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Computational Gene Finding. Greg Voronin Hui Zhao Xueyi(Judy) Xiao. CIS786 Intro to Comp Biol Instructor: Dr. Barry Cohen. The Challenge. Presented By Greg Voronin. Generate predictions of gene locations from primary genomic sequence by computational means Two principle means: - PowerPoint PPT Presentation
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Computational Gene Finding
Greg VoroninHui ZhaoXueyi(Judy) Xiao
CIS786 Intro to Comp BiolInstructor: Dr. Barry Cohen
The Challenge
Generate predictions of gene locations from primary genomic sequence by computational means
Two principle means:– Database searching– Statistical Methods
Presented By Greg Voronin
The Biological Model
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?
Exon Classification Scheme
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
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
MZEF
Michael Zhang’s 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
QDA
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.
Mahalanobis Distance
The actual posterior probabillity function is more complex, but this is the distance component:
( x – i )T i-1 ( x – i )
MZEF Specifics
MZEF uses the following features:– Exon length, exon-intron transition,
branch site score, 3’ss score, exon score, strand score, frame score, 5’ss 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
…GATC… to Gene
Cells recognize genes from DNA sequence.
Can we??Can we??The Hidden Markov Model Method
HMMgene Presented By Hui Zhao
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…
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.
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?
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)
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
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
A HMM genefinder States represent standard gene features: intergenic
region, exon, intron, perhaps more (promotor, 5’UTR, 3’UTR, 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.
5’
correct gene structure
extended exon
missing exon
additional exon
missing intron
extended gene model
3’
Several problems can occur
HMMgene
•Predicts whole genes in any given stretch of DNA •Uses Hidden Markov Models (HMM) to maximize
probability of accurate prediction •This allows confidence levels to be determined and
"Best Prediction" as well as potential alternative splicing predictions •Outputs splice sites, start and stop codons, alternative predictions •Trained for human and C. elegans
Krogh (1997) In Proc. 5th Conf. Intel. Sys. Mol Biol. pp179-186
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
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..
e.g: P(G, I | A,C,G,G,T) P(G, E | A,C,G,G,T)
2) Construct the model
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.
3) In a CHMM states emit a pair
labelclass
nucl
.
EG
IG
ge or ..
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.
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.
HMMgene http://www.cbs.dtu.dk/services/HMMgene/)
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
P(x) … P(y)
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.
GENSCAN (v1.0)
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 @Stanford New server @MIT (seq_len <= 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
Presented By Xueyi (Judy) Xiao
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
Model of Genomic Sequence Structure
Fig. 3, Burge and Karlin 1997
Input http://genes.mit.edu/GENSCAN.html
Output
Graphic View
Initial Exon
Internal Exon
TerminalExon
Single-Exon gene
Optimal ExonSuboptimal Exon
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 C+G content and for distinct groups of vertebrates.
Why not Perfect? Gene Number
usually approximately correct, but may not
Organismprimarily for human/vertebrate seqs; maybe lower accuracy for non-vertebrates. ‘Glimmer’ & ‘GeneMark’ for prokaryotic or yeast seqs
Exon and Feature Type
Internal exons > Initial or Terminal exons;Exons > 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 <- 08/1997); source organism specified; consider genomic seqs containing exactly one gene; seqs>200kb were discarded; mRNA seqs and seqs containing pseudo genes or
alternatively spliced genes were excluded.
What are They doing NOW?
The research group @MITis currently developing another program,
GenomeScan, which is more accuratewhen a moderate or closely related
protein seq is available.
TEST OF METHODS
Sample Tests reported by Literature Test on the set of 570 vertebrate gene seqs (Burset&Guigo 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
Where to get the dataset for Self-Test?
http://www.ncbi.nlm.nih.gov/genome/guide/human/
Accuracy Measures
Sensitivity vs. Specificity (adapted from Burset&Guigo 1996)
Sensitivity (Sn) Fraction of actual coding regions that are correctly predicted as coding
Specificity (Sp) Fraction of the prediction that is actually correctCorrelation Coefficient (CC)
Combined measure of Sensitivity & Specificity Range: -1 (always wrong) +1 (always right)
TP FP TN FN TP FN TNActual
Predicted
Coding / No Coding
TNFN
FPTP
Pred
icte
d
Actual
No
Cod
ing
/ Cod
ing
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
Results: Accuracy Statistics
Test By Rogic 2001 Self-Test 2002 Nucleotide accuracy
Exon accuracy Multi-Exon Single-Exon
Programs # of seq
Sn Sp CC ESn ESp # of Seq ESn ESp # of
Seq ESn ESp
Genscan 195(3) 0.95 0.90 0.91 0.70 0.70 5 0.57 0.63 5 0.60 0.50 HMMgene 195(5) 0.93 0.93 0.91 0.76 0.77 5 0.42 0.42 5 0.60 0.30
MZEF 119(8) 0.70 0.73 0.66 0.58 0.59 5 0.76 0.62 5 0.40 0.40
Testing ‘Random’ Sequences
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
Presented By Greg Voronin
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
•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.
New directionsPresented By Hui Zhao
•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
New directions
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
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