Research in Focus is a series that highlights Neurobiology faculty members’ papers in peer-reviewed journals.
A study led by Lara Ianov, Ph.D., assistant professor in the Department of Neurobiology, was published in Bioinformatics for the paper, “scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.” Co-authors include Austyn Trull, Computational Scientist Associate in the UAB Institutional Research Core Program, and Liz Worthey, Ph.D., associate professor in the Department of Genetics. All authors are members of the UAB Biological Data Science Core (U-BDS).
This publication introduces scnanoseq, a novel secondary analysis workflow for long-read single-cell RNA-sequencing (scRNA-seq) data. Although the workflow was formally released in October 2024, the peer-reviewed article describes its implementation, validation efforts, and the downstream analyses it enables.
To better understand the significance of this research, we sat down with Ianov to discuss her findings and the development process behind scnanoseq.
Q: Can you describe the key findings of your recent publication?
In our recent publication, “scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing,” my co-authors and I introduce a novel secondary analysis workflow designed for long-read scRNA-seq data. The paper provides a detailed overview of the workflow’s implementation, highlights the validation steps we took to ensure reliability, and demonstrates a range of downstream analyses enabled by this resource.
Q: What inspired you to pursue this area of research?
As co-director of the UAB Biological Data Science Core (U-BDS), one of my key responsibilities is to support the development of workflows that integrate cutting-edge practices in research. Long-read single-cell RNA-sequencing emerged as a critical area for investment because of its unique advantages over short-read sequencing—for instance, the ability to quantify isoforms rather than only gene-level expression.
To meet this need, we set out to create a workflow that could establish standard practices in the field. We developed scnanoseq within the nf-core community—a global network dedicated to curating open-source analytical workflows—to ensure broad accessibility and adherence to principles of reproducibility and rigor. This work reflects the infrastructure and best practices we prioritize at U-BDS.
Q: What were some of the biggest challenges you faced during this project?
When we began developing nf-core/scnanoseq, very few computational methods existed for analyzing long-read scRNA-seq data. This meant several modules had to be built from scratch. As the field rapidly evolved, we continually updated the workflow to integrate newly released algorithms and methods.
Another major challenge was scalability. Long-read data, particularly single-cell datasets, are much larger than short-read data. We implemented several steps to improve scalability and continue evaluating ways to optimize performance. We also provide detailed user guidelines to help researchers leverage available resources for large-scale analyses.
Q: Can you explain the methodology you used and why it was particularly suited for this study?
At its core, scnanoseq was developed within the nf-core framework, adhering to community standards such as using Nextflow as the workflow manager, maintaining strict coding guidelines for consistency, providing containerized environments, and incorporating automated testing.
Each computational method was carefully selected and benchmarked against ground-truth data to ensure reliability at every step. Because few workflows exist for long-read scRNA-seq, developing within the nf-core community was a natural choice—advancing rigor and reproducibility while showcasing the computational biology expertise available at UAB.
Q: Collaboration is so important to research at UAB. Was there collaboration involved in your project, and if so, with whom?
Yes. The development of this workflow was a collaborative effort between Dr. Liz Worthey and me, as co-directors of U-BDS, to strengthen research infrastructure at UAB. Austyn Trull and I co-designed, co-developed, and validated the workflow. Members of the nf-core community also played a key role, providing peer review and feedback prior to the pipeline’s release—one of the greatest strengths of this global, open-source community.