Ciccolella Simone, Cozzi Davide, Della Vedova Gianluca, Kuria Stephen Njuguna, Bonizzoni Paola, Denti Luca
Department of Computer Science, University of Milano-Bicocca, Milan, Italy.
Department of Biochemistry and Biotechnology, Pwani University, Kilifi, Kenya.
PLoS Comput Biol. 2024 Dec 9;20(12):e1012665. doi: 10.1371/journal.pcbi.1012665. eCollection 2024 Dec.
Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes.
泛基因组正成为一个强大的框架，用于进行许多生物信息学分析，同时考虑到群体的遗传变异性，从而减少单个参考基因组引入的偏差。随着泛基因组的更广泛传播，将遗传变异性与转录组多样性相结合正成为一种自然的扩展，这需要特定的方法来进行探索。在这项工作中，我们将剪接泛基因组的概念扩展到注释剪接泛基因组；这使我们能够在图结构上引入可变剪接（AS）事件的正式定义。为了研究图泛基因组在跨条件定量AS事件中的应用，我们开发了pantas，这是第一种用于从短RNA测序读数中检测和差异分析AS事件的泛基因组方法。与基于线性参考的最新方法进行比较证明，pantas具有有竞争力的准确性，使剪接泛基因组能够有效地进行AS事件定量，并为基于群体的转录组分析开辟了未来的方向。
