# RNA-seq Scientific Workflow Workflow for RNA sequencing using the Parallel Scripting Library - Parsl. **Reference:** Cruz, L., Coelho, M., Terra, R., Carvalho, D., Gadelha, L., Osthoff, C., & Ocaña, K. (2021). *Workflows* Científicos de RNA-Seq em Ambientes Distribuídos de Alto Desempenho: Otimização de Desempenho e Análises de Dados de Expressão Diferencial de Genes. In *Anais do XV Brazilian e-Science Workshop*, p. 57-64. Porto Alegre: SBC. DOI: https://doi.org/10.5753/bresci.2021.15789 ## Requirements In order to use RNA-seq Workflow the following tools must be available: - [Bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) You can install Bowtie2 by running: > bowtie2-2.3.5.1-linux-x86_64.zip Or > sudo yum install bowtie2-2.3.5-linux-x86_64 - [Samtools](http://www.htslib.org/) Samtools is a suite of programs for interacting with high-throughput sequencing data. - [Picard](https://github.com/broadinstitute/picard) Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. - [HTSeq](https://htseq.readthedocs.io/en/master/) HTSeq is a native Python library that folows conventions of many Python packages. You can install it by running: > pip install HTSeq HTSeq uses [NumPy](https://numpy.org/), [Pysam](https://github.com/pysam-developers/pysam) and [matplotlib](https://matplotlib.org/). Be sure this tools are installed. - [R](https://www.r-project.org/) To use [DESEq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) script make sure R language is also installed. You can install it by running: > sudo apt install r-base - [Parsl - Parallel Scripting Library](https://parsl.readthedocs.io/en/stable/index.html) The recommended way to install Parsl is the suggest approach from Parsl's documentation: > python3 -m pip install parsl - [Python (version >= 3.5)](https://www.python.org/) To use Parsl, you need Python 3.5 or above. You also need Python to use HTSeq, so you should load only one Python version. ## Workflow invocation First of all, make a Comma Separated Values (CSV) file. So, onto the first line type: ``sampleName,fileName,condition``. **Remember, there must be no spaces between items**. You can use the file *"table.csv"* in this repository as an example. Your CSV file will be like this: | sampleName | fileName |condition| |------------------|------------------|---------| | tissue control 1 | SRR5445794.merge.count | control | | tissue control 2 | SRR5445795.merge.count | control | | tissue control 3 | SRR5445796.merge.count | control | | tissue wntup 1 | SRR5445797.merge.count | wntup | | tissue wntup 2 | SRR5445798.merge.count | wntup | | tissue wntup 3 | SRR5445799.merge.count | wntup | The list of command line arguments passed to Python script, beyond the script's name, must be: 1. The indexed genome; 2. The number of threads for bowtie task, sort task, number of splitted files for split_picard task and number of CPU running in htseq task; 3. Path to read fastaq file, which is the path of the input files; 4. Directory's name where the output files must be placed; 5. GTF file; 7. and, lastly the DESeq script. Make sure all the files necessary to run the workflow are in the same directory and the fastaq files in a dedicated folder, as a input directory. The command line will be like this: > python3 rna-seq.py ../mm9/mm9 24 ../inputs/ ../outputs ../Mus_musculus.NCBIM37.67.gtf ../DESeq.R **Remember to adjust the parameter multithreaded and multicore according with your computational environment.** Example: If your machine has 8 cores, you should set the parameter on 8.