Medical Informatics

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Medical Informatics
Bioinformatics
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Biomedical informatics The broad discipline
concerned with the study and application of
computer science, information science,
informatics, cognitive science and human-computer
interaction in the practice of biological
research, biomedical science, medicine and
healthcare. Bioinformatics, clinical informatics
and public health informatics or medical
informatics can be considered as sub-domains
within biomedical informatics.
Bioinformatics The merger of biotechnology and
information technology with the goal of revealing
new insights and principles in biology OR The
science of managing and analyzing biological data
using advanced computing techniques. Especially
important in analyzing genomic research
data. Health Informatics or Medical Informatics
The intersection of information science, computer
science, and health care. It deals with the
resources, devices, and methods required to
optimize the acquisition, storage, retrieval, and
use of information in health and biomedicine.
Wikipedia
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Mining Bio-Medical Mountains How Computer Science
can help Biomedical Research and Health Sciences
Anil Jegga Division of Biomedical Informatics,
Cincinnati Childrens Hospital Medical Center
(CCHMC) Department of Pediatrics, University of
Cincinnati http//anil.cchmc.org Anil.Jegga_at_cchmc.
org
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Algorithm A fixed procedure embodied in a
computer program. Base One of the molecules
that form DNA and RNA molecules. Base pair Two
nitrogenous bases (adenine and thymine or guanine
and cytosine) held together by weak bonds. Two
strands of DNA are held together in the shape of
a double helix by the bonds between base pairs.
Wikipedia
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Nucleotide A subunit of DNA or RNA consisting of
a nitrogenous base (adenine, guanine, thymine, or
cytosine in DNA adenine, guanine, uracil, or
cytosine in RNA), a phosphate molecule, and a
sugar molecule (deoxyribose in DNA and ribose in
RNA). Thousands of nucleotides are linked to form
a DNA or RNA molecule. Genome All the genetic
material in the chromosomes of a particular
organism its size is generally given as its
total number of base pairs. Genomics The study
of genes and their function. Functional
Genomics The study of genes, their resulting
proteins, and the role played by the proteins the
body's biochemical processes.
Wikipedia
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Two Separate Worlds..
Medical Informatics
Bioinformatics the omes
PubMed
Proteome
Disease Database
Patient Records
OMIM Clinical Synopsis
Clinical Trials
382 omes so far and there is UNKNOME too -
genes with no function known http//omics.org/inde
x.php/Alphabetically_ordered_list_of_omics
With Some Data Exchange
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The genome is our Genetic Blueprint

• Nearly every human cell contains 23 pairs of
  chromosomes
• 1 to 22 and
• XY or XX
• XY Male
• XX Female
• Length of chromosomes 1 to 22, X, Y together is
  3.2 billion bases.

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The Genome is Who We Are on the inside!

• Chromosomes consist of DNA
• molecular strings of A, C, G, T
• base pairs, A-T, C-G
• Genes
• DNA sequences that encode proteins
• less than 3 of human genome

