Biological Data, in its truest sense, presents an enormous challenge to researchers who need to analyze massive amounts of complex high throughput data before they can unlock the secrets of life.
Reema Singh, our next pathbreaker, currently works as Bioinformatician and Data Manager at the Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Canada.
Reema talks to Shyam Krishnamurthy from The Interview Portal about her work in Bioinformatics, and collaboration with scientists to discuss their biological questions and how bioinformatics can help answer them through brainstorming, designing experiments, and interpreting results.
For students, make sure to build a “toolbox” of skills, like coding or good communication, because the more you know, the more opportunities you’ll find.
Reema, can you share how your growing up years were?
Hello everyone! It’s an honor to share my story with you today. My journey in the world of science has been incredibly exciting, filled with learning, challenges, and amazing discoveries. I hope by sharing my experiences, I can inspire some of you to explore your own passions and consider a career in science, especially in a field like Computational biology and Bioinformatics,which is all about understanding the secrets of life hidden in data!
I grew up in India, a land of diverse cultures and vibrant traditions. From a young age, I was fascinated by the world around me, especially how living things worked. I remember spending hours observing insects, plants, and even just the way our bodies functioned. This curiosity wasn’t something I learned in school; it was just a part of who I was. My parents, while not scientists themselves, always encouraged my inquisitive nature. They taught me the value of hard work and the importance of continuous learning. They supported my interest in science, and I think that early encouragement played a big role in shaping my career path.
What did you do for graduation/ post-graduation?
My formal education started with a Bachelor’s degree with a major in Biotechnology from KVA DAV College for Women (Karnal), affiliated with Kurukshetra University in India. Think of Biotechnology as the science of using living systems and organisms to make products or solve problems – like developing new medicines or improving crops. It was here that I got a taste of real-world biological science.
After that, I decided to dive deeper and pursued a Master’s degree in Bioinformatics from CCS Haryana Agricultural University, Hisar, India. This was a crucial step! Bioinformatics is where biology meets computer science. It’s about using computers to understand the huge amounts of biological data we collect, like the genetic code of living things. My thesis focused on “Application of entropy and mutual information in Bioinformatics,” which sounds complicated, but it’s basically about using mathematical tools to find patterns and relationships in biological data.
Finally, I earned my PhD in Computational Biology and Bioinformatics from Jawaharlal Nehru University, New Delhi, India. My PhD thesis was all about “Development and application of data management and analysis protocols for high throughput data analysis.” This means I spent years learning how to handle massive amounts of biological information generated by modern technologies, and how to build “pipelines” – like automated factories for data – to make sense of it all. It was during my PhD that I truly honed my skills in programming, data analysis, and understanding complex biological systems through a computational lens.
What were some of the key influences that led you to such an offbeat, unconventional, and unique career in Bioinformatics?
Choosing a career in Bioinformatics wasn’t a sudden decision for me, but a path I discovered step-by-step. It began with my natural curiosity about how livingthings work, especially how DNA and genes control everything. When I learned that computers could help unlock these biological secrets from vast amounts of data, it felt incredibly powerful. Along the way, inspiring teachers and mentors showed me the excitement of scientific research, and attending workshops revealed how computers were solving real biological mysteries, making me see the potential to understand diseases and find new cures. A major turning point came during my Master’s, when new technologies started generating huge amounts of genetic data, creating a clear need for people who could use computers to make sense of it all. That’s when I knew Bioinformatics, combining my love for biology and computers, was the perfect fit for me – like learning the secret language of life with digital tools.
How did you plan the steps to get into the career you wanted? Or how did you make a transition to a new career? Tell us about your career path.
My career path in Bioinformatics wasn’t just about jobs; it was a planned journey focused on continuous learning, solving real-world health problems, and collaborating with other scientists.
After my Master’s degree, my first step was actually as a guest faculty, teaching bioinformatics to Master’s of Biotechnology students, which solidified my understanding and communication skills. Following that, I became a Scientist-I at ICMR, where I gained hands-on experience building databases for antibiotic resistance and analyzing genetic data related to HIV, laying a strong foundation in handling large biological datasets.
