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Bioinformatics: Its Application And Perspective In Fisheries And Aquaculture

Dharmendra Kumar Meena*., A. K. Sahoo*., Debabrata Panda* and B.K Bahera*.

*Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700 120

Corresponding author: Dharmendra Kumar Meena

* Email : dkmeenafnb@gmail.com

Bioinformatics is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project. The goal of this project - determination of the sequence of the entire human genome (approximately three billion base pairs) - will be reached by the year 2002. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists. Such groups will be key to unraveling the mass of information generated by large scale sequencing efforts underway in laboratories around the world.

Molecular medicine requires the integration and analysis of genomic, molecular, cellular, as well as clinical data and it thus offers a remarkable set of challenges to bioinformatics. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic, and proteomic data generated by high-throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine.Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and-most importantly-well trained and educated personnel to design, maintain and use these environments. This review outlines the most promising trends in bioinformatics, which may play a major role in the pursuit of future biological discoveries and medical applications. Bioinformatics is a comparatively younger discipline that bridges the life sciences and computer sciences. The explosive growth of biological sequence information has made it imperative to integrate these two disciplines. Organization and analysis of biological data are the main activities of bioinformatics. Algorithms to create, maintain and access the sequence databases are among the most important contributions that bioinformatics has made for the life sciences. In the flow of genetic information from sequence to function, the stored information is translated twice: first from DNA to mRNA in the process of transcription, then from mRNA to protein in the process of translation. DNA and protein sequence comparisons have become routine steps in biochemical characterization, from newly cloned proteins to entire genomes. Genomics attempts to make a complete inventory of genes and nucleic acid sequences. In contrast to genomics approach, proteomics attempts to study the expressed proteins. Protein manifest physiological as well as pathophysiological processes in a cell or an organism and proteomics describe the complete inventory of proteins in dependence on in vivo parameters. Proteomics is complementing genomics as a tool to study life sciences. The two key technologies in experimental proteomics are: 1) 2-D PAGE with image analysis and 2) biological mass spectrometry (MS) with database searching. 2-D PAGE technique is finding application in fisheries for identification of serum/plasma proteins that might be involved in the constitutive resistance to infections, muscle protein characterization, and biochemical analysis of cross-reactive antigens, understanding the molecular pathogenesis and genetics of disease resistance. We are developing 2D-refernce maps of commercially important fish and shellfish and plan to construct an index of the piscine proteins, by the construction of 2D-database that may be useful in identification of quantitative trait loci (QTL).

Bioinformatics tools

BioInformatics Tools BioInformatics Tools The Bioinformatics tools are the software programs for the saving, retrieving and analysis of Biological data and extracting the information from them.

Factors that must be taken into consideration when designing these tools are

  1. The end user (the biologist) may not be a frequent user of computer technology and thus it should be very user friendly. 

  2. These software tools must be made available over the internet given the global distribution of the scientific research community

Types of bioinformatics tools

1. Homology and Similarity Tools

The term homology implies a common evolutionary relationship between two traits - whether they are DNA sequences or bristle patterns on a fly's nose. Homologous sequences are sequences that are related by divergence from a common ancestor. Thus the degree of similarity between two sequences can be measured while their homology is a case of being either true of false. This set of tools can be used to identify similarities between novel query sequences of unknown structure and function and database sequences whose structure and function have been elucidated.

2. Protein Function Analysis

Function Analysis is Identification and mapping of all functional elements (both coding and non-coding) in a genome. This group of programs allows you to compare your protein sequence to the secondary (or derived) protein databases that contain information on motifs, signatures and protein domains. Highly significant hits against these different pattern databases allow you to approximate the biochemical function of your query protein.

3. Structural Analysis

This set of tools allows you to compare structures with the known structure databases. The function of a protein is more directly a consequence of its structure rather than its sequence with structural homologs tending to share functions. The determination of a protein's 2D/3D structure is crucial in the study of its function

4. Sequence Analysis

This set of tools allows you to carry out further, more detailed analysis on your query sequence including evolutionary analysis, identification of mutations, hydropathy regions, CpG islands and compositional biases. The identification of these and other biological properties are all clues that aid the search to elucidate the specific function of your sequence.

Important bioinformatics tools used in fisheries and aquaculture

BLAST:
The Basic Local Alignment Search Tool (BLAST) for comparing gene and protein sequences against others in public databases, now comes in several types including PSI-BLAST, PHI-BLAST, and BLAST 2 sequences. Specialized BLASTs are also available for human, microbial, malaria, and other genomes, as well as for vector contamination, immunoglobulins, and tentative human consensus sequences.

FASTA
A database search tool used to compare a nucleotide or peptide sequence to a sequence database. The program is based on the rapid sequence algorithm described by Lipman and Pearson. It was the first widely used algorithm for database similarity searching. The program looks for optimal local alignments by scanning the sequence for small matches called "words". Initially, the scores of segments in which there are multiple word hits are calculated ("init1").

Later the scores of several segments may be summed to generate an "initn" score. An optimized alignment that includes gaps is shown in the output as "opt". The sensitivity and speed of the search are inversely related and controlled by the "k-tup" variable which specifies the size of a "word".

EMBOSS
EMBOSS (The European Molecular Biology Open Software Suite) is a new, free open source software analysis package specially developed for the needs of the molecular biology user community. Within EMBOSS you will find around 100 programs (applications) for sequence alignment, database searching with sequence patterns, protein motif identification and domain analysis, nucleotide sequence pattern analysis, codon usage analysis for small genomes, and much more.