Information coded in DNA
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5000 bases per page..
CACACTTGCATGTGAGAGCTTCTAATATCTAAATTAATGTTGAATCATT
ATTCAGAAACAGAGAGCTAACTGTTATCCCATCCTGACTTTATTCTTTAT
GAGAAAAATACAGTGATTCCAAGTTACCAAGTTAGTGCTGCTTGCTTTAT
AAATGAAGTAATATTTTAAAAGTTGTGCATAAGTTAAAATTCAGAAATAA
AACTTCATCCTAAAACTCTGTGTGTTGCTTTAAATAATCAGAGCATCTGC
TACTTAATTTTTTGTGTGTGGGTGCACAATAGATGTTTAATGAGATCCTG
TCATCTGTCTGCTTTTTTATTGTAAAACAGGAGGGGTTTTAATACTGGAG
GAACAACTGATGTACCTCTGAAAAGAGAAGAGATTAGTTATTAATTGAAT
TGAGGGTTGTCTTGTCTTAGTAGCTTTTATTCTCTAGGTACTATTTGATT
ATGATTGTGAAAATAGAATTTATCCCTCATTAAATGTAAAATCAACAGGA
GAATAGCAAAAACTTATGAGATAGATGAACGTTGTGTGAGTGGCATGGTT
TAATTTGTTTGGAAGAAGCACTTGCCCCAGAAGATACACAATGAAATTCA
TGTTATTGAGTAGAGTAGTAATACAGTGTGTTCCCTTGTGAAGTTCATAA
CCAAGAATTTTAGTAGTGGATAGGTAGGCTGAATAACTGACTTCCTATCA
TTTTCAGGTTCTGCGTTTGATTTTTTTTACATATTAATTTCTTTGATCCA
CATTAAGCTCAGTTATGTATTTCCATTTTATAAATGAAAAAAAATAGGCA
CTTGCAAATGTCAGATCACTTGCCTGTGGTCATTCGGGTAGAGATTTGTG
GAGCTAAGTTGGTCTTAATCAAATGTCAAGCTTTTTTTTTTCTTATAAAA
TATAGGTTTTAATATGAGTTTTAAAATAAAATTAATTAGAAAAAGGCAAA
TTACTCAATATATATAAGGTATTGCATTTGTAATAGGTAGGTATTTCATT
TTCTAGTTATGGTGGGATATTATTCAGACTATAATTCCCAATGAAAAAAC
TTTAAAAAATGCTAGTGATTGCACACTTAAAACACCTTTTAAAAAGCATT
GAGAGCTTATAAAATTTTAATGAGTGATAAAACCAAATTTGAAGAGAAAA
GAAGAACCCAGAGAGGTAAGGATATAACCTTACCAGTTGCAATTTGCCGA
TCTCTACAAATATTAATATTTATTTTGACAGTTTCAGGGTGAATGAGAAA
GAAACCAAAACCCAAGACTAGCATATGTTGTCTTCTTAAGGAGCCCTCCC
CTAAAAGATTGAGATGACCAAATCTTATACTCTCAGCATAAGGTGAACCA
GACAGACCTAAAGCAGTGGTAGCTTGGATCCACTACTTGGGTTTGTGTGT
GGCGTGACTCAGGTAATCTCAAGAATTGAACATTTTTTTAAGGTGGTCCT
ACTCATACACTGCCCAGGTATTAGGGAGAAGCAAATCTGAATGCTTTATA
AAAATACCCTAAAGCTAAATCTTACAATATTCTCAAGAACACAGTGAAAC
AAGGCAAAATAAGTTAAAATCAACAAAAACAACATGAAACATAATTAGAC
ACACAAAGACTTCAAACATTGGAAAATACCAGAGAAAGATAATAAATATT
TTACTCTTTAAAAATTTAGTTAAAAGCTTAAACTAATTGTAGAGAAAAAA
CTATGTTAGTATTATATTGTAGATGAAATAAGCAAAACATTTAAAATACA
AATGTGATTACTTAAATTAAATATAATAGATAATTTACCACCAGATTAGA
TACCATTGAAGGAATAATTAATATACTGAAATACAGGTCAGTAGAATTTT
TTTCAATTCAGCATGGAGATGTAAAAAATGAAAATTAATGCAAAAAATAA
GGGCACAAAAAGAAATGAGTAATTTTGATCAGAAATGTATTAAAATTAAT
AAACTGGAAATTTGACATTTAAAAAAAGCATTGTCATCCAAGTAGATGTG
TCTATTAAATAGTTGTTCTCATATCCAGTAATGTAATTATTATTCCCTCT
CATGCAGTTCAGATTCTGGGGTAATCTTTAGACATCAGTTTTGTCTTTTA
TATTATTTATTCTGTTTACTACATTTTATTTTGCTAATGATATTTTTAAT
TTCTGACATTCTGGAGTATTGCTTGTAAAAGGTATTTTTAAAAATACTTT
ATGGTTATTTTTGTGATTCCTATTCCTCTATGGACACCAAGGCTATTGAC
ATTTTCTTTGGTTTCTTCTGTTACTTCTATTTTCTTAGTGTTTATATCAT
TTCATAGATAGGATATTCTTTATTTTTTATTTTTATTTAAATATTTGGTG
ATTCTTGGTTTTCTCAGCCATCTATTGTCAAGTGTTCTTATTAAGCATTA
TTATTAAATAAAGATTATTTCCTCTAATCACATGAGAATCTTTATTTCCC
CCAAGTAATTGAAAATTGCAATGCCATGCTGCCATGTGGTACAGCATGGG
