Concentration in Bioinformatics


Scientific research is rapidly becoming a digital, information-driven field resulting in lots of data that’s increasingly more difficult to manage and understand. Bioinformatics strives to adapt the powerful information processing techniques from computer sciences to yield new and exciting discoveries from biological, medical, and health data. Bioinformaticians use powerful computational techniques such as data modeling, machine learning, data mining, and data visualization to analyze and observe biological processes like never before. They play a pivotal role in modern research within both wet laboratories and digital labs.

Bioinformatics @UNO

Bioinformatics at UNO is overseen by recognized experts in the the field whom perform ongoing scientific research with industry support. There are many exciting research opportunities for our students to participate in with our Bioinformatics and Machine Learning (BML) lab, Bioinformatics and Molecular Modeling (BMM) lab, and a wet lab at Research Institute for Children. Additionally, the Computer Science department collaborates with the Chemistry department to share domain knowledge through student presentations. This exchange provides students with practical experience in preparing, presenting, and discussing research with a group of their peers. Ongoing research at UNO includes machine learning applications, protein structure prediction and design.


The Bioinformatics concentration at UNO provides the most promising avenue for anyone interested in pursuing a career in scientific research. Bioinformatics is one of the the fastest growing careers involved in scientific research. Employment opportunities are projected to grow by over 20% from 2012-2022. Bioinformaticians are highly sought by pharmaceutical companies, universities, hospitals, clinics, research facilities of companies, educational and medical institutions, and government regulatory agencies to fill jobs paying an average of over $81k/yr.

Objectives of Bioinformatics Concentration

  • Impart upon the student a deep knowledge of biology.
  • Encourage student to exercise and develop very efficient algorithms to extract biological knowledge from complex and challenging dataset.
  • Train students to manipulate scientific datasets and retrieve and access them through databases.
  • Expose students to the standard tools, frameworks, and libraries in preparation for industry.
  • Engage students in active research in the field of bioinformatics.

Highlights of Bioinformatics Concentration

Computational Biology and Molecular Biology: Understanding the science behind the biological data is necessary for fostering the insight required for making meaningful observations. So students will become well versed in the biological sciences, with an emphasis on microbiology and cellular biology, central dogma as well as algorithmic approaches for effective computation DNA/RNA sequencing and mapping, sequence alignment, and gene prediction.

Databases: Researchers must archive very large amounts of data and share it amongst multiple people or groups. So Bioinformaticians must routinely retrieve and update datasets from databases. Students will understand how to interface with such databases and learn to acquire, store, and organize their data using common SQL operations.

Machine Learning: Use methodologies that automate the knowledge discovery process. Machine learning provides techniques for creating software that dynamically determines the criteria for making decisions about the data without human intervention. This includes techniques such as evolutionary algorithms, support vector machines, and neural networks.

Data Mining: Bioinformaticians must extract important features from extremely large datasets or identify similarities across multiple datasets. Students will apply knowledge of statistics to build software capable of clustering and classifying unknown or hidden patterns within data.

Data Visualization: Learn to create informative visual representations of complex data. In many scientific applications the data is too complex for traditional charts, graphs, and plots. As such, data visualization experts must use special techniques to develop new ways of representing this complex data. The importance of data visualization is to intuitively model complex or abstract data so that we can make observations that were difficult to make before.