Computational Gene Finding

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Computational Gene Finding
CIS786 Intro to Comp Biol Instructor Dr. Barry
Cohen

• Greg Voronin
• Hui Zhao
• Xueyi(Judy) Xiao

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The Challenge
Presented By Greg Voronin

• Generate predictions of gene locations from
  primary genomic sequence by computational means
• Two principle means
• Database searching
• Statistical Methods

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The Biological Model
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The Computational Model

• Representing the biology in a framework amenable
  to mathematical/statistical methods
• Exon classification, sequence features, signal
  profiles
• What is an exon and what properties does the
  sequence of an exon hold?
• How is an exon recognized and processed?

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Exon Classification Scheme
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The Nature of The Data

• What is the primary genomic sequence?
• Nor is the available sequence a single
  continuous and exact sequence for each
  chromosome the HGP is represented by a set
  of sequences that cover the genome is a
  statistical sense but have a very large number of
  gaps.
• Many genes are as large or larger than the
  contigs in the HGP
• Finding genes will depend on the accuracy of the
  scaffold of their contigs

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Back to Beginning

• What is a gene?
• A biological model, a mathematical model and
  computational representation

The programs we evaluate take these factors into
account in their underlying model
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MZEF

• Michael Zhangs Exon Finder
• Utilizes quadratic discriminant analysis (QDA) to
  classify sequence into gene and non-gene groups
• QDA is a multivariate statistical pattern
  recognition method
• Draws a curved boundary between groups of
  different classes

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QDA
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Key Elements of QDA

• Entities are represented by an n-dimensional
  vector of feature values
• Two classes of entities are categorized by their
  respective multinormal distribution
• Each class has its own mean vector
• The mean of each feature
• An appropriate distance function is central to
  the calculation of the posterior probabillity of
  group membership of a given unknown entity given
  its specific feature vector.

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Mahalanobis Distance

• The actual posterior probabillity function is
  more complex, but this is the distance component

( x mi )T Si-1 ( x mi )
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MZEF Specifics

• MZEF uses the following features
• Exon length, exon-intron transition, branch site
  score, 3ss score, exon score, strand score,
  frame score, 5ss score, intron-exon transition
• 9 dimensional feature vector
• Training sets of known exons and non-exons are
  used to establish the class characterisitics
• Supervised learning

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GATC to Gene

• Cells recognize genes from DNA sequence.

Can we??
The Hidden Markov Model Method
HMMgene Presented By Hui Zhao
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HMMs are Statistical Models

• Definition
• Any mathematical construct that attempts to
  parameterize a random process
• Example A normal distribution
• Assumptions
• Parameters
• Estimation
• Usage
• HMMs are just a little more complicated

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Primary HMM Assumptions

• Observations are ordered
• Random processes can be represented by a
  stochastic finite state machine with emitting
  states
• transition probabilities and emission
  probabilities.

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How do we find the model probabilities?

• This is called training
• We start with an architecture and a set of
  observed sequences
• The training process iteratively alters its
  parameters to fit the training set
• The trained model will assign the training
  sequences high probability
• but can it generalize?

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HMM Usage two major tasks

• Evaluate the probability of an observed sequence
  given the model (Forward)
• Find the most likely path through the model for a
  given observation sequence (Viterbi)

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Gene Finding An Ideal HMM Application

• Our Objective
• To find the coding and non-coding regions of an
  unlabeled string of DNA nucleotides
• Our Motivation
• Assist in the annotation of genomic data produced
  by genome sequencing methods
• Gain insight into the mechanisms involved in
  transcription, splicing and other processes

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Why HMMs might be a good fit for Gene Finding

• The observations within a sequence are ordered
• A DNA sequence is a set of ordered observations
• Designing the architecture is straight forward
• Easy to measure success
• Training data is available from various genome
  annotation projects

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A HMM genefinder

• States represent standard gene features
  intergenic region, exon, intron, perhaps more
  (promotor, 5UTR, 3UTR, Poly-A,..).
• Observations are things like state-dependent
  base composition.
• In a HMM, length of each state must be included
  as well.
• Finally, reading frames and both strands must be
  dealt with.

