Bioinformatics: Applications

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Bioinformatics Applications

• ZOO 4903
• Fall 2006, MW 1030-1145
• Sutton Hall, Room 312
• Jonathan Wren
• Final Review

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Strategic overview
Instruction set ? Active programs ? Product ? Sys
tem ? Network
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Exam 1 material

• Genomes sequences

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Genomics

• How are genomes sequenced?
• What are the challenges in sequencing?
• What kinds of large-scale functional features are
  found in genomes?
• How is similarity visualized for large genomic
  regions?

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Sequence alignment

• Know what approaches are used to align sequences
• Be able to score a given alignment
• Be able to conduct a local and/or global
  alignment via a dynamic programming matrix
• Understand how alignment approaches differ when
  using DNA versus amino acid sequences (e.g., PAM
  matrices)

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Multiple Sequence Alignment

• Know the basics of how BLAST works and how
  results might be interpreted
• Understand conceptually why people are interested
  in conducting multiple sequence alignments
• Know how to create a sequence profile score a
  multiple alignment

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Phylogenetics

• Know how to interpret a given phylogenetic tree
• Be able to construct a phylogenetic tree

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Genome comparison diversity

• Understand the different ways SNPs can impact
  phenotype
• Whats more likely to have an impact SNP in
  coding sequence or SNP that changes a splice site?

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Exam 2 material

• Genes and transcription

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Gene finding

• Prokaryotes
• How are genes located in prokaryotes?
• What type of gene finding systems are there?
• Eukaryotes
• How do gene finding approaches differ between
  eukaryotes prokaryotes?
• How would you calculate specificity and
  sensitivity given a prediction from some gene
  finding program?

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Alternative Splicing

• How are genes alternatively spliced?
• What are the evolutionary advantages of having an
  alternative splicing system?
• How would a microarray detect alternative splice
  variants?

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Microarray Analysis

• Technology
• Technology for measuring transcription
• Image processing whats done, why, and
  advantages/disadvantages
• Normalization what is it, what kinds of data
  are normalized, what kinds of methods are used
  for normalization?
• Analysis
• Clustering goals methods
• Analysis of gene lists in terms of their
  biological meaning/significance
• Genetic networks, what are they and how are they
  being approximated computationally?

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RNA 2ndary structure

• How does one identify secondary structure?
• General strategies for trying to calculate
  secondary structures

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Exam 3 material

• Proteins Structure, function and interactions

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Proteomics

• Be familiar with techniques used to detect
  changes in protein levels and techniques used to
  identify what proteins are in a given sample

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Protein Domains

• Understand what protein domains are
• Understand how they can be detected
• Understand how they can be predicted

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Protein 3D Structure

• Know how one would go about predicting a 3D
  protein structure
• What are some of the assumptions in predicting 3D
  structure

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Protein-Protein Interactions

• Be familiar with the basics of the technologies
  used to detect PPIs
• Know the different structures that a network can
  have and what these structures imply in terms of
  their behavior
• Remember some of the basics that we talked about
  on how order, complexity and chaos are emergent
  properties of a system

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Immunoinformatics

• We focused on epitopes know what they are and
  how they are processed by the immune system

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Cheminformatics

• How does one go about designing molecules to
  interact with proteins?

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Theme Area 4

• Systems Biology

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Biological Pathways Network Motifs

• What is a network motif and how are they found?
• Why do some motifs predominate (e.g. the FFL
  motif)?
• Alon paper

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Artificial Life

• What is AL and why do we fiddle with such things
  that arent real?
• How does AL work in a general sense?
• What is evolutionary computing (e.g. genetic
  algorithms) and how does it work?
• Artificial life paper (Kim Cho)

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Text Mining

• Why mine text? What sort of things in it and why
  bother extracting them?
• How can text mining be used to analyze
  microarrays or discover new knowledge?
• Bork Nature Reviews paper

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Cellular Simulations

• What strategies are used to simulate cellular
  models and where is the current state of the art?
• What is the range of modeling techniques, from
  the quantum level to the static level and what
  are their advantages/disadvantages?
• IEEE paper

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Systems Biology

• What is SB and what is the strategy being used to
  try and model systems?
• How can we begin to deal with the complexity in
  biological systems and the differences in time
  scale?
• What are the features that define complex
  systems?
• Nature SB paper

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Synthetic Biology

• How is SynthBio different from genetic
  engineering?
• What is the strategy people are taking to attempt
  SynthBio?
• What are the problems and limitations in
  SynthBio?
• Bioinformatics review paper

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Feedback

• How was the workload compared to other classes?
• Were there any areas you felt were over- or
  underemphasized?
• What was the most interesting lecture/series, in
  your opinion?
• What was the most boring lecture/series, in your
  opinion?

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Feedback

• How much of the material we covered do you feel
  will (or might) be useful to your own research or
  career interests in the future?
• One of my goals was to have more breadth than
  depth in this introductory class do you think
  more depth would be better in the future?

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Good luck!