Bioinformatics Approach in Biological Sciences: History and Uses

1. Introduction:

Biological data is defined as any data that can be derived from living organisms as well as their products. It means any genetic, biochemical, physiological, and biological activities, characteristics, or structures of living organisms and their products. In fact, biological data is considered complex data that can include linking between protein structure and genomic sequence. Therefore, the science of bioinformatics has emerged 1-5.

Bioinformatics is a scientific interdisciplinary field that integrates biological sciences with computer and information technology to collect, organize, store, analyze, and interpret all types of biological data (Figure 1) 5-11.

Fig 1: Bioinformatics approach for biological data7

In 1970, Paulien Hogeweg, a Dutch scientist, was the first scientist to use the term "Bioinformatics" in order to study biological data by using information technologies. In the past, before the era of bioinformatics, there were only two ways to conduct biological experiments including: 1) in vivo, in a living organism, and 2) in vitro, in an artificial environment. However, in the era of bioinformatics, biological experiments can be conducted in silico, computer experiments, within a few hours 12-14.

Bioinformatics has many significant roles in different sciences, especially -omics in biology. Also, Bioinformatics has different applications, tools, and databases in order to collect, organize, store, analyze, and interpret all types of biological data. Therefore, this paper would summarize the history, applications, databases, tools, advantages, limitations, and future of bioinformatics in biology.

2. History of bioinformatics:

Although it is common for researchers and students to think that current bioinformatics is a relatively new field, bioinformatics first emerged over 50 years ago, when desktop computers were still a theory and deoxyribonucleic acid (DNA) sequencing was not yet possible 15. Even if Paulien Hogeweg, in 1970, was the first scientist to use the term "Bioinformatics", the real moment for the beginning of bioinformatics was the discovery of DNA structure by Watson, Crick in 1953 2,3,16. Since bioinformatics is considered a major leader in today's biological sciences 15, it is important to have a connective timeline of the past, present and future of bioinformatics.

2.1. The origin of deoxyribonucleic acid (DNA) analysis (1950s - 1960):

Little was known about DNA at the beginning of the 1950s. In 1944, MacLeod and McCarty stated that the virulence in a bacteria species would be switched off by DNA 15, 17. However, the scientific community, at that time, had a different opinion that genetic information are carried on proteins 18. In 1952, Hershey and Chase proved the theory of MacLeod and McCarty 19. Finally, in 1953, Watson and Crick solved the double-helix structure of DNA 20. Also, in the end of 1950s, crystallography had been developed in defining protein structures leading to publish the first sequence of the insulin 15,21,22. At that period of time, DNA analysis had several unsolved issues and scientists were not sure.

2.2. The rise of deoxyribonucleic acid (DNA) analysis (1956s - 1970):

There was still an issue that it was hard to sequence several thousands or even hundreds of residues. This issue was solved at the beginning of the 1960s by the first known bioinformatics software 15. Margaret Dayhoff, American physical chemistry scientist, was the first scientist to use computational technologies in electrochemistry in the early 1960s 23. Then, COMPROTEIN was developed in 1962. It is a complete computer program for the IBM 7090 that used data of Edman peptide sequencing to define the primary structures of proteins. It was coded in FORTRAN on cards that are well known today as a de novo sequence assembler 15.

In 1963, Pauling and Zuckerkandl came up with ‘Paleogenetics’ as a new area of growing biology 24. Also, in 1965, Dayhoff developed a one-letter amino acid code that is still in use today. Then, Dayhoff and Eck’s first used the biological sequence database “Atlas of Protein Sequence and Structure” 15,25. At that period of time, there was a wonder whether DNA was coming to be the as the initial source of biological data or not.

2.3. Shifting protein to deoxyribonucleic acid (DNA) analysis (1970s - 1980):

In the early of this decade, using computational technologies for sequencing proteins with unequal length of sequences leading to improper results 15. However, in the early 1970s, this issue was solved when Needleman and Wunsch came up with the first dynamic programming algorithm in order to pairwise protein sequence alignments 15,26. Also, at the beginning of the 1970s, it was well known that proteins of living organisms are encoded with specific arrangements of nucleotide of the DNA. Also, all of the 64 codons of the genetic code were decoded, and DNA was readable, and it was clear to obtain DNA sequences 15.

In 1976, the Maxam–Gilbert sequencing method was the first widely accepted DNA sequencing method 27. Also, in 1977, Sanger’s team developed the DNA sequencing method that rely on primed synthesis with DNA polymerase 15. In order to predict the primary structure of a protein, sequencing must be performed. So, to obtain a protein sequence, this protein must be purified, while obtaining a genome sequence is directly from a genomic DNA extract, giving DNA sequencing an advantage over protein sequencing. Manual information extraction from DNA sequences includes: 1) comparisons to find homology among different sequences, 2) calculations to build phylogenetic trees of multiple proteins, and 3) pattern matching to find reading frames in DNA sequences 15.