TTTGGGCTTGCTTTCTTCTTTTTTTTTTAACTTTTATTTTAGGTTTGGGA
GTACCTGTGAAAGTTTGTTATATAGGTAAACTCGTGTCACCAGGGTTTGT
TGTACAGATCATTTTGTCACCTAGGTACCAAGTACTCAACAATTATTTTT
CCTGCTCCTCTGTCTCCTGTCACCCTCCACTCTCAAGTAGACTCCGGTGT
CTGCTGTTCCATTCTTTGTGTCCATGTGTTCTCATAATTTAGTTCCCCAC
TTGTAAGTGAGAACATGCAGTATTTTCTAGTATTTGGTTTTTTGTTCCTG
TGTTAATTTGCCCAGTATAATAGCCTCCAGCTCCATCCATGTTACTGCAA
AGAACATGATCTCATTCTTTTTTATAGCTCCATGGTGTCTATATACCACA
TTTTCTTTATCTAAACTCTTATTGATGAGCATTGAGGTGGATTCTATGTC
TTTGCTATTGTGCATATTGCTGCAAGAACATTTGTGTGCATGTGTCTTTA
TGGTAGAATGATATATTTTCTTCTGGGTATATATGCAGTAATGCGATTGC
TGGTTGGAATGGTAGTTCTGCTTTTATCTCTTTGAGGAATTGCCATGCTG
CTTTCCACAATAGTTGAACTAACTTACACTCCCACTAACAGTGTGTAAGT
GTTTCCTTTTCTCCACAACCTGCCAGCATCTGTTATTTTTTGACATTTTA
ATAGTAGCCATTTTAACTGGTATGAAATTATATTTCATTGTGGTTTTAAT
TTGCATTTCTCTAATGATCAGTGATATTGAGTTTGTTTTTTTTCACATGC
TTGTTGGCTGCATGTATGTCTTCTTTTAAAAAGTGTCTGTTCATGTACTT
TGCCCACATTTTAATGGGGTTGTTTTTCTCTTGTAAATTTGTTTAAATTC
CTTATAGGTGCTGGATTTTAGACATTTGTCAGACGCATAGTTTGCAAATA
GTTTCTCCCATTCTGTAGGTTGTCTGTTTATTTTGTTAATAGTTTCTTTT
GCTATGCAGAAGCTCTTAATAAGTTTAATGAGATCCTGATATGTTAGGCT
TTGTGTCCCCACCCAAATCTCATCTTGAATTATATCTCCATAATCACCAC
ATGGAGAGACCAGGTGGAGGTAATTGAATCTGGGGGTGGTTTCACCCATG
CTGTTCTTGTGATAGTGAATGAGTTCTCACGAGATCTAATGGTTTTATGA
GGGGCTCTTCCCAGCTTTGCCTGGTACTTCTCCTTCCTGCCGCTTTGTGA
AAAAGGTGCATTGCGTCCCTTTCACCTTCTTCTATAATTGTAAGTTTCCT
GAGGCCTTCCCAGCCATGCTGAACTTCAAGTCAATTAAACCTTTTTCTTT
ATAAATTACTCAGTCTCTGGTGGTTCTTTATAGCAGTGTGAAAATGGACT
AATGAAGTTCCCATTTATGAATTTTTGCTTTTGTTGCAATTGCTTTTGAC
ATCTTAGTCATGAAATCCTTGCCTGTTCTAAGTACAGGACGGTATTGCCT
AGGTTGTCTTCCAGGGTTTTTCTAATTTTGTGTTTTGCATTTAAGTGTTT
AATCCATCTTGAGTTGATTTTTGTATATTGTGTAAGGAAGGGGTCCAGTT
TCAATCTTTTGCATATGGCTAGTTAGTTATCCCAGTACCATTTATTGAAA
AGACAGTCTTTTCCCCATCGCTCGTTTTTGTCAGTTTTATTGATGATCAG
ATAATCATAGCTGTGTGGCTTTATTTCTGGGTTCTTTATTCTGTTCTATT
GGTTTATGTCCCTGTTTTTGTGCCAGTACCATGCTGTTTTGGTTAACATA
GCCCTGTAGTATAGTTTGAGGTCAGATAGCCTGATGCTTCCAGCTTTGTT
CTTTTTCTTAAGATTGCCTTGGCTATTTGGCCTCTTTTTTGGTTCCACAT
GAATTTTAAAACAGTTGTTTCTAGTTTTTGAAGAATGTCATTGGTAGTTT
GATAGAAATAGCATTTAATCTGTAAATTGATTTGTGCAGTATGGCCTTTT
AATGATATTGATTCTTCCTATCCATGAGCATGATATGTTTTCCATTTTGT
TTGTATCCTCTCTGATTTCTTTGTGCAGTGTTTTGTAATTCTCATTGTAG
AGATTTTTCACCTCCCTGGTTAGTTGTATTTTACCCTAGATATTTTATTC
TTTTTGTGAAAATTGTGAATGGGATTGCCTTCCTGATTTGACTGCCAGCT
TGGTTACTGTTGGTTTATAGAAATGCTAGTGATTTTTGTACATTGATTTT
CTTTCTAAAACTTTGCTGAAGTTTTTTTTATTAGCAGAAGGAGCTTTGGG
GCTGAGACTATGGGGTTTTCTAGATATAGAATCATGTCAGCTTCAAATAG
GGATAATTTTACTTCCTCTCTTCCTATTTGGATGCCCTTTATTTCTTTCT
CTTGCCTGATTACTCTGGCTGGGATTTCCTATGTTGAATAGGAGTCATGA
GAGAGGGCATCAAATCTACACATATCAAATACTAACCTTGAATGTCTAGA
T
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How much data make up the human genome?

