Pharmacogenomics and Bioinformatics

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Pharmacogenomics and Bioinformatics

• M. Saleet Jafri

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What is pharmacogenomics?

• Pharmacogenomics is the use genomic and sequence
  data of host and pathogens to identify potential
  drug targets
• Involves a variety of techniques/disciplines such
  as sequence analysis, protein structure,
  genomics, micorarray analysis and others
• These fields rely heavily on bioinformatics
• Usually focuses on medical or agricultural
  applications

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Human Genome Project

• Project goals are to
• identify all the approximately 20,000-25,000
  genes in human DNA,
• determine the sequences of the 3 billion chemical
  basepairs that make up human DNA,
• store this information in databases,
• improve tools for data analysis,
• transfer related technologies to the private
  sector, and
• address the ethical, legal, and social issues
  (ELSI) that may arise from the project.

From http//www.ornl.gov/hgmis/
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Human Genome Project

• Progress
• - Several types of genome maps have already
  been completed, and a working draft of the entire
  human genome sequence was announced in June 2000,
  with analyses published in February 2001.
• - An important feature of this project is the
  federal government's long-standing dedication to
  the transfer of technology to the private sector.
  By licensing technologies to private companies
  and awarding grants for innovative research, the
  project is catalyzing the multibillion-dollar
  U.S. biotechnology industry and fostering the
  development of new medical applications.

From http//www.ornl.gov/hgmis/
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Human Genome Project

• Seven organisms were originally chosen for
  sequencing.
• E. coli
• Yeast
• Fly
• Worm
• Arabidopsis
• Mouse
• human
• Why were these chosen?

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Genome Projects

• As of January 2005 there were many more sequenced
• 25 non-plant eukaryotes
• 5 plants
• 213 microbes completed
• 21 Archae
• 274 microbes in progress
• 1431 viruses in progress
• 833 non-virus organisms with at least on
  nucleotide sequence submitted
• Why were these chosen?

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Genome Projects

• Chosen by funding agencies
• Four main categories
• Medical applications
• Evolutionary significance
• Environmental impact
• Food production

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How are genomics used for drug target
identification?

• The basic idea is to look for genes unique to the
  pathogen that are crucial for its survival. This
  would be the drug target.
• If this is a pathogen in the host, the gene would
  be in the pathogen and not in the host.
• If this was in the environment, the gene should
  be as specific as possible for the pathogen to
  avoid harming other organisms that might be
  beneficial.

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How can this be done?

• To do this genomics, proteomics and
  bioinformatics are involved.
• In any of these cases bioinformatics tools are
  necessary.

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Genome Sequencing and Comparison

• As mentioned earlier, many pathogen (virus,
  bacteria, and other microorganisms) have been
  sequenced.
• Once they are sequenced, they are annotated.
  Annotation is the process by which the functions
  of the different proteins (genes) are determined.
• In this way, an understanding of the organisms
  metabolism is gained.

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Malaria

• Malaria is caused by the genus Plasmodium, with
  Plasmodium falciparum being the most lethal.
• Its genome has been sequenced
• It is a pathogen that digests proteins for food.
  It does not contain any amino acid producing
  genes in its genome, i.e. it does not make its
  own amino acids.
• Purines are recycled, but there are not genes for
  purine synthesis.
• Has many solute ATP dependent transporters and
  one novel multifunctional transporter.

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How is annotation done?

• Annotation is the process of predicting the
  function of genes in a genome.
• First all the genes have to be found. This is
  done by finding the open reading frame (ORF).
• This is done by gene finding or gene prediction
  software.

