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What's Bioinformatics ?!?!

Bioinformatics is a method to predict the possible outcome of biological problems




Collection of Bioinformatics Tools


Collection of Biological Databases


List of Universities offering Bioinformatics Courses


List of universitites offering Bioinformatics Courses all over the world


List of Bioinformatics Companies


Some Important Definitions in Bioinformatics


Useful Bioinformatics Notes.

WHO we Are ?

Its About us

Perl for Bioinformatics

Why ? How? Do we need Perl.

Linux for Bioinformatics

Why ? How Do we need Linux.?

Different Bioinformatics Fields

What are all the Other Fields in Bioinformatics.


List of Symposiums.

Need a Help in Bioinformatics ?

If you need a help feel free to contact us.


Structural Bioinformatics:

Structural bioinformatics refers to the branch of bioinformatics which is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as computational structural biology

Computational Genomics:

Computational genomics is the study of deciphering biology from genome sequences using computational analysis. including both DNA and RNA. Modern genomics has been defined in many ways:

* The study of genomes.
* The molecular characterization of all the genes in a species.
* The study of genes and their biochemical function in an organism.
* The comprehensive study of the interactions and functional dynamics of whole sets of genes and their products.
* The study of the genome and its significance to pathology and disease

whichever definition we choose, it is impossible for genomics to achieve its fundamental goals without the use of advanced computational tools. The computational aspects of modern genomics go under the name computational genomics. Among other topics, computational genomics includes: bio-sequence analysis, gene expression data analysis, phylogenetic analysis, and more specifically pattern recognition and analysis problems such as gene finding, motif finding, gene function prediction, fusion of sequence and expression information, and evolutionary models.

Systems Biology:

Systems biology is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway.

The systems biology approach is characterised by a cycle of theory, computational modelling and experiment to quantitatively describe cells or cell processes. Since the objective is a model of all the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.


In biology, phylogenetics (Greek: phylon = tribe, race and genetikos = relative to birth, from genesis = birth) is the study of evolutionary relatedness among various groups of organisms (e.g., species, populations). Also known as phylogenetic systematics, phylogenetics treats a species as a group of lineage-connected individuals over time. Phylogenetic taxonomy, which is an offshoot of, but not a logical consequence of, phylogenetic systematics, constitutes a means of classifying groups of organisms according to degree of evolutionary relatedness.

All birds and reptiles are believed to be descended from a single common ancestor, so this taxonomic grouping (yellow in the diagram) is called monophyletic. "Modern reptile" is a grouping that contains a common ancestor, but not all descendents of that ancestor (cyan in the diagram) because birds are excluded -- and is called paraphyletic. A grouping such as warm-blooded animals would include only mammals and birds (red/orange in the diagram) and is called polyphyletic because the members of this grouping do not include the most recent common ancestor. Warm-blooded animals are all descended from a cold-blooded ancestor. Warm-bloodedness evolved independently in both mammals and birds.

Phylogeny (or phylogenesis) is the origin and evolution of a set of organisms, usually a set of species. A major task of systematics is to determine the ancestral relationships among known species (both living and extinct). The most commonly used methods to infer phylogenies include parsimony, maximum likelihood, and MCMC-based Bayesian inference. Distance-based methods construct trees based on overall similarity which is often assumed to approximate phylogenetic relationships. All methods depend upon an implicit or explicit mathematical model describing the evolution of characters observed in the species included, and are usually used for molecular phylogeny where the characters are aligned nucleotide or amino acid sequences.

Population Genetics:

Population genetics is the study of the allele frequency distribution and change under the influence of the four evolutionary forces: natural selection, genetic drift, mutation, and gene flow. It also takes account of population subdivision and population structure in space. As such, it attempts to explain such phenomena as adaptation and speciation. Population genetics was a vital ingredient in the modern evolutionary synthesis, its primary founders were Sewall Wright, J. B. S. Haldane and R. A. Fisher, who also laid the foundations for the related discipline of quantitative genetics

Mathematical Modelling:

A mathematical model is an abstract model that uses mathematical language to describe the behaviour of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science); physicists, engineers, computer scientists, and economists use mathematical models most extensively.

