Bioinformatics & Computational Biology Career

Bioinformatics & Computational Biology Career

The first and foremost question that everyone has when they hear about Computational Biology and Bioinformatics is whether are they the same or different. Should one be choosing Computational Biology or Bioinformatics as their degree course and career option? So, let’s start with finding answers to these questions first and then we will move to further discussions about the scopes of Computational Biology and Bioinformatics in the short and long term.

What is the Difference Between Computational Biology & Bioinformatics?

Aspect Computational Biology Bioinformatics
Similarity
  • Solving similar biological problems
  • Performing similar tasks with similar outcomes
  • Multi-omics data analysis
  • In situ cancer biology research
  • Use of AI, ML, deep learning, neural networks
  • Use of coding languages like Python and R
  • Involvement in drug biology
  • Solving similar biological problems
  • Performing similar tasks with similar outcomes
  • Multi-omics data analysis
  • In situ cancer biology research
  • Use of AI, ML, deep learning, neural networks
  • Use of coding languages like Python and R
  • Involvement in drug biology
Starting Point
  • Initially trained in computer/software
  • Focus more on computational aspects
  • Example: Developing databases related to biological systems
  • Initially trained in biological research
  • Focus more on biological problems
  • Example: Drug designing and discovery
Technical Approach
  • Uses more mathematical and physical calculations
  • Coding languages are central
  • Uses statistics, chemistry, biology, and coding
  • Coding is important but integrated with biological sciences
Interdisciplinarity Interdisciplinary with a focus on computational methods Interdisciplinary with a focus on biological applications
Future Prospects
  • Can switch between fields without problems
  • Interchangeable in real-life experience, jobs, applications
  • Can switch between fields without problems
  • Interchangeable in real-life experience, jobs, applications

Computational Biology and Bioinformatics share many similarities and very less differences, It can be referred to as two sides of the same coin. The similarities include solving similar Biological problems and performing similar tasks with more or less near similar outcomes as well as Multi-omics data analysis, In situ cancer biology research, and the Use of Artificial Intelligence, Machine Learning, deep learning and Neural networks to solve complex problems, Use of coding languages like python and R to analyse as well as visualize the data in a more accurate manner, Involvement in drug biology etc.

There is only a fine line between which separates the two fields is the starting point like if you are initially a trained computer/software person and you start doing biological research afterwards you can opt for computational biology as in computational biology, computational is more focused upon example includes developing databases related to Biological/Biotechnological systems and more whereas Bioinformatics is just the vice versa meaning Bioinformatics focuses on Biological problems first and apply the computer-based knowledge later on example includes drug designing and discovery and more.

So, majorly the difference between Computational Biology and Bioinformatics lies in the approach of how they solve a biological problem but the outcome is somewhat similar hence, these two career paths and courses are used interchangeably in job portals as well as by the colleges that provide the course in either of the two.

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Apart from the overview, if we move into the technicalities of both the fields we can say that computational biology uses more mathematical, and physical calculations and coding languages to solve biological problems whereas in Bioinformatics statistics, chemistry, biology and coding play a major part. It is not that either of the fields is deprived of or let go and disciplines after all both are interdisciplinary fields but the focus on some subjects is more in one field than the other and vice versa. So, further in this article, we will be discussing some of the future prospects of both fields, we are going discuss them as one because they can used interchangeably when it comes to real-life experience, jobs, applications, higher studies or future prospects for that matter because one can switch between both the field at higher levels and that won’t be facing any problems what so ever. 

Definitions of Computational Biology & Bioinformatics

Last but not least of this introduction we should at least once go through the definitions of both fields:

Stanford University defines Computational biology as “A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions sand this field encompasses all computational methods and theories applicable to molecular biology and areas of computer-based techniques for solving biological problems including manipulation of models and datasets.” [1]

On the other hand National Human Genome Research Institute (NHGRI), NIH defines Bioinformatics as “Bioinformatics, as related to genetics and genomics, is a scientific subdiscipline that involves using computer technology to collect, store, analyze and disseminate biological data and information, such as DNA and amino acid sequences or annotations about those sequences. Scientists and clinicians use databases that organize and index such biological information to increase our understanding of health and disease and, in certain cases, as part of medical care.” [2].

According to the National Cancer Institute, the definition of both field is somewhat similar as they mentions in there website that “Computational Biology is a field of science that uses computers, databases, math, and statistics to collect, store, organize, and analyze large amounts of biological, medical, and health information. Information may come from many sources, including genetic and molecular research studies, patient statistics, tissue specimens, clinical trials, and scientific journals. Also called Bioinformatics.” [3].

The future of both fields is very bright and sustainable which will be discussed now.

