SAMAR-lite lets you perform quick-and-dirty differential gene expression analysis in organisms when you don't have a reference transcriptome sequence.
For non-model organisms, conventional DE analysis pipelines requires de-novo transcriptome assembly, which requires massive computational resources. This pipeline provides a shortcut in which RNA-seq reads are directly mapped to the reference proteome which would have been otherwise used for annotation and functional inference in the assembly-based approach. It is faster than SAMAR, but less accurate.
This pipeline requires the package manager Conda and the workflow management system Snakemake. All other dependencies are handled automatically by Snakemake.
Download Miniconda3 installer for Linux from here. Installation instructions are here. Once installation is complete, you can test your Miniconda installation by running:
$ conda list
Snakemake recommends installation via Conda:
$ conda install -c conda-forge mamba
$ mamba create -c conda-forge -c bioconda -n snakemake snakemake
This creates an isolated enviroment containing the latest Snakemake. To activate it:
$ conda activate snakemake
To test snakemake installation
$ snakemake --help
To deactivate the Snakemake environment:
$ conda deactivate
$ git clone https://github.com/bioinfodlsu/samar_lite.git
$ cd samar_lite
The pipeline requires, at the very least: (1) RNA-seq fastq reads, (2) a reference protein database in fasta format, (3) a k-mer size, and (4) a coverage threshold. These and other input parameters are specified via a YAML-format config file -- a template config.yaml is provided in the config folder within the workflow folder.
The output of the pipeline is stored inside the folder specified in the out_dir entry of the config file. Inside it, the mappings can be found in the mappings folder, the count data in the counts folder, and the results of differential expression analysis in the DEanalysis folder.
In the DEanalysis folder in test_output, you will find the following files:
- DE_count_summary.txt, which contains the count of up- and downregulated genes
- Test_result-Group_treated_vs_control.csv, which contains the table of the results of hypothesis testing for each gene in the reference.
- DESeq2_fit.RDS which can be loaded using R, if further analysis is required.
The parameters in the YAML config file can be seen below:
| Keyword | Possible values | Default | Description |
|---|---|---|---|
| reference | path/to/reference/file.fasta | - | Reference protein database in fasta format |
| k-size | positive integer value | 7 | Size of the k-mers |
| threshold | positive float value | 40.0 | Minimum mapping coverage |
| reads: sample_i | path/to/query/file_i.fastq | - | RNA-seq fastq reads |
| out_dir | path/to/output/dir | - | Directory where outputs will be stored where, the mappings can be found in the mapping folder, the count data in the counts folder, and the results of differential expression analysis in the DEanalysis folder |
| deanalysis | yes, no | yes | If set to "no", the pipeline does not proceed to DE anlaysis and halts after counting. If keyword is not provided, value defaults to "yes" |
| factor | [factor1, factor2, .. ] | - | Name of factors in the study |
| sample_info: sample_i: | [level of factor1, level of factor2, ..] | - | Describes the factor levels which the sample corresponds to |
After constructing a config.yaml file and with the snakemake conda environment you created earlier activated, you can call the pipeline from the top-level directory of SAMAR_lite:
$ snakemake --configfile <workflow/config/config.yaml> --use-conda --cores all
A toy example is provided in the test_data folder. In this example, there are 6 RNA-seq samples, of which 3 are the "control" group replicates and 3 are the "treated" group replicates. The paths to the files and information about the experiment design is specified in config_test.yaml. From the top directory of the pipeline and with the snakemake conda environment activated, run:
$ snakemake --configfile testdata/config_test.yaml --use-conda --cores all
Once the run is complete, you can check the output in the folder test_output which is created in the top-level directory of SAMAR-lite.
Kyle Christian L. Santiago and Anish M.S. Shrestha: DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms, GIW XXXI/ISCB-Asia V, Dec 12 – 14, 2022, Tainan, Taiwan. To appear in BMC Supplements. Preprint
For comments, suggestions, issues, please send an email to anish.shrestha--atmark--dlsu.edu.ph