{"id":979,"date":"2024-11-11T16:33:02","date_gmt":"2024-11-11T16:33:02","guid":{"rendered":"https:\/\/alex-jimenez.com\/?post_type=rara-portfolio&p=979"},"modified":"2024-11-19T23:03:31","modified_gmt":"2024-11-19T23:03:31","slug":"nextflow-rnaseq-pipeline","status":"publish","type":"rara-portfolio","link":"https:\/\/alex-jimenez.com\/?rara-portfolio=nextflow-rnaseq-pipeline","title":{"rendered":"NextFlow RNAseq Pipeline"},"content":{"rendered":"\n

Nextflow<\/a> is a powerful workflow management tool designed to streamline and automate data processing pipelines, particularly in bioinformatics. By leveraging domain-specific language (DSL) syntax, Nextflow enables researchers to write complex workflows that are portable, reproducible, and scalable across various computing environments, including local workstations, high-performance computing clusters, and cloud platforms like AWS. Its ability to manage dependencies, parallelize tasks, and efficiently handle large datasets makes it particularly valuable in RNA sequencing (RNAseq) workflows, where it can orchestrate the alignment, quantification, and differential expression analysis steps. With support for containerization through Docker or Singularity, Nextflow ensures consistent environments across different stages of the workflow, significantly enhancing reproducibility and minimizing issues related to software versioning. This robust framework is thus instrumental in advancing reproducible research in data-intensive fields.<\/p>\n\n\n\n

The following writeup will go over the practical implementations of a NextFlow RNAseq pipeline from the nf-core<\/a> community, more specifically Patel et al., 10.5281\/zenodo.1400710<\/a>. <\/p>\n\n\n\n

I. Background and Methods<\/h2>\n\n\n\n
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Figure 1: Summary of the tools used in rnaseq nf-core pipeline<\/figcaption><\/figure>\n\n\n\n

The pipeline seen above is the RNAseq pipeline created by the nf-core community. It offers a series of quantification, quality, and visualization tools that together provide a gold standard for analysis. To use the NextFlow pipeline above, each of these tools needs to be installed and properly setup, along with a containerized service such as Docker or Singularity. Using a Linux environment is preferred since many of these tools have linux-based binaries. Depending on the Linux operating software, sudo yum or sudo apt-get can be used to install many of these softwares, while some of these softwares are more easily installed using the python installation (pip) manager or R’s installation manager. <\/p>\n\n\n\n

After installing all necessary software, the next step is to create a sample sheet for the samples intended for the pipeline. To set up the workflow, an E. coli<\/em> sample was used initially to test the pipeline before moving on to more computationally intensive samples, such as those from human tissue. This test sample (SRR959239<\/a>) was downloaded from NCBI’s Sequence Read Archive<\/p>\n\n\n\n

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Figure 3: Shell script used to run the nf-core\/rnaseq pipeline<\/figcaption><\/figure>\n\n\n\n

Once the sample sheet is setup, the following additional parameters need to be specified. <\/p>\n\n\n\n