FUTURE OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

Keeping the fact in mind that both Computational Biology and Bioinformatics are computer-oriented fields so as the computational technologies grow, the same reciprocates to both fields. The advantages involve object-oriented and future-proof applications because computing power is evolving day by day in a fastidious manner from home desktops/laptops to High-performance computing clusters (HPCC), Cloud computing, supercomputers to now quantum computers.

As the computing power evolves so will the fields associated with it like Computational Biology and Bioinformatics, for example in the late 90’s simulating a simple protein in water for 10 nanosec. took months and now with the use of HPCC or supercomputers we can simulate more complex simulations of protein-ligand for 100 milisec. in a few days or even hours. And who knows that shortly it may take only a few seconds to minutes with the use of quantum computers which have not yet been used in drug biology but will be used someday sooner. 

Most of the biological problems are data-oriented which involves handling large amounts of data whether it be from Next-gen sequencing platforms or medical-related cohort data, all the data is to be analysed using Computational Biology and Bioinformatics pipelines using computers so that we can have highly accurate and non-redundant results. With the advancement of computational power, handling more and more data at once would become easier which will save us time as researchers. So, in the future Computational Biology and Bioinformatics will have more speed and accuracy which is the foremost requirement of any research and both fields will set an example of the same.

Bioinformatics & Computational Biology Career
Image: Future of Computational Biology & Bioinformatics

The present and the future are coding and fields associated with it like Artificial intelligence, Machine Learning, Deep Learning, Computer vision and Neural networks. Computational Biology and Bioinformatics use all the above mentioned as tools to solve complex problems related to biology which implies that both of these fields are already setting the path for the future and will thrive in the future with the advancement of all the associated fields.

Furthermore, Computational Biology and Bioinformatics always look to solve problems by using the latest and future-ready coding languages, for example, previously bioperl was used to analyse and visualise biological data which got backdated and is now replaced by more advanced languages like bio-python, python and R. Similarly, FASTA (Fast-All) which was used to match/align sequences in the past got replaced by must faster tool like BLAST (Basic Local Alignment Search Tool). So, adaptability is one of the finest qualities of both Bioinformatics and Computational Biology which ensures that it doesn’t get degraded but instead flourish in the upcoming years.

As Bioinformatics and Computational Biology use the latest possible techniques to solve complex biological problems another example comes in as the use of Artificial Intelligence and Machine learning-powered tools in every subfield of Bioinformatics and Computational Biology like for protein modelling we have AlphaFold, AI/ML based Drug discovery tools by Schrödinger and MGL, Machine Learning algorithms to train the machine using genomic cohort datasets like Genome Wide Association Studies (GWAS), Phenome Wide Association Studies (PhWAS), HapMap, 1000 Genome etc. and later on using it to classify and cluster between the mix of different test sets in order to do genomic variance analysis, genomic expression profiling and designing personalised medicine based on a specific population or individual.

Computer-aided drug designing and discovery have played a major role in the advancement of medical science by adding more accuracy and being time efficient. Now with the incorporation of AI/ML tools for drug designing has further pushed the boundaries of way the drugs are designed in-silico, Both AI/ML are CADD are part of Bioinformatics and Computational Biology so with the advancement of all these tools, medical industries, pharma industries simultaneously the fields will grow to it’s true potential and will have limitless possibilities. AI/ML is the present and the future as it has immense potential to grow, Bioinformatics and Computational Biology utilises AI/ML in many fields of biology which are as follows with examples –

Applications of AI ML in Computational Biology & Bioinformatics

  1. Spotting genes associated with diseases-
  • Scientists at the University of Washington used several machine learning algorithms to design bioinformatics pipeline which includes neural networks, support vector machines, and decision tree to test their ability of predicting and classify cancer types. [4]
  • Researchers handling The Cancer Genome Atlas project deployed RNA sequencing data  and discovered that linear support vector machine was the most precise as compared to others, hitting a 95.8% accuracy in cancer classification. [5]
  • Researchers used Machine Learning algorithms to classify breast cancer types based on gene expression data. For the same experiment they also referred  the Cancer Genome Atlas project’s data for validation. [6]
  • Researchers at the University of Pennsylvania used the ML-powered Tree-based Pipeline Optimization Tool (TPOT) to pinpoint a combination of Single Nucleotide Polymorphisms (SNPs) related to Coronary Artery Disease (CAD). [7]

2. Facilitating gene editing experiments-

  • A research team employed ML algorithms to discover the most optimal combinational variants of amino-acid residues that allow genome-editing protein Cas9 to bind with the target DNA. Due to the large number of these variants, such an experiment would have been too large, but using an ML-driven engineering approach reduced the screening burden by around 95%. [8]

3. ML to identify the primary kind of cancer from a liquid biopsy-

  • A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity. [9]

