BIOINFOMATICS

BIOINFOMATICS
1. The beginning of the pioneering work by Margaret Dayhoff, Richard Eck, and Robert Ledley in computer-aided analysis of protein data goes back to the period around 1960.
2. The term was used to mean the study of informatics processes in biotic systems.
3. Bioinformatics is basically informatics as applied to biology—that is, computer-aided analysis of biological data.
4. Computational biology is an umbrella term that includes any subdiscipline in biology that uses computer-aided analysis, modeling, and prediction. In contrast, bioinformatics can be regarded as computational molecular biology, as indicated above. Therefore, according to the definitions discussed in this book, computational biology is much broader in scope and bioinformatics is a part of it.
Other areas of computational biology, is essentially a multidisciplinary science because it uses techniques and concepts from a number of disciplines, such as molecular biology and biochemistry, computer science, statistics and mathematics, and informatics (information science).
Ultimate goal of bioinformatics
1. The ultimate goal of bioinformatics is to be able to predict the biological processes in health and disease. In order to acquire such an ability, a thorough understanding of the biological processes is necessary.
2. Therefore, the proximate goal of bioinformatics is to develop such an understanding through analysis and integration of the information obtained on genes and proteins, as well as to develop new tools and continuously improve the existing set of tools for diverse types of analyses.
3. Bioinformatics also aims to develop tools that help in the management of and access to data and information, including improved search and retrieval capability of genomic data and information from various types of databases.
Some examples of common bioinformatic tools and analyses that are continuously being improved and refined are:
Data capture and storage capability; the usability of databases; data analysis; nucleic acid and protein sequence analysis and sequence annotation;
Structural analysis of proteins and prediction of protein structure, including three-dimensional (3D) structure; protein domain prediction; gene prediction; analysis of functional studies; analysis of gene and protein networks; and phylogenetic analysis.

BIOINFORMATICS TECHNICAL TOOLBOX
1. Bioinformatic analysis requires data (such as sequence information), databases, and analysis tools.
2. Databases are built from data obtained through wet laboratory experiments. Some of the original nucleotide and protein-sequence databases were created more than 30 years ago.
3. The bioinformatics technical toolbox provides analysis tools (algorithms) and visualization techniques of the data generated through high-throughput experiments, such as high-throughput sequencing, microarray analysis, mass spectrometry, and other proteomic techniques.
4. The analysis tools are computer based (software), and the development of newer tools is driven by various needs, such as an increased need for handling the huge body of data, faster analysis, expanded scope of the analysis, multiple simultaneous analyses, to name a few.


A few examples of software-driven analysis that have tremendously facilitated bioinformatics research are:
1. Analysis of nucleotide sequences
2. Detection of single nucleotide polymorphisms (SNPs) and copy number variation (CNV).
3. Understanding the sequence features and differences between coding and noncoding regions.
4. Alignment of nucleotide sequences.
5. Prediction of open reading frames (ORFs), restriction-enzyme cutting sites in DNA, various cis-acting regulatory DNA elements in the gene, and putative miRNA-encoding sequences in the genome
6. Gene-expression analysis.
7. Designing probes and primers.
8. Analysis of protein sequences.
9. Alignment of amino-acid sequences.