What is bioinformatics ?
It’s method to predict the biological outcomes before anyone go for full fledged research. It’s a method to compare the biological data. Ex: sequence analysis. It’s a way to predict or solve the protein structure.
It’s the only way for PERSONALIZED MEDICINE in this post genomic era. It’s the method to do comparative genomics and predict the Human homolog genes in other species.
It’s the method to annotate the newly sequenced genomes.
How the biological problems can be predicted ?
We are living in the world of Computers. By analyzing the existing biological data using Information Technology we can predict the biological outcomes.
What is the HOTTEST branch of Bioinformaics in this post genomic era ?
Personalized medicine is the most hottest and fastest growing field. Personalized medicine can be achieved through bioinformatics only.
Friday, January 2, 2009
Branches of Bioinformatics
These are some of the important fields in bioinformatics
1. Structural Bioinformatics:
Predicting the 3D structure of a protein from its protein sequence. Homology modelling is the best method for predicting the protein structures by using already structured or crystallized protein as a template. MODELLER is one of the best software for Homology modelling. Protein Data Bank is the data base for 3D co-ordinates of a protein.
Recent Studies ..
Crystal structure of Mycobacterium tuberculosis Rv0760c at 1.50 A resolution, a structural homolog of Delta(5)-3-ketosteroid isomerase.
2. 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.
Computer-assisted drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. Click to see the drug discovery softwares.
3. Phylogenetics:
Predicting the genetic or evolutionary relation of set of organisms. Mitochondrial SNPs and Microsatellites ( DNA repeats) are mostly used in Phylogenetics. MEGA,PAUP are PAUP* are some of the important softwares. Maximum Parsimony and Maximum Likelyhood are mostly used methods.
4. Computational biology:
Computational biology is an interdisciplinary field that applies the techniques of computer science, applied mathematics, and statistics to address problems inspired by biology.
5. Population Genetics:
Population Genetics is a study of genotype frequency distribution and the change in the genotype frequencies under the influence of Natural selection, genetics drift, mutation and gene flow. Coalescent theory is one of the most used theory to predict the most recent ancester. Arlequin is one of the best and most used software in population gentics.
6. Genotype Analysis:
Genotype = Genetic variation, SNP,Mutation ….
1. Studying Genotype and phenotype association.
2. Studying Genotype frequencies. There is no specific software for genotype analysis. But its called the “Generation Next Market using Bioinformatics….”. Genotyping is mostly done using Illumina and Affy microarry chips.
2008 July - Recent Studies….
Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease.
Estimating coverage and power for genetic association studies using near-complete variation data.
Genetic diversity patterns at the human clock gene period 2 are suggestive of population-specific positive selection.
Environment And Genetics in Lung cancer Etiology (EAGLE) study: an integrative population-based case-control study of lung cancer.
7. Splicing Site prediction:
Splicing prediction is a very important application of Bioinformatics which is very important in Gene expression studies. Visit also Alternative Splicing site Predictior.
For More info
2008 July - Recent Studies ..
ASPicDB: a database resource for alternative splicing analysis.
Diagnostics of pathogenic splicing mutations: does bioinformatics cover all bases?
8. MiRNA prediction:
MiRNA = MicroRNA. MiRNA emerged as a new Gene regulatory element and gained more space in research. 20 -23 base pair RNA which regulates a gene or genes. So many methods and softwares have been developed to predicting this tiny RNAs. But still they are not precise in predicting. It means that we need some more information from experimental labs to predict.
MiRNA binds to the gene and regulates the gene. Most of the time it down regulate the gene expression. Predicting the MiRNA target is also a very important problem in Bioinformatics.
Database..
miRNA Registry from Sanger Institute.
MiRNA target prediction software
There are so many softwares for miRNA and Target prediction….
Recent Studies..
MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer.
Accelerated sequence divergence of conserved genomic elements in Drosophila melanogaster.
miRNA expression in the failing human heart: Functional correlates.
Computational analysis of miRNA-mediated repression of translation: Implications for models of translation initiation inhibition.
9. RNA Structure prediction:
The functional form of single stranded RNA molecules frequently requires a specific tertiary structure. The scaffold for this structure is provided by secondary structural elements which are hydrogen bonds within the molecule. This leads to several recognizable “domains” of secondary structure like hairpin loops, bulges and internal loops. There has been a significant amount of bioinformatics research directed at the RNA structure prediction problem.
10. Gene Prediction:
Predicting the Gene by the predefined conditions. Comparative genomics is the best method for predicting the gene.
Some of the softwares..
GeneMark, Genscan
11. Transcription factor binding site prediction:
Predicting the transcription factor. Most common method is to use “Comparative genomics”. And finding clusters of motifs in the noncoding part of gene.
Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences.
12. Genome Annotation:
Predicitng the genes, coding and noncoding sequences are called genome annotation.
Most of the people follow comparative genomics to annotate the newly sequenced genomes.
GOLD is the database for ongoing genome projects.
13. Ancestry Prediction:
Predicting the Ancestry of an individual based on his/her genetic signatures or SNPs.
mitochondrial SNPs are used in predicting Maternal ancestry because Mitochondria is passed ONLY through mother to the child.
Y chromosome SNPs are used in predicting paternal ancestry becuase Y chromsome is passed from Father to the child.
Ancestry is one of the successful field in Bioinformatics. Genography project by Dr. Spencer Wells is one of the finest one.
Recent studies..
Mitochondrial DNA haplogroup D4a is a marker for extreme longevity in Japan.
Analysis of Y-chromosomal biallelic polymorphisms in Sichuan Han of Chinese population
14. Mathematical Modelling:
Using mathemetics to predict the out come of some complex real time problems which cannot be done in lab or in reality. Ex: population dynamics.
Recent Studies..
Diagnosed and undiagnosed HIV-infected populations in Europe.
15. Ethnicity Prediction:
Predicting the ethnicity of an individual by using genetics variations. Each ethnicity is defined by a set of genetic variations.
16. Functional Domains prediction:
Predicting the protein domains which are functionaly important from its protein sequence like active sites in a protein.
Recent studies ..
Predicting protein function from domain content.
17. Motif Prediction /Pattern matching:
Predicting the motifs or motif clusters which are functionaly important.
Ex: regulatory motifs, Binding site motifs …miRNA motics ..repeat motis …Microsatellites are also a kind of motifs.
Recent studies…
Biomolecular network motif counting and discovery by color coding.
18. Protein - protein interaction:
19. Protein folding:
One of the famous and most important and still unsolved problem.
20. Database development:
In some sense Bioinformatics is called as “Comparative Method”. Because Bioinformatics depends on Databases for all of its analysis. So developing data base is a very important project. Many companies surviving by devloping and updating the databases.
NCBI , PDB and UCSC genome browser are some of the very important databases.
21. Software development:
Incorporating the usage of Softwares in Biological analysis is called “Bioinformatics”.
22. Developing Bioinformatics Methods/Approaches :
23. Primer designing:
24. Modeling genetics History:
25. Ancient DNA:
26. Population Genetics Simulations:
27. Finding SNPs:
28. Genome wide Association Studies:
29. Systems Biology:
30. Homology Search:
31. Computational Genomics:
1. Structural Bioinformatics:
Predicting the 3D structure of a protein from its protein sequence. Homology modelling is the best method for predicting the protein structures by using already structured or crystallized protein as a template. MODELLER is one of the best software for Homology modelling. Protein Data Bank is the data base for 3D co-ordinates of a protein.
Recent Studies ..
Crystal structure of Mycobacterium tuberculosis Rv0760c at 1.50 A resolution, a structural homolog of Delta(5)-3-ketosteroid isomerase.
2. 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.
Computer-assisted drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. Click to see the drug discovery softwares.
3. Phylogenetics:
Predicting the genetic or evolutionary relation of set of organisms. Mitochondrial SNPs and Microsatellites ( DNA repeats) are mostly used in Phylogenetics. MEGA,PAUP are PAUP* are some of the important softwares. Maximum Parsimony and Maximum Likelyhood are mostly used methods.
4. Computational biology:
Computational biology is an interdisciplinary field that applies the techniques of computer science, applied mathematics, and statistics to address problems inspired by biology.
5. Population Genetics:
Population Genetics is a study of genotype frequency distribution and the change in the genotype frequencies under the influence of Natural selection, genetics drift, mutation and gene flow. Coalescent theory is one of the most used theory to predict the most recent ancester. Arlequin is one of the best and most used software in population gentics.
6. Genotype Analysis:
Genotype = Genetic variation, SNP,Mutation ….
1. Studying Genotype and phenotype association.
2. Studying Genotype frequencies. There is no specific software for genotype analysis. But its called the “Generation Next Market using Bioinformatics….”. Genotyping is mostly done using Illumina and Affy microarry chips.
2008 July - Recent Studies….
Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease.
Estimating coverage and power for genetic association studies using near-complete variation data.
Genetic diversity patterns at the human clock gene period 2 are suggestive of population-specific positive selection.
Environment And Genetics in Lung cancer Etiology (EAGLE) study: an integrative population-based case-control study of lung cancer.
7. Splicing Site prediction:
Splicing prediction is a very important application of Bioinformatics which is very important in Gene expression studies. Visit also Alternative Splicing site Predictior.
For More info
2008 July - Recent Studies ..
ASPicDB: a database resource for alternative splicing analysis.
