Zika virus sequence and secondary structure analysis using VADR
Zika virus is a mosquito-borne flavivirus that caused a major epidemic in the Americas in 2015 and 2016 and is associated with congenital birth defects and neurological disease. Like other flaviviruses, it depends on conserved sequence and RNA structural elements that carry out essential steps in its life cycle and in evading host immune defenses. Public sequence databases and annotation tools…
Zika virus, a mosquito-transmitted flavivirus, triggered a major epidemic in the Americas during 2015 and 2016. This virus is linked to birth defects and neurological conditions. Similar to other flaviviruses, Zika's existence hinges on crucial sequence and RNA structural components that facilitate key life cycle stages and immune evasion techniques.
While there are public sequence databases and annotation tools offering Zika sequence data for free, there is a scarcity of structural information. To address this gap, researchers constructed a computational model for Zika's sequence and secondary structure. This model is compatible with the VADR software package, a tool that authenticates and annotates Zika sequences for GenBank submission and assigns each sequence to a genetic lineage based on a previously established classification system.
Upon testing the model on a dataset of 1254 Zika sequences, it achieved a validation rate of 97.2%. Furthermore, the model's annotations for these sequences matched those of an existing tool, VIGOR4, in 99.3% of the cases examined. The researchers also enhanced VADR's capabilities to generate structural diagrams of each sequence's elements in both linear and circular forms using the R2DT software.
Additionally, they submitted a recently identified Zika pseudoknotted RNA element to the Rfam database. These newly developed resources provide comprehensive secondary structure information for all Zika sequences, making this valuable data accessible to the scientific community.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.