ├── run_snakemake.pbs
    └── snakefile

First time here?

Biosnaker is a Snakemake workflow, aimed at performing a typical RNA-seq workflow in a reproducible, automated, and partially contained manner. It is implemented such that alternative or similar analysis can be added or removed.

Biosnaker consists of a Snakefile, a conda environment file (envs/environment.yaml) a configuration file (config.yaml) and a set of R scripts, to perform quality control, preprocessing and differential expression analysis of RNA-seq data. The output can be integrated with Python/R scripts for further analysis, allowing for more in-depth exploration and sharing of the results.

The config.yaml file contains several important parameter configurations that facilitate the automation of downstream data analysis workflows. The key parameters are as follows:

• Sample List: The samples section includes identifiers for a series of samples that are the focus of the data analysis. These identifiers are categorized into different classes, such as P7-DTP, P7-Par, and P7-Reg, with each category further divided into sub-samples like D12, D4, D9, etc. Each sample sequence has multiple replicates, such as "1", "2", "3", "4", to ensure the reliability and reproducibility of the experiments.

• Data Path: The path specifies where the raw omics data is stored, set to /public5/home/t6s001510/data1/omics_data/Rawdata/.

• Output Path: The output_path is the destination for the analysis results, configured as /public5/home/t6s001510/data1/omics_data/outputs/.

• Genome Index Path: The genome_index_path is used to store the genome indexes, supporting alignment for transcriptome data, specified as /public5/home/t6s001510/data1/omics_data/ref/index/STAR/genome.

• rRNA Index Path: The rRNA_index provides the reference index for rRNA to filter out rRNA fragments from the data, located at /public5/home/t6s001510/data1/omics_data/ref/index/rRNA/Homo_sapiens.rRNA.

• GTF File Path: The GTF parameter specifies the annotation file for precise gene localization and functional annotation, at /public5/home/t6s001510/data1/omics_data/ref/index/GTF/Homo_sapiens.GRCh38.110.gtf.

• Count Matrix Script: The count_matrix indicates the script path for generating the gene expression count matrix, located at /public5/home/t6s001510/data1/omics_data/scripts/Count_matrix.py.

• Sample Information Script: The sample_info provides the script path for generating sample information, helping organize metadata about the samples, at /public5/home/t6s001510/data1/omics_data/scripts/generate_sample_info.py.

• Pathway Information Reference File: pathway_info contains the file path with information about biological pathway classifications, available for reference in the analysis, located at /public5/home/t6s001510/data1/omics_data/scripts/refer/pathway_class_information.csv.

• Human Metabolic Genes Reference File: The hm_gene lists human genes involved in metabolic processes, specified at /public5/home/t6s001510/data1/omics_data/scripts/refer/human_metabolic_genes.csv.

samples:
- Sample1
- Sample2
  
path: "Your_input_data"
sc_fastq_path: "Your_input_scRNA_data"
output_path: "output_path"
genome_index_path: "Your_STAR_genome_path"
rRNA_index: "rRNA_path"
GTF: "gtf_doc_path"
count_matrix: "scripts/Count_matrix.py"
sample_info: "scripts/generate_sample_info.py"
pathway_info: "refer/pathway_class_information.csv"
hm_gene: "refer/human_metabolic_genes.csv"
cellranger_transcriptome: "path_to_cellranger_transcriptome_GRCh38-2020-A"

How-to use it？

By default, the pipeline performs all the steps shown in the below. However, you can turn off any combination of the light-colored steps (e.g STAR alignment or DRIMSeq analysis) in the config.yaml file.

Advanced use: If you prefer other software to run one of the outlined steps (e.g. limma-voom over DESeq2, or kallisto over Salmon), you can use the software of your preference provided you have your own script(s), and change some lines within the Snakefile. If you think your "custom rule" might be of use to a broader audience, let us know by opening an issue.