Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 Review Article Bioinformatics International Journal of Pharma and Bio Sciences ISSN 0975-6299 BIOINFORMATICS WITH COMPUTATIONAL BIOLOGY- A SYNERGISTIC EFFECT MARGI GANDHI AND RAJASHREE MASHRU* Faculty of pharmacy, Kalabhavan, The M.S.University of Baroda, Baroda, India ABSTRACT Nowadays Bioinformatics and computational biology has emerged as an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data. Earlier bioinformatics term had different meaning, it referred to the study of information processes in biotic systems like biochemistry and biophysics. However with the emergence of bioinformatics tools it lead to the development of protein sequencing methods from a variety of organisms and with the availability of protein sequences it helped in determining sequences of insulin. The major challenge to bioinformatics tools was to manually handle large number of protein sequences of different organisms. Thus to overcome this limitation new computer methods were developed. The objective of the current review article is to addresses the limitations of bioinformatics field and how these limitations were overcome when computational biology was combined with bioinformatics. This article also includes recent application highlighting synergistic effect of bioinformatics with computational biology in various fields like drug discovery and development, food, treatment of neglected tropical diseases like TB, HIV, Leishmaniasis and malaria, Influenza surveillance and vaccine strain selection and development of precision medicine. KEYWORDS: Biochemistry, Bioinformatics, Computational Biology, Synergistic effect, Drug discovery and development. RAJASHREE MASHRU* Faculty of pharmacy, Kalabhavan, The M.S. University of Baroda, Baroda, India Received on: 18-06-2019 Revised and Accepted on: 26-07-2019 DOI: http://dx.doi.org/10.22376/ijpbs.2019.10.3.b224-239 Creative commons version 4.0 This article can be downloaded from www.ijpbs.net B-224 Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 INTRODUCTION with biological data to solve problems.2 Computational biology: It is about studying biology using computational Bioinformatics: Biologists who specialize in the use of techniques, which further the understanding of science. computational tools and systems to answer problems of Computer scientists, Mathematicians, Statisticians, and 1 Engineers who specialize in developing theories, biology are bioinformaticians. They tend to draw upon 3 skills in software development, database development algorithms and techniques for tools and systems developed by bioinformatics are called computational and management and visualization methods to convey 2 information contained within data sets. It focuses more biologists. on the engineering side and creation of tools that work HISTORY FROM BIOINFORMATICS TO COMPUTATIONAL BIOLOGY High quality data for life sciences ↓ Network and system biology Computational system-level Analysis ↓ Data Analysis Figure 15 Databases HIGH-THROUGHPUT DATA PROCESSING 1. precisely the transcription unit (5’ and 3’ Bioinformatics was coined in 1990 to define the use of boundaries) of genes in metazoan genomes was to computers in sequence analysis. But it had major difficult with bioinformatics. limitations which restricted its application. These 2. To identify correct sequence of mRNA (exon includes: parsing) was also a major challenge of bioinformatics. This article can be downloaded from www.ijpbs.net B-225 Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 From 1990 to 1997 many software were developed to 1. Common statistical regularities 2. Plain sequence overcome the limitation in the field of bioinformatics. Out similarity. of which one software was the state-of-the-art gene The drawback of this software was that it had no prediction software. It was based on two main principles relationship to actual molecular mechanism of gene expression.4 Figure 25 Flow chart showing relationship between Bioinformatics and computational Biology Thus bioinformatics was linked to computational biology. CELL Their synergism involved using computer science in The basic unit of biological activity is called Cell. The understanding data collected by biologists and health cells of living kingdom are divided to two categories sciences and professionals. Together as an namely prokaryotic and eukaryotic cells. interdisciplinary field they aim to solve problems by their open sourced approach in development and sharing EUKARYOTIC CELLS research. Both fields interact with a wide range of These have advanced and complete cells. These cells disciplines within biology, including genetics, are found in unicellular and multicellular plants and biochemistry, biophysics, cell