Deciphering the Genetic Code

When it comes to data, our genetic codes are a veritable treasure trove. According to the National Human Genome Research Institute1, just a single human genome sequence takes up approximately 200 gigabytes (GB)—that’s the same size as around 200 full-length, standard-definition movies! With the rise of precision medicine approaches and sequencing efforts worldwide, roughly two to 40 billion GB of genomic data are generated each year. Helping make sense of this data deluge are bioinformaticians like Assistant Professor Lim Weng Khong from Duke-NUS Medical School, concurrently Director of Bioinformatics at the SingHealth Duke-NUS Genomic Medicine Centre.

By processing, visualising and interpreting individual -omics data on a population-level scale, bioinformaticians play a crucial role in drawing crucial insights that can help clinicians stratify patients according to disease risk. In this feature, learn about how Lim got his start in bioinformatics and how he intends to further the field in Singapore and beyond.

1. What first drew you to computational biology and bioinformatics in the context of biomedicine?

My Computer Science background gave me tremendous freedom in choosing the area to hone my skills on. In fact, apart from bioinformatics, I have previously done data science work in the semiconductor and transportation industries. However, I feel that bioinformatics is the area where my expertise will not only have a lasting impact in terms of scientific discoveries but also directly influence healthcare outcomes—in particular, how patients with genetic diseases are managed.

2. Could you give us an example of how bioinformatics helps with precision medicine initiatives?

Genomics has progressed significantly over the past ten years, and we are generating vast amounts of biomedical data. This includes whole-genome sequencing, biomedical imaging and electronic health records. Skilled bioinformaticians develop algorithms and workflows to process large amounts of raw data into useful information that can then be acted upon by the clinical team. Meanwhile, bioinformaticians can analyse large-scale population genomics data to identify genetic disorders prevalent in our population, thus guiding genomic medicine implementation efforts.

3. You have been working to set up a genome analytics platform at the Institute of Precision Medicine (PRISM) since 2016. Which genomic medicine initiatives does it support and how?

My team and I play a key role in genomic medicine, both at the institutional and national level. At the institutional level, I lead the bioinformatics division of the SingHealth Duke-NUS Genomic Medicine Centre, which aims to deliver high-quality genomic medicine across the entire SingHealth Duke-NUS Academic Medical Centre. We do so by providing not only clinical genetics services and genetics training to the clinical community, but also bioinformatics analysis capabilities at the highest level to ensure that patients get accurate results from their genome sequencing tests.

4. Could you tell us more about the precision cardiovascular medicine platform you are building at Singapore’s National Heart Centre?

Together with our colleagues at the National Heart Centre Singapore, we developed a proof-of-concept Precision Cardiovascular Medicine Platform (PCMP) that enabled us to comprehensively analyse genomic data from both healthy volunteers and patients with cardiovascular disorders. The platform allows various types of users such as bioinformaticians, biomedical researchers and clinicians to perform complex analysis on genomic and phenotypic data through user-friendly interfaces. Our experience with this proof-of-concept project provided very useful information for national-level initiatives for genomic data analysis platforms.

5. What is your role in PRISM’s contributions to international data sharing projects? Why are such projects important?

PRISM shares data with the global genomics community through several channels. First, we are members of an international initiative aimed at furthering data sharing and interoperability at an international level. On that basis, we share aggregated genomic data with international collaborators using standardised protocols (i.e., Beacon servers). Secondly, our team submits results from our variant curation and interpretation efforts to the public archive ClinVar, which then helps other clinical genomics teams when they encounter the same variants. We have likewise benefited from variant submissions that have been made by other teams.

Finally, we take part in expert workgroups run by the Clinical Genome Resource (ClinGen) to establish gene-disease associations and guidelines that have been published and are being used in the clinic.

6. What are your hopes for the future of data science in Singapore’s precision medicine initiatives?

I hope that Singapore will do better at attracting and retaining data science talent in the biomedical sector. To this end, we not only need greater awareness of the role data science plays in biomedicine—and particularly in genomic medicine—but we also need to have well-defined career progression pathways for data scientists and bioinformaticians. The latter is particularly important because there is currently a very strong demand for this skill set, with the tech sector offering attractive salaries for skilled data scientists.

References:

1. Fact Sheet: Genomic Data Science [Online]. https://www.genome.gov/about-genomics/fact-sheets/Genomic-Data-Science