Automating SRA data access and processing with Nextflow

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Introduction

Handling large-scale genomic data efficiently is one of the most pressing challenges in bioinformatics. My work on pediatric gliomas requires processing vast amounts of RNA-seq data from publicly available repositories like the Sequence Read Archive (SRA). With over 4,662 datasets matching my initial query on gliomas and RNA-seq, managing, filtering, and processing such a large collection of datasets presented a significant challenge.

Traditional methods of handling these datasets involved labor-intensive manual downloads and data validation processes. This approach was time-consuming, error-prone, and lacked reproducibility. To address these issues, I developed biopy_sra.nf, a Nextflow pipeline designed to automate the process of accessing, downloading, and preprocessing RNA-seq data from SRA.

While tools like SRA Explorer and nf-core/fetchngs provide great solutions for accessing SRA data, we opted for a custom approach to better fit the specific needs of our pediatric glioma research. BioPySRA automates data retrieval, conversion, and quality control, ensuring seamless integration with the following analysis steps in the pipeline. Building our own solution also gave us the opportunity to learn Nextflow, gaining hands-on experience with workflow automation while tailoring the pipeline to our exact requirements. This approach deepened our understanding of scalable bioinformatics workflows.

The challenge: Managing massive SRA repositories

The Sequence Read Archive (SRA) is an invaluable resource for genomic data, but working with it comes with significant challenges. The scale of the data is immense, with thousands of repositories to manage, making manual downloading and processing impractical. The metadata is complex, spanning clinical, biospecimen, and raw sequencing information, which requires careful curation to ensure relevance and quality. Traditional approaches relying on ad-hoc scripting with tools like Bash or Python often led to inconsistencies across datasets and environments, compromising reproducibility. Steps such as downloading files, converting them to FastQ format, and performing quality control were error-prone when done manually. Additionally, running large-scale analyses demanded efficient computational resource management, which further complicated the process.

Why Nextflow?

Nextflow provided an ideal solution to these challenges. It automated repetitive tasks like downloading and converting SRA files, reducing the need for manual intervention. Its scalability allowed pipelines to run seamlessly on local machines, high-performance clusters, or cloud environments. By containerizing dependencies with Docker, Nextflow ensured reproducibility, enabling identical execution across different setups. Its robust error-handling capabilities allowed the pipeline to retry failed tasks automatically, minimizing interruptions and ensuring a smooth workflow, even with thousands of datasets.

Building the biopy_sra.nf pipeline

The biopy_sra.nf pipeline was built to integrate a custom Python tool, BioPySRA.py, for automating SRA data handling. The pipeline begins with a curated list of SRA accession numbers (e.g., SRA_list.txt) based on metadata such as biospecimen type, clinical data, and sequencing method. It processes these numbers through key steps: downloading files using prefetch, converting them to FastQ format using fastq-dump, and running quality control analyses with FastQC. The output includes preprocessed FastQ files and quality control reports, which are ready for downstream analyses.

Challenges addressed by biopy_sra.nf

The pipeline addressed several key challenges effectively. Managing large accession lists became straightforward with Nextflow’s input channels, which allowed the pipeline to read from files and process datasets in parallel. Instead of manually handling downloads, the pipeline fetched and preprocessed hundreds of datasets concurrently, saving days of effort. Reproducibility was ensured by using Docker to containerize dependencies like prefetch, fastq-dump, and FastQC, eliminating version conflicts and ensuring consistency. Nextflow’s built-in error handling allowed automatic retries for failed downloads or conversions, reducing the need for manual oversight. Additionally, its ability to parallelize tasks and maximize CPU utilization made it possible to process large datasets in hours instead of days.

Impact of biopy_sra.nf

The biopy_sra.nf pipeline revolutionized the workflow for handling SRA data. It enabled a faster turnaround, with tasks that previously took weeks now completed in a fraction of the time. Its capacity to process hundreds of datasets simultaneously ensured higher throughput and efficient use of resources. Automated quality control provided confidence in the integrity of the data, setting a strong foundation for downstream analyses and research insights. By automating this labor-intensive workflow, Nextflow has empowered me to focus on advancing genomic research rather than managing the technical complexities of data preprocessing.

Conclusion

The challenges of managing massive genomic repositories like SRA are daunting, but tools like Nextflow make them surmountable. The biopy_sra.nf pipeline not only addressed the technical hurdles of data access and preprocessing but also laid the foundation for more ambitious analyses. By embracing automation and reproducibility, we can focus on what truly matters: uncovering the molecular secrets of pediatric gliomas.

My journey motivated by advances in pediatric glioma research

As a physician, data scientist and bioinformatician, my journey began with the need to efficiently process vast amounts of RNA-seq data from SRA repositories. Using Nextflow, I developed the biopy_sra.nf pipeline, which automated data retrieval, preprocessing, and quality control, transforming a labor-intensive process into a streamlined, scalable workflow.

Building on this foundation, I will apply the same principles to develop a pipeline that will enable the identification and annotation of genetic variations in pediatric gliomas. The evolution will reflect my commitment to integrating clinical, molecular, and genomic data into a unified resource for advancing biomarker discovery and personalized medicine. My ultimate goal is to create a comprehensive and useful database to compare with the variations in Argentinian pediatric gliomas, driving more precise diagnostics and personalized treatments in my country.

This post was contributed by a Nextflow Ambassador. Ambassadors are passionate individuals who support the Nextflow community. Interested in becoming an ambassador? Read more about it here.