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Several problems can occur
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correct gene structure extended exon missing
exon additional exon missing intron extended gene
model
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HMMgene

Krogh (1997) In Proc. 5th Conf. Intel. Sys. Mol
Biol. pp179-186


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HMMGene

• Uses an extended HMM called a CHMM
• CHMM HMM with classes
• Takes full advantage of being able to modify the
  statistical algorithms
• Uses high-order states
• Trains everything at once

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How does HMMGene work?
1) 5th order HMM assumes P(xi xi-1,xi-2,
xi-3, xi-4, xi-5) is different in Introns, Exons,
etc..
2) Construct the model
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2. How does HMMGene work?
4) Use Viterbi (n-best) to find a path
through the CHMM a labeled gene
5) Use the forward algorithm to measure P(gene
model) using n-best.
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A DNA sequence containing one gene. For each
nucleotide its label is written below. The coding
regions are labeled C, the introns I, and the
intergenic regions 0. HMMGene calls these
class labels in a CHMM.
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HMMGene

• Does not use the standard ML method which
  optimizes the probability of the observed
  sequence instead it maximizes the probability
  of the correct prediction.
• Only one conference paper describes the
  algorithm. There is a web site to run the
  algorithm, and it's performance has been compared
  to other algorithms.
• No complete description of the algorithm is
  available in the 1997 paper the author states
  " the details of HMMGene will be described
  elsewhere (in prep)" but unfortunately the
  detailed paper has not been published.

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HMMgene http//www.cbs.dtu.dk/services/HMMgene/)
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HMMgene and HMM Disadvantages

• Markov Chains
• States should be independent
• P(y) must be independent of P(x) -usually not
  true
• Local maxima
• Model may not converge the optimal parameter set
• Over-fitting
• More training is not always good-set may be too
  small

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Summary

• HMMgene finds whole genes in anonymous DNA with
  correctly spliced exons.
• It can predict several whole or partial genes in
  one sequence.
• If some features of a sequence are known, such as
  hits to ESTs, proteins, or repeat elements, these
  regions can be locked as coding or non-coding and
  then the program will find the best gene
  structure under these constraints.

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GENSCAN (v1.0)
Presented By Xueyi (Judy) Xiao

• A computer program identifying complete exon
  intron structures of genes in genomic DNA.
• Developed by Chris Burge (Burge 1997), in the
  research group of Samuel Karlin, Dept of
  Mathematics, Stanford Univ. 
• Original server _at_Stanford ? New server _at_MIT
  (seq_len lt 500 kb)
• Servers are also maintained by the Pasteur
  Institute, Paris and by the GENSCAN web server at
  DKFZ/EMBnet, Heidelberg
• Implementations
• web server http//genes.mit.edu/GENSCAN.html
• email server http//genes.mit.edu/GENSCANM.html
• local copy downloaded under a license agreement

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How does It Work?

• Designed to predict complete gene structures
• Introns and exons
• Promoter sites
• Polyadenylation signals
• Larger predictive scope
• Partial and Complete genes
• Multiple genes separated by intergenic DNA in a
  seq
• Consistent sets of genes on either/both DNA
  strands
• Not use similarity-based methods
• Based on a general probabilistic model of genomic
  sequences composition and gene structure

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Model of Genomic Sequence Structure
Fig. 3, Burge and Karlin 1997
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Input
http//genes.mit.edu/GENSCAN.html
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Output
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Graphic View
Optimal Exon Suboptimal Exon
Initial Exon
Internal Exon
Terminal Exon
Single-Exon gene
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Is It Good?

