Computational Biology Overview

Fellow presentation and intro to your career path

‍My name is Salih Topal, and I am from Turkey. I work as a Senior Bioinformatics Scientist at a biotech company called Foghorn Therapeutics. I enjoy traveling, reading books and hiking. I was always interested in science, specifically biology, during my high school years and decided to start my scientific journey with a bachelor’s degree in Molecular Biology and Genetics. During my bachelor years, I was involved in organizing a scientific symposium focused on Neuroscience and I was amazed by the scientific findings and potentials to cure neurodegenerative diseases presented by professors from all around the world. That was the moment that I decided to pursue a higher degree in Neuroscience under Molecular Biology field.

It was a wonderful opportunity for me to specialize and obtain a master's degree in Neuroscience and decide whether I would want a career in this field or not. During my last year of the Neuroscience program, I did my thesis research in Developmental Neuroscience and most of my findings were highly relevant to Epigenetics, which introduced me further into this field. At this point, I decided to continue with a doctoral degree in Epigenetics. During my doctoral degree, I had the opportunity to combine epigenetics with computational biology.

As a Senior Bioinformatics Scientist, I analyze both in-house and public large sequencing datasets to understand the mechanisms of actions of certain drugs, extract information to build new hypotheses and provide novel ideas or targets for the company to pursue.

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‍Computational Biology career options 

Computational Biology consists of various sub-fields including Bioinformatics, AI/Machine Learning, Genomics, Functional Genomics, Computational Genomics, Data Analysis and Data Mining, etc. The following example career paths are for Scientist level positions, which requires for a PhD degree (or similar amount of industry experience). You can also pursue these paths without a PhD degree as Research Associate (Junior/Senior/Principal levels).

• ‍Scientist, Computational Biology
  Analyzing large sequencing datasets to understand mechanism of actions of certain drugs and answer biological questions
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• ‍Scientist, Bioinformatics
  Using bioinformatics technologies to build new pipelines to analyze sequencing datasets
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• ‍Scientist, Functional Genomics
  Discussing experimental ideas with other scientists and analyzing genomics-based datasets
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• ‍Scientist, AI/Machine Learning
  ‍Using AI or machine learning technologies to build new pipelines for data analysis, infer or simulate different biological phenomenon, build, and train different datasets
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• ‍Scientist, Computational Genomics
  ‍Building computational flowcharts and experimental designs for genomics experiments, analyzing genomics datasets
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• Scientist, Oncology Bioinformatics
  Using bioinformatics technologies to build new pipelines to specifically analyze oncology datasets
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• ‍Scientist, Bioinformatics Software Engineer
  Building, maintaining and troubleshooting softwares for data analysis and data mining

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Senior or Principal scientist levels of the above paths also require additional roles such as coordinating with other scientists, mentoring research associates or other scientists, leading computational biology efforts of different biological programs.

After the Principal Scientist position, you can continue in the career as Associate Director or Director (or Senior Director). The role of these positions would be managing the whole computational biology (or bioinformatics) team, coordinating with the project leaders from the biology side and maintaining a healthy, interactive relationship between their team and the rest of the research organization.

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‍Main hard skills you use on daily basis in your current job

As a Senior Computational Biologist in Chromatin and Oncology fields, I use following skills on daily basis:

• Advance knowledge of programming/coding (Python, R, etc.)
  I have learned Python and R during my PhD project to be able to analyze my sequencing datasets that I had been producing in the wetlab. I took a couple of different courses available in the graduate program and asked for help from Senior Bioinformaticians within the program. I use both languages daily in my work to analyze large datasets and visualize them.
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• Writing, maintaining and updating codes (Jupyter Notebook, GitHub)
  One important part of a computational biologist's life is documenting the codes that are written, so the other computational biologists can replicate the work. I use Jupyter Notebook to write the codes and use them. I also deposit these codes in GitHub, so other team members can use them as well.
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• Deep understanding of biology (specifically chromatin and epigenetics, oncology)
  Depending on which career path you choose, you might need to have a good understanding of biology to be able to interpret the data you analyze. I took multiple courses in molecular biology, epigenetics, chromatin, and oncology during my undergraduate and graduate years. Knowing biology helps by asking the right questions and doing the most efficient analyses.
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• Advance knowledge of public databases (TCGA, Dependency Map)
  ‍I often need to check certain parameters for certain cell lines while doing analysis. Dependency Map is the best resource for checking any type of sequencing information for cancer cell lines. I also frequently compare it to patient samples, which is the information that can be extracted from TCGA. I learned these resources when I started working as a computational biologist and they are indispensable for my daily work.
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• Documenting the experiments and/or analyses (Labguru – ELN)
  ‍I always write my analysis in the ELN (Electronic Lab Notebook), so it can be easily shared with other scientists, and it also acts as an evidence of the analysis with a time stamp.
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• Presenting the results (scientific work) to the whole research team
  ‍I regularly present my results to the other members of the computational biology team, biology team or the whole company. Depending on the audience, I need to adjust my slides to have detailed bioinformatics or more biology-oriented visualization. This was a skill I acquired over time by presenting in different courses or programs during my undergraduate and graduate years.

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‍Soft skills you use on daily basis in your current job

• ‍Collaboration
  ‍One of the most important soft skills that I need to use in my daily job is collaborating with other computational biologists, but more importantly with other bench scientists. Apart from analyzing public datasets to find new targets or understand a mechanism of action, I also analyze datasets produced by bench scientists. So, it is key to collaborate with them, understand the experimental setup and follow up with the right analysis.
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• ‍Communication
  Actually, collaboration and communication are in balance together. During my collaborations with bench scientists, it is important to be able to communicate clearly, so I can understand the wet lab experiment clearly and the bench scientist can understand the analysis I am about to do clearly.
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• ‍Managing projects
  As a Senior Computational Biologist, I lead computational analysis efforts of multiple projects that we are interested in in the company. To be able to manage projects well, I need to collaborate (analyze the data) and communicate (get updates from bench scientists).
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• ‍Leadership
  As part of my job, I mentor junior Research Associates about how to do certain analyses, how to manage time efficiently, follow their progress and make appropriate suggestions to help them excel in their jobs.

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‍Your personal path

My personal experience with job search was not an easy one. I completed my graduate work and got my PhD degree in January 2020, and started looking for jobs in computational biology field in February 2020, when Covid pandemic started showing its impact in the industry. At that point, I had applied for about 25-30 jobs and had some interviews lined up. However, those interviews were not successful for me, because it was either not the right job, or I did not perform well during the interview process, or they just announced hiring freeze due to the pandemic. When most of the companies announced hiring freezes in April 2020, I had decided to do post-doctoral research in my graduate work lab until the market was going upwards again.

During this time, I also tried to expand my networking, I attended Zoom meetings with people from industry organized by my school, attended some LinkedIn webinars, had informational interviews with some companies. By August 2020, companies started posting jobs again and I started applying for more jobs. It is not easy to find your dream job, but luckily, I saw this post about a computational biologist needed in chromatin/epigenetics field (which is the field that I did my PhD in) and applied for it. The interview process was exceptionally smooth, because I really enjoyed talking with everyone that I interacted with, and my background was a great fit for the job.

So, as you can see from my experience, it takes some time to find a job or even your dream job. However, do not be discouraged! As long as you are persistent in finding a job and keep applying, you will find a job that you really like.

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