Bioinformatics, Computational Biology, and Biostatistics

- Overview

Computational biology and bioinformatics is an interdisciplinary field that develops and applies computational methods to analyze large amounts of biological data, such as gene sequences, cell populations, or protein samples, to make new predictions or discover new biology. Computational methods used include analytical methods, mathematical modelling and simulations. 

Biologists encounter a staggering amount of data in their daily work. Since 2015, genomic data alone has grown faster than any other data type, and is expected to reach 40 exabytes per year by 2025. This rapid growth presents new challenges for industry professionals in acquisition, storage, distribution and analysis.

While bioinformatics and computational biology sound similar, they are different disciplines that scientists can use to help manage and understand all of this data. Here are some of the key differences between computational biology and bioinformatics, and when scientists should turn to them for analysis.

- The Key Difference

The terms computational biology and bioinformatics are often used interchangeably. However, computational biology sometimes means the development of algorithms, mathematical models, and statistical inference methods, while bioinformatics is more associated with the development of software tools, databases, and visualization methods. 

Computational biologists and bioinformaticians routinely utilize data generated by modern high-throughput analyses, including microarrays, mass spectrometry, confocal microscopy, sequencing, and other biotechnological advances. 

The respective fields of bioinformatics and computational biology are often integrated in laboratories, research centers, or colleges. Since both fields rely on the availability and accuracy of datasets, they often help each other achieve their respective project goals. Computational biology emphasizes the development of theoretical methods, computational simulation, and mathematical modeling, while bioinformatics emphasizes informatics and statistics. 

Although the two fields are interrelated, bioinformatics and computational biology differ in the types of needs they address. While both fields seek to make greater use of our collective understanding of biology, bioinformatics tends to focus on the collection and management of biological data, while computational biology focuses on the practical application of biological data. 

Bioinformatics systems engineering is generating new knowledge for patient diagnosis and treatment as well as for animal and plant sciences. Genetic modification has the potential to impact the future of global drug delivery.

- Bioinformatics

Bioinformatics refers to the data management and processing of biomolecular data, often collected on a genome-wide scale. Bioinformatics is the process of interpreting and analyzing biological questions posed by evaluating or studying biological data. Bioinformatics professionals develop algorithms, programs, codes, and analytical models to record and store biologically relevant data. This includes studies of the human genome, biochemical proteins, pharmacological components, metabolic pathway readouts, and more. These datasets form the basis for what is often seen as the next step in the process: computational biology. 

Bioinformatics is key to the future of precision medicine and aims to store, organize, explore, extract, analyze, interpret and utilize information from biological data. Translational bioinformatics is a rapidly emerging field of biomedical data science and informatics technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health impacts. It focuses on applying informatics methods to growing biomedical and genomic data to form knowledge and medical tools that can be used by scientists, clinicians and patients. In addition, it involves applied biomedical research to improve human health through the use of computer-based information systems. Translational bioinformatics employs data mining and analytical biomedical informatics to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to make treatment recommendations, and predicting health outcomes. 

The vast amount of molecular biology information generated by high-throughput genomics, proteomics and other 'omics' projects presents challenges to understanding the roles of genes, proteins and other molecules and their applications in health, wellbeing, agriculture, social applications , and the environment.

- Computational Biology

Computational biology is concerned with solutions to problems posed by bioinformatics research. In many cases, the phrases "bioinformatics" and "computational biology" are used interchangeably, especially in job descriptions or job titles. That's partly because both fields have only been around for a few decades. Computational biology has been used to build highly detailed models of the human brain, map the human genome, and assist in modeling biological systems. Computational biology research, development and implementation of algorithms or tools to solve biological problems, concerns or challenges presented by bioinformatics analysis. 

While computational biology relies on computers and technology, it generally does not imply the use of machine learning and other more recent computational developments. Computational biology deals with all parts of biology that are not included in big data.

- Genomics and Bioinformatics

Genome technologies are generating an unprecedented amount of information in the history of biology. Bioinformatics addresses the specific needs of data acquisition, storage, analysis, and integration from genomics research. 

Bioinformatics systems engineering is generating new knowledge for patient diagnosis and treatment as well as for animal and plant sciences. Genetic modification has the potential to impact the future of global drug delivery. 

In the past few years, tremendous progress has been made in unraveling the mysteries of life. This advance is driven by technological advances that enable sequencing of biomolecules (DNA/proteins) and the development of efficient computational algorithms to analyze the vast amounts of data generated by these techniques. These advances have allowed us to decipher the genomes of thousands of species and have provided important insights into the inner workings of cells. 

Genomic technologies are best defined as technologies used to manipulate and analyze genomic information. The evolution of this collective power began with the invention of DNA cloning in the 1970s, with much of the technology coming from the second half of the 20th century. The historical impact of these technologies is obviously enormous.

- Biostatistics

Biostatistics is a compound word consisting of biology and statistics, as defined by etymology; the application of statistics in biology. Historically, the field of statistics has emerged and systematically developed to address various problems in biology, particularly in morphometry (measurement of morphological characteristics) and population genetics. 

It was only later that the field began to find application in various other disciplines, especially in the quantitative fields of the humanities (psychology and economics), so the original meaning of statistics was steadily expanded, so the use of biostatistics was needed to refers to biological statistics. The word statistics now has a new meaning,

- Next-Generation Sequencing (NGS)

Next-generation sequencing (NGS) is a massively parallel sequencing technology that provides ultra-high throughput, scalability, and speed. This technique is used to determine the sequence of nucleotides throughout the genome or in target regions of DNA or RNA.

Next-generation sequencing (NGS), massively parallel or deep sequencing are related terms to describe DNA sequencing technologies that have revolutionized genomic research. The entire human genome can be sequenced in one day using NGS. 

Whole genome sequencing (WGS) provides the most comprehensive data on a given organism. Using NGS can provide a large amount of data in a short period of time. Since you are analyzing the entire genome, previously unknown genes or variants can be discovered. The key difference is that NGS sequences millions of fragments in a massively parallel fashion, increasing speed and accuracy while reducing sequencing costs.

DNA sequencing refers to a general laboratory technique used to determine the exact sequence of nucleotides or bases in DNA molecules. Base sequences (usually represented by the initials of their chemical names: A, T, C, and G) encode the biological information that cells use for development and manipulation. NGS is sequencing volume. 

While the Sanger method only sequences a single DNA fragment at a time, NGS is massively parallel, sequencing millions of fragments simultaneously per run. This process translates into sequencing hundreds to thousands of genes at a time. RNA sequencing (RNA-Seq) takes advantage of next-generation sequencing (NGS) to detect and quantify RNA in biological samples at a given time point. 

High-throughput sequencing is another name for NGS. NGS allows rapid base pair sequencing of DNA samples. NGS is advancing drug development and paving the way for the future of personalized medicine, genetic diseases, and clinical diagnostics. In addition, NGS is a massively parallel sequencing method capable of determining the sequence of nucleotides in the whole genome, which is scalable, ultra-high-throughput, and fast.