Capture of transcriptomic events in Stomach Adenocarcinoma integrating alignment-based and alignment-free protocols

Abstract

Abstract Background Transcriptome expression variations and abnormalities in peptides and proteins play a crucial role in phenotypic differences. RNA-seq data provides valuable insights for identifying disease-causing mutations. However, traditional RNA-seq analysis techniques heavily rely on reference sequences and alignment procedures, limiting their effectiveness. In this study, we employed k-mer technology to overcome these constraints and comprehensively identify pathogenic mutations. Methods Our investigation focused specifically on individuals diagnosed with stomach adenocarcinoma (STAD). By leveraging k-mer technology, we were able to detect frequent alterations occurring in various genomic elements and post-transcriptional modifications. We also explored the significance of previously overlooked events in typical transcriptomics pipelines, which may serve as potential indicators for tumor prediction, prognosis, tumor neoantigen prediction, and their correlation with the immune microenvironment. Additionally, we considered the impact of unannotated long intergenic non-coding RNA, newly discovered splice variants, repetitive sequences, and pathogenic microbial RNA on understanding STAD. Results By utilizing our method, which does not depend on a reference sequence or mapping, we anticipate simplifying the analysis of differential RNA-seq in tumor/normal sample collections. This approach offers a more comprehensive framework for evaluating crucial cancer-related occurrences and addresses the limitations of traditional techniques. Conclusion The application of k-mer technology in analyzing RNA-seq data provides a robust platform for identifying disease-causing mutations in STAD patients. Our method offers a promising alternative to traditional approaches by incorporating unannotated elements and overcoming reference-dependent constraints. The comprehensive insights gained from this analysis have implications for tumor prediction, prognosis, tumor neoantigen prediction, and understanding the immune microenvironment in STAD.