Clustalw
ClustalW is a general purpose multiple sequence alignment program for DNA or proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences, calculates the best match for the selected sequences, and lines them up so that the identities, similarities and differences can be seen.

RasMol
It is a powerful research tool to display the structure of DNA, proteins, and smaller molecules. Protein Explorer, a derivative of RasMol, is an easier to use program.

Soft ware application programs

JAVA in Bioinformatics

Due to Platform independence nature of  Java, it is emerging as a key player in bioinformatics. Physiome Sciences' computer-based biological simulation technologies and Bioinformatics Solutions' PatternHunter are two examples of the growing adoption of Java in bioinformatics.

Perl in Bioinformatics:
Perl is also being used in the processing of biological data. One example of perl project is BioPerl project.

Bioinformatics Projects:

BioJava:
The BioJava Project is providing the Java tool for the processing of data in Java

BioPerl:
The BioPerl project many module for biological data processing.

BioXML:
A part of the BioPerl project, this is a resource to gather XML documentation, DTDs and XML aware tools for biology in one location.

Open access bioinformatics soft ware

 The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have helped to create opportunities for all research groups to contribute to both bioinformatics and the range of open source software available, regardless of their funding arrangements. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications. They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration.

The range of open source software packages includes titles such as Bioconductor, BioPerl, BioJava, Bioclipse, EMBOSS, Taverna workbench, and UGENE. In order to maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundationhave supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.



Branches of applied bioinformatics

1. Sequence analysis

2. Genome annotation

3. Computational evolutionary biology

4. Literature analysis

5. Analysis of gene expression

6. Analysis of regulation

7. Analysis of protein expression

8. Modeling biological systems

9. High-throughput image analysis

10. Molecular Interaction

11. Prediction of protein structure


Current Limitations and future perspective for the Future

The field of biology has undergone several rounds of transformation in the approaches taken, ranging from theoretical to experimental perturbation to discovering molecular components. In the next decades to come, I believe it will take on another transformation to bioinformatical, where computational models of systems-wide properties could serve as the basis for experimentation and discovery. The ramifications of this will be not only the precise understanding of how organisms are built, but also the ability to engineer organisms to exhibit specified traits, to discover the causality of diseases, and to predict organisms' responses to changes in the environment. This could lead to prevention and targeted treatment of diseases, improved food production, and preservation of the environment.

Urgently, bioinformatics is conducted by a specialized group of individuals, such as database curators database and software engineers, and computational biologists. On the fringe of this are the collaborative entities of biologists, mechanical or electric engineers (bioengineers), computer scientists, and mathematicians. The majority of the biologists, however, are on the other end of the spectrum in that they are users of the most basic bioinformatical tools. I see this as the major limitation of bioinformatics today. It is simply not as accessible to most biologists as it should be. In the future, I see that the distribution of people in this spectrum will change to a bell curve where the majority of biologists will have some basic skills such as programming, database development and management of large datasets, and quantitative and statistical analysis of data (Fig. 1). This change will not be unlike how molecular biology penetrated the field of biology some 30 years ago in changing how people thought about and conducted biological research. Recent publication of the Current Protocols in Bioinformatics series provides an example of this trend already in motion. The infiltration of bioinformatical biology may be more profound in shifting the biological research paradigm than molecular biology ever was. The richness and enormity of information, such as understanding the function of every gene in an organism, will shift research into more theoretical biology using bioinformatical approaches, with experiments carried out to find supporting or refuting evidence for the theories, models, and hypotheses. Biologists will generally have a much larger circumference of their domain of expertise and spend more of their time on the computer than at the bench. The concept of ownership of data will also change, and analyzing other people's data will be much more common place. This change will encourage, if not force, scientists to pay more attention to the quality of data annotation and actively participate in their improvement. Other problems in bioinformatics we are facing today include the heterogeneity of how data are analyzed, annotated, and displayed and the lack of connectivity among the available data. These problems arose partially because of the young age of the field of bioinformatics, with independent and disparate efforts carried out without the conventions and discipline associated with an established scientific community. Recent movements toward the creation of a scientific society for database curators and projects that bring together the efforts of different model organism databases provide early hints to the development of bioinformatics into a more coherent discipline of biology.

References

  1. http://www.hgmp.mrc.ac.uk/Software/EMBOSS/Apps/

  2. http://www.plantphysiol.org/cgi/doi/10.1104/pp.104.900153

  3. http://www.does.org/cp/bioinfo.html

  4. www.biocurator.org

  5. www.gmod.org

  6. Hogeweg, P. (1978). "Simulating the growth of cellular forms". Simulation 31 (3): 90–96.

  7.  Hogeweg, P. (2011). Searls, David B.. ed. "The Roots of Bioinformatics in Theoretical Biology". PLoS Computational Biology 7 (3): e1002021. 

  8. Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, Hutchison CA, Slocombe PM, Smith M (February 1977). "Nucleotide sequence of bacteriophage phi X174 DNA". Nature 265 (5596): 687–95. 

  9.  Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (January 2008). "GenBank". Nucleic Acids Res. 36 (Database issue): D25–30. 

  10.  Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM (July 1995). "Whole-genome random sequencing and assembly of Haemophilus influenzae Rd". Science 269 (5223): 496–512.


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