Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Bioinformatics: Introduction, Objective of Bioinformatics, Bioinformatics Databases, Concept of Bioinformatics, Impact of Bioinformatics in Vaccine Discovery
With advances in technology, enormous amounts of data have become available for bioscience researchers. While this high volume of information holds tremendous promise for expanding the science knowledge base, it must be organized for meaningful study. Bioinformatics is a discipline that devises methods for storing, distributing, and analyzing biological data used by diverse areas of research. Bioinformatics professionals develop software and tools that assist researchers in the analysis of data related to molecular biology and genome studies.
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Bioinformatics: Introduction, Objective of Bioinformatics, Bioinformatics Databases, Concept of Bioinformatics, Impact of Bioinformatics in Vaccine Discovery
With advances in technology, enormous amounts of data have become available for bioscience researchers. While this high volume of information holds tremendous promise for expanding the science knowledge base, it must be organized for meaningful study. Bioinformatics is a discipline that devises methods for storing, distributing, and analyzing biological data used by diverse areas of research. Bioinformatics professionals develop software and tools that assist researchers in the analysis of data related to molecular biology and genome studies.
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Bioinformatics & It's Scope in BiotechnologyTuhin Samanta
As an interdisciplinary field of science, bioinformatics consolidates science, software engineering, data building, arithmetic and measurements to dissect and decipher organic information. Bioinformatics has been utilized for in silico investigations of organic inquiries utilizing numerical and measurable methods.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
Bioinformatics is a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine.
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
Applications of bioinformatics, main by kk sahuKAUSHAL SAHU
Introduction
Goals of Bioinformatics
Bioinformatics & Human Genome
Project
What can we do using bioinformatics ?
Applications of bioinformatics in various fields
1) Medicine
2) Evolutionary studies
3) Agriculture
4) Microbiology
5) Biotechnology
Conclusion
References
this ppt contains information regarding Bioinformatics database. introduction, objectives of database, database management, application of database management, types of database management. Its a part of subject pharmacy, 2nd semester computer application.
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
Bioinformatics is the branch of life science that deals with the use of mathematical, statistical and computer methods to analyze biological and biochemical data.
Types of Bioinformatics (see the slides)
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Bioinformatics & It's Scope in BiotechnologyTuhin Samanta
As an interdisciplinary field of science, bioinformatics consolidates science, software engineering, data building, arithmetic and measurements to dissect and decipher organic information. Bioinformatics has been utilized for in silico investigations of organic inquiries utilizing numerical and measurable methods.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
Bioinformatics is a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine.
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
Applications of bioinformatics, main by kk sahuKAUSHAL SAHU
Introduction
Goals of Bioinformatics
Bioinformatics & Human Genome
Project
What can we do using bioinformatics ?
Applications of bioinformatics in various fields
1) Medicine
2) Evolutionary studies
3) Agriculture
4) Microbiology
5) Biotechnology
Conclusion
References
this ppt contains information regarding Bioinformatics database. introduction, objectives of database, database management, application of database management, types of database management. Its a part of subject pharmacy, 2nd semester computer application.
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
Bioinformatics is the branch of life science that deals with the use of mathematical, statistical and computer methods to analyze biological and biochemical data.
Types of Bioinformatics (see the slides)
PharmaCon2007 Congress, Dubrovnik, Croatia "New Technologies and Trends in Pharmacy, Pharmaceutical Industry and Education" http://www.pharmacon2007.com
Abstract is available at http://www.pharmaconnectme.com
Genomics is the study of the structure and action of the genome, i.e. the sum total of genetic material present in an organism. Genetics is the study of heredity and of the mechanisms by which genetic factors are transmitted from one generation to the next.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
1. Bioinformatics
Bioinformatics is about finding and interpreting
biological data using informatic tools, with the goal of
enabling and accelerating biological research
2. Bioinformatics spans a wide range of activities
- Data capture
- Automated recording of experimental results
- Data storage
- Visualization of raw data and analytical results
- Access to data using a multitude of databases
and query tools
7. Modern Methods of Drug Discovery
What’s different?
• Drug discovery process begins
with a disease (rather than a treatment)
• Use disease model to pinpoint relevant
genetic/biological components (i.e. possible
drug targets)
8. Defining genetic disease
Genetic disorders are caused by abnormalities in the
genetic material
Abnormalities can range from a small mutation in a single gene to the
addition or subtraction of an entire chromosome or set of
chromosomes.
In general, four types of genetic disorders can be distinguished
9. Monogenetic
Monogenetic (also called Mendelian or single gene) disorders are caused by
a mutation in one particular pair of gene.
A mutated gene can result in a mutated protein, which can no longer carry
out its normal function.
Over 10,000 human diseases are known to be caused by defects in single
genes, affecting about 1% of the population as a whole.
Monogenetic disorders often have simple and predictable inheritance
patterns.
11. Polygenic
Polygenic disorders are due to mutations in multiple genes in combination
with external factors, such as lifestyle and environment
Heritability presents the contritution of genetic factors in the formation of
multiple gene diseases. Higher heritability is generally interpreted as a larger
contribution of genes.
