Data Analytics Boosting Next-Gen Sequencing Methodologies

Dr. Tan engaged in a conversation with the India Pharma Outlook magazine in order to answer queries on how data analytics is boosting next generation sequencing methodologies. He is currently leading the Southeast Asia and India commercial teams at Illumina, a leader in sequencing-and array-based solutions for genetic analysis, where we are continuing to impact human lives by unlocking the power of the genome. He is passionate about technology and how it can make a difference for the better, especially in the emerging markets.

How is data analytics transforming next-generation sequencing (NGS) methodologies?

We are standing at a juncture where the overall cost for sequencing has decreased. In general, NGS is quite expensive and as we see that the cost is going down, there is an increase in the volume of projects that are being performed. Here, we need to first understand that sequencing is just the first step of the process. The motive of performing the sequencing and data analytics appears as the most important question in the industry today. All the sequencing that gets done empowers the whole process of data analytics and will be able to ignite all the discoveries of the future. All the data analytics that is being done today will be the pathways to all the sequencing that will be performed in the coming years. In this regard, one of the biggest challenges that the industry is facing is the interpretation of data. The primary reason for this is the complexity of the human genome which has the ability to deliver vast amounts of data. The interpretation of that data requires two things. First is human expertise and next is the availability of advanced bioinformatics tools. Currently, these are the only limitations being faced by experts in delivering results through NGS projects. 

What are the primary challenges faced in analyzing NGS data, and how can data analytics address these challenges?

As I mentioned earlier that there are two primary challenges that the industry is facing in NGS. First is the lack of skilled workforce, the human talent that is required who will go in and perform the analysis and second is the unavailability of advanced bioinformatics tools. Overall if we look, modern innovations have indeed greatly enabled us is performing complex next-gen sequencing projects and has also taught the industry to use the retrieved data in various kinds of ways and forms. There is a challenge regarding increased adoption of NGS technologies in a larger scale in terms of how the data is retrieved and results are driven from it. Another obstacle that is evident is how do we manage and store data efficiently. Whenever an organization wants to step into a NGS process, one of the primary problems is to find out the location of the data and how difficult will it be to access that data for the purpose of analytics. In terms of data analytics tools that are out there in the market, solutions have been developed where interpretation of data from a secondary as well as a tertiary perspective can be made possible. Both are the processes are extremely different because the outcome completely lies on the the understanding as how to streamline all the data and analyze it in a complete frame.

How has data analytics contributed to advancements in personalized medicine through NGS?

When we look at advancements in personalized medicine, the availability of data has made it possible to deepen the search and find for underlying causes. This is helping the industry provide advanced treatments to patients followed by better outcomes. These are the areas where precision medicine is making progress at a whole new level. Another problem that is being faced by scientists is finding out the disease causing variants. We have been developing tools that can help us do it better. Some of these diseases can also be rare diseases and it is very important that we find the rare disease causing variants. We are able to generate results and were able to increase our risk prediction for common diseases and complex treatments. It was difficult to achieve because for something to be called personalized medicine, it needs to be different for every individual. Using tools like these in data analytics helps us look and inspect the data from a different set of lens and use predictive technologies to assess all the variants that may occur.

What ethical considerations arise from the use of data analytics in NGS, particularly concerning patient data privacy?

Genomic data is a very sensitive topic especially with regards to the amount of data that is retrieved and also the value of that data. Data is something which must always be protected and Illumina always promotes and practices a high level of data privacy. The handlers of data bear a lot of responsibility as they need to make ethical and responsible use of the data. They are responsible for privacy and confidentiality. This means that the data needs to be received on consent and anonymizing data wherever possible and abiding to all the data protection regulations. From Illumina's perspective, we develop, we innovate and we review all privacy related policies and practices. We take the ultimate responsibility on how data is getting managed.