• Accuracy
• Substantially higher accuracies when tested on
  standardized sets of human vertebrate genes,
  with 75-80 of exons identified exactly.
• Reliability
• Able to indicate fairly accurately the
  reliability of each predicted exon.
• Consistency
• Consistently high levels of accuracy, for seqs
  of differing CG content and for distinct groups
  of vertebrates.

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Why not Perfect?

• Gene Number
• usually approximately correct, but may not
• Organism
• primarily for human/vertebrate seqs maybe lower
  accuracy for non-vertebrates. Glimmer
  GeneMark for prokaryotic or yeast seqs
• Exon and Feature Type
• Internal exons gt Initial or Terminal exons
• Exons gt Polyadenylation or Promoter
  signals(NNPP)
• Biases in Test Set
• The Burset/Guigó (1996) dataset
• toward short genes with relatively simple
  exon/intron structure
• The Rogic (2001) dataset
• DNA seqs GenBank r-111.0 (04/1999 lt- 08/1997)
• source organism specified
• consider genomic seqs containing exactly one
  gene
• seqsgt200kb were discarded mRNA seqs and seqs
  containing pseudo genes or alternatively spliced
  genes were excluded.

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What are They doing NOW?

• The research group _at_MIT
• is currently developing another program,
  GenomeScan, which is more accurate
• when a moderate or closely related
• protein seq is available.

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TEST OF METHODS

• Sample Tests reported by Literature
• Test on the set of 570 vertebrate gene seqs
  (BursetGuigo 1996) as a standard for comparison
  of gene finding methods.
• Test on the set of 195 seqs of human, mouse or
  rat origin (named HMR195) (Rogic 2001).
• Self-Test done by our group
• Dataset Intron-less(Single-exon),
  -rich(Multi-exon), -poor(Random)
• Organism Human
• Methods all of the three
• Steps

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Where to get the dataset for Self-Test?
http//www.ncbi.nlm.nih.gov/genome/guide/human/
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Accuracy Measures
Sensitivity vs. Specificity (adapted from
BursetGuigo 1996)
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Results Accuracy Statistics

• Table Relative Performance (adapted added from
  Rogic 2001)

of seqs - number of seqs effectively analyzed
by each program in parentheses is the number of
seqs where the absence of gene was predicted Sn
-nucleotide level sensitivity Sp - nucleotide
level specificity CC - correlation coefficient
ESn - exon level sensitivity ESp - exon level
specificity
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Testing Random Sequences
Presented By Greg Voronin

• These gene finding programs model statistical
  trends and properties
• Can they be fooled by random sequences
• Generate a preliminary measure of accuracy
• Java program written to generate random
  sequences of a,t,g,c
• 3 groups of sequences 5k, 10k 30K
• Sent to BLAST then GeneMachine

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Testing Results

• BLAST
• bit score E-value
• 5k 42 5.7
• 10k 44 3.0
• 30k 42 8.7
• GeneMachine
• 5k 10k 30K
• MZEF 1 5 14
• GenScan 3 11 26
• HMMgene 7 11 42

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New directions
Presented By Hui Zhao

• Computational Gene Finding has rapidly evolved
  since it started 20 years ago.
• The advent of full-length genomic sequences has
  provided data and increased the requirements.
• Gene annotation has direct medical implications
  on the design of pharmaceuticals and the
  understanding of the genetic component of
  diseases.
• Gene finding remains largely an unsolved problem.

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New directions

• The growing quantities of training data for the
  models should improve their performance.
• Algorithms that combine the inputs from several
  models in a weighted voting scheme should be
  considered to try to get the best from all of the
  methods.
• Many other AI approaches can be used to meet this
  challenge including decision trees, neural
  networks and rule-based systems

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Challenges and Discoveries Ahead

• Eukaryotic gene finding continues to be an active
  and important area more research is required
  into algorithms with greater accuracy
• Expertise in computational biology is also
  required which means training in both computer
  science and molecular biology
• More classes like this