Bioinformatics – the link between molecular biology and reality – The Oakland Post


Rachel Yim, science and technology journalist

While high-throughput data generation technologies have revolutionized biological and biomedical sciences, a parallel revolution has occurred in the development of large-scale analytical tools for computational biology and bioinformatics. Although one of the most important components of science, bioinformatics is unfamiliar to many.

What is bioinformatics?

As a field of computer science, Bioinformatics is the study of biological systems through the implementation of data analysis and computer simulation approaches. This involves a variety of sequence analyzes – usually indicating those of genes and proteins. It is a particularly valuable method for comparing genes and protein sequences within an organism or between species, examining the evolutionary links between creatures, and exploring the patterns found in these sequences to identify their functions.

Computational techniques – with the aim of better understanding protein structure, gene function, disease mechanism, drug discovery, imaging of biological systems, precision medicine and analysis of other enormous datasets – should make significant contributions to the study of biological systems and problems.

What is offered at OU regarding bioinformatics?

ORGANIC 4412 – Functional Genomics and Bioinformatics

This course demonstrates “the use and implementation of computer software for sequence analysis” and covers “gene discovery, annotation, construction of phylogenetic stories, and state-of-the-art strategies used for analysis of an organism’s gene expression from a genome-wide perspective.”

ISC 4780 – Bioinformatics

This course introduces basic algorithms and computational approaches to study “biological sequence data for comparative biology and evolution,” with an emphasis on genomic content, function, and structure. Sequence database mining algorithms, pairwise and multiple sequence alignment, phylogenetic approaches, and methods for pattern identification and functional inference from sequence data are also discussed.

The two courses mentioned above are just a few of the many options available to OU students interested in such a study approach. Students can get more information from Course Catalog.

If students find this information interesting and wish to participate in research, there are a variety of research labs at UO that use this method in their studies. An example is the laboratory of Dr. Fabia Battistuzzi.

“We analyze large amounts of data such as genomes using bioinformatics technology,” said Randy Karana, a recent OU graduate and former undergraduate research assistant in Dr. Battistuzzi’s lab. “As it is impossible to analyze it by hand, we use coding to have the computer do the initial analyzes so that we can interpret the data we get.”

Another undergraduate researcher in Dr. Battistuzzi’s lab is Caesar Abaas, a top student majoring in biomedical sciences.

“My research with Dr. Fabia is trying to find an objective way to differentiate between prokaryotic species,” Abaas said. “Prokaryotes become genetically diverse through horizontal gene transfer and mutations, so our goal is to identify where the species boundaries are. My role is to create code that will simplify our genetic analysis step so that it becomes more fast.

According to Abaas, the point that connects their research to bioinformatics and computational biology is the “analysis part of the research” which includes running the genome of prokaryotes through an application called “Roary” which analyzes the genome and categorizes it into main and accessory genes. genes for later comparison.

“Our goal is to analyze currently classified species using a genome-wide approach to determine if the current classification is consistent with the expected basic genetic distribution,” Abaas said. “There will always be new strains of prokaryotes emerging, and tracking and classifying them correctly is essential. This plays an important role in the formation of many microbial populations.


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