An Interdisciplinary Field
We are entering the era of global genomics. In the coming years, genomics will dramatically change businesses, public health, the way we age, and as such, also policymaking. Combining genomics with other technologies such as artificial intelligence, blockchain, and cryptography, will revolutionize the practice of medicine and bring about a paradigm shift in the way we think about healthcare.
By far the most effective way to reduce healthcare costs is reducing the rates of illness. One way to achieve this is by using preventive and precision medicine utilizing the information stored in our genomes. A new wellness era has emerged, using personalized big data as a source of predictive information about our future health conditions. Personalized genomics is a core health data stream for preventive medicine and empowers individuals as knowledgeable, self-interested, action-taking agents.
Data Organization and Applications in Healthcare
Appropriate, effective, and sustainable integration of genomics into our healthcare requires an organized approach. To make genetic data actionable, a new kind of genomics ecosystem is needed that provides a space where people own their data and can profit from it. Already, many global initiatives have invested heavily in precision medicine projects, featuring, among other things, genome sequencing of individuals in the population or health systems.
For example, in the video above, we have seen with the UK governmental project Genomics England that integrating genome screening in a national health system creates a fantastic resource of data, offering better clinical understanding of diseases. The resulting data pool enables earlier diagnoses and personalized care for cancer, rare diseases, and infectious diseases.
The time is now ripe for the use of genome sequencing in regular preventive care and health promotion. Expect that within a few years almost everyone in countries with developed healthcare systems will have their DNA sequenced and stored for preventive healthcare. We can use the resulting data to understand the interaction between our genomes and our environment and lifestyles. In recent years, scientific research in this area has contributed significantly to our knowledge about health, improving our ability to understand disease etiology, risk, prevention, diagnosis, and treatment.
Skills for Success
The field of genomics, depending on the job, can draw from a variety of highly technical disciplines that are normally disparate bedfellows. Generally, it is beneficial to have multidisciplinary expertise with a sound knowledge of various scientific subjects to enter the highly complex world of genomics. Traditionally, we had the computer science Ph.D. coming into the world of biology. Now, aside from the male-dominated world of computing, we have a more female-dominated science environment. Moreover, the clash of “intellectual cultures” can hinder progress if not dealt with maturely. Now we see more informatics and bioinformatics courses at universities. Often, hardcore programmers have problems understanding the biological complexities, and that has led to some unfavourable results. Nevertheless, genetic labs need to attain a level of computational competence to guide programmers; and programmers need to be open to learning from their non-programming peers, particularly around groups that are not mathematically oriented. Teamwork is essential for this proper merging of left and right brains.
Ideal Genome Talent: You have a degree in biological science, a Masters in computer science and a PhD in a related field involving the two and an extensive history of the project. Experience with Github, Diploit, Docker, Python, and some basic understanding of AI and Machine Learning methods help. And, depending on the level of need – experience, experience, and more experience matter.
Acquire More Technical Knowledge
You still need to know a relational, object-relational database management system — and that has not changed since the 60s. There are many commercial applications to master, and due to the costs of these applications, it’s hard to find someone who has “played with” all. For growing your analytical skillsets, data science, Github, Docker containers, Diploit, and R are critical. For subject-matter knowledge, acquire outside of the wet-lab techniques: Next-Generation Sequencing (NGS), RNAseq, Exomeseq, Epigenetics, Microbiome, Omics profiling, ChiPseq data types, and finally some interaction with patient groups or the public health systems make for an excellent genetic researcher.
The Future of Genomics
Two disruptive trends will change genomics as we know it:
- The decentralization of Omics data, and
- The advance of Artificial Intelligence (AI)
Decentralization will improve data sharing and data security. Healthcare data, when trapped in silos, does not yield maximum value. Breaking up genomics data silos is of utmost importance because the value of genomics data silos is very limited in their current state. But when deployed to aggregate genomes globally, a gain in value will be observed according to the law of accelerating returns rather than a law of diminishing returns. One way to decentralize data is with blockchain. A blockchain is a distributed tamper-proof database, shared and maintained by multiple parties that secures all records. Records can only be added to the database, never removed, with each new record cryptographically linked to all previous records in time.
The combination of blockchain with artificial intelligence and big data will be indispensable in building a global precision-medicine ecosystem that optimally connects patients, clinicians, researchers, and healthy individuals. AI systems improve in performance with more data, and blockchain-based data platforms are ideal for collecting this vast array of data. The combination of those technologies is of enormous value. AI researchers are already working towards an “Artificial General Intelligence” (AGI), a computer than can think and reason about the world in general, on its own. AI and AGI are critical areas for integrating massive genome projects and is a key enabler of the above happening in the future. AIs that “talk” to each other will also happen in the coming years. They allow for inference of greater data volume understanding, new data ideas, speedier results, reduction in adverse drug reactions, and more patient-physician integrations.
This post was created with inputs from the global outreach, data science and leadership teams at Project Shivom.