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Gene Prediction

• Analysis by sequence similarity can only reliably
  identify about 30 of the protein-coding genes in
  a genome
• 50-80 of new genes identified have a partial,
  marginal, or unidentified homolog
• Frequently expressed genes tend to be more easily
  identifiable by homology than rarely expressed
  genes

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Gene Finding

• Process of identifying potential coding regions
  in an uncharacterized region of the genome
• Still a subject of active research
• There are many different gene finding software
  packages and no one program is capable of finding
  everything

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(No Transcript)
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Eukaryotes vs Prokaryotes

• Eukaryotic DNA wrapped around histones that might
  result in repeated patterns (periodicity of 10)
  for histone binding. The promotor regions might
  be near these sites so that they remain hidden.
• Prokaryotes have no introns.
• Promotor regions and start sites more highly
  conserved in Prokaryotes
• Different codon use frequencies

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Gene finding is species-specific

• Codon usage patterns vary by species
• Functional regions (promoters, splice sites,
  translation initiation sites, termination
  signals) vary by species
• Common repeat sequences are species-specific
• Gene finding programs rely on this information to
  identify coding regions

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The genetic code
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Codon usage
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Identifying ORFs

• Simple first step in gene finding
• Translate genomic sequence in six frames.
  Identify stop codons in each frame
• Regions without stop codons are called "open
  reading frames" or ORFs
• Locate and tag all of the likely ORFs in a
  sequence
• The longest ORF from a Met codon is a good
  prediction of a protein encoding sequence.
• SOFTWARE NCBI ORF Finder

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ORF Finder input
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ORF finder results
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Tests of the Predicted ORF

• Check if the third base in the codons tends to be
  the same one more often than by chance alone.
• Are the codons used in the ORF the same as those
  used in other genes (need codon usage frequency).
• Compare the amino acid sequence for similarity
  with other know amino acid sequences.

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Problems with ORF finding

• A single-character sequencing error can hide a
  stop codon or insert a false stop codon,
  preventing accurate identification of ORFs
• Short exons can be overlooked
• Multiple transcripts or ORFs on complementary
  strand can confuse results

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Pattern-based gene finding

• ORF finding based on start and stop codon
  frequency is a pattern-based procedure
• Other pattern-based procedures recognize
  characteristic sequences associated with known
  features and genes, such as ribosome binding
  sites, promoter sites, histone binding sites,
  etc.
• Statistically based.

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Content-based gene finding

• Content-based gene finding methods rely on
  statistical information derived from known
  sequences to predict unknown genes
• Some evaluative measures include "coding
  potential" (based on codon bias), periodicity in
  the sequence, sequence homogeneity, etc.

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A standard content-based alignment procedure

• Select a window of DNA sequence from the unknown.
  The window is usually around 100 base pairs long
• Evaluate the window's potential as a gene, based
  on a variety of factors
• Move the window over by one base
• Repeat procedure until end of sequence is
  reached report continuous high-scoring regions
  as putative genes

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Combining measures

• Programs rarely use one measure to predict genes
• Different values are combined (using
  probabilistic methods, discriminant analysis,
  neural net methods, etc.) to produce one "score"
  for the entire window

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Drawbacks to window-based evaluation

• A sequence length of at least 100 b.p. is
  required before significant information can be
  gained from the analysis
• Results in a /- 100 b.p. uncertainty in the
  start site of predicted coding regions, unless an
  unambiguous pattern can also be found to indicate
  the start.

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Most are web-based, but...

• Submit sequence input sequence length may be
  limited
• Select parameters, if any
• Interpret results
• Most software is first or second generation
  results come in non-graphical formats.
• GeneMark, GenScan, Glimmer

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How is annotation done?

• This is done by comparing the DNA sequences of
  the genes to known genes in a database. If they
  sequences are similar, the a similar function is
  assumed.
• The comparison is done using sequence comparison
  tools such as BLAST

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Database Searching for Similar Sequences

• Database searching for similar sequences is
  ubiquitous in bioinformatics.
• Databases are large and getting larger
• Need fast methods

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Types of Searches

• Sequence similarity search with query sequence
• Alignment search with profile (scoring matrix
  with gap penalties)
• Serch with position-specific scoring matrix
  representing ungapped sequence alignment
• Iterative alignment search for similar sequences
  that starts with a query sequence, builds a
  multiple alignmnet, and then uses the alignment
  to augment the search
• Search query sequence for patterns representative
  of protein families

From Bioinformatics by Mount
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DNA vs Protein Searches

• DNA sequences consists of 4 characters
  (nucleotides)
• Protein sequences consist of 20 characters (amino
  acids)
• Hence, it is easier to detect patterns in protein
  sequences than DNA sequences
• Better to convert DNA sequences to protein
  sequences for searches.

