Role of Bioinformatics in Biotechnology
Forensic Bioinformatics offers professional corporate consulting. We advise and support you in all the processes and structures of your company. Forensic DNA typing often requires the use of techniques that useful in forensic science, particularly in the determination .. single SNP and haplotype association tests, permutation. Bioinformatics also has a wide spectrum of utility in Forensic science especially in DNA Forensics area where the identification of DNA Profiles becomes a.
Similarly, pollen and spores analysis, i. Feline, canine and white-tailed deer DNA evidence has been presented in court, and follows the procedures for human DNA forensics. Linkage disequilibrium and haplotyping In order to avoid pitfalls in the inference process of the forensic evidences, it is important to discuss the patterns of occurrences of mutations in the human genome. Linkage disequilibrium LDis the nonrandom pattern of association between alleles at different loci within a population.
An association in inheritance between characters means that the parental character combinations appear among the progeny more often than the nonparental.
The closer two or more markers are on a chromosome the greater the probability that they will be inherited together [ 22—24 ]. LD is generally low near telomeres, elevated near centromeres and correlated with chromosome length, particularly high in few regions, termed recombination hotspots which are enriched of retrotransposon-like elements. LD is low in regions containing genes involved in immune responses and neurophysiological processes, and high in regions containing genes involved in DNA and RNA metabolism, response to DNA damage and the cell cycle [ 13 ].
Therefore, a haplotype is a set of closely linked genetic markers present on one chromosome which tend to be inherited together. Intuitively, haplotypes which can be regarded as a collection of ordered markers may be more powerful than individual, unorganized markers [ 23 ]. Haplotype patterns reflect the fact that all modern humans originated in Africa more than years ago. Some of the descendents of this group remained in Africa, whereas others migrated, eventually reaching all parts of the world.
DNA events such as mutations and recombinations, natural selection and random drift which have caused population expansions and bottlenecks, founder effects, have influenced generated or eliminated the haplotype patterns in populations in different parts of the world.
While the reference sequence constructed by the Human Genome Project is informative of the vast majority of bases that are invariant across individuals, the HapMap project http: The Hapmap project consisted of compiling data on groups of individuals representative of four populations for more than a million single nucleotide polymorphisms, or SNPs.
If a similar project will be carried out for CNV, identification of risk factors for common human diseases will be helpful in treatment or prevention and forensic information on human population will be complete.
Software for haplotype scoring, selection, visualization There is a large variety of software useful for haplotype analysis. Most of this software comes with example data sets and manuals so it is easy to try different programs and make comparison on the basis of the specific needs and data sets. We describe a list of software relevant to haplotyping and linkage disequilibrium analysis that we have found particularly useful in forensic bioinformatics [ 25—33 ].
Haploview currently supports the following functionalities. LD and haplotype block analysis, haplotype population frequency estimation, single SNP and haplotype association tests, permutation testing for association significance, implementation of Tagger see subsequentlytag SNP selection algorithm. Haploview computes single locus and multimarker haplotype association tests, outputting the chi square and P-value for the allele frequencies in cases versus control.
For family trios, all probands affected individual with genotyped parents are used to compute transmission disequilibrium test TDT values. Haploview can only interpret biallelic markers—markers with greater than two alleles e.
Haplofreq's approach incorporates a maximum likelihood model based on a simple random generative model which assumes that the genotypes are independently sampled from the population. Haplofreq accepts as an input a set of gentoypes of the same length, and produces the haplotype distribution in the population, estimated from these haplotypes.
The input format contains a genotype in each line. It combines the simplicity of pair-wise tagging methods with the efficiency benefits of multimarker haplotype approaches. Alternatively, users can specify chromosomal landmarks to indicate genomic regions of interest within which tag SNPs are to be picked.
This feature will be particularly useful for multiplex tag SNP design of candidate genes. Tagger has been implemented in the stand-alone program Haploview [ 25 ] see above.
Distribution also includes some handy programs for calculation of LD coefficients [ 31 ]. Simple representations of observed allele sharing in families to highlight erroneous coding of relationships amongst members [ 32 ]. The functions accommodate uncertain haplotype phase and can handle missing genotypes at some SNPs.
Likelihood inference of trait associations with SNP Haplo. Estimates haplotype and covariate relative risks in case-control data by weighted logistic regression. Diplotype probabilities, which are estimated by the Expectation Maximization algorithm with progressive insertion of loci, are utilized as weights. Here, in this communication, we have tried to explain the importance of bioinformatics in various fields of biotechnology viz.
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This is considered to be an amalgam of biological sciences and computer science and now a days, many scientists prefer to use the term, computational biology.
This branch of science became more popular when human genome project came into existence. Bioinformatics merges biology, computer science and information technology to form a single discipline. It covers many areas of biological science especially of modern biology viz. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be recognized.
