Introduction to Bioinformatics: the beginning of a career

Along with the recent advances to scientific research in different fields, a lot of attention has been given to the important role of bioinformatics. Because of this, professionals and enthusiasts have shown interest in learning and applying the different tools born from the intersection of informatics and biology.  However, everyone needs a starting point, and for this, Omics Logic developed, especially for undergraduate and graduate students, an introductory course that addresses topics of big data bioinformatics and their applications, such as in healthcare, agriculture, environment, and others.

The Introduction to Bioinformatics course is presented in 3 main topics: 

  • Introduction to Big Data Bioinformatics; 
  • Bioinformatics in Healthcare;
  • Translational Bioinformatics.

But how does it feel to start a journey on this topic?

Well, first of all, my main motivation to start the lessons was to develop a skill which will likely be very useful in my career but that I am not very familiar with. It is important to say that learning something new is always a challenge, but even the topics that I did not have knowledge of before, I could understand based on the didactics present in the course, which is structured in texts followed by diagrams and also videos that contextualize more each topic. As an undergraduate student, the main goal of completing this course is to be exposed to different and new areas of knowledge that could pique my interest and so stimulate me to keep going and continue to learn more about them.

In the Introduction to Big Data Bioinformatics, we look back in time to understand that so much data has been generated in the last years, due to lower costs, improved storage capacity, and new computational solutions that have been created. But, it is crucial to use this data with the correct tools and skills! For this, a new form of doing scientific research emerged: the data-driven model. It was through this concept that opposes hypothesis-driven research that I could understand that data can allow us to determine our hypothesis.

An interesting path that scientists can follow is to use machine learning tools. Computers can understand and explore relationships or trends that we, as researchers, might not think about immediately. This process is known by having six steps — Identify Question, Review Findings, Design Model, Gather Data, Analyze Data, and Implement Results. Data-driven decisions can be very useful to transform large amounts of information into knowledge and solutions.

Identify Question, Review Findings, Design Model, Gather Data, Analyze Data, and Implement Results. (1)

However, all of these aspects can seem too distant from our realities, so several examples are introduced by the course. The applications in health care can be found in the laboratory but also in industry and clinics. One of the examples is that of companies that work to reduce time and cost in cancer diagnoses based on the genomic profiles of patients or that use sequencing to identify mutations in genes to make the right decision about medications and procedures. 

With these in mind, I was taken into the second lesson: Bioinformatics in Healthcare. Here the theme of precision (or personalized) medicine was in the spotlight. This term refers to the use of new diagnostics and therapeutics based on characteristics like genetics, biomarkers, phenotypic, or even psychosocial. Precision medicine is a very relevant topic when it comes to computational biology because several benefits can be brought by omics: the reduction of costs, early discovery of diseases are just two of them. 

Different Omics types

Before the approach of the personalized treatment itself, the molecular diagnostic has a relevant role, because the technique detects specific sequences in DNA or RNA associated with a disease is less invasive and less costly than other methods, and informatics help to validate it. At this point in the course, I got very curious about the possibility of this type of diagnosis in the field of mental health. It is common sense that depression is a very serious issue and each time it is being more and more discussed, not only in academia but also in the daily life of people all over the world. This possibility may be a way to shorten the search for the ideal antidepressive avoiding a long, expensive, and stressful treatment. 

Another gain of the use of informatics is that some tests discovered variants that can help to determine which drugs tend to be effective or to predict certain side effects, before deciding which one to prescribe. To sum up, the biomedical data can be used in treatment and diagnostics mainly by biomarkers specific to each patient or using the data-driven that we learned before drug discovery.

Lastly, my favorite topic is translational bioinformatics. In this lesson, we go beyond biomedical research and learn about agricultural and environmental applications!!! 

Since next-generation sequencing, it has been possible to study an immense quantity and variety of microorganisms without culturing necessity. It means that environmental biology research has been changed forever due to the fact that entire populations can be analyzed in soil, water, and also in extreme environments that are difficult to reproduce the conditions in laboratories. 

And why is this important?

The results have high value in view of the fact that it is a holistic study, searching for a complete understanding of the microbiome.  And so forth, the microorganisms are used as biomarkers for pollution. A great case is through the use of Vibrio fischeri, a bacteria bioluminescent in the presence of certain substances. Not only that but transcriptomic is shown to be valuable for revealing gene expression in microbial communities; for example, the changes in their expression can be involved in the metabolization of toxins.

As one of the last applications mentioned, agriculture is different from others, which is explained by the great importance of genetic engineering and plant breeding via biotechnology. This manipulation can increase yields, decrease pesticide use, turn plants resistant to difficult conditions, or turn foods more nutritional. So the best thing about agrigenomics, in my point of view, is the possibility of developing crops more sustainably — focusing on the impacts on the ecosystem and climate change — that will be used to produce food, biopharmaceuticals and so much more.


The highlight in agriculture through transcriptomic, proteomic, and epigenomic studies is the development of new crop varieties that can be grown under different conditions and locations! And besides, genomics also can improve the understanding of botanical diseases. Metabolomics is also relevant in crops, by evaluating microbial communities that live in the rhizome, which can help us to optimize production in a farm, for example.

After studying the materials and watching the videos I was surprised by the next subtopic: Bioinformatics in Defense, Public Health, and More. One great sample of this use is the vaccine for influenza, every year we have to be vaccinated again, not only for the decline of protection in the immune system but also because a virus changes very fast. 

This way, researchers can use tools of bioinformatics to understand in less time how a virus is changing and how to combat it! A way of doing it is through circular sequencing, or CirSeq, developed by the Andino Lab at UCSF, which allows us to investigate viruses’ population and evolution. 

To conclude, I really enjoyed this time of reviewing subjects that I saw before in university classes and also learning new applications of bioinformatics. I let a bit of advice for others that will register for this course: explore the links such as articles and platforms shown and read with critical thinking, to really understand what you like the most and how you want to follow or apply this content in your life.

Check the platform T-BioInfo  – a User-Friendly, Visual Solution for Education and Practical Applications

T-BioInfo is a multi-omics analysis platform, developed by the Tauber Bioinformatics Research Center at the University of Haifa, Israel, designed to work with genomic, transcriptomic, epigenomic, metagenomic, metabolomic, proteomic, and structural biology data. T-BioInfo has an interface simple and intuitive and using it anyone can start to analyze data because the platform is designed to allow flexibility and choice according to the user’s expertise.


Get started in bioinformatics for free with courses by OmicsLogic!  

The Omics Logic Introduction to Bioinformatics program is an introduction to the field of bioinformatics or the intersection of informatics and biology. This course is about the changing landscape of data-driven research in life sciences and the opportunities that come with the widely accessible high throughout technologies and computational methods for analysis and annotation of such detailed data in research and industry. Throughout the classes are discussed various data types, emerging technologies, and their impact on health care, agriculture, environmental sciences, public health, and more.


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