Examples of polygenic diseases include coronary heart disease, diabetes,
hypertension, and peptic ulcers.
At present, there are still many difficulties in prenatal diagnosis for multiple-
gene diseases, however, as technology develops, prenatal diagnosis for
common multiple-gene diseases will be available in the near future.
13. Chromosomal
Abnormalities in the chromosomal number or structure, e.g. (partial)
deletion, extra copies, breakage, and (partial) rearrangements, can result in
disease.
15. Mitochondrial
Mitochondria, like the cell nucleus, contains DNA (mtDNA), which is the
biggest difference between mitochondria and other sub-units. mtDNA is only
inherited from the mother and exhibits higher mutation rate than that of
nuclear DNA as well as low repair capacity.
Mitochondrial diseases have threshold effects. That means mitochondrial
diseases could occur only if the abnormal mtDNA exceeds the threshold.
Although sometimes diseases would not happen in the female carriers, for
their underthreshold abnormal mtDNA or certain nuclear effects, mutant
mtDNA can also be passed from generation to generation.
17. DISEASE
Gene identification/finding of inherited disease
Every gene has a specific task
Identification of disease genes is similar to finding genes
responsible for normal functions
The mutation may be within a gene/protein or within a
regulatory part of the genome that, e.g., affects the amount of
protein being produced.
The mutation changes the protein, which alters the way the
task is usually performed
18. Gene identification/finding of inherited disease
Timeline
1983 – Invention of Polymerase Chain Reaction (PCR) technique by Kary Mullis
1989 – the National Center for Human Genome Research is created
1990 – the Human Genome Project (HGP) starts to map and sequence human DNA
1996 – the DNA sequence of the first eukaryotic genome (S. Cerevisiae) is completed
2003 – the human genome sequence is completed
Now – the genome sequences are still frequently updated with new and rearranged
sequences, and some parts are still missing.
2002 – the mouse genome sequence is completed
19. Gene identification/finding of inherited disease
We have a huge amount of genetic data in place.
And now?
Find a candidate gene!
20. Candidate gene definition
A candidate gene is a gene that is suspected
to be involved in a genetic disease
It is located in a chromosome region suspected of
being involved in the expression of a trait such as a
disease, whose protein product suggests that it could
be the gene in question.
21. Disease genes identification in complex disorders
Complex disorders are multifactorial and many such
diseases, like heart and vascular disease are quite
common.
Five steps are applicable to research of
a complex disease:
22. I. Establish that the disease is indeed (partially ) caused
by genetic factors
To prove that the candidate is in fact a gene, demonstration of a
genetic mutation is needed.
Mutation analysis can be done by direct sequencing. Changes in
the splicing process of the gene may be missed when screening
protein-coding DNA sequences only, but are detectable at the
RNA level using RT-PCR. With RT-PCR and related methods it is
possible to evaluate whether the spatio-temporal gene
expression pattern is compatible with the phenotype of
interest.
Final proof may require the examination of the effect of
induced mutation in model organisms.
24. II. Perform segregation analysis on individual pedigree to
determine the type of inheritance.
Inheritance can vary from Mendelian to polygenic,
depending on penetrance and environment.
The mode of inheritance determines the linkage analysis
methods applicable (next step).
25. Segregation analysis
E.g. Genetic Association Interaction Analysis Software (GAIA)
http://www.bbu.cf.ac.uk/html/research/biostats.htm
26. III. Perform linkage analysis to map susceptibility loci
Genetic linkage analysis is a statistical method that is used to
associate functionality of genes to their location on
chromosomes. The main idea is that markers which are found in
vicinity on the chromosome have a tendency to stick together
when passed on to offsprings. Thus, if some disease is often
passed to offsprings along with specific markers, then it can be
concluded that the gene(s) which are responsible for the disease
are located close on the chromosome to these markers.
Parametric, useful for Mendelian traits;
Non parametric, useful for complex diseases.
27. IV. Fine mapping of the susceptibility gene by
population-association studies
Association studies require the use of DNA from many
individuals. However, association studies do not use families.
Rather, they look at DNA from affected individuals compared
to DNA of controls (non-affected individuals who do not have
to be relatives.)
After using linkage to get an idea where disease genes may be
located, use association to try to better locate the gene.
Association allows to test candidate genes, or very small
genetic regions, to see if they are associated with the
phenotype in study. These tests can result in the location of a
risk gene.
28. V. Elucidation of the DNA sequences/genes
Confirm their molecular and biochemical action and involvement.
Relatively easy in case of Mendelian disorders, because the
disease is due to a single change.
In complex disorders the susceptibility is often modelled as a
quantitative trait locus (QTL)
29. In silico positional cloning
Once the critical region for a genetic disease has been
determined by linkage analysis, population-association, etc., the
human genome sequence can be used to identify positional
candidate disease genes.
Genome browsers, biological databases, and other bioinformatics
tools all contribute to the gene finding strategy.
30. Bioinformatics approach to disease gene identification
The release of genomic sequences, full-lenght cDNA sequences,
expressed sequence tags (ESTs), and large-scale expression micro-
array data of human and model organisms (e.g. Mus Musculus)
offer invaluable resources for studying genetic diseases.