Bioinformatics is a fascinating subject having input of engineering art as well as of science. Bioinformaticians are mostly engaged in designing new algorithms, software, developing updated databases which all help in solving many biological problems [ 1 ]. A number of bioinformatics tools, software and databases are available for better understanding of biological complexity and analyze and store the biological data. Thus, the bioinformatics research is used to avoid time, cost and wet lab practice.
The human genome sequence data is so huge that if compiled in books, the data would run into volumes of pages each and reading alone would require 26 years working around the clock. This challenge of handling such a huge data can only be possible because of bioinformatics [ 2 ]. The growth of the biotechnology industry in recent years is unprecedented, and advancements in molecular modeling, disease characterization, pharmaceutical discovery, clinical healthcare, forensics, and agriculture fundamentally impact economic and social issues worldwide.
As a result, with people confidence and development of biotechnology, bioinformatics also reached to new heights among all the biological sciences. There exists a number of applications of bioinformatics for accelerating research in the area of biotechnology that include automatic genome sequencing, gene identification, prediction of gene function, prediction of protein structure, phylogeny, drug designing and development, identification of organisms, vaccine designing, understanding the gene and genome complexity, understanding protein structure, functionality and folding.
By using bioinformatics in research, many long term projects are turned up so fast like genome mapping of human and other organisms. Similarly, it is expected that bioinformatics innovation in future will also meet the demands of biotechnology. Here, we have tried to explain the role of bioinformatics in various fields of biotechnology like drug deigning and development, genomics, proteomics, environment biotechnology and others [ 3 ]. Genomics The study of genes and their expression is called as Genomics.
This field generates a vast amount of data from gene sequences, their interrelation and functions. To manage this vast enormous data, bioinformatics plays a very important role. With the complete genome sequences for an increasing number of organisms, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviours of the cell and the organism [ 4 ].
Bioinformatics plays a vital role in the areas of structural genomics, functional genomics and nutritional genomics. Proteomics The study of protein structure, function, and interactions produced by a particular cell, tissue, or organism is called as proteomics. It deals with techniques of genetics, biochemistry and molecular biology. Advanced techniques in biology led to accumulate enormous data of protein-protein interactions, protein profiles, protein activity pattern and organelles compositions.
This vast data can be managed and access easily by using bioinformatics tools, software and databases. Till now, many algorithms in the field of proteomics viz.
Forensic DNA and bioinformatics | Briefings in Bioinformatics | Oxford Academic
Transcriptomics The study of sets of all messenger RNA molecules in the cell is called as transcriptomics. The microarray technique generates vast amount of data, single run generates thousands of data value and one experiment requires hundreds of runs. Analysis of such vast data is done by numerous software packages. In this way, bioinformatics is used for transcriptome analysis where mRNA expression levels can be determined [ 6 ].
It is carried out using next generation sequencing to determine the presence and quantity of RNA in a sample at a given time. It is used to analyze the continuously changing cellular transcriptome.
Cheminformatics Cheminformatics chemical informatics focuses on storing, indexing, searching, retrieving, and applying information about chemical compounds. It involves organization of chemical data in a logical form to facilitate the retrieval of chemical properties, structures and their relationships. Using bioinformatics, it is possible through computer algorithm to identify and structurally modify a natural product, to design a compound with the desired properties and to assess its therapeutic effects, theoretically.
Cheminformatics analysis includes analyses such as similarity searching, clustering, QSAR modeling, virtual screening, etc. Drug Discovery Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment and drug development.
This growing importance is not because bioinformatics handles large volumes of data but also in the utility of bioinformatics tools to predict, analyze and help interpretation in clinical and preclinical findings [ 8 ].
Traditionally, pharmacology and chemistry-based drug discovery approaches face various difficulties in finding new drugs. The increasing pressure to generate more and more drugs in a short period of time with low risk has resulted in remarkable interest in bioinformatics.
In fact, now there is an existence of new separate field known as computer-aided drug design CADD. Bioinformatics provides a huge support to overcome the cost and time context in various ways.
It provides wide range of drug-related databases and softwares which can be used for various purposes related to drug designing and development process [ 9 ]. Taxonomists find the evolutionary relationship using various anatomical methods that takes too much time.
Using Bioinformatics, phylogenetic trees are constructed based on the sequence alignment using various methods.
Various algorithmic methods are developed for the construction of phylogenetic tree that are used depending on the various evolutionary lineages [ 10 ].
Role of Bioinformatics in Biotechnology
Crop Improvement Sustainable agricultural production is an urgent issue in response to global climate change and population increase. Innovations in omics based research improve the plant based research.
Genomics strategy, especially comparative genomics helps in understanding the genes and their functions, and also the biological properties of each species. Bioinformatics databases are also used in designing new techniques and experiments for increased plant production [ 11 ].