• 3 pallets with 40 boxes per pallet x 5000 pages
  per box x 5000 bases per page 3,000,000,000
  bases!

• To get an accurate sequence requires
• 6-fold coverage!
• Now imagine shredding 18 pallets and reassembling!

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Human Genome ProjectInitial Stages

• Most of the initial phases were primarily focused
  on improving speeding the technology to
  sequence and analyze DNA.
• Scientists all around the world worked to make
  detailed maps of our chromosomes and sequence
  model organisms, like worm, fruit fly, and mouse.

Image Courtesy Google Images
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Overwhelming Challenges

• First there was the Assembly
• The DNA sequence is so long that no
  technology can read it all at once, so it was
  broken into pieces.
• There were millions of clones (small sequence
  fragments).
• The assembly process included finding where
  the pieces overlapped in order to put the draft
  together.

3,200,000 piece puzzle anyone?
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The Completion of the Human Genome Sequence

• One June 26, 2000 President Clinton, with J.
  Craig Venter, and Francis Collins, announces
  completion of "the first survey of the entire
  human genome - 80 working draft.
• Publication of 90 percent of the sequence in the
  February 2001 issue of the journal Nature.
• Completion of 99.99 of the genome as finished
  sequence on July 2003.

Image Courtesy Google Images
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Butthe Project is not Done
Human Genome is finally Sequenced!!!

• Next there is the Annotation
• The sequence is like a topographical map, the
  annotation would include cities, towns, schools,
  libraries and coffee shops!
• So, where are the genes?

• How do genes function?
• How do we use this information for scientific
  understanding?
• How does it benefit or improve the health care?

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What do genes do anyway?

• As per current estimate, we only have 27,000
  genes! That means each gene has to do a lot!
• Genes make proteins that make up nearly all we
  are (bones, muscles, hair, eyes, etc.).
• Almost everything that happens in our bodies
  happens because of proteins (walking, digestion,
  fighting disease).

Image Courtesy Google Images
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Of Mice and Men Its all in the genes

• Humans and Mice have about the same number of
  genes. But then why are we so different from each
  other, how is this possible?

Did you say cheese?
Mmm, Cheese!

• While one human gene can make many different
  proteins a mouse gene can only make a few
  probably!

Image Courtesy Google Images
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Genes are important

• By selecting different pieces of a gene, your
  body can make many kinds of proteins. (This
  process is called alternative splicing.)
• If a gene is expressed that means it is turned
  on and it will make proteins.

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What weve learned from our genome so far

• There are a relatively small number of human
  genes, less than 30,000, but they have a complex
  architecture that we are only beginning to
  understand and appreciate.
• We know where 85 of genes are in the sequence.
• We dont know where the other 15 are because we
  havent seen them on (they may only be
  expressed during fetal development).
• We only know what about 50 of our genes do so
  far.
• So it is relatively easy to locate genes in the
  genome, but it is hard to figure out what they
  do.

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How do scientists find genes?

• The genome is so large that useful information is
  hard to find.
• Researchers use a computational microscope to
  help scientists search the genome.
• Just as you would use google to find something
  on the internet, researchers can use the Genome
  Browser to find information in the human genome.

