RNA-seq Analysis as a Cloud Service: Toward AI-Driven Computational Resource Efficiency

• Elisson Silva UFPE https://orcid.org/0000-0001-6998-7809
• Rosana Blawid UFRPE https://orcid.org/0000-0003-1001-7191
• Stefan Blawid UFPE https://orcid.org/0000-0001-6982-4575

DOI: https://doi.org/10.5753/pesquisanuvem.2026.23134

Resumo

Analysis of RNA-seq data generated by high-throughput sequencing poses substantial computational challenges due to its scale and complexity, often exceeding the resources available in typical laboratory environments. In this work, we present a cloud-centric perspective on RNA-seq analysis by deploying a phytosanitary pipeline as a cloud service based on a conventional bioinformatics workflow and analyzing its computational characteristics. Building on this baseline, we investigate the potential of artificial intelligence to improve computational resource efficiency by introducing an attention-based neural network for early-stage read classification. Our results indicate that AI-based filtering can distinguish relevant reads and reduce the volume of data processed by downstream, resource-intensive stages. This suggests a means to reduce compute time and memory usage through selective data reduction, although full integration into the pipeline is left for future work. We discuss how the combination of cloud-native execution and AI-driven preprocessing can enable more resource-efficient and accessible RNA-seq analysis services.

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