However, in 1979, Roger Staden issued a software that analyzed Sanger sequencing reads 28. It uses included: 1) looking for overlaps among readings of Sanger gel, 2) verifying, editing and joining sequence reads into contigs, and 3) annotating and manipulating sequence files 15. However, at that period of time, there were some limitations of using computer technologies in DNA analysis.

2.4. Development of biology and computer sciences (1980s - 1990):

In fact, the period of the 1980s saw increased development of both computers and bioinformatics softwares 15. In the early 1980s, multiple sequence alignment (MSA) algorithms were published, leading to solve improper results due to unequal lengths of protein sequences 29. However, the actual practice of MSA was developed by Feng and Doolittle in 1987 30. In 1984, the University of Wisconsin Genetics Computer Group issued the ‘GCG’ software suite 31. Also in 1984, the DNASTAR software suite was published, as well as other suites. In 1986 and 1987, sequence databases such as GenBank, the European Molecular Biology Laboratory (EMBL), DNA Data Bank of Japan (DDBJ) united in order to regulate data formatting, define information, and facilitate data sharing. Today, this union is represented by the International Nucleotide Sequence Database Collaboration 15.

In the 1980s, the use of computers in biology became broad. Mainframe computers were used instead of microcomputers with different programming languages such as: C and FORTRAN. However, desktop workstations emerged as well as some programming languages such as: Perl (Practical Extraction and Reporting Language) and Python 15. Even at that period of time, there was increasing development of both computers and bioinformatics software; tools for facilitating DNA analysis in vitro or in silico were needed.

2.5. Genomics, bioinformatics, and information technologies (1990s - 2000):

In 1991, the National Institutes of Health (NIH) in the United States of America initiated the Human Genome Project (HGP). Institute for Genomic Research (TIGR), in 1995, sequenced the complete genome of Haemophilus influenzae considered the first sequencing of a living organism's genome 15. Therefore, the 1990s are well known as the start of the genomic era 15,32,33. In 1998, Celera Genomics assembled and sequenced one-tenth of the human genome in an NIH-funded project 34. Also, different software were developed for whole-genome sequencing reads such as Celera Assembler, PHRAP, EULER, TIGR Assembler, and MIRA 15,35-38.

In addition, in the 1990s, the globalized information network was published, and it came wide-used for data and software sharing 15,39. Also, the World Wide Web was initiated by Tim Berners at Conseil European pour la Recherche Nuclear (CERN) led to publishing different bioinformatics resources in the world such as: European Molecular Biology Laboratory (EMBL) which is the first database of nucleotide sequence 15,40. Furthermore, in 1990s, National Center for Biotechnology Information (NCBI) became responsible of GenBank database as well as establishing Basic Local Alignment Search Tool (BLAST). Then, different well-known databases were established such as: PubMed, Genomes, and Human Genome 15.

However, bioinformatics software required some requirements including: 1) knowledge of UNIX-like operating systems, 2) utilization of command lines, and 3) installation of software libraries before use. Also, internet use was restricted to analyzing data and sharing scientific studies through publications 15. Even at that period of time, there were some requirements of bioinformatics software as well as internet restrictions; that period was known as the era of genomic and with the power of computers, developments led to what is known as biological big data.

2.6. Development of bioinformatics (2000s - 2010):

In the 2000s, several technologies were available in the market 41. With the public appearance of next-generation sequencing (NGS) in 2005, sequencing thousands to millions of DNA molecules in a single run reopened the topic of computer technology requirements that were needed to keep up with sequencing technologies 15. In the 2000s, with the availability of different technologies, it became difficult to compare results among different research groups. Also, changing from one bioinformatics tool to another requires additional training 42.

Even if the sequencing and computer technologies were available in the market in the 2000s, the increase of biological data dwarfed these technologies. Therefore, different organizations that were supported by governments worked on high-performance computing, such as New York State’s high-performance computing program, Compute Canada, the European technology platform for high-performance computing, and China’s national center for high-performance computing. Also, Microsoft and Amazon are offering services in bioinformatics. At that period of time, the way that people can contribute to bioinformatics has changed with the rise of community computing such as Berkeley Open Infrastructure for Network Computing (BOINC). BOINC is a software platform that uses volunteer resources to compute. So, experts send computing tasks to BOINC, and nonexperts can volunteer 15.

