Institute for Integrative Systems Biology (I2SysBio), CSIC-Universitat de València, Paterna, València 46980, Spain.
Departament de Física Teòrica, Universitat de València, Burjassot, València 46100, Spain.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae651.
Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete autonomously an infectious cycle. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation, and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the host's immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing. An accurate quantification is essential for understanding their very dynamical interactions with the helper virus.
We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs genome wide from short-reads RNA sequencing.
The Matlab code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.
缺陷型病毒基因组（DVGs）是野生型（wt）病毒的变体，它们缺乏独立完成感染周期的能力。然而，在其亲本（辅助）wt 病毒存在的情况下，DVGs 可以干扰功能性基因组的复制、包装和传播，成为病毒进化的重要选择力量。DVGs 还影响宿主的免疫反应，并与慢性感染和较轻的症状有关。因此，鉴定和表征 DVG 对于了解感染预后至关重要。由于 DVG 自身无法维持，因此难以对其进行定量，这使得它们很难与辅助病毒区分开来，尤其是使用高通量 RNA 测序时。准确的定量对于理解它们与辅助病毒的非常动态的相互作用至关重要。
我们提出了一种通过识别与预期测序深度有显著偏差的基因组区域来同时估计样本中 DVG 和 wt 基因组丰度的方法。我们的方法通过线性方程组来重建深度分布，从而提供每种类型的 wt 和 DVG 基因组数量的估计。到目前为止，基于计算的方法仅估计了局部基因组区域的 DVG-to-wt 比值。这是第一个从短读 RNA 测序中同时估计 wt 和 DVG 基因组比例的方法。
Matlab 代码和合成数据集可在 https://github.com/jmusan/wtDVGquantific 上免费获得。
