Bioinformatics: Introduction, History, Goal, Applications & Future
Bioinformatics is an interdisciplinary field that develops biological data comprehension methods and software tools, especially when the data set is large and complicated. Bioinformatics combines biology, computer science, information engineering, math, and statistics as an interdisciplinary field in science in order to analyze and interpret biological information. For In-silico analysis of biological questions using mathematical and computational methods, Bioinformatics was used.
In bioinformatics, biological studies using computer programming in their process and specific analyses, especially in the field of genomics, contain “pipelines” which are repeatedly used. Bioinformatics common uses include identifying candidate genes and single nucleotide polymorphisms (SNPs).
Such identification is also performed in order to better understand the genetic base of illness, the special adaptations, beneficial properties (specifically in agricultural species), or demographic differences. Bioinformatics also attempts in a less systematic way to explain the organizational concepts in nucleic acid and protein sequences, known as proteomics.
Introduction to Bioinformatics
In several fields of biology, bioinformatics has become an important aspect. Bioinformatics techniques, such as image and signal processing, allow the extraction of useful results from large numbers of raw data in experimental molecular biology. It helps to sequence and annotate genomes and their mutations in the field of genetics.
It plays a role to organize and question biological data in the mining of biological literature and the development of biological and gene ontological research. It also plays a role in the study and control of gene and protein expression.
Bio-informatics instruments help to compare, analyze, and interpret genetic and genomic data and to consider evolutionary aspects of molecular biology in more general terms. It helps to examine and catalog the biological trajectories and networks that are an integral part of system biology at a more integrative level. In structural biology, DNA, RNA, proteins[and biomolecular interactions are simulated and modeled.
History of the Subject
The term bioinformatics has historically not meant what it means today. The definition of bioinformatics as a parallel to biochemistry (the study of chemical processes in biological systems) was adopted by Paulien Hogeweg and Ben Hesper in 1970.
Sequences in the study
Computers became essential in molecular biology when protein sequences were available after Frederick Sanger determined the insulin sequence at the beginning of the fifties. Comparisons of several sequences turned out not to be practical manually. Margaret Oakley Dayhoff was one of the pioneers in this field.
Aim of the Study
In order to investigate how normal cellular activity in various disease states is modified, biological data should be combined to provide a comprehensive overview of these activities. The field of bioinformatics has therefore evolved so that the most urgent task now is the analysis and interpretation of different types of data.
This includes nucleotide and amino acid sequences, protein domains, and protein structures. The actual data process is known as computational biology. Important bioinformatics and computer biology subdisciplines include:
- Computer programs developed and implemented that enable effective access, management, and use of different types of information.
- Development of new algorithms (mathematical formulas) and statistical measures to evaluate relations between large data sets members. For instance, methods exist for finding a gene within a sequence, for predicting protein structure and/or function, and for clustering protein sequences in families of associated sequences.
Bioinformatics’ primary goal is to increase the understanding of biological processes. But its focus on developing and applying computationally intensive techniques to achieve this goal is what distinguishes it from other approaches. Examples include pattern recognition, data mining, algorithms for machine learning, and visualization.
Major research efforts in this field include sequence alignment; gene finding; genome assembly; drug design; drug discovery; alignment of protein structure; prediction of protein structure; prediction of gene expression, and interactions between protein and protein; genome-wide association studies; modeling of development; and cell division/mitosis.
Bioinformatics now involves the creation and promotion of databases, algorithms, computational and statistical techniques, and theory to address the formal and practical issues of biological data management and analysis.
In the past decades, rapid developments in genomics and other molecular research technologies and IT technologies have combined to produce an enormous amount of molecular biological information. These mathematical and computing approaches used to gather an understanding of biological processes are called bioinformatics.
Common bioinformatics activities include mapping and analysis of DNA and protein sequences, comparing DNA and protein sequences, and developing and viewing 3D protein structures.
Example of the capabilities of bioinformatics
Consider the human genome to gain an idea of the stunning amounts of data and information bioinformatics have to deal with. A genome is a complete set of DNA of an organism.
DNA molecules consist of two twisting strands, each of which consists of nucleotide bases, including the following:
- Adenine (A)
- Thymine (T)
- Guanine (G)
- Cytosine (C)
The human genome consists of approximately 3 billion base pairs. Genome sequence involved the exact order of all 3 billion of these nuclear DNA nucleotides, which without a massive amount of calculation power would not have been possible.
The DNA of thousands of organisms was decoded and a vast library of genetic data was created.
This has created many subfields that use these data in various ways. Computer evolutionary biology is an example. This field of study examines how the DNA of a species changes over time and provides far greater detail than physical comparison could provide.
Why should you learn and study bioinformatics?
Bioinformatics has become an interdisciplinary science and you will find that your experiments and research can greatly help you if you are a biologist.
The job industry today is full of vacancies for people with bioinformatics skills. Major pharmaceutical, biotech, and software companies seek to employ professional bioinformatics experts to provide them with enormous amounts of information on biology and healthcare. You can check out our job section for a number of bioinformatics jobs.
A major application of bioinformatics in precision medicine and preventive medicine is found. Precision medicine consists of individual medical techniques, including treatments and practices, tailored to each patient. The focus of precision medicine is not on treating or treating diseases but on developing disease prevention measures. Some of the diseases are influenza, cancer, cardiovascular conditions, and diabetes.
Research is underway to identify genetic changes in patients that allow scientists to develop better treatments and even possible prevention measures. Some types of cancer caused by such genetic changes can be previously identified and treated before conditions get worse. More information on the role of bioinformatics in the treatment of cancer is provided at the National Cancer Institute.
How to approach bioinformatics?
You will have to learn a little bit about biology before you get deeper into the subject, as the beginning steps. The study includes genes, DNA, RNA, protein structures, different processes of synthesis, etc.
Next, you will need to study biological sequences (e.g. DNA, RNA, and protein sequences) and techniques to identify and analyze diverse patternings and informative sites within them.
Different algorithms using different techniques are found. You will also have the opportunity to use different techniques like hidden Markov models, neural networks, and clusters for machine learning and data mining.
As you deal with large amounts of data, a good understanding of statistics is crucial because data needs to be analyzed according to specific requirements.
You will need good programming skills, of course. R, Python, and Bash are the most common programming languages used in the analysis of biological data. Decide which one, to begin with, depends on your objectives. It is also possible to use other languages such as C/C++ and Java.
After understanding the fundamental concepts, you can explore other areas like structural bioinformatics, biological systems, and biological networks.
The human being is a fascinating creature, with an even more fascinating genome. The whole-genome that is stored in the DNA molecule is mind-blowing as to how large amounts of data can be coded in a single minute and precisely decoded in order to create unique human beings with their own distinctive characters. Some changes in gene expression, however, may lead to fatal genetic diseases. Healthy ecosystems need measures to identify such diseases and provide treatment and preventive actions to save lives.
Bioinformatics has proven to be very capable of identifying diseases in advance, determining treatment, and helping to improve human lives. Inspired by computer science, the fields of genetics, medicine, and health care can evolve from healing individual patients to healing entire populations.