Image Courtesy Google Images
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The Continuing Project

• Finding the complete set of genes and annotating
  the entire sequence. Annotation is like
  detailing scientists annotate sequence by
  listing what has been learnt experimentally and
  computationally about its function.
• Proteomics is studying the structure and function
  of groups of proteins. Proteins are really
  important, but we dont really understand how
  they work.
• Comparative Genomics is the process of comparing
  different genomes in order to better understand
  what they do and how they work. Like comparing
  humans, chimpanzees, and mice that are all
  mammals but all quite different.

Image Courtesy Google Images
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Who works on this stuff anyway?

• Biologists and Chemists understand the physical
  sciences-they take biology and chemistry classes.
• Computer Scientists program the computers (the
  same people who make video games!)-they take math
  and computer classes.
• Computer Engineers try to build better, faster,
  smarter computers-they take math, physics and
  computer classes.
• Social Scientists try to understand how this new
  information and technology will impact our
  lives-they take sociology and philosophy classes.

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How can I work on this project, or something like
it?

• Read about it, online at http//www.genome.gov,
  or in Nature, Science, or other scientific
  magazines.
• Take classes in biology, chemistry, mathematics
  and physics classes at high school.
• Go to college and get a degree in science,
  engineering, mathematics, or social sciences.

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Bioinformatics Opportunities
Director/Professor - University Company
(Pharmaceutical) National Laboratory Research
Foundation
Ph.D.
Bioinformatics Biochemistry Biology Computer
Science Computer Engineering Mathematics Physics L
inguistics Education, Sociology, Philosophy,
Psychology, Community Studies) A research degree
in any of these majors will take you far!
Research Staff - Company/University National
Laboratory Research Foundation Teaching
- Community College Public Schools
M.S. (M.A.)
Entry-Level - Company National Laboratory Teaching
Private Schools
B.S. (B.A.)
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now. The number 1 FAQ
How much biology should I know??
No simple or straight-forward answer
unfortunately!
But the mantra is Take the classes and Interact
routinely with biologists OR Work with the
biologists or the biological data
High School Senior Summer Internship http//www.ci
ncinnatichildrens.org/ed/research/undergrad/hs/def
ault.htm Summer Undergraduate Research
Fellowship http//www.cincinnatichildrens.org/ed/r
esearch/undergrad/surf/default.htm
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But I want to start with some basics..

• http//www.ncbi.nlm.nih.gov/Education
• http//www.ebi.ac.uk/2can/
• http//www.genome.gov/Education/
• http//genomics.energy.gov/
• Books
• Introduction to Bioinformatics by Teresa Attwood,
  David Parry-Smith
• A Primer of Genome Science by Gibson G and Muse
  SV
• Bioinformatics A Practical Guide to the Analysis
  of Genes and Proteins, Second Edition by Andreas
  D. Baxevanis, B. F. Francis Ouellette
• Algorithms on Strings, Trees, and Sequences
  Computer Science and Computational Biology by Dan
  Gusfield
• Bioinformatics Sequence and Genome Analysis by
  David W. Mount
• Discovering Genomics, Proteomics, and
  Bioinformatics by A. Malcolm Campbell and Laurie
  J. Heyer

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Biological Challenges - Computer Engineers

• Post-genomic Era and the goal of bio-medicine
• to develop a quantitative understanding of how
  living things are built from the genome that
  encodes them.
• Deciphering the genome code
• Identifying unknown genes and assigning function
  by computational analysis of genomic sequence
• Identifying the regulatory mechanisms
• Identifying their role in normal
  development/states vs disease states

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Biological Challenges - Computer Engineers

• Data Deluge exponential growth of data silos and
  different data types
• Human-computer interaction specialists need to
  work closely with academic and clinical
  biomedical researchers to not only manage the
  data deluge but to convert information into
  knowledge.
• Biological data is very complex and interlinked!
• Creating information systems that allow
  biologists to seamlessly follow these links
  without getting lost in a sea of information - a
  huge opportunity for computer scientists.

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Biological Challenges - Computer Engineers
A major goal in molecular biology is Functional
Genomics Study of the relationships among genes
in DNA their function in normal and disease
states

• Networks, networks, and networks!
• Each gene in the genome is not an independent
  entity. Multiple genes interact to perform a
  specific function.
• Environmental influences Genotype-environment
  interaction
• Integrating genomic and biochemical data together
  into quantitative and predictive models of
  biochemistry and physiology
• Computer scientists, mathematicians, and
  statisticians will ALL be an integral and
  critical part of this effort.

