COVID-19 Data and Non-coding RNA Impact Factors: What Vinod Scaria Can Tell Us about the Numbers

While Vinod Scaria specializes in computational biology regarding non-coding RNAs, he's lab has diverted their energies to focus most of their data analysis specifically on the genomics of COVID-19. He gives listeners a global picture of the data and also explains the significance of small non-coding RNAs, long non-coding RNAs as well as categorizing functional RNA types. Listeners will learn

• How computational biologists assess the potential anti-viral load of microRNAs to understand their regulatory mechanisms,
• How they've turned their data skills to the COVID-19 pandemic spread and what they hope to accomplish, and
• What significant findings their analysis has established, such as the impact of local spread versus spread through travel.

Vinod Scaria is a principal scientist at the CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB). His lab works with clinicians across India to help them with difficult diagnoses that often end up being rare genetic conditions. However, the pandemic has caused them to repurpose their labs and into the space of virology. First, he gives the audience a nice refresher on what computational biologists do. Computational biologists, he says, look at genomes and the proteomes of organisms but with a computer instead of a microscope. They work on algorithms and sequences to develop hypotheses that can later be validated in labs.

He gives a really interesting glimpse into their COVID research, explaining that they look at the genome of the virus and try to understand its genetic epidemiology. The virus mutates at a very constant rate, he explains, and therefore they can use information in a specific way to trace the epidemic spread. These computations tell them about how the virus spreads, if there have been undocumented outbreaks, and the origin of outbreaks. All this together helps inform policies for better containment such as helpful social measures and where lockdowns might be most effective. Basically, he says, they use computational biology to make far more effective interventions to prevent spread.

For more about his work, see his lab's website: vinodscaria.rnabiology.org/home. Available on Apple Podcasts: apple.co/2Os0myK