Drug Designing:

Drug design is the approach of finding drugs by design, based on their biological targets. Typically a drug target is a key molecule involved in a particular metabolic or signalling pathway that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial pathogen.

Some approaches attempt to stop the functioning of the pathway in the diseased state by causing a key molecule to stop functioning. Drugs may be designed that bind to the active region and inhibit this key molecule. However these drugs would also have to be designed in such a way as not to affect any other important molecules that may be similar in appearance to the key molecules. Sequence homologies are often used to identify such risks.Other approaches may be to enhance the normal pathway by promoting specific molecules in the normal pathways that may have been affected in the diseased state.

The structure of the drug molecule that can specifically interact with the biomolecules can be modeled using computational tools. These tools can allow a drug molecule to be constructed within the biomolecule using knowledge of its structure and the nature of its active site. Construction of the drug molecule can be made inside out or outside in depending on whether the core or the R-groups are chosen first. However many of these approaches are plagued by the practical problems of chemical synthesis.Newer approaches have also suggested the use of drug molecules that are large and proteinaceous in nature rather than as small molecules. There have also been suggestions to make these using mRNA. Gene silencing may also have therapeutical applications.


Comparative Genomics:

Comparative genomics is the study of relationships between the genomes of different species or strains. Comparative genomics is an attempt to take advantage of the information provided by the signatures of selection to understand the function and evolutionary processes that act on genomes. While it is still a young field, it holds great promise to yield insights into many aspects of the evolution of modern species. The sheer amount of information contained in modern genomes (several gigabytes in the case of humans) necessitates that the methods of comparative genomics are mostly computational in nature. Gene finding is an important application of comparative genomics, as is discovery of new, non-coding functional elements of the genome.

Comparative genomics exploits both similarities and differences in the proteins, RNA, and regulatory regions of different organisms to infer how selection has acted upon these elements. Those elements that are responsible for similarities between different species should be conserved through time (stabilizing selection), while those elements responsible for differences among species should be divergent (positive selection). Finally, those elements that are unimportant to the evolutionary success of the organism will be unconserved (selection is neutral).

Identifying the mechanisms of eukaryotic genome evolution by comparative genomics is one of the important goals of the field. It is however often complicated by the multiplicity of events that have taken place throughout the history of individual lineages, leaving only distorted and superimposed traces in the genome of each living organism. For this reason comparative genomics studies of small model organisms (for example yeast) are of great importance to advance our understanding of general mechanisms of evolution.

Having come a long way from its initial use of finding functional proteins, comparative genomics is now concentrating on finding regulatory regions and siRNA molecules. Recently, it has been discovered that distantly related species often share long conserved stretches of DNA that do not appear to code for any protein. It is unknown at this time what function such ultra-conserved regions serve.

Molecualar Docking:

Computational molecular docking is a research technique for predicting whether one molecule will bind to another, usually a protein. Protein-protein, protein-DNA and protein-ligand docking predictions are all performed, though the techniques employed in each area are highly various. Protein-ligand docking is done by modelling the interaction between protein and ligand: if the geometry of the pair is complementary and involves favorable biochemical interactions, the ligand will potentially bind the protein in vitro or in vivo Protein Protein interaction. Protein-protein interactions refer to the association of protein molecules and the study of these associations from the perspective of biochemistry, signal transduction and networks.

The interactions between proteins are important for many biological functions. For example, signals from the exterior of a cell are mediated to the inside of that cell by protein-protein interactions of the signalling molecules. This process, called signal transduction, plays a fundamental role in many biological processes and in many diseases (e.g. cancer). Proteins might interact for a long time to form part of a protein complex, a protein may be carrying another protein (for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins), or a protein may interact briefly with another protein just to modify it (for example, a protein kinase will add a phosphate to a target protein). This modification of proteins can itself change protein-protein interactions. For example, some proteins with SH2 domains only bind to other proteins when they are phosphorylated on the amino acid tyrosine. In conclusion, protein-protein interactions are of central importance for virtually every process in a living cell. Information about these interactions improves our understanding of diseases and can provide the basis for new therapeutic approaches.












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