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Informatics Biologists Expectations

• Representation, Organization, Manipulation,
  Distribution, Maintenance, and Use of
  information, particularly in digital form.
• Functional aspect of bioinformatics
  Representation, Storage, and Distribution of
  data.
• Intelligent design of data formats and databases
• Creation of tools to query those databases
• Development of user interfaces or visualizations
  that bring together different tools to allow the
  user to ask complex questions or put forth
  testable hypotheses.

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Informatics Biologists Expectations

• Developing analytical tools to discover knowledge
  in data
• Levels at biological information is used
• comparing sequences predict function of a newly
  discovered gene
• breaking down known 3D protein structures into
  bits to find patterns that can help predict how
  the protein folds
• modeling how proteins and metabolites in a cell
  work together to make the cell function.

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Finally.What does informatics mean to
biologists?

• The ultimate goal of analytical bioinformaticians
  is to develop predictive methods that allow
  biomedical researchers and scientists to model
  the function and phenotype of an organism based
  only on its genomic sequence. This is a grand
  goal, and one that will be approached only in
  small steps, by many scientists from different
  but allied disciplines working cohesively.

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Biology Data Structures

• Four broad categories
• Strings To represent DNA, RNA, amino acid
  sequences of proteins
• Trees To represent the evolution of various
  organisms (Taxonomy) or structured knowledge
  (Ontologies)
• Sets of 3D points and their linkages To
  represent protein structures
• Graphs To represent metabolic, regulatory, and
  signaling networks or pathways

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Biology Data Structures

• Biologists are also interested in
• Substrings
• Subtrees
• Subsets of points and linkages, and
• Subgraphs.

Beware Biological data is often characterized by
huge size, the presence of laboratory errors
(noise), duplication, and sometimes unreliability.
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Support Complex Queries A typical demand

• Get me all genes involved in or associated with
  brain development that are differentially
  expressed in the Central Nervous System.
• Get me all genes involved in brain development in
  human and mouse that also show iron ion binding
  activity.
• For this set of genes, what aspects of function
  and/or cellular localization do they share?
• For this set of genes, what mutations are
  reported to cause pathological conditions?

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Model Organism Databases Common Issues

• Heterogeneous Data Sets - Data Integration
• From Genotype to Phenotype
• Experimental and Consensus Views
• Incorporation of Large Datasets
• Whole genome annotation pipelines
• Large scale mutagenesis/variation projects
  (dbSNP)
• Computational vs. Literature-based Data
  Collection and Evaluation (MedLine)
• Data Mining
• extraction of new knowledge
• testable hypotheses (Hypothesis Generation)

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Human Genome Project Data Deluge
No. of Human Gene Records currently in NCBI
29413 (excluding pseudogenes, mitochondrial genes
and obsolete records). Includes 460 microRNAs
NCBI Human Genome Statistics as on February12,
2008
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The Gene Expression Data Deluge
Till 2000 413 papers on microarray!
Problems Deluge! Allison DB, Cui X, Page GP,
Sabripour M. 2006. Microarray data analysis from
disarray to consolidation and consensus. Nat Rev
Genet. 7(1) 55-65.
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Information Deluge..

• 3 scientific journals in 1750
• Now - gt120,000 scientific journals!
• gt500,000 medical articles/year
• gt4,000,000 scientific articles/year
• gt16 million abstracts in PubMed derived from
  gt32,500 journals

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Data-driven Problems..

• Generally, the names refer to some feature of the
  mutant phenotype
• Dickies small eye (Thieler et al., 1978, Anat
  Embryol (Berl), 155 81-86) is now Pax6
• Gleeful "This gene encodes a C2H2 zinc finger
  transcription factor with high sequence
  similarity to vertebrate Gli proteins, so we have
  named the gene gleeful (Gfl)." (Furlong et al.,
  2001, Science 293 1632)

Whats in a name!
Rose is a rose is a rose is a rose!
Gene Nomenclature

• Disease names
• Mobius Syndrome with Polands Anomaly
• Werners syndrome
• Downs syndrome
• Angelmans syndrome
• Creutzfeld-Jacob disease

• Accelerin
• Antiquitin
• Bang Senseless
• Bride of Sevenless
• Christmas Factor
• Cockeye
• Crack
• Draculin
• Dickies small eye

• Draculin
• Fidgetin
• Gleeful
• Knobhead
• Lunatic Fringe
• Mortalin
• Orphanin
• Profilactin
• Sonic Hedgehog

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Rose is a rose is a rose is a rose.. Not Really!
What is a cell?