biology, and 2 5 5 animals and contain plasma membrane, nucleus, DNA, evaluation. figure 1 ,2 represents the flow chart of cytoplasm with ribosomes and cellular organelles process starting with bioinformatics tools to collect [Figure 3]5 databases and ending with computational software to analyse the collected data. PROKARYOTIC CELLS OVERVIEW ON BIOLOGY prokaryotic is derived from Greek word “v” Biology is all about the science of life and living where “”(pro) means before and “v” (karyon) organisms. The main vocabulary of our interest Means nucleus. They lack well defined nucleus and 5 includes: possess relatively simple structure [Figure 4] 1. Cell 2. Prokaryotic and eukaryotic cells 3. Nucleus, chromosomes, DNA, DNA bases A,G,C,T 4. RNA, 5. Gene Figure 35 Eukaryotic Cells5 This article can be downloaded from www.ijpbs.net B-226 Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 Figure 45 Prokaryotic Cells5 NUCLEUS, CHROMOSOMES, DNA, DNA of genetic substance called DNA . DNA along with BASES A,G,C,T chromosomes are together known as the genome. The Nucleus is the largest cellular organelle. Its contain specific regions of the genomes are called genes. These chromatin material which gets condensed into two or genes control the protein synthesis through the more thick ribbon-like structure called chromosomes mediation of RNA. during cell division. These chromosomes are composed Figure 56 The Central Dogma of life called the “central dogma of molecular biology”. [Figure 1. MessengerRNA [mRNA]: It specifies the 5]6. In eukaryotic organisms, DNA is also present in sequence of amino acid in protein synthesis. mitochondria and chloroplast. It performs specified 2. Ribosomal RNA [rRNA]; It is associated with function of protein synthesis. DNA is a chain of four structure and function of ribosome’s [factories of types of molecules A,G,C and T where A always links protein synthesis] with T and C with G. Thus, these sequence of AGCT 3. Transfer RNA [tRNA]: It delivers amino acid to gives complete blueprint of our lives including indication ribosomes for protein synthesis. of the diseases that are likely to occur. RNA contains 4 types of molecule A,G,C and U. The last one replaces T in DNA. Here is the list of bioinformatics tools [Refer Table 2]8and Computational RNA 7 These are single stranded molecules. They perform Biology Software. [Table-1] several functions carried out by three types of RNA This article can be downloaded from www.ijpbs.net B-227 Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 Table 17 List of computational biology softwares used for ages Year Software Purpose Creators Key capabilities released Citations BLAST Sequence Stephen Altschul, Warren First program to provide 1990 35,617 alignment Gish, Gene Myers, Webb statistics for sequence Miller, David Lipman alignment, combination of sensitivity and speed R Statistical Robert Gentleman, Ross Interactive statistical analysis, 1996 N/A analyses Ihaka extendable by packages ImageJ Image analysis Wayne Rasband Flexibility and extensibility 1997 N/A Cytoscape Network Trey Ideker et al. Extendable by plugins 2003 2,374 visualization and analysis Bioconductor Analysis of Robert Gentleman et al. Built on R, provides tools to 2004 3,517 genomic data enhance reproducibility of research Galaxy Web-based Anton Nekrutenko, Provides easy access to high- 2005 309 analysis James Taylor performance computing platform MAQ Short-read Heng Li, Richard Durbin Integrated read mapping and 2008 1,027 mapping SNP calling, introduced mapping quality scores Bowtie Short-read Ben Langmead, Cole Fast alignment allowing gaps 2009 1,871 mapping Trapnell, Mihai Pop, and mismatches based on Steven Salzberg Burrows-Wheeler Transform Tophat RNA-seq read Cole Trapnell, Lior Discovery of novel splice sites 2009 817 mapping Pachter, Steven Salzberg BWA Short-read Heng Li, Richard Durbin Fast alignment allowing gaps 2009 1,556 mapping and mismatches based on Burrows-Wheeler Transform Circos Data Martin Krzywinski et al. Compact representation of 2009 431 visualization similarities and differences arising from comparison between genomes SAMtools Short-read data Heng Li, Richard Durbin Storage of large nucleotide 2009 1,551 format and sequence alignments utilities Cufflinks RNA-seq Cole Trapnell, Steven Transcript assembly and 2010 710 analysis Salzberg, Barbara Wold, quantification Lior Pachter IGV Short-read data James Robinson et al. Scalability, real-time data 2011 335 visualization exploration N/A, paper not available in Web of Science. This article can be downloaded from www.ijpbs.net B-228 Int J Pharma Bio Sci 2019 July; 10(3): (B) 224-239 Table28 Role of bioinformatics software and computational methods in clinical research LIST OF SOFTWARES