Diagnostics of pathogenic splicing mutations: does bioinformatics cover all bases?
8. MiRNA prediction:
MiRNA = MicroRNA. MiRNA emerged as a new Gene regulatory element and gained more space in research. 20 -23 base pair RNA which regulates a gene or genes. So many methods and softwares have been developed to predicting this tiny RNAs. But still they are not precise in predicting. It means that we need some more information from experimental labs to predict.
MiRNA binds to the gene and regulates the gene. Most of the time it down regulate the gene expression. Predicting the MiRNA target is also a very important problem in Bioinformatics.
Database..
miRNA Registry from Sanger Institute.
MiRNA target prediction software
There are so many softwares for miRNA and Target prediction….
Recent Studies..
MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer.
Accelerated sequence divergence of conserved genomic elements in Drosophila melanogaster.
miRNA expression in the failing human heart: Functional correlates.
Computational analysis of miRNA-mediated repression of translation: Implications for models of translation initiation inhibition.
9. RNA Structure prediction:
The functional form of single stranded RNA molecules frequently requires a specific tertiary structure. The scaffold for this structure is provided by secondary structural elements which are hydrogen bonds within the molecule. This leads to several recognizable “domains” of secondary structure like hairpin loops, bulges and internal loops. There has been a significant amount of bioinformatics research directed at the RNA structure prediction problem.
10. Gene Prediction:
Predicting the Gene by the predefined conditions. Comparative genomics is the best method for predicting the gene.
Some of the softwares..
GeneMark, Genscan
11. Transcription factor binding site prediction:
Predicting the transcription factor. Most common method is to use “Comparative genomics”. And finding clusters of motifs in the noncoding part of gene.
Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences.
12. Genome Annotation:
Predicitng the genes, coding and noncoding sequences are called genome annotation.
Most of the people follow comparative genomics to annotate the newly sequenced genomes.
GOLD is the database for ongoing genome projects.
13. Ancestry Prediction:
Predicting the Ancestry of an individual based on his/her genetic signatures or SNPs.
mitochondrial SNPs are used in predicting Maternal ancestry because Mitochondria is passed ONLY through mother to the child.
Y chromosome SNPs are used in predicting paternal ancestry becuase Y chromsome is passed from Father to the child.
Ancestry is one of the successful field in Bioinformatics. Genography project by Dr. Spencer Wells is one of the finest one.
Recent studies..
Mitochondrial DNA haplogroup D4a is a marker for extreme longevity in Japan.
Analysis of Y-chromosomal biallelic polymorphisms in Sichuan Han of Chinese population
14. Mathematical Modelling:
Using mathemetics to predict the out come of some complex real time problems which cannot be done in lab or in reality. Ex: population dynamics.
Recent Studies..
Diagnosed and undiagnosed HIV-infected populations in Europe.
15. Ethnicity Prediction:
Predicting the ethnicity of an individual by using genetics variations. Each ethnicity is defined by a set of genetic variations.
16. Functional Domains prediction:
Predicting the protein domains which are functionaly important from its protein sequence like active sites in a protein.
Recent studies ..
Predicting protein function from domain content.
17. Motif Prediction /Pattern matching:
Predicting the motifs or motif clusters which are functionaly important.
Ex: regulatory motifs, Binding site motifs …miRNA motics ..repeat motis …Microsatellites are also a kind of motifs.
Recent studies…
Biomolecular network motif counting and discovery by color coding.
18. Protein - protein interaction:
19. Protein folding:
One of the famous and most important and still unsolved problem.
20. Database development:
In some sense Bioinformatics is called as “Comparative Method”. Because Bioinformatics depends on Databases for all of its analysis. So developing data base is a very important project. Many companies surviving by devloping and updating the databases.
NCBI , PDB and UCSC genome browser are some of the very important databases.
21. Software development:
Incorporating the usage of Softwares in Biological analysis is called “Bioinformatics”.
22. Developing Bioinformatics Methods/Approaches :
23. Primer designing:
24. Modeling genetics History:
25. Ancient DNA:
26. Population Genetics Simulations:
27. Finding SNPs:
28. Genome wide Association Studies:
29. Systems Biology:
30. Homology Search:
31. Computational Genomics:
Bioinformatics Opportunities in India
Outsourcing to India, compared to other developed countries, offers about 30-40% costs savings in overall drug discovery research, and close to 60% cost savings when outsourcing core bioinformatics services. This is due to the lower wage costs for skilled manpower, and lower infrastructure costs.