This huge amount of data is stored in numerous different
databases, thus making the use of high performance computing an
essential tool for decoding the information contained in these
databases.
35. First Step
To search for all genes between two genetic markers on the
chromosome under study
Essential is a proper description of the location of genes and
other annotations like regulatory elements
Databases and computational tools have been developed to
identify all genes on the human genome sequence. None is
perfect and genes may be missed, or false genes may be
annotated manual evaluation is necessary or..
Multiple sequence analyses on different databases
should be performed
37. Second Step
Functional cloning and candidate gene selection
We identified all the genes between the genetic markers
In theory, every gene within the disease critical region can cause the disease.
When the critical region is large, or the gene density is high,
positional candidates are many.
Strategies:
•There may be already a suspicion on the biochemical/pathogenic background of the
disease
•If a genetic disorder affects e.g. the liver, select only genes expressed in liver
•For known genes, the knowledge in literature can be used to select the candidate genes
•Genes located within the critical disease region that have a functional similarity to genes
involved in related diseases are good candidates
38. The Gene Ontology project is a major bioinformatics initiative with the aim
of standardizing the representation of gene and gene product attributes
across species and databases. The project provides a controlled vocabulary
of terms for describing gene product characteristics and gene product
annotation data from GO Consortium members, as well as tools to access
and process this data.
39. Database for Annotation, Visualization and Integrated Discovery
http://david.abcc.ncifcrf.gov/home.jsp
Systematic and integrative analysis of large gene lists using DAVID Bioinformatics
Resources. (2009) Nat Protoc. 4(1):44 -57.
40.
41.
42.
43. Further, knowledge of model organisms makes
comparative candidate selection possible
This situation applies when a gene is known, which
causes a similar phenotype in other species.
Transfer of knowledge by phenotype is strightforward
in Mus Musculus, being evolutionarily close to humans
44. This grid, called Oxford grid, shows the relationship between human and mouse
chromosomes. Chromosome location of either of the species often predicts the
chromosome location in the other species.
45. When none of the known genes has mutations, it is possible to
try to find new genes in the critical region.
Comparative genome analysis of related species
present us with a wealth of opportunities for studying
evolution and gene/protein function.
Homology-based function-prediction transfers information from
known genes/proteins to unknow sequences and remains the
primary method to determine the function of a new gene
Example of homology-based methods are
Basic Local Alignment Search Tool (BLAST)
Evolutionary annotation database (EVOLA)
49. Biological Networks
Over the last years, the wealth of information derived from
high-throughput interaction screening methods have been
used to map different biological interactions.
These maps provide a vision of the molecular networks in
biological systems.
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B
A
D
F E
50. Protein-protein interaction networks represent the interaction
between proteins such as the building of protein complexes and the
activation of one protein by another protein.
Gene regulatory and signal transduction networks describe how genes
can be activated or repressed and therefore which proteins are
produced in a cell at a particular time.
Metabolic networks show how metabolites are transformed, for
example to produce energy or synthesize specific substances.
Biological Networks
Gene regulatory, protein-protein interaction and metabolic networks
interact with each other and build a complex network of interactions.
51. The study of biological network is essential to understand the
role of candidate genes in genetic diseases
Biological Networks
Finally they are very useful to identify the
genotypes that are associated with phenotypes,
a major goal in genetic research
53. Confirming a candidate gene
Selected genes have to be tested individually to see if there is
evidence that mutations in them do cause the disease in question.
Mutation screening. Identifying mutations in several unrelated affected individuals
strongly suggests that the correct candidate gene has been chosen, but formal proof
requires additional evidence.
Restoration of normal phenotype in vitro. If a cell line that displays the mutant
phenotype can be cultured from the cells of a patient, transfection of a cloned normal
allele into the cultured disease cells may result in restoration of the normal phenotype
by complementing the genetic deficiency.
Production of a mouse model of the disease. Once a putative disease gene is
identified, a transgenic mouse model can be constructed. If the human phenotype is
known to result from loss of function, gene targeting can be used to generate a
germline knockout mutation in the mouse ortholog. The mutant mice are expected to
show some resemblance to humans with the disease.
54. Suggested readings
1. A. L. Barabási, N. Gulbahce, J. Loscalzo, Network medicine: a network-based
approach to human disease. Nature Reviews Genetics 12, 56 (2011).
2. J. K. DiStefano, Disease Gene Identification: Methods and Protocols. (Humana Press,
2011).
3. S. D. Mooney, V. G. Krishnan, U. S. Evani, Bioinformatic tools for identifying disease
gene and SNP candidates. Methods Mol. Biol 628, 307 (2010).
4. A. Schlicker, T. Lengauer, M. Albrecht, Improving disease gene prioritization using the
semantic similarity of Gene Ontology terms. Bioinformatics 26, i561 (2010).
5. N. Tiffin et al., Integration of text-and data-mining using ontologies successfully
selects disease gene candidates. Nucleic acids research 33, 1544 (2005).
6. Y. Zhang et al., Systematic analysis, comparison, and integration of disease based
human genetic association data and mouse genetic phenotypic information. BMC
medical genomics 3, 1 (2010).