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K.Gobalan Et.Al APPLICATIONS of BIOINFORMATICS in GENOMICS View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Journal of Advanced Applied Scientific Research (JOAASR) Journal of Advanced Applied Scientific Research - ISSN: 2454-3225 K.Gobalan et.al JOAASR-RTB-2016- February-2016:29- 42 APPLICATIONS OF BIOINFORMATICS IN GENOMICS AND PROTEOMICS K.Gobalan 1, Dr.S.Ahamed John2 1. PG and Research Department of Biotechnology, Jamal Mohamed College, Trichy-20. 2. PG and Research Department of Botany Jamal Mohamed College, Trichy –20. ABSTRACT Bioinformatics is the application of statistics and computer science to the field of molecular biology. The term bioinformatics was coined by Paulien Hogeweg in 1979 for the study of bioinformatics processes in biotic systems. Its primary use since at least the late 1980s has been in genomics and proteomics, particularly in those areas of genomics involving in large-scale DNA sequencing and proteomics in protein structure prediction. Bioinformatics now entitle the creation and advancement of data bases, algorithms, computational and statistical techniques and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and proteomics. Research technologies and developments in information technologies have combined to produce tremendous amount of information related to molecular biology. It is the name given to these mathematical and computing approaches used to clear understanding of biological processes. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them and creating and viewing 3-D models of protein structures. The primary goal of bioinformatics is to increase the understanding of biological processes. Bioinformatics is focus on developing and applying computationally intensive techniques (e.g., data mining, machine learning algorithms, and visualization) to achieve this goal. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein- protein interactions, genome-wide association studies and the modeling of evolution. 29 Journal of Advanced Applied Scientific Research - ISSN: 2454-3225 K.Gobalan et.al JOAASR-RTB-2016- February-2016:29- 42 1. Introduction statistical modeling of protein-protein In recent years, rapid developments interaction, etc. Therefore, we see a great in genomics and proteomics have potential to increase the interaction generated a large amount of biological between data mining and bioinformatics. data. Drawing conclusions from these data 2. Bioinformatics requires sophisticated computational The term bioinformatics was coined by analyses. Bioinformatics, or computational Paulien Hogeweg in 1979 for the study of biology, is the interdisciplinary science of informatics processes in biotic systems. It interpreting biological data using was primary used since late 1980s has been information technology and computer in genomics and genetics, particularly in science. The importance of this new field those areas of genomics involving large- of inquiry will grow as we continue to scale DNA sequencing. Bioinformatics generate and integrate large quantities of can be defined as the application of genomic, proteomic, and other data. A computer technology to the management particular active area of research in of biological information. Bioinformatics bioinformatics is the application and is the science of storing, extracting, development of data mining techniques to organizing, analyzing, interpreting and solve biological problems. Analyzing large utilizing information from biological biological data sets requires making sense sequences and molecules. It has been of the data by inferring structure or mainly fueled by advances in DNA generalizations from the data. Examples of sequencing and mapping techniques. Over this type of analysis include protein the past few decades rapid developments in structure prediction, gene classification, genomic and other molecular research cancer classification based on microarray technologies and developments in data, clustering of gene expression data, information technologies have combined 30 Journal of Advanced Applied Scientific Research - ISSN: 2454-3225 K.Gobalan et.al JOAASR-RTB-2016- February-2016:29- 42 to produce a tremendous amount of software system was designed in 1995 by information related to molecular biology. Dr. Owen White. The primary goal of bioinformatics is to 2.3. Analysis of gene expression increase the understanding of biological The) tag sequencing, massively processes. Some of the grand area of parallel signature sequencing (MPSS), or research in bioinformatics includes: various applications of expression of many Major research areas: genes can be determined by measuring 2.1. Sequence analysis mRNA levels with various techniques such as microarrays, expressed cDNA sequence Sequence analysis is the most tag (EST) sequencing, serial analysis of primitive operation in computational gene expression (SAGE multiplexed in- biology. This operation consists of finding situ hybridization etc. All of these which part of the biological sequences are techniques are extremely noise-prone and alike and which part differs during medical subject to bias in the biological analysis and genome mapping processes. measurement. Here the major research area The sequence analysis implies subjecting a involves developing statistical tools to DNA or peptide sequence to sequence separate signal from noise in alignment, sequence databases, repeated High-throughput gene expression studies. sequence searches, or other bioinformatics methods on a computer. 2.4. Analysis of protein expression 2.2. Genome annotation Gene expression is measured in In the context of genomics, annotation many ways including mRNA and protein is the process of marking the genes and expression; however protein expression is other biological features in a DNA one of the best clues of actual gene activity sequence. The first genome annotation since proteins are usually final catalysts of 31 Journal of Advanced Applied Scientific Research - ISSN: 2454-3225 K.Gobalan et.al JOAASR-RTB-2016- February-2016:29- 42 cell activity. Protein microarrays and high nucleotide polymorphism arrays to detect throughput (HT) mass spectrometry (MS) known point mutations. Another type of can provide a snapshot of the proteins data that requires novel informatics present in a biological sample. development is the analysis of lesions Bioinformatics is very much involved in found to be recurrent among many tumors. making sense of protein microarray and 2.6. Protein structure prediction HT MS data. The amino acid sequence of a 2.5 Analysis of mutations in cancer protein (so-called, primary structure) can In cancer, the genomes of affected be easily determined from the sequence on cells are rearranged in complex or even the gene that codes for it. In most of the unpredictable ways. Massive sequencing cases, this primary structure uniquely efforts are used to identify previously determines a structure in its native unknown point mutations in a variety of environment. Knowledge of this structure genes in cancer. Bioinformaticians is vital in understanding the function of the continue to produce specialized automated protein. For lack of better terms, structural systems to manage the sheer volume of information is usually classified as sequence data produced, and they create secondary, tertiary and quaternary new algorithms and software to compare structure. Protein structure prediction is the sequencing results to the growing one of the most important for drug design collection of human genome and the design of novel enzymes. A Sequences and germline general solution to such predictions polymorphisms. New physical detection remains an open problem for the technologies are employed, such as researchers. oligonucleotide microarrays to identify chromosomal gains and losses and single- 32 Journal of Advanced Applied Scientific Research - ISSN: 2454-3225 K.Gobalan et.al JOAASR-RTB-2016- February-2016:29- 42 2.7. Comparative genomics simulations of biological systems, like Comparative genomics is the study cellular subsystems such as the networks of the relationship of genome structure and of metabolites and Enzymes, signal function across different biological transduction pathways and gene regulatory species. Gene finding is an important networks to both analyze and visualize the application of comparative genomics, as is complex connections of these cellular discovery of new, non-coding functional processes. Artificial life is an attempt to elements of the genome. Comparative understand evolutionary processes via the genomics exploits both similarities and computer simulation of simple life forms differences in the proteins, RNA, and 2.9. High-throughput image analysis regulatory regions of different organisms. Computational technologies are used to Computational approaches to genome accelerate or fully automate the comparison have recently become a processing, quantification and analysis of common research topic in computer large amounts of high-information-content science. biomedical images. Modern image 2.8. Modeling biological systems analysis systems augment an observer's Modeling biological systems is a ability to make measurements from a large significant
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