GSBS Core Module 7 Bioinformatics

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GSBS Core Module 7Bioinformatics

• February 8, 2008
• Bruce Byrne, PhD
• Informatics Institute of UMDNJ
• http//informatics.umdnj.edu

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What to Expect?

• Class Notes
• http//informatics.umdnj.edu/bioinformatics/course
  s/CORE/
• Interactive style of teaching
• Dont be shy
• Ask questions
• Self-testing exercises prepare you for the exam
• Not graded, but answers supplied
• Exam, February 20
• Part I 25
• Solve an individualized problem, on your own,
  before coming into the examination. I will
  distribute those problems on February 18th.
• Part II 75
• A written exam, executed on February 20
• Definitions, explanation of facts, and
  interpretations of results.
• Bring any notes you have taken or other documents
  for reference.
• You may not use computers or wireless devices
  during the exam.

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What Constitutes a Good Answer?

• For an exam
• A definition?
• An explanation?
• An interpretation?
• For a scientific inquiry?
• A good question
• Good data
• Good interpretation
• Knowledge and use of the literature
• Novel or solid use of analytical skills

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Criteria for AnswersClass Discussion 2/8/08

• Good Answers
• To the point
• Factual based on evidence
• Best answer
• Apply concept pointers.
• See the flow of reasoning in an explanation or
  interpretation
• Well said
• Bad Answers
• Rambling
• Wrong
• Unclear or too clear! No copy/paste!

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Goals for the Bioinformatics Unit

• Appreciate the scope of a dynamic field
• Know next year when things you learn in the next
  few sessions will be available
• Understand that the resources are continuously
  changing and be prepared to adapt to those
  changes
• Associate available data sets and analytical
  tools to research problems
• Find sequence, gene, and genomic data
• Evaluate data quality
• Compare sequences
• Important areas not covered in sufficient detail
• Molecular modeling and visualization or
  Structural Bioinformatics
• Measures of gene activity Functional Genomics
  and/or Proteomics

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Goals for the First Lecture

• What is Bioinformatics?
• Review fundamental bits of biology necessary to
  the unit as a whole
• Consider the types of data we could be looking at
  and how a biologist adds value to the
  bioinformatics team
• Take these issues in some historical perspective
  to see where we came to this point and how far we
  have to go

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The Scope of Bioinformatics

• References
• National Center for Biotechnology Information.
• http//www.ncbi.nlm.nih.gov/
• UCSC Genome Browser
• http//genome.ucsc.edu/
• Molecular Cell Biology
• Lodish, et al., 2008. Freeman and Company, NY.
• Genomes. T.A. Brown, Second Ed.
• http//www.ncbi.nlm.nih.gov/books/bv.fcgi?ridgeno
  mes
• Nature Human Genome Collection (May, 2006).
  Nature Supplements/Collections Human Genome
• http//www.nature.com/nature/supplements/collectio
  ns/humangenome/index.html
• Human Genome Project
• http//www.ornl.gov/sci/techresources/Human_Genome
  /home.shtml

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Biology Review

• Genetics of Disease
• Lodish Section 5.4
• Genomic markers
• RFLP
• SNP
• Association studies
• Simple patterns of inheritance
• Complex patterns of susceptibility

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Biology Review

• Eukaryotic Gene Structure
• Lodish Section 6.3, 8.1, 8.2
• Introns, exons, and splicing

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Overview of Bioinformatics

• Large number of disciplines and techniques
• Large and complex problems and data sets
• Practitioners
• biologists, chemists, librarians, computer
  scientists, statisticians, engineers

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Why is Bioinformatics Required?

• Massive quantities of information
• are not easily comprehended by inspection
• must be created, stored, managed, retrieved, and
  disseminated efficiently
• stored in databases
• analyzed by computer programs (applications) that
  distinguish patterns.
• The patterns can be used to compare two
  sequences, structures, or images to approximate
  similarity with each other or similarity to a
  recognized sequence, structure or image.
• What about the human?
• Endpoints are statistical analyses and
  visualizations
• What does statistics do for you?
• What is visualization?

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What Is Your Kind of Problem?

• Detail
• interested in exact and detailed understanding
  of problems?
• Big pictures?
• Systems Biology
• unites the sub-disciplines directed at genes,
  proteins, protein expression, metabolism and
  physiology to model larger entities in the
  analysis of networked information from organs to
  organisms and populations.