2.7. Present of bioinformatics (2010s - Present):

In these recent years, bioinformatics became a specialization for researchers to specialize, called bioinformaticians 43. Also, bioinformatics became an academic specialization in universities due to its necessity in the job market, whether in private or governmental institutions 44. In addition, scientists who are not specialized in bioinformatics need to be skilled at basic levels of bioinformatics tools to prevent abuse and incorrect interpretations of the data 45,46. Finally, even if in recent years bioinformatics has become a scientific field, it still faces some challenges, including managing big biological data, guaranteeing the results, and integration in academic programs 15.

3. Applications of bioinformatics in biology:

The bioinformatics approach contributes to several sciences, such as: biology science, medical and clinical sciences, pharmaceutical sciences, data science, forensic sciences, and veterinary sciences 47. However, in biology science, the bioinformatics approach contributes to various fields, especially -omics fields, mainly including: genomics, proteomics, metabolomics, transcriptomics, interactomics, lipidomics, mechanomics, microbiomics, and other -omics fields.

3.1. Genomics and Proteomics:

Genomics is a branch of biological science that studies the whole set of genes, called the genome, of organisms with regard to the interactions and functions of these genomes 48. A bioinformatics approach is used to deal with the results of the interaction of the sequences and variations of genomes 47. Proteomics is a branch of biological science that studies the entire proteins of organisms 47,49. A bioinformatics approach is used to deal with the results of the interaction of the structure and functions of proteins 47. In other words, the genomics branch can be the intersection of the study of genes in biological science and bioinformatics, while the proteomics branch can be the intersection of the study of proteins in biological science and bioinformatics.

3.2. Metabolomics and Transcriptomics:

Metabolomics is a branch of biological science that is considered the next phase in the sequence after genomics and proteomics branches. It is the study of small molecules produced by the metabolic processes of organisms 50. A bioinformatics approach is used to deal with the results of the interaction of these small molecules 47. Transcriptomics is a branch of biological science that studies all molecules of RNA of organisms 51. A bioinformatics approach is used to deal with the results of all interactions of these RNA molecules 47. Simply, the metabolomics branch can be the intersection of the study of small molecules from metabolic processes in biological science and bioinformatics, while the transcriptomics branch can be the intersection of the study of RNA molecules in biological science and bioinformatics.

3.3. Interactomics and Lipidomics:

Interactomics is a branch of biological science that studies the whole set of molecular interactions in organisms’ cells 52. A bioinformatics approach is used to deal with the results of these interactions 52. Lipidomics is a branch of biological science that studies the lipid profiles of organisms’ cells based on chemical analysis 53. A bioinformatics approach is used to deal with the results of these chemical analysis profiles 53. Purely, interactomics branch can be the intersection of the study of the whole set of molecular interactions in biological science and bioinformatics, while lipidomics branch can be the intersection of the study of lipid profiles based on chemical analysis in biological science and bioinformatics.

3.4. Mechanomics and Microbiomics:

Mechanomics is a branch of biological science that studies all biological processes at organ, tissue, cell, and even molecular levels 54. A bioinformatics approach is used to deal with the results of these processes 54. Microbiomics is a branch of biological science that studies the characteristics of molecules that are responsible for the structure and functions of microbes 55. A bioinformatics approach is used to deal with the results of all these characteristics 55. In other words, the mechanomics branch can be the intersection of the study of all biological processes in biology science and bioinformatics, while the microbiomics branch can be the intersection of the study of microbial characteristics in biological science and bioinformatics.

4. Biological databases in bioinformatics approach:

Biological databases can be classified according to different criteria including: the level of data curation, the coverage of data, and the method of data curation. According to the level of data curation, biological databases are classified as primary and secondary biological databases. According to coverage of data, biological databases are classified as comprehensive and specialized biological databases. According to the method of data curation, biological databases are classified as expert-curated biological databases and community-curated biological databases 56.

4.1. Primary and secondary biological databases:

According to the level of data curation, biological databases are classified as primary and secondary biological databases. Primary biological databases include only raw sequences or structural information that are not processed in any way. This type of database is called an archival database because it saves all the results of the submitted experiments. Primary biological databases store nucleotide sequences, protein sequences, and molecular structures. Popular examples of primary biological databases include: National Center for Biotechnology Information (GenBank) and Protein Data Bank (PDB) 56,57. Secondary biological databases include information that has been processed based on the raw data from primary biological databases by using different computational and manual methods and algorithms. Popular examples of secondary biological databases include: Protein Families Database (Pfam) and Protein Information Resource (PIR) 56,57.

4.2. Comprehensive and specialized biological databases:

According to coverage of data, biological databases are classified as comprehensive and specialized biological databases. Comprehensive biological databases include data from different organisms as well as different types of sequencing data, including nucleotide sequence and protein sequence. Popular examples of comprehensive biological databases include: DNA Data Bank of Japan (DDBJ) and European Molecular Biology Laboratory (EMBL) 56,57. Specialized biological databases include data from specific organisms as well as specific types of data. Popular examples of specialized biological databases include: European Genome–phenome Archive (EGA) and Human Gene Mutation Database (HGMD) 56,57.