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Database Searching Efficacy

• To evaluate searching methods, selectivity and
  sensitivity need to be considered.
• Selectivity is the ability of the method not to
  find members known to be of another group (i.e.
  false positives).
• Sensitivity is the ability of the method to find
  members of the same protein family as the query
  sequence.

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Protein Searches

• Easier to identify protein families by sequence
  similarity rather than structural similarity.
  (same structure does not mean same sequence)
• Use the appropriate gap penalty scorings
• Evaluate results for statistical significance.

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History

• Historically dynamic programming was used for
  database sequence similarity searching.
• Computer memory, disk space, and CPU speed were
  limiting factors.
• Speed still a factor due to the larger databases
  and increase number of searches.
• FASTA and BLAST allow fast searching.

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History

• The PAM250 matrix was used for a long time. It
  corresponds to a period of time where only 20 of
  the amino acids have remained unchanged.
• BLOSUM has replace PAM250 in most applications.
  BLAST use the BLOSUM62 matrix. FASTA uses the
  BLOSUM50 matrix.

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Search Tools

• Similarity Search Tools
• Smith-Waterman Searching
• Heuristic Search Tools
• FASTA
• BLAST

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Malaria Vaccine

• A German and American Team used reverse genetics
  i.e. they used the sequenced genome, deduced the
  candidate genes, and then knocked out a
  particular gene (Uis3).
• This give 30 day immunity in mice which is better
  than vaccines made by traditional methods

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Microarray Data Analysis

• Gene chips allow the simultaneous monitoring of
  the expression level of thousands of genes. Many
  statistical and computational methods are used to
  analyze this data. These include
• statistical hypothesis tests for differential
  expression analysis
• principal component analysis and other methods
  for visualizing high-dimensional microarray data
• cluster analysis for grouping together genes or
  samples with similar expression patterns
• hidden Markov models, neural networks and other
  classifiers for predictively classifying sample
  expression patters as one of several types
  (diseased, ie. cancerous, vs. normal)

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What is Microarray Data?

• In spite of the ability to allow us to
  simultaneously monitor the expression of
  thousands of genes, there are some liabilities
  with micorarray data. Each micorarray is very
  expensive, the statistical reproducibility of the
  data is relatively poor, and there are a lot of
  genes and complex interactions in the genome.
•  
• Microarray data is often arranged in an n x m
  matrix M with rows for the n genes and columns
  for the m biological samples in which gene
  expression has been monitored. Hence, mij is the
  expression level of gene i in sample j. A row ei
  is the gene expression pattern of gene i over all
  the samples. A column sj is the expression level
  of all genes in a sample j and is called the
  sample expression pattern.

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Types of Microarrays

• cDNA microarray
• Nylon membrane and plastic arrays (by Clontech)
• Oligonucleotide silicon chips (by Affymetrix)
• Note Each new version of a microarray chip is
  at least slightly different from the previous
  version. This means that the measures are likely
  to change. This has to be taken into account
  when analyzing data.

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cDNA Microarray

• The expression level eij of a gene i in sample j
  is expressed as a log ratio, log(rij/gi), of the
  log of its actual expression level rij in this
  sample over its expression level gi in a control.
  
• When this data is visualized eij is color coded
  to a mixture of red (rij gtgt gi) and green (rij
  ltlt gi) and a mixture in between.

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Nylon Membrane and Plastic Arrays (by Clontech)

• A raw intensity and a background value are
  measured for each gene.
• The analyst is free to choose the raw intensity
  or can adjust it by subtracting the background
  intensity.

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Oligonucleotide Silicon Chips (by Affymetrix)

• These arrays produce a variety of numbers derived
  from 16-20 pairs of perfect match (PM) and
  mismatch (MM) probes.
• There are several statistics related to gene
  expression that can be derived from this data.
  The most commonly used one is the average
  difference (AVD), which is derived from the
  differences of PM-MM in the 16-20 probe pairs.
• The next most commonly used method is the log
  absolute value (LAV), which comes from the ratios
  PM/MM in the probe pairs.
• Note The Affymetrix gene-chip software has a
  absent/present call for each gene on a chip.
  According to Jagota, the method is complex and
  arbitrary so they usually ignore it.