4. Predicting how a certain kind of cancer will progress in a patient-

  • ML trained using multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). The study provides a means of classifying patients based on how their tumor evolved, with implications for the anticipation of disease progression. [10]

5. Identifying disease-causing genomic variants compared to benign variants using machine learning-

  • Train a deep neural network that identifies pathogenic mutations in rare disease patients with 88% accuracy  and enables the discovery of 14 new candidate genes in intellectual disability at genome-wide significance. [11]

6. AI in immunotherapy-

  • A research team at UT Southwestern Medical Center and MD Anderson Cancer Center built an AI-powered technique for identifying which neoantigens (peptides produced by mutations in cancer cells’ genomes) are recognized by a patient’s immune system. [12]

7. Identifying protein structure-

  • One of the most successful applications in this field is using SVM and CNN to position proteins’ amino acids into three classes — sheet, helix, and coil. Neural networks can achieve an accuracy of 84% with the theoretical limit being 88%–90%. [13]
  • SML in protein model scoring, a task essential to predict protein structure. In their machine learning approach to bioinformatics, researchers from the Fayetteville State University deployed SML to improve protein model scoring. They divided protein models under question into groups and used an SML interpreter to decide on the feature vector to evaluate models belonging to each group. These feature vectors were used later to further improve the SML algorithms while training them on each group separately. [14]

8. Traversing the knowledge base in search of meaningful patterns-

  • Researchers uses CNN and SVM algorithms to traverse PubMed papers on protein-protein interactions, searching for residues that could help generate these constraints for model scoring. [15]

9. Repurposing drugs-

  •  Researchers from the China University of Petroleum and the Shandong University developed a deep neural network algorithm and used it on the DrugBank database. [16]
  • They wanted to study drug-target interactions between drug molecules and the mitochondrial fusion protein 2 (MFN2), which is one of the main proteins that can possibly cause Alzheimer’s disease. The study identifies 15 drug molecules with binding potential. Upon further investigation, it appeared that 11 of them can successfully dock with MFN2. And five of them have medium to strong binding force. [16]

 10. AI in cancer prediction through medical imaging. [17]

11. Machine learning cancer detection through self-diagnosing apps

  • SkinVision, based in Amsterdam, developed a mobile app that assists users in screening their skin abnormalities for cancer with 95% accuracy. [18]

 12. Personalizing therapies-

  • AI supported by big data, enables doctors to study diverse information about the patient and the cancer cells coming up with personalized treatments. [19] 

 13. Reducing false positives and negatives-

  • Google’s research team built an AI-powered software that cut false positives in mammogram readings down by 6% and false negatives by 9%. Another group of scientists developed an AI algorithm for breast cancer detection. During an evaluation, the model helped radiologists reduce false-positive rates by 37.3%. [20]

Bioinformatics & Computational Biology Career

Some of the salient features of Bioinformatics and Computational Biology which make them future-proof are as follows-

  1. Adaptability towards newer tools and technologies for biological data analysis including coding languages like Biopython, python, R, Artificial intelligence, Machine Learning, Deep learning and Natural Language processing etc.
  2. Use of advanced computational systems in order to handle large amounts of data in less time, example includes the use of High-performance computers like High-Performance Computing Clusters (HPCC) and Supercomputers.
  3. Remote accessibility of computers from any end of the world to the other end of the world. So, that the work can be done from anywhere without being physically present there to execute any job.
  4. Betterment in the data visualization day by day by the use of AL/ML-based tools as well as in Python and R programming. The figurative representation of any data using classic techniques was not that good and also it used to take a lot of time but now you can do that in literally no time with a much better visual graphic that further aids in the quality of the research paper publication.
  5. Time efficient as a large amount of data can be handled at once in very little time, be it Multi-omics data analysis or computer-aided drug designing and discovery or any other complex analysis. For example, the screening of thousands of drug candidates for the drug discovery process used to take years previously using wet lab techniques which can now be done within a week or so using Bioinformatics and Computational Biology pipelines.
  6. Wide range of applications in different fields of life science i.e. not only confined to a specific area but takes up biology as a whole. The application ranges from the field of molecular biology, microbiome, gene expression, genetic variance analysis to medical science, cancer biology, ecology, Sequencing data analysis and drug discovery etc.

CONCLUSION

Before concluding the article, we must discuss a very important question that arises many times while discussing any of the computer-associated fields nowadays is that:

Question: “Will modern advancements in Computational Biology and Bioinformatics, such as the inclusion of AI/ML-based tools like chatbots and GPTs, replace human jobs in these fields?”

