Bioinformatics Databases

Information science has been applied to biology to produce a field called Bioinformatics. For understanding biological data different methods and software tools are developed using an interdisciplinary field (combining or involving two or more academic disciplines or fields of study) is said to be a bioinformatics database. 

The central theme of modern biological researchers is to get access to the genomic information and synthesize it for the discovery of new knowledge. Finding these genomic sequences requires advanced tools.

It is essential for biologists to initiate and familiarize with a field of study that is concerned with the careful storage, organization and indexing of information in order to tackle the new challenges in the genomic era.

Bioinformatics has been used in silico analysis of biological queries using mathematical and statistical techniques. To explain it in a single word, Bioinformatics databases are databases consisting of biological data. 

Objectives of Bioinformatics

Bioinformatics mainly has three objectives

  • To organize data in an efficient manner
  • To develop tools that help to analyze such data
  • To interrupt the results accurately. 

What is the Concept of Bioinformatics?

Simply, bioinformatics is the science of storing, retrieving and analyzing large amounts of biological information. 

It is a highly interdisciplinary field involving many different types of specialists, including biologists, molecular life scientists, computer scientists and mathematicians.

The term bioinformatics was coined by Paulien Hogeweg and Ben Hesper to describe "the study of informatic processes in biotic systems" and it found early use when the first biological sequence data began to be shared.

Whilst the initial analysis methods are still fundamental to many large-scale experiments in the molecular life sciences, nowadays bioinformatics is considered to be a much broader discipline, encompassing modeling and image analysis in addition to the classical methods used for comparison of linear sequences or three-dimensional structures.

Traditionally, bioinformatics was used to describe the science of storing and analyzing biomolecular sequence data, but the term is now used much more broadly, encompassing computational structural biology, chemical biology and systems biology (both data integration and the modeling of systems).

molecular life sciences have become increasingly data driven by and reliant on data sharing through open-access databases. 

This is as true of the applied sciences as it is of fundamental research.

Furthermore, it is not necessary to be a bioinformatician to make use of bioinformatics databases, methods and tools.

However, as the generation of large data-sets becomes more and more central to biomedical research, it’s becoming increasingly necessary for every molecular life scientist to understand what can (and, importantly, what cannot) be achieved using bioinformatics, and to be able to work with bioinformatics experts to design, analyze and interpret their experiments.

Advantages of Bioinformatics

  • Theoretically justified very well. 
  • Assumptions are explicit resulting in that they can be improved and evaluated. 
  • ML applications are gently consistent. 
  • In most cases by applying sequence simulation experiments, it has shown that this approach outperforms all others.

Disadvantages/ Limitations of Bioinformatics

  • Bioinformatics is Bioinformatics and experimental biology are independent, but complementary, activities.
  • Bioinformatics depends on experimental  science to produce raw data for analysis.
  • It, in turn, provides useful interpretation  of experimental data and important leads for further experimental research.
  • Quality of bioinformatics predictions depends on the quality of data and the sophistication of the algorithms being used.
  • Sequence data from high throughput analysis often contain errors. If the sequences are wrong or annotations incorrect, the results from the downstream analysis are misleading as well.
  • They often make incorrect predictions that make no sense when placed in a biological context. Errors in sequence alignment.

Applications of Bioinformatics

Bioinformatics has not only become essential for basic genomic and molecular biology research, but is having a major impact on many areas of biotechnology and biomedical sciences. The main uses of bioinformatics include:

  • Bioinformatics plays a vital role in the areas of structural genomics, functional genomics, and nutritional genomics.
  • It covers emerging scientific research and the exploration of proteomes from the overall level of intracellular protein composition (protein profiles), protein structure, protein-protein interaction, and unique activity patterns (e.g. post-translational modifications).
  • Bioinformatics is used for transcriptome analysis where mRNA expression levels can be determined.
  • Bioinformatics is used to identify and structurally modify a natural product, to design a compound with the desired properties and to assess its therapeutic effects, theoretically.
  • Cheminformatics analysis includes analyses such as similarity searching, clustering, QSAR modeling, virtual screening, etc.
  • Bioinformatics is playing an increasingly important role in almost all aspects of drug discovery and drug development.
  • Bioinformatics tools are very effective in prediction, analysis and interpretation of clinical and preclinical findings.

What Are Bioinformatics Databases? 

Databases are essential for bioinformatics research and applications. There are many types of databases like DNA and protein sequences, molecular structures, phenotype and biodiversity. Databases may contain empirical data or predicted data or both. These databases may be specific to particular organisms, pathways or molecules of interest. 

Types of Bioinformatics Databases

These are classified on the basis of:

  1. Data type
  2. Data source
  3. Database design
  4. Special category

01. Data Type

Database data types refer to the format of data storage that can hold a distinct type or range of values.  When computer programs store data in variables, each variable must be designated a distinct data type.  Some common data types are as follows: Integers, characters, strings, floating-point numbers and arrays. 

More specific data types are as follows: Varchar (variable character) formats, Boolean values, dates and timestamps. There are programming languages that require the programmer to determine the data type of a variable before attaching a value to it.  While some programming languages can automatically attach a data type to a variable based on the initial data assigned to the variable. 

For example, a variable is assigned with the value “3.75”, then the data type that will be attached to the variable is floating point. Most of the programming languages enable each variable to store only a single data type.  For example, if the data type attached to the variable is integer, when you assign a string data to the variable, the string data will be converted to an integer format.

02. Data Source

A data source is simply the source of the data. It can be a file, a particular database on a DBMS, or even a live data feed. The data might be located on the same computer as the program, or on another computer somewhere on a network.