The Indian bioinformatics market has grown from $18 million in 2003-04 to $35 million in 2006-07, at a CAGR of 25%. Interestingly, owing to low local demand, $32 million or about 90% of bioinformatics revenues in India are derived from outsourcing activities. “Local demand for bioinformatics services is low due to low investment in new drug discovery. Even though research investments of life sciences companies are increasing, they are still small compared to global standards” says Ashutosh Mundkur, VBU Life Sciences, Service Offerings, Satyam Computer Services Ltd.
The Indian bioinformatics outsourcing services opportunity is estimated to grow at 25% per annum during 2007-2010 raising its share of the global market from 1.4% in 2007 to 1.7% in 2010. These estimates are made based on the current plans of Indian vendors, as well as considering the impact of scarcity in human resources. Improved availability of skilled workers could help take growth rates higher. Similarly, positive actions by the Indian government to enhance IP rights could also help raise growth.
Indian Bioinformatics Market:
Pharmaceutical companies are under constant pressure to develop new “blockbuster” drugs to replace older ones that are going off patent. With costs to launch a new drug crossing $1 billion and the number of drugs approved for commercial launch decreasing, pharmaceutical companies are increasingly looking at biotechnology to deliver results.
The Indian bioinformatics industry comprises vendors with origins in the life sciences domain or the IT domain. We estimate that there are about 45-50 companies active in the bioinformatics segment in India. Of these, about 35 are involved in the development of software tools, database solutions and providing bioinformatics services; the remaining largely into marketing third party products and services.
2005 Report:
The global bioinformatics market is currently estimated at about $1.4 billion (€1.1 billion). It is expected to grow at an average annual growth rate of 15.8 per cent to reach nearly $3 billion by 2010
Pharmaceutical companies are expected to increase their R&D expenditure in the future. A major portion of this R&D spending is expected to go into bioinformatics. Global drug discovery spending is estimated to increase from $19.6 billion in 2002 to $25.1 billion in 2006.
Scientists are acquiring genomics data through the use of techniques such as amplification, DNA microarray expression, real-time PCR and genotyping. Instrumentation, hardware and software are then required to analyse, integrate and transmit this vast amount of data, which has resulted in significant IT challenges for those in the field.
The segment is estimated to grow at an average annual growth rate (AAGR) of 21.2 per cent from $444.7 million in 2005 to $1.16 billion in 2010. Growth in the genomics-based content will be the key driver for the rise in genomics-based analysis software and services segment.
The Indian bioinformatics market has grown from $18 million in 2003-04 to $35 million in 2006-07, at a CAGR of 25%. Interestingly, owing to low local demand, $32 million or about 90% of bioinformatics revenues in India are derived from outsourcing activities. “Local demand for bioinformatics services is low due to low investment in new drug discovery. Even though research investments of life sciences companies are increasing, they are still small compared to global standards” says Ashutosh Mundkur, VBU Life Sciences, Service Offerings, Satyam Computer Services Ltd.
The Indian bioinformatics outsourcing services opportunity is estimated to grow at 25% per annum during 2007-2010 raising its share of the global market from 1.4% in 2007 to 1.7% in 2010. These estimates are made based on the current plans of Indian vendors, as well as considering the impact of scarcity in human resources. Improved availability of skilled workers could help take growth rates higher. Similarly, positive actions by the Indian government to enhance IP rights could also help raise growth.
Indian Bioinformatics Market:
Pharmaceutical companies are under constant pressure to develop new “blockbuster” drugs to replace older ones that are going off patent. With costs to launch a new drug crossing $1 billion and the number of drugs approved for commercial launch decreasing, pharmaceutical companies are increasingly looking at biotechnology to deliver results.
The Indian bioinformatics industry comprises vendors with origins in the life sciences domain or the IT domain. We estimate that there are about 45-50 companies active in the bioinformatics segment in India. Of these, about 35 are involved in the development of software tools, database solutions and providing bioinformatics services; the remaining largely into marketing third party products and services.
2005 Report:
The global bioinformatics market is currently estimated at about $1.4 billion (€1.1 billion). It is expected to grow at an average annual growth rate of 15.8 per cent to reach nearly $3 billion by 2010
Pharmaceutical companies are expected to increase their R&D expenditure in the future. A major portion of this R&D spending is expected to go into bioinformatics. Global drug discovery spending is estimated to increase from $19.6 billion in 2002 to $25.1 billion in 2006.
Scientists are acquiring genomics data through the use of techniques such as amplification, DNA microarray expression, real-time PCR and genotyping. Instrumentation, hardware and software are then required to analyse, integrate and transmit this vast amount of data, which has resulted in significant IT challenges for those in the field.
The segment is estimated to grow at an average annual growth rate (AAGR) of 21.2 per cent from $444.7 million in 2005 to $1.16 billion in 2010. Growth in the genomics-based content will be the key driver for the rise in genomics-based analysis software and services segment.
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