4.3. Expert-curated and public-curated biological databases:

According to the method of data curation, biological databases are classified as expert-curated biological databases and community-curated biological databases. Expert-curated biological databases contain information that is curated by experts in the field. Popular examples of specialized biological databases include: National Center for Biotechnology Information Reference Sequence Database (RefSeq) and The Arabidopsis Information Resource (TAIR) 56. Community-curated biological databases contain information that is curated by a number of collaborative researchers. Popular examples of specialized biological databases include: human long non-coding RNAs (LncRNAWiki) and GeneWiki 56.

In addition, biological databases can be categorized based on data management. These categories include: 1) DNA databases such as: Allele Frequency Net Database (AFND), 2) protein databases such as: Protein structure classification (CATH), 3) RNA databases such as: DAtabase of RNa EDiting in humans (DARNED), 4) expression databases such as: Multi-Omics Profiling Expression Database (MOPED), 5) pathway databases such as: Database of human interaction networks (CPDB), 6) disease databases such as: Gene expression-based Outcome for Breast cancer Online (GOBO), 7) literature databases such as: PubMed, 8) standard and ontology databases such as: Database of human gene names (HGNC) 56.

5. Tools of bioinformatics in biological databases:

Bioinformatics approach includes not only the creation and maintenance of biological databases but also the development of computer tools and software to analyze these biological databases 2,58. Depending on these tools’ uses, complications, and functions, they can be divided into several categories. Examples of bioinformatics tools’ categories include: sequence analysis tools, structure analysis tools, and function analysis tools 59,60.

5.1. Sequence analysis tools:

Sequence analysis tools are used to analyze DNA sequences and protein sequences, and to identify homologous sequences in order to recognize relationships among different organisms. Specifically, this category includes tools that are used for sequence database searching, genome assembly, sequence alignment, genome comparison, and phylogeny. Popular examples of sequence analysis tools include: Basic Local Alignment Search Tool (BLAST) and Molecular Evolutionary Genetics Analysis (MEGA) 2,58-60.

5.2. Structure analysis tools:

Structure analysis tools are used to analyze DNA structure and protein structure. Specifically, this category includes tools that are used to compare, classify, and predict DNA and protein structures. Popular examples of structure analysis tools include: PyMOL and RasMol 2,58-60.

5.3. Function analysis tools:

Function analysis tools are used to understand functions and relationships among DNA and protein. Specifically, this category includes tools that are used to model pathways and predict protein interactions. Popular examples of function analysis tools include: Gene Expression Omnibus (GEO) and Pathway Tools 2,58-60.

6. Advantages, limitations, and future of bioinformatics approach:

Generally, there are various advantages of using bioinformatics approach including: 1) organizing biological data allowing researchers to access, create, analyze, and interpret the data, 2) enabling researchers to discover new connections among huge biological data through in silico, computer experiments, within a few hours, 3) improving biological experiments for the designing and using of other experiments through in silico experiments, 4) enabling the researchers to isolate scant biological data for better interpretations of the whole data, 5) developing new tools to analyze and interpret new data 3,14,61-65.

However, there are also some limitations of using the bioinformatics approach, including: 1) requirement of advanced laboratories in biology, 2) complexity of using some computational technologies, 3) high-funded researches, 4) integration of different sources of data 47.

Finally, the bioinformatics approach is facing massive amounts of biological data such as the human genome projects, which are producing an increasing number of unique sequences not only for humans, but also for other species 66. Future directions of the bioinformatics approach are discussed to solve these issues, including integration of multiple biological data, integration of various bioinformatics methods, and integration of artificial intelligence (AI) with bioinformatics 66.

7. Conclusion:

Bioinformatics is a scientific interdisciplinary field that integrates biological science with computer and information technologies in order to collect, organize, store, analyze, and interpret all types of biological data. The bioinformatics approach has many significant roles in different sciences. However, in biological science, the bioinformatics approach contributes to various fields, especially -omics fields, notably: genomics, proteomics, metabolomics, transcriptomics, interactomics, lipidomics, mechanomics, microbiomics, and other -omics fields. Also, the bioinformatics approach includes not only creating and maintaining biological databases but also the development of computer tools and software to analyze these biological databases. Finally, the use of bioinformatics has different advantages and also some limitations which could be addressed in the future by integrating more biological data, different bioinformatics methods, and integrating artificial intelligence with bioinformatics.

Declaration of Competing Interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding:

This paper did not receive any sort of financial support.

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