My Time at ICMR
My 6 years at the Indian Council of Medical Research (ICMR) was my second job after completing my Master’s degree. I also pursued my PhD studies side-by-side while I was employed at ICMR, though the research for my PhD was not part of my work projects there.
At ICMR, I got hands-on experience applying bioinformatics to important health problems right away. I worked on:
Building an Antimicrobial Resistance Gene Database: Imagine a huge, organized library of information about how different bacteria are becoming resistant to the medicines we use to fight them. I designed and built this database, which is super important for doctors and scientists to track antibiotic resistance.
Analyzing HIV Data: I also analyzed biological data called “microarray data” to understand how HIV infection changes our genes. This helped us get clues about how the virus affects our bodies at a very detailed level.
This experience at ICMR was a fantastic foundation, teaching me how to manage large scientific datasets and apply my bioinformatics skills to direct health challenges, even while pursuing my PhD.
Can you talk about your PhD research? What was your research about?
My PhD journey was indeed about tackling some big problems in biology using computers.
Think of every living thing, from tiny bacteria to us humans, as having its own unique instruction manual written in its DNA. When scientists use advanced machines to read these manuals, they get incredibly long strings of genetic code – billions and billions of pieces of information! This is what we call genomics – studying all the genes and DNA in an organism.
My PhD work focused on a few key areas that really show what Bioinformatics is all about and the challenges it helps solve:
1. Created a custom tool for biological data analysis using R and the Bioconductor framework: Imagine you have a huge, messy pile of Lego bricks (which are like our biological data from studies like transcriptomics, proteomics, and metabolomics). You can’t build anything useful unless you have the right tools to sort them, connect them, and build structures. Bioconductor is like a specialized, digital toolbox full of amazing software tools and programs specifically designed for biological data. In my PhD, I used these tools, and even developed my own integrated R/Bioconductor package, named HTDA, which stands for High-Throughput Data Analysis. This package was a generic tool designed to help analyze vast and complex datasets from different types of “omics” studies. It helps researchers turn raw genetic information into meaningful biological discoveries.
2. Developing a TB Gene-Expression Analysis Protocol: Beyond analyzing just the genes, I also developed a special integrated protocol (a step-by-step guide for analysis) to look at how genes are expressed (switched on or off) in relation to Mycobacterium tuberculosis, the specific bacteria causing TB. This “meta-analysis” helped identify which biological processes and pathways in our bodies are involved when we’re dealing with TB, giving us deeper insights into the disease.
So, the big picture is: modern biology generates mountains of data. The challenge is to make sense of it quickly and accurately. Bioinformatics helps us by providing the computational tools, methods, and skills to analyze this “big data,” uncover hidden patterns, and gain insights that can improve human health, like fighting diseases such as TB. It’s like finding the secret codes in the book of life to solve real-world medical mysteries.
Can you explain your work as a Postdoc in Scotland ?
After submitting my PhD thesis, I strategically transitioned to a postdoctoral position in Scotland, UK to dive deeper into advanced genomic and transcriptomics analysis, for piecing together genetic information from various organisms. My postdoctoral work in Scotland was a fascinating continuation of my journey in bioinformatics, but with a new twist.
Imagine an architect who studies how different buildings are put together. My work was a bit like that, but for tiny living things! I focused on something called transcriptomics and genomic analysis in social amoeba species.
Let’s break that down:
● Social Amoeba: These are really interesting, tiny single-celled organisms. What makes them “social” is that sometimes they come together and act like a multicellular organism, developing into different structures. It’s a great model to understand how individual cells cooperate and specialize.
● Transcriptomics: If genomics is like reading the entire instruction manual (DNA), transcriptomics is like looking at which parts of the manual are actively being used at any given time. It tells us which genes are “switched on” or “expressed” and how much.
● Assembly: When we get transcriptomics data from these tiny amoeba, it comes in millions of short genetic “reads” – like shredded pieces of paper from the instruction manual. My job was to use powerful computer algorithms to “assemble” these tiny pieces back together, like solving a giant jigsaw puzzle. This allowed us to reconstruct the full genetic messages (RNA sequences) being used by the amoeba.