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For What Do We Use Microarray Data?

• Genes with similar expression patterns over all
  samples We can compare the expression patterns
  ei and ei of two genes i and i' over all
  samples.
• If we use cluster analysis, we can separate the
  genes into groups of genes with similar
  expression patterns (trees).
• This will allow us to find what unknown genes
  have altered expression in a particular disease
  by comparing the pattern to genes know to be
  affiliated with a disease.
• It can also find genes that fit a certain pattern
  such as a particular pattern of change with time.
  
• It can also characterize broad functional classes
  of new genes from the known classes of genes with
  similar expression.

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For What Do We Use Microarray Data?

• Genes with unusual expression levels in a sample
  In contrast to standard statistical methods
  where we ignore outliers, here outliers might
  have particular importance. Hence, we look for
  genes whose expression levels are very different
  from the others.
• Genes whose expression levels vary across samples
  We can compare gene expression levels of a
  particular gene or set of genes in different
  samples. This can be used to look compare normal
  and diseased tissues or diseased tissue before
  and after treatment.

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For What Do We Use Microarray Data?

• Samples that have similar expression patterns
  We might want to compare the expression patters
  of all genes between two samples. We might
  cluster the genes into gene with similar
  expression patterns to help with the comparison.
  This can be used to look compare normal and
  diseased tissues or diseased tissue before and
  after treatment.
• Tissues that might be cancerous (diseased) We
  can take the gene expression pattern of sample
  and compare it to library expression patterns
  that indicate diseased or not diseased tissue.

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Statistical Methods Can Help

• Experimental Design Since using microarrays is
  costly and time consuming, we want to design
  experiments to use the minimal number of
  micorarrays that will give a statistically
  significant result.
• Data Pre-processing It is sometimes useful to
  preprocess the data prior to visualization. An
  example of this is the log ratio mentioned
  earlier. It is often necessary to rescale data
  from different microarrays so that they can be
  compared. This is due to variation in chip to
  chip intensity. Another type of preprocessing
  is subtracting the mean and dividing by the
  variance.

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Statistical Methods Can Help

• Data Visualization Principle component analysis
  and multidimensional scaling are two useful
  techniques for reducing multidimensional data to
  two and three dimensions. This allows us to
  visualize it.
• Cluster Analysis By associating genes with
  similar expression patterns, we might be able to
  draw conclusions about their functional
  expression.
• Probability Theory We can use statistical
  modeling and inference to analyze our data.
  Probability theory is the basis for these.

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Statistical Methods Can Help

• Statistical Inference This is the formulation
  and statistical testing of a hypothesis and
  alternative hypothesis.
• Classifiers for the Data We can construct
  classes from data, such a diseased vs.
  non-diseased tissue. We can build a model (such
  as a hidden Markov model) that fits know data for
  the different classes. This can then be used to
  classify previously unclassified data.

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Preprocessing Microarray Data

• Before microarray data can be analyzed or stored,
  a number of procedures or transformations must be
  applied to it.
• In order to analyze the data correctly, it is
  important to understand what the transformations
  might be doing to the data.

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Preprocessing Microarray Data

• Ratioing the data
• Log-tranforming ratioed data
• Alternative to ratioing the data
• Differencing the data
• Scaling data across chips to account for
  chip-to-chip difference
• Zero-centering a gene on a sample expression
  pattern
• Weighting the components of a gene or sample
  expression pattern differently
• Handling missing data
• Variation filtering expression patterns
• Discretizing expression data

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Cluster Analysis of Microarray Data

• Recall that microarray data can be thought of as
  gene expression patterns or sample expression
  patterns. These can be each considered to be
  vectors. The first thing we have to do before
  applying cluster analysis is to find a distance
  between the various expression pattern vectors.
  This is done using similarity/dissimilarity
  measures such as Euclidean distance, Mahalonobis
  distance, or linear correlation coefficients.
  Once a distance matrix is computed, the following
  clustering algorithms can be used. The clusters
  formed can differ significantly depending upon
  the distance measure used.