For example, a data source might be an Oracle DBMS running on an OS/2® operating system, accessed by Novell® Netware; an IBM DB2 DBMS accessed through a gateway; a collection of Xbase files in a server directory; or a local Microsoft® Access database file.

The purpose of a data source is to gather all of the technical information needed to access the data - the driver name, network address, network software, and so on - into a single place and hide it from the user.

The user should be able to look at a list that includes Payroll, Inventory, and Personnel, choose Payroll from the list, and have the application connect to the payroll data, all without knowing where the payroll data resides or how the application got to it.

The term data source should not be confused with similar terms. In this manual, DBMS or database refers to a database program or engine.

A further distinction is made between desktop databases, designed to run on personal computers and often lacking in full SQL and transaction support, and server databases, designed to run in a client/server situation and characterized by a stand-alone database engine and rich SQL and transaction support. Database also refers to a particular collection of data, such as a collection of Xbase files in a directory or a database on SQL Server. 

It is generally equivalent to the term catalog, used elsewhere in this manual, or the term qualifier in earlier versions of ODBC.

03. Database Design

Database design is the organization of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. Database management system manages the data accordingly.

Database design involves classifying data and identifying interrelationships. This theoretical representation of the data is called an ontology. The ontology is the theory behind the database's design. In a majority of cases, a person who is doing the design of a database is a person with expertise in the area of database design, rather than expertise in the domain from which the data to be stored is drawn e.g. financial information, biological information etc. 

Therefore, the data to be stored in the database must be determined in cooperation with a person who does have expertise in that domain, and who is aware of what data must be stored within the system.

This process is one which is generally considered part of requirements analysis, and requires skill on the part of the database designer to elicit the needed information from those with the domain knowledge. 

This is because those with the necessary domain knowledge frequently cannot express clearly what their system requirements for the database are as they are unaccustomed to thinking in terms of the discrete data elements which must be stored. Data to be stored can be determined by Requirement Specification.

04. Special Category

The UK GDPR singles out some types of personal data as likely to be more sensitive, and gives them extra protection:

  • Personal data revealing racial or ethnic origin;
  • Personal data revealing political opinions;
  • Personal data revealing religious or philosophical beliefs;
  • Personal data revealing trade union membership;
  • Genetic data;
  • Biometric data (where used for identification purposes);
  • Data concerning health;
  • Data concerning a person’s sex life; and
  • Data concerning a person’s sexual orientation.

In this guidance we refer to this as ‘special category data’.

The majority of the special categories are not defined and are fairly self-explanatory. However specific definitions are provided for genetic data, biometric data and health data.

Importance of Bioinformatics Database

  • Bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data.
  • In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. 
  • It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data.
  • It also plays a role in the analysis of gene and protein expression and regulation. 
  • Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology.
  • At a more integrative level, it helps analyze and catalog the biological pathways and networks that are an important part of systems biology.
  • In structural biology, it aids in the simulation and modeling of DNA, RNA, proteins as well as biomolecular interactions.


As we discussed above, the Bioinformatics database has become an essential interdisciplinary scientific field to the life science helping to “omics” field and technologies and mainly handling and analyzing “omes” data. Accumulation of high-throughput biological data due to the technological advances in “omics” fields required and prioritized the use of bioinformatics resources, and research and application for the analysis of complex and even further enlarging “Big Data” volumes, which would be impractical and useless without bioinformatics.

Therefore, as highlighted herein, there is a critical need for the preparation of well-qualified, new generation scientists with integrated knowledge, multilingual ability, and cross-field experience who are capable of using sophisticated operating systems, software and algorithms, and database/networking technologies to handle, analyze, and interpret high-throughput and increasing volume of complex biological data.

So reading the above information, now you got a sound knowledge on

  • What a bioinformatics database is
  • The objectives of bioinformatics database
  • The limitations, applications, advantages and importance of bioinformatics database
  • Types of databases 

Hope this information is useful for you  and gives you some knowledge to perform well in your exam. 


Here are some FAQs related to Bioinformatics databases.

1. What are the branches of bioinformatics?

It can be classified into various categories but broadly into plant and animal bioinformatics.

Hence, the application of Bioinformatics is multi-fold and can be found in various domains! Are you looking forward to pursuing higher education in this field from universities abroad? Let the experts at Leverage Edu assist you in starting your academic journey. Book your free 30 minutes e-counselling session with the team and discover new educational possibilities.

2. What are examples of bioinformatics?

Genbank, Predictprotein, BLAST are some of the examples of bioinformatics.

3. What are the advantages of applications of bioinformatics?

The major advantage of the application of bioinformatics is in the field of medical science. With the help of bioinformatics, it is possible to identify and develop new treatments for diseases

4. What are the top application tools of bioinformatics?

The application of bioinformatics is in various sectors, fields, and different purposes. Some of them are the plant genetic resources database, biometrical analysis, and storage and retrieval of data.

5. What are the top application tools of bioinformatics?

Biojava, GALAXY, Autodock are the top application tools of bioinformatics.


  1. Bioinformatics by Dr. Siddhi Upadhyay |
  2. Bioinformatic - Computer Application in Pharmacy | 
  3. Bioinformatics- Introduction and Applications |
  4. Bioinformatics – Computer Application in Pharmacy |
  5. Application of Bioinformatics |
  6. Importance of Bioinformatics |

Dr. V Bhavyasri

She's is a Pharm.D student who loves to collect and review papers. She's an expert with slides, and feels that confidence and persistence are the keys to success.

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