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Data Types

• Sequences
• Nucleic acids
• Protein
• Molecular structures
• Proteins
• Nucleic acids
• Small molecules (ligands/drugs)
• Complexes of large and small molecules
• Indicators of cellular function
• Micorarray profiles
• Proteomic profiles

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A Brief History of Sequences

• Sequences
• 1951
• Pauling and others
• Margaret Dayhoff and colleagues began to assemble
  family trees as reflected by sequence similarity
• Not all differences are equal.
• Since the substitution of biochemically similar
  amino acids is likely functionally less
  disruptive than the substitution of dissimilar
  amino acids, comparisons needed to be weighted
• PAM (Percent Accepted Mutation) accounts for
  observed changes among closely related sequences
  of proteins.
• PAM1 matrix derived from sequences having only 1
  sequence dissimilarity
• DNA sequencing
• Chain Termination Method (Frederick Sanger)
• PhiX-174 genome sequenced 1977 5400 bp
• DNA sequence databases
• Los Alamos / GenBank
• Conceived in 1979 (Walter Goad)
• In production 1982 1992
• Transferred to NCBI in 1992
• Coordinated with international databases
• 6.3 billion bases in 80 million sequence files
  (Dec., 2007)
• complete, structured output of all sequences from
  one organism constitutes the sequence of the
  genome

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Sequence Data

• How is sequence data obtained?
• What is the nature of the data that is produced
  using current technology?
• What are the weaknesses and strengths of these
  data?
• Reference
• http//en.wikipedia.org/wiki/DNA_sequencing
• Commentary Wikipedia

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Genomes

• Human
• 1985 Proposal gains strength at Department of
  Energy (DOE)
• 1987 Congress approves 15-year Human Genome
  Project NIH begins funding projects
• 1988 HUGO (European Effort) takes hold
• 1989 Sequence Tagged Sites (STS) strategy
  developed
• 1990 15-year plan begins
• 1994 5-year goals beat target
• 1999 First complete chromosome (22)
• 2000 Working Draft of complete genome by
  International Human Genome Sequencing Consortium
• 2003 Chromosomes 6, 7, Y and 14
• 2004 Chromosomes 5, 9,10, 13 and 19
• Gene count estimate set at 25,000
• 2005 Chromosomes 2, 4, and X
• 2006 Chromosomes 1, 3, 8, 11, 12, 15, and 17
• 2007 and on no one has gotten around to listing
  later milestones!
• Non-human
• 1995 First non-viral genome sequenced
  (Haemophilus influenzae)
• 1996 First eukaryote Sacchromyces
• 1997 E. coli

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Molecular Structures

• 100-year history of X-ray crystallography
• 1971 Seven protein structures
• 1988 Rutgers University, University of Wisconsin,
  National of Standards and Technology and the San
  Diego Supercomputer Center
• Approaching 49,000 structures
• X-ray, NMR and other advanced methods employed
• Highly curated and structured database
• Each structure described the a pattern in an XYZ
  matrix describing the position of each heavy atom
  in the protein

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Gene Activity

• Northern Blot (PubMed 1980)
• mRNA separated by gel electrophoresis,
  transferred to membrane, probed with DNA
• highly labor intensive
• DNA Micorarray (PubMed 1991-1999)
• Short oligonucleotides or cDNAs bound to
  substrate in a matrix
• Population of mRNA extracted from experimental
  material and controls
• reverse transcribed to fluorescent-tagged cDNA
• hybridized to matrix
• fluorescent pattern representative of up- or
  down-regulated genes
• output said to be a profile of the transcriptome
• Proteomics (PubMed 1997)
• Population of proteins extracted from
  experimental material and controls
• Proteins partially digested to peptides and
  separated by gel electrophoresis into a pattern
  of peptides
• Peptides generate characteristic patterns during
  mass spectrometry analyses which, in mature
  systems, can be associated with specific protein
  sequences and gene
• Output said to be a profile of the proteome.

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Review of goals for this lecture

• What is Bioinformatics?
• What biology must we remember
• What kinds of data will bioinformaticists look
  at?
• What good is the biologist to bioinformatics
• Whats left to do?

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BREAK

• Next Genes and Genomes