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Cluster Analysis of Microarray Data

• Hierarchical Clustering Assume each data point
  is in a singleton cluster.
• Find the two clusters that are closest together.
  Combine these to form a new cluster.
• Compute the distance from all clusters to the new
  cluster using some form of averaging.
• Find the two closest clusters and repeat.

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Cluster Analysis of Microarray Data

• k-Means Clustering An alternate method of
  clustering called k-means clustering, partitions
  the data into k clusters and finds cluster means
  ?i for each cluster. In our case, the means will
  be vectors also. Usually, the number of clusters
  k is fixed in advance. To choose k something
  must be know about the data. There might be a
  range of possible k values. To decide which is
  best, optimization of a quantity that maximizes
  cluster tightness ie. minimizes distances between
  points in a cluster.

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Cluster Analysis of Microarray Data

• Self-organizing Maps This is basically an
  application of neural networks to microarray
  data. Assume that there is a 2-dimensional grid
  of cells and a map from a given set of expression
  data vectors in Rn, ie, there are n nodes in the
  input layer and a connection neuron from each of
  these to each cell. Each cell (i, j) gets it own
  weight from n input neurons. The weight vector
  mij is the mean of the cluster associated with
  cell (i, j). Each data vector d gets mapped to
  the cell (i, j) that is closest to d using
  Euclidean distance.In order to train the network,
  the mean vectors mij for the cells (i, j) must be
  learned.

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Sample Microarray
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Correlations
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Clustering of Genes
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Personalized Medicine

• There is a new buzz word called personalized
  medicine.
• The idea is to develop medicine and treatment
  plan based on an individuals genetic make-up.

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Proteomics

• Understanding protein function
• Functional genomics
• Multiple approaches structure, expression
  levels, biochemistry, modeling etc.
• Combining technologies is necessary to understand
  in vivo protein functional

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Approach

• Use data to determine pathway.
• Use biochemistry to figure out kinetics and
  concentrations.
• Use new proteomic approaches to determine
  relative concentrations.
• Apply pathway model to determine functional
  consequence.

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Pathway Data

• Using molecular biological techniques we can
  determine what proteins make up a biochemical
  pathway.

A
B
C
D
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Pathways

• Biochemical Pathways form complex biochemical
  reaction networks.
• There might be multiple ways to get from A to B.
• The path chosen depends on biochemical kinetics.

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Biochemistry

• Classical biochemistry isolates proteins from
  tissue or cells.
• Modern molecular biology allows the production of
  purified protein.
• The concentration of the protein is determined
• The kinetic properties of the proteins is
  determined by biochemical assay rates of
  reactions, modulating factors, etc.

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Pathway Modeling Methods

• Boolean Models
• Metabolic Control Theory Flux Balance Analysis
• Biochemical Systems Analysis
• Kinetic Modeling Approach

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Disorders of Thrombophilia

• The functional consequences of nonsynonymous SNPS
  can be predicted by comparison of protein
  structures.
• There are various SNPs know
• Activated protein C resistance by Arg 506 to Glu
• Prothrombing polymorphism (G20210A) causing
  elevated prothrombin levels
• Protein C deficiency
• Protein S deficiency
• Antithormbin deficiency
• Elevated factor VIII levels

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Fibrinogen Abnormalities

• Various polymorphisms found in the long arm of
  chromosome 4
• Two dimorphisms of the b-chain gene are of major
  importance and in linkage disequilibrium with
  each other.
• These affect plasma fibrogen levels

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Prothrombin G20210 Polymorphism

• Replacement of a G by A at nucleotide 20210 in
  the untranslated section of the prothrombin gene
  increases translation without altering
  transcription of the gene.
• This results in elevated synthesis and secretion
  of prothrombin by the liver.
• This results in increased thrombin levels

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Activated protein C deficiency

• Factor V Leiden R506Q mutation occurs in 8 of
  the population.
• It is a G?A substitution at nucleotide 1691 in
  the gene for factor V.
• Factor V is cleaved less efficiently by activated
  protein C
• Results in deep vein thrombosis, early kidney
  transplant loss, recurrent miscarriages and other
  disorders