Meme kanserinde izoform değişikliklerinin tanımlanması

Characterizing the human genome's molecular functions and their variations across people is vital for understanding the cellular processes behind human genetic characteristics and diseases. With the advent of single-cell RNA sequencing (scRNA-seq), it is now possible to investigate gene expression in individual cells. Although a number of scRNA-seq bioinformatics tools are now available, many of them focus on overall gene expression levels and, as a result, often ignore heterogeneity caused by individual transcript expression. Differences in the relative abundance of expressed isoforms, such as those that occur between normal and diseased states, may have dramatic effects on phenotype or prognosis. This variation in expression may aid in the discovery of novel therapies as well as the better management of patients in certain situations. We propose a computational workflow for scRNA-seq data that identifies differential transcript usage from transcript abundances produced by widely used alignment tools such as Salmon. This approach enabled us to detect alterations in gene expression that were previously overlooked, in patients with breast cancer.