Bioinformatics Research Overview

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Bioinformatics Research Overview Li
Liao Develop new algorithms and (statistical)
learning methods gt Capable of incorporating
domain knowledge gt Effective, Expressive,
Interpretable
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Motivations

• Understanding correlations between genotype and
  phenotype
• Predicting genotype ltgt phenotype
• Phenotypes
• Protein function
• Drug/therapy response
• Drug-drug interactions for expression
• Drug mechanism
• Interacting pathways of metabolism

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Projects

• Homology detection, protein family classification
  
• (funded by a DuPont SE award)
• Support Vector Machines
• Hidden Markov models
• Graph theoretic methods
• Probabilistic modeling for BioSequence (funded by
  NIH)
• HMMs, and beyond
• Motifs finding
• Secondary structure
• Comparative Genomics
• Identify genome features for diagnostic and
  therapeutic purposes
• (funded by an Army grant)
• Evolution of metabolic pathways
• Tree and graph comparisons

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Detect remote homologues

• Attributes to be looked at
• Sequence similarity, Aggregate statistics (e.g.,
  protein families), Pattern/motif, and more
  attributes (presence at phylogenetic tree).
• How to incorporate domain specific knowledge into
  the model so a classifier can be more accurate?
• Results
• Quasi-consensus based comparison of profile HMM
  for protein sequences (submitted to
  Bioinformatics)
• Using extended phylogenetic profiles and support
  vector machines for protein family classification
  (SNPD 04)
• Combining Pairwise Sequence Similarity and
  Support Vector Machines for Detecting Remote
  Protein Evolutionary and Structural Relationships
  (JCB 2003)

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Support Vector Machines
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• Data phylogenetic profiles
• - How to account for correlations among
  profile components?
• profile extension (Narra Liao, SNPD 04)

Tree-based distance
Hamming distance
0 1 1 1 1
x
3 0.1
1 1 1 1 1
y
3 0.5
1 1 1 1 0
z
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Quasi consensus based comparison of HMMs

• From MSA to profile HMMs using
• existing packages (SAM-T99 or HMMER)
• Generation of quasi consensus
• sequence from the model
• Alignment of consensus sequence of a
• model with the other model

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(No Transcript)
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Sequence Models (HMMs and beyond)

• Motivations What is responsible for the
  function?
• Patterns/motifs
• Secondary structure
• To capture long range correlations of bio
  sequences
• Transporter proteins
• RNA secondary structure
• Methods generative versus discriminative
• Linear dependent processes
• Stochastic grammars
• Model equivalence

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TMMOD An improved hidden Markov model for
predicting transmembrane topology (to appear in
IEEE ICTAI04)
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Mod. Reg. Data set Correct topology Correct location Sens- itivity Speci- ficity
TMMOD 1 (a) (b) (c) S-83 65 (78.3) 51 (61.4) 64 (77.1) 67 (80.7) 52 (62.7) 65 (78.3) 97.4 71.3 97.1 97.4 71.3 97.1
TMMOD 2 (a) (b) (c) S-83 61 (73.5) 54 (65.1) 54 (65.1) 65 (78.3) 61 (73.5) 66 (79.5) 99.4 93.8 99.7 97.4 71.3 97.1
TMMOD 3 (a) (b) (c) S-83 70 (84.3) 64 (77.1) 74 (89.2) 71 (85.5) 65 (78.3) 74 (89.2) 98.2 95.3 99.1 97.4 71.3 97.1
TMHMM S-83 64 (77.1) 69 (83.1) 96.2 96.2
PHDtm S-83 (85.5) (88.0) 98.8 95.2
TMMOD 1 (a) (b) (c) S-160 117 (73.1) 92 (57.5) 117 (73.1) 128 (80.0) 103 (64.4) 126 (78.8) 97.4 77.4 96.1 97.0 80.8 96.7
TMMOD 2 (a) (b) (c) S-160 120 (75.0) 97 (60.6) 118 (73.8) 132 (82.5) 121 (75.6) 135 (84.4) 98.4 97.7 98.4 97.2 95.6 97.2
TMMOD 3 (a) (b) (c) S-160 120 (75.0) 110 (68.8) 135 (84.4) 133 (83.1) 124 (77.5) 143 (89.4) 97.8 94.5 98.3 97.6 98.1 98.1
TMHMM S-160 123 (76.9) 134 (83.8) 97.1 97.7
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Genomics study of enterobacterial BT agents
(funded by the US Army via Center for Biological
Defense, USF )

• Goals
• Identification of genes and sequence tags as
  targets for novel diagnosis and therapy
• BT agents Yersinia pestis, Salmonella,
  Escherichia coli O157H7)
• Methods
• Various bioinformatics tools and databases

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Comparative Genomics

• Motivation
• Evolution of metabolic pathways
• Gene functions
• De novo (alternative pathways)
• Genetic engineering
• Drug discovery
• Methods
• Put data into a context knowledge/data
  representation
• Trees, graphs, etc.
• Learning models/methods

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Profiling pairs of attribute-value
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• What we found
• Informative way to compare genomes
• Majority pathways (or rather their enzyme
  components) evolve in congruence with species

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What we do next

• Database and search engine
• Off-line self-consistent iteration
• Pathways in a network
• Graph comparisons
• Identify key components of networks
• Small world topology
• Cross-level interactions with regulatory networks