Answer: The answer is a straight no, given you are learning the new technologies and trying to train your AI/ML algorithm in a better way so that it can solve more complex problems and so on. The data analysis and what kind of pipeline is to be used for analysis to get the desired result will always be there for humans to decode. So, it is foolish to think that AI/ML will take all our jobs instead we should think about how many opportunities it brings to the table in terms of learning, expanding boundaries and also it opens a window of limitless possibilities. 

As far as the job placements are concerned, the field of  Computational Biology and Bioinformatics is going to create a lot of job opportunities in the future as the next biggest revolution will be in Bio-IT fields and the initiative has already started and it’s growing at a faster pace. So, It is to be advised to the younger generation to take up Bio-IT, like till now we have Bio-IT fields like Bioinformatics and Computational Biology but soon some other course or field related to Bio-IT might emerge which can add to revolutionizing the future of Bio-IT in further smoother fashion.

From the above discussed we can conclude that Bio-IT fields like Computational Biology and Bioinformatics are the present as well as the future of the Bio-research and Bio-pharmaceutical industries. Our responsibility as researchers/students of biology is to explore more in the field, also deep dive into the field looking for newer possibilities and alongside helping the field grow to its truest potential which will ensure a much brighter future.

Bioinformatics & Computational Biology Career

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REFERENCES:

  1. https://cs.stanford.edu/people/eroberts/courses/soco/projects/1999-00/computational-biology/definition.html
  2. https://www.genome.gov/genetics-glossary/Bioinformatics
  3. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/computational-biology
  4. Alharbi F, Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering (Basel). 2023 Jan 28;10(2):173. 
  5. Hsu YH, Si D. Cancer Type Prediction and Classification Based on RNA-sequencing Data. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5374-5377.
  6. Wu J, Hicks C. Breast Cancer Type Classification Using Machine Learning. J Pers Med. 2021 Jan 20;11(2):61. 
  7. Manduchi E, Le TT, Fu W, Moore JH. Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection. IEEE/ACM Trans Comput Biol Bioinform. 2022 May-Jun;19
  8. Thean DGL, Chu HY, Fong JHC, Chan BKC, Zhou P, Kwok CCS, Chan YM, Mak SYL, Choi GCG, Ho JWK, Zheng Z, Wong ASL. Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities. Nat Commun. 2022 Apr 25;13(1):2219.
  9. Cristiano, S. et. al., Nature 570, 385–389, 2019.
  10. Caravagna, G. et. al., Nat Methods 15, 707–714, 2018. 
  11. Sundaram, L. et. al., Nat Genet 50, 1161–1170, 2018.
  12. Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol. 2023 Jan 9
  13. Romana Rahman Ema et. al., IJACSA,Vol. 13, No. 11, 2022
  14. Czejdo, Denny & Bhattacharya, Sambit & Spooner, Catherine. (2019). Improvement of Protein Model Scoring Using Grouping and Interpreter for Machine Learning.
  15. Xie Z, Deng X, Shu K. Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int J Mol Sci. 2020 Jan 11.
  16. https://itrexgroup.com/blog/ai-and-machine-learning-in-bioinformatics/
  17. Koh, DM., Papanikolaou, N., Bick, U. et al. Artificial intelligence and machine learning in cancer imaging. Commun Med 2, 133 (2022).
  18. https://www.skinvision.com.
  19. https://news.microsoft.com/source/features/digital-transformation/how-ai-can-help-cancer-patients-receive-personalized-and-precise-treatment-faster/
  20. https://www.cnbc.com/2020/01/02/googles-deepmind-ai-beats-doctors-in-breast-cancer-screening-trial.html

Bioinformatics & Computational Biology Career

About the Author: 

Mr. Prodyot Banerjee, Bioinformatics Scientist at Biotecnika. is a seasoned professional in Computer-Aided Drug Designing, Bioinformatics Analysis, and Genomics, with experience from CSIR-IGIB, CSIR-CLRI, IIT Madras, and Delhi Technological University. Holding an M.Tech in Bioinformatics from Delhi Technological University, he has presented his work at IIT Kharagpur and published in journals like IEEE and Frontiers in Pharmacology. A GATE 2019 qualifier from IIT Madras, Prodyot is dedicated to academic excellence and professional growth, making him a valuable asset in his field.

Shekhar
Shekhar Suman is the Co-founder of BioTecNika Info Labs Pvt. Ltd. He is an Entrepreneur, Writer, Public Speaker, and a Motivational Coach. In his career, he has mentored more than 100,000+ students toward success in the Biopharma Industry. He heads the BioTecNika Group, which comprises BioTecNika.com, BioTecNika.org, and Rasayanika.com. An avid reader and listener who is passionate about BioSciences. Today Biotecnika is India's largest Biotech Career portal, with over 5 Million subscribers from academia & Industry. It's ranked among the top 50 websites worldwide in the Biology category.

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