Beyond transcriptomics, I also worked on core genome phylogeny. Think of a family tree, but for genes or entire organisms. Core genome phylogeny means studying the genes that are shared by all members of a group of organisms and using those shared genes to figure out their evolutionary relationships – basically, how they are related to each other over time.
By piecing these together, we could understand how these amoeba developed and behaved, and identify important genes that played roles in their “social” interactions and development. It was all about making sense of incredibly large and complex genetic datasets to understand the biology of these unique organisms.
Was it in a different field from your PhD?
Not at all a completely different field! It was very much a continuation and application of my core skills from my PhD. My PhD focused on “Development and application of data management and analysis protocols for high-throughput data,” and I even developed an R/Bioconductor package (HTDA) that could handle ‘omics’ data including transcriptomics.
So, while the Dictyostelium discoideum (social amoeba) and the biological question were new, the field itself was still Bioinformatics and Computational Biology, with a strong emphasis on high-throughput data analysis, especially transcriptomics and genomics. It was an exciting opportunity to apply the advanced computational techniques I mastered during my PhD to a new biological system, deepening my expertise in genomics and gene expression analysis. It also broadened my experience in research project management and supervising a student in a new international setting.
You then transitioned to the field of infectious diseases which is a very pressing challenge in Biology
My next move to University of Saskatchewan, Canada for another postdoctoral role was a deliberate step into the field of infectious disease research, a field I’m deeply passionate about. Here, I developed powerful computer tools, like “Gen2Epi,” to automatically link bacterial genome to antibiotic resistance, directly contributing to public health. All these roles were stepping stones, leading me to my current position as a Bioinformatician and Data Manager at Vaccine and Infectious Disease Organization, Saskatoon, Canada. While in this current role, I also broadened my scope by taking on a remote Visiting Associate position at Colorado State University, USA, applying my expertise to a wider range of diseases and collaborating across borders. Every step was taken with the intention of acquiring new skills in programming, data analysis, and machine learning, always aiming to apply them to important biological questions, ultimately helping to develop new vaccines and treatments. My academic degrees (BSc., MSc., Ph.D. and constant networking with other researchers were crucial to planning and navigating this evolving career path.
How did you get your first break?
My very first break was getting my job as a guest faculty, which helped me solidify my understanding and communication skills. After that, my “first break” in a research role was landing the Scientist-I position at the Biomedical Informatics Centre at ICMR, India. This happened because my Master’s thesis was very practical and focused on a developing area of bioinformatics. During my interviews, I was able to demonstrate my theoretical knowledge and the practical skills I had developed in programming and data analysis. I think showcasing my practical work and my enthusiasm for applying computational tools to biological problems really helped me stand out. It was a testament to the importance of building practical skills alongside theoretical knowledge.
After I completed my PhD, my first big break into a postdoctoral position was at the University of Dundee in Scotland,UK. It wasn’t just luck; it was a direct result of the skills and expertise I developed during my PhD. My doctoral research focused heavily on developing and applying advanced data analysis tools for complex biological data, including transcriptomics. The lab in Scotland was specifically looking for someone with those exact computational and data analysis skills to work on understanding social amoeba using high-throughput sequencing data.
During the application and interview process, I was able to show how my PhD work was directly relevant and how my knowledge of building data pipelines and handling massive datasets would be valuable to their research. It was a perfect match between my specialized skills and their research needs, which led to that exciting opportunity abroad.
What were some of the challenges you faced? How did you address them?
My career journey had its tough moments, but each challenge helped me learn and grow. One big challenge was handling massive amounts of complex data; I overcame this by constantly learning new computer languages and using supercomputers. Another challenge was keeping up with the fast-changing world of science and technology; I tackled this by always being open to learning new tools and methods, like learning how to analyze new types of high throughput sequencing data as soon as they came out. Finally, it was tricky to explain complicated scientific findings to people without a science background, but I improved my communication skills by practicing how to simplify ideas and focus on why the information was important.
Where do you work now?
I currently work as a Bioinformatician and Data Manager at the Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, CANADA.
What problems do you solve?