• any small compartment
• (biology) the basic structural and functional
  unit of all organisms they may exist as
  independent units of life (as in monads) or may
  form colonies or tissues as in higher plants and
  animals
• a device that delivers an electric current as a
  result of chemical reaction
• a small unit serving as part of or as the nucleus
  of a larger political movement
• cellular telephone a hand-held mobile
  radiotelephone for use in an area divided into
  small sections, each with its own short-range
  transmitter/receiver
• small room in which a monk or nun lives
• a room where a prisoner is kept

Image Sources Somewhere from the internet and
Google Images
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Foundation Model Explorer
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• COLORECTAL CANCER 3-BP DEL, SER45DEL
• COLORECTAL CANCER SER33TYR
• PILOMATRICOMA, SOMATIC SER33TYR
• HEPATOBLASTOMA, SOMATIC THR41ALA
• DESMOID TUMOR, SOMATIC THR41ALA
• PILOMATRICOMA, SOMATIC ASP32GLY
• OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC
  SER37CYS
• HEPATOCELLULAR CARCINOMA SOMATIC SER45PHE
• HEPATOCELLULAR CARCINOMA SOMATIC SER45PRO
• MEDULLOBLASTOMA, SOMATIC SER33PHE

The REAL Problems
Many disease states are complex, because of many
genes (alleles ethnicity, gene families, etc.),
environmental effects (life style, exposure,
etc.) and the interactions.
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The REAL Problems
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Methods for Integration

• Link driven federations
• Explicit links between databanks.
• Warehousing
• Data is downloaded, filtered, integrated and
  stored in a warehouse. Answers to queries are
  taken from the warehouse.
• Others.. Semantic Web, etc

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Link-driven Federations

• Creates explicit links between databanks
• query get interesting results and use web links
  to reach related data in other databanks
• Examples NCBI-Entrez, SRS

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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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Link-driven Federations

• Advantages
• complex queries
• Fast
• Disadvantages
• require good knowledge
• syntax based
• terminology problem not solved

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Data Warehousing
Data is downloaded, filtered, integrated and
stored in a warehouse. Answers to queries are
taken from the warehouse.

• Advantages
• Good for very-specific, task-based queries and
  studies.
• Since it is custom-built and usually
  expert-curated, relatively less error-prone.

• Disadvantages
• Can become quickly outdated needs constant
  updates.
• Limited functionality For e.g., one
  disease-based or one system-based.

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Algorithms in Bioinformatics

• Finding similarities among strings
• Detecting certain patterns within strings
• Finding similarities among parts of spatial
  structures (e.g. motifs)
• Constructing trees
• Phylogenetic or taxonomic trees evolution of an
  organism
• Ontologies structured/hierarchical
  representation of knowledge
• Classifying new data according to previously
  clustered sets of annotated data

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Algorithms in Bioinformatics

• Reasoning about microarray data and the
  corresponding behavior of pathways
• Predictions of deleterious effects of changes in
  DNA sequences
• Computational linguistics NLP/Text-mining.
  Published literature or patient records
• Graph Theory Gene regulatory networks,
  functional networks, etc.
• Visualization and GUIs (networks, application
  front ends, etc.)

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Disease Gene Identification and Prioritization
Hypothesis Functionally similar or related genes
cause similar disease.

• Functional Similarity Common/shared features
• Gene Ontology term
• Pathway
• Phenotype
• Chromosomal location
• Expression
• Cis regulatory elements (Transcription factor
  binding sites)
• miRNA regulators
• Interactions
• Other features..

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PPI - Predicting Disease Genes

• Direct proteinprotein interactions (PPI) are one
  of the strongest manifestations of a functional
  relation between genes.
• Hypothesis Interacting proteins lead to same or
  similar diseases when mutated.
• Several genetically heterogeneous hereditary
  diseases are shown to be caused by mutations in
  different interacting proteins. For e.g.
  Hermansky-Pudlak syndrome and Fanconi anaemia.
  Hence, proteinprotein interactions might in
  principle be used to identify potentially
  interesting disease gene candidates.

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• Prioritize candidate genes in the interacting
  partners of the disease-related genes
• Training sets disease related genes
• Test sets interacting partners of the training
  genes

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• Example Breast cancer

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PubMed
OMIM
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http//sbw.kgi.edu/