Current trends in Bioinformatics: An Insight

Journal of Computational Biology, 1996

1. Biological data is highly complex when compared with most other domains or applications. Definitions of such data must thus be able to represent a complex substructure of data as well as relationships and to ensure that information is not lost during biological data modeling. Biological information systems must be able to represent any level of complexity in any data schema, relationship, or schema substructure. A good example of such system is MITOMAP database documenting the human mitochondrial genome (http://www.mitomap.org). The database information include data and their relationship about ca. 17,000 nucleotide bases of the mitochondrial DNA; 52 gene loci encoding mRNAs, rRNAs and tRNAs; over 1,500 known population variants and over 60 disease associations. MITOMAP includes links to over 3,000 literature references.

Bioinformatics was applied in the creation and maintenance of a database to store biological information at the beginning of the "genomic revolution", such as nucleotide and amino acid sequences. Development of this type of database involved not only design issues but the development of complex interfaces whereby researchers could both access existing data as well as submit new or revised data. Bioinformatics and computational biology are frequently integrated with experimental studies as well, with bioinformatics emphasizing informatics and statistics, while computational biology emphasizes development of theoretical methods, mathematical modeling, and computational simulation techniques to find solution for genetic problems. Bioinformatics studies include analysis and integration of genomics data, prediction of protein function from sequence, and comparisons of protein ligands to identify target effects of drugs. Computational biology includes simulation of protein motion and folding and how proteins interact with each other.

IOSR Journal of Computer Engineering, 2014

The amount and variety of data in natural sciences increases rapidly. Data abstraction, data manipulation and pattern discovery techniques are of great need in order to deal with such large quantities. Integration between different sources of data is also of major interest, as complex relations may arise. Biology is a good example of a field that provides extensive, highly variable and multi-sources data. The scope of these investigations has now expanded greatly owing to the development of high throughout sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. We have developed a new software package, molecular genetics analysis to integrate their diverse data and tools under Common Graphical User Interfaces (GUIs) by using BLAST, FASTA data searches. One of the most exciting things about being involved in computer programming and biology is that both fields are rich in new techniques and results. The illumina is sequence analyzer in Next Generation Sequence Technology.

Biological data available today surpasses information content in several fields. It is critical to logically organize and disseminate these contents to end users. In this chapter, we learn about biological databases that serve as the gateway for researchers.

Journal of Data Mining in Genomics & Proteomics, 2014

The pace, by which scientific knowledge is being produced and shared today, was never been so fast in the past. Different areas of science are getting closer to each other to give rise new disciplines. Bioinformatics is one of such newly emerging fields, which makes use of computer, mathematics and statistics in molecular biology to archive, retrieve, and analyse biological data. Although yet at infancy, it has become one of the fastest growing fields, and quickly established itself as an integral component of any biological research activity. It is getting popular due to its ability to analyse huge amount of biological data quickly and cost-effectively. Bioinformatics can assist a biologist to extract valuable information from biological data providing various web-and/or computer-based tools, the majority of which are freely available. The present review gives a comprehensive summary of some of these tools available to a life scientist to analyse biological data. Exclusively this review will focus on those areas of biological research, which can be greatly assisted by such tools like analysing a DNA and protein sequence to identify various features, prediction of 3D structure of protein molecules, to study molecular interactions, and to perform simulations to mimic a biological phenomenon to extract useful information from the biological data.

Advanced Techniques in Biology & Medicine, 2015

With the completion of human genome project, a plethora of information had been available for exploring various unanswered questions related to cell and molecular biology. Bioinformatics has been instrumental in unravelling the genetic, phenotypic, structural and functional aspects of the whole genome by using this information. Genomics and proteomics have become one of the most relevant fields after the advancements in computational analysis, interpretation, and modelling software. It can not only quite categorically describe the position of nucleotides and amino acids throughout genomes and proteomes respectively, but it also helps in performing the phylogenetic analysis, search for associated transcription factors, multiple sequence alignments, and many other relevant explorations/hunts. The advances in the knowledge of genetics acquired from molecular biology and bioinformatics are applied and point towards potential therapeutic strategies such as genome editing. This review has an aim of discussing some of the bioinformatics databases and software, which has been utilized for exploring the position of a DNA sequence, any associated single nucleotide polymorphism (SNP) related to a disease, on or nearby situated transcription factor binding sites followed by multiple sequence alignment of this sequence with other organisms. This study provides the insights in to the functional elements of any DNA, RNA or Protein sequence prior to exploring the structural polymorphism, which may regulate the gene expression. Also, this review briefly discusses the tools used for programmable nuclease-based genome editing technology.

2013

A flood of data means that many of the ch allenges in biology are now challenges in computing . Bioinformatics, the application of computational te chniques to analysis the information associated wit h bimolecular on a large-scale, has now firmly establ ished itself as a discipline in molecular biology, and encompasses a wide range of subject areas from stru ctural biology, genomics to gene expression studies . The underlying motivation for many of the bioinformatics and DNA sequencing approaches is the evolutio n of organisms and the complexity of working with err oneous data. This article also describes the kind o f software programs which were developed by the resea rch community in order to (1) search, classify and mine different available biological databases; simulate biological experiments with and without errors.

PROCEEDINGS OF THE PAKISTAN ACADEMY OF SCIENCES, 2015

Abstract: Building block of life is DNA which produces RNA and protein sequences; all these molecular blocks lie in the category of biological data. Biological data which comprises various organisms like animals, plants, viruses, bacteria and humans,throughout the globe has heterogeneous nature. This heterogeneity is caused by different parameters such as storage mechanism, information representation and content format. The abundance of heterogeneity creates a havoc towards optimized exploitation, integration, storage and retrieval of existing biological data. This work introduces a computational framework for achieving a unified format for biological data, which can accommodate different formats of storage; and data presentation. An information retrieval system for biological data has been developed which sheds light on different recompenses gained by this unified format of the biological data.