My main job is to use computers to understand and fight infectious diseases. I help scientists track down how viruses and bacteria spread and change, almost like being a detective for tiny germs! I also look for their weak spots in their genetic code, which helps in creating new medicines or vaccines. Plus, I study how our bodies react to infections at a very detailed level,and even use smart computer programs (machine learning) to predict how diseases might progress, which treatments would work best, or how to develop better diagnostic tools.
What skills are needed for this job? How did you acquire the skills?
For my job, I need many skills, which I learned through my academic studies, doing research, and teaching myself. I have strong coding skills in languages like Python and R, which are like talking to computers to handle data. I also know how to analyze the huge amounts of information that come from advanced DNA sequencing machines – this is a big part of my work. I learned how to set up and manage databases to store all this data efficiently. I also use machine learning, which helps computers find patterns and make predictions from the data.
Beyond computer skills, I’m good at solving complex problems and breaking them down into smaller steps. Lastly, I’ve learned to explain my scientific findings clearly to everyone, not just other scientists.
What’s a typical day like?
My day is usually a mix of different activities. I start by checking on my ongoing computational analyses; sometimes, these analyses can run for hours or even days on a supercomputer! I’ll then spend time writing and debugging code for new analysis pipelines or refining existing ones. I often have meetings with scientists and researchers to discuss their biological questions and how bioinformatics can help answer them. This involves brainstorming, designing experiments, and interpreting results. I also spend time analyzing data, looking for interesting patterns, and creating visualizations (charts, graphs, genetic trees) to make those patterns understandable. I regularly write scientific manuscripts or work on grant proposals. Every day is a puzzle, and it’s exciting to piece together the solutions!
What is it you love about this job?
What I love most about my job is the feeling that I am contributing to something meaningful. I get to use my skills to help solve real-world problems related to health and disease. It’s incredibly satisfying to take raw, complex data and transform it into knowledge that can lead to new vaccines, better diagnostic tools, or more effective treatments. The constant challenge of learning new things, the opportunity to work with brilliant scientists, and the potential for my work to positively impact society are what make this job truly rewarding.
How does your work benefit society?
My work in bioinformatics supports public health by helping track and control infectious diseases, such as COVID-19 and antibiotic-resistant infections, through genomic and transcriptomics analysis. I contribute to developing better treatments by uncovering how diseases work at the molecular level, and I support faster diagnostics by identifying unique genetic markers of pathogens. Beyond my research, my role as an Associate Editor and Academic Editor for scientific journals also helps society by ensuring that high-quality, reliable research is published and shared widely, which is crucial for advancing scientific understanding globally. My research also aids in predicting future outbreaks and advancing scientific understanding, while mentoring the next generation of bioinformaticians to continue this important work.
Tell us an example of a specific memorable work you did that is very close to you!
One project especially close to my heart is the development of “Gen2Epi,” a computational pipeline I designed for Neisseria gonorrhoeae, a bacterium increasingly resistant to antibiotics.The challenge was to link large-scale genome data to antibiotic resistance patterns and transmission dynamics. Gen2Epi automates this process—it takes raw genome sequences and identifies resistance markers and relationships between strains, helping public health officials track and manage outbreaks more effectively. This project was memorable for two reasons:
First, it was an amazing feeling to build something that could do days of work automatically.
Second, knowing that my work directly helps fight antibiotic resistance and improves public health is incredibly rewarding. We even made it easy for others to use, which makes its impact even bigger!
Your advice to students based on your experience?
Here’s my advice for all of you: always follow your curiosity and keep learning, because the world is always changing. Practice solving problems, and don’t be scared of challenges or even making mistakes – they help you grow stronger. Make sure to build a “toolbox” of skills, like coding or good communication, because the more you know, the more opportunities you’ll find. Reach out to others and work together, and most importantly, find something you’re passionate about that also helps make the world a better place.
Future Plans?
My future plans in bioinformatics focus on applying advanced computational methods to address global health challenges. I aim to enhance disease prediction through improved machine learning models, support the One Health approach by exploring connections between human, animal, and environmental health, and create accessible, user-friendly bioinformatics tools. I’m also dedicated to mentoring future scientists and building international collaborations. Ultimately, I want to drive data-driven biological research, develop innovative solutions, and contribute to better public health outcomes through bioinformatics.