gene prediction. gene prediction: computational challenge...
TRANSCRIPT
Gene prediction
Gene Prediction: Computational Challenge
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Gene Prediction: Computational Challenge
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Gene Prediction: Computational Challenge
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Gene!
• Newspaper written in unknown language– Certain pages contain encoded message, say 99 letters on page
7, 30 on page 12 and 63 on page 15.
• How do you recognize the message? You could probably distinguish between the ads and the story (ads contain the “$” sign often)
• Statistics-based approach to Gene Prediction tries to make similar distinctions between exons and introns.
Gene Prediction Analogy
Noting the differing frequencies of symbols (e.g. ‘%’, ‘.’, ‘-’) and numerical symbols could you distinguish between a story and the stock report in a foreign newspaper?
Statistical Approach: Metaphor in Unknown Language
• Statistical: coding segments (exons) have typical sequences on either end and use different subwords than non-coding segments (introns).
• Similarity-based: many human genes are similar to genes in mice, chicken, or even bacteria. Therefore, already known mouse, chicken, and bacterial genes may help to find human genes.
Two Approaches to Gene Prediction
If you could compare the day’s news in English, side-by-side to the same news in a foreign language, some similarities may become apparent
Similarity-Based Approach: Metaphor in Different Languages
Annotation of Genomic Sequence
• Given the sequence of an organism’s genome, we would like to be able to identify:– Genes– Exon boundaries & splice sites– Beginning and end of translation– Alternative splicings– Regulatory elements (e.g. promoters)
• The only certain way to do this is experimentally, but it is time consuming and expensive.
Computational methods can achieve reasonable accuracy quickly, and help direct experimental approaches.
primary goals
secondary goals
Prokaryotic Gene Structure
Promoter CDS Terminator
transcription
Genomic DNA
mRNA
Most bacterial promoters contain the Shine-Delgarno signal, at about -10 that has the consensus sequence: 5'-TATAAT-3'.
The terminator: a signal at the end of the coding sequence that terminates the transcription of RNA
The coding sequence is composed of nucleotide triplets. Each triplet codes for an amino acid. The AAs are the building blocks of proteins.
Pieces of a (Eukaryotic) Gene(on the genome)
5’
3’
3’
5’
~ 1-100 Mbp
5’
3’
3’
5’
……
……
~ 1-1000 kbp
exons (cds & utr) / introns(~ 102-103 bp) (~ 102-105 bp)
Polyadenylation site
promoter (~103 bp)
enhancers (~101-102 bp)other regulatory sequences (~ 101-102 bp)
What is Computational Gene Finding?
Given an uncharacterized DNA sequence, find out:
– Which region codes for a protein?– Which DNA strand is used to encode the gene?– Which reading frame is used in that strand?– Where does the gene starts and ends?– Where are the exon-intron boundaries in eukaryotes?– (optionally) Where are the regulatory sequences for that
gene?
Prokaryotic Vs. Eukaryotic Gene Finding
Prokaryotes:
• small genomes 0.5 – 10·106 bp
• high coding density (>90%)• no introns
– Gene identification relatively easy, with success rate ~ 99%
Problems:
• overlapping ORFs• short genes• finding TSS and promoters
Eukaryotes:
• large genomes 107 – 1010 bp• low coding density (<50%)• intron/exon structure
– Gene identification a complex problem, gene level accuracy ~50%
Problems:• many
What is it about genes that we can measure (and model)?
• Most of our knowledge is biased towards protein-coding characteristics
– ORF (Open Reading Frame): a sequence defined by in-frame AUG and stop codon, which in turn defines a putative amino acid sequence.
– Codon Usage: most frequently measured by CAI (Codon Adaptation Index)
• Other phenomena– Nucleotide frequencies and correlations:
• value and structure– Functional sites:
• splice sites, promoters, UTRs, polyadenylation sites
General Things to Remember about (Protein-coding) Gene Prediction Software
• It is, in general, organism-specific
• It works best on genes that are reasonably similar to something seen previously
• It finds protein coding regions far better than non-coding regions
• In the absence of external (direct) information, alternative forms will not be identified
• It is imperfect! (It’s biology, after all…)
Gene Finding: Different Approaches
• Similarity-based methods (extrinsic) - use similarity to annotated sequences:
– proteins– cDNAs– ESTs
• Comparative genomics - Aligning genomic sequences from different species
• Ab initio gene-finding (intrinsic)
• Integrated approaches
Similarity-based methods
• Based on sequence conservation due to functional constraints
• Use local alignment tools (Smith-Waterman algo, BLAST, FASTA) to search protein, cDNA, and EST databases
• Will not identify genes that code for proteins not already in databases (can identify ~50% new genes)
• Limits of the regions of similarity not well defined
Comparative Genomics
• Based on the assumption that coding sequences are more conserved than non-coding
• Two approaches:– intra-genomic (gene families)– inter-genomic (cross-species)
• Alignment of homologous regions
• Difficult to define limits of higher similarity
• Difficult to find optimal evolutionary distance (pattern of conservation differ between loci)
Summary for Extrinsic Approaches
Strengths:
• Rely on accumulated pre-existing biological data, thus should produce biologically relevant predictions
Weaknesses:
• Limited to pre-existing biological data• Errors in databases• Difficult to find limits of similarity
Ab initio Gene FindingInput: A DNA string over the alphabet {A,C,G,T}
Output: An annotation of the string showing for every nucleotide whether it is coding or non-coding
AAAGCATGCATTTAACGAGTGCATCAGGACTCCATACGTAATGCCG
AAAGC ATG CAT TTA ACG A GT GCATC AG GA CTC CAT ACG TAA TGCCG
Gene finder
•Using only sequence information
•Identifying only coding exons of protein-coding genes (transcription start site, 5’ and 3’ UTRs are ignored)
•Integrates coding statistics with signal detection
A eukaryotic gene
• This is the human p53 tumor suppressor gene on chromosome 17.• Genscan is one of the most popular gene prediction algorithms.
This particular gene lies on the reverse strand.
3’ untranslated region
Final exon
Initial exon
Introns
Internal exons
Observations
• Given (walk, shop, clean) – What is the probability of this sequence of
observations? (is he really still at home, or did he skip the country)
– What was the most likely sequence of rainy/sunny days?
Signals vs contents
• In gene finding, a small pattern within the genomic DNA is referred to as a signal, whereas a region of genomic DNA is a content.
• Examples of signals: splice sites, starts and ends of transcription or translation, branch points, transcription factor binding sites
• Examples of contents: exons, introns, UTRs, promoter regions
The CpG island problem
• Methylation in human genome– “CG” -> “TG” happens in most places
except “start regions” of genes and within genes
– CpG islands = 100-1,000 bases before a gene starts
• Question– Given a long sequence, how would we find
the CpG islands in it?
Promoters
• Promoters are DNA segments upstream of transcripts that initiate transcription
• Promoter attracts RNA Polymerase to the transcription start site
5’Promoter 3’
Splice signals (mice): GT , AG
Splice site detection
5’ 3’Donor site
Position
% -8 … -2 -1 0 1 2 … 17
A 26 … 60 9 0 1 54 … 21C 26 … 15 5 0 1 2 … 27G 25 … 12 78 99 0 41 … 27T 23 … 13 8 1 98 3 … 25
Real splice sites
• Real splice sites show some conservation at positions beyond the first two.
• We can add additional arrows to model these states.
weblogo.berkeley.edu
Ribosomal Binding Site
Prior knowledge
• The translated region must have a length that is a multiple of 3.• Some codons are more common than others.• Exons are usually shorter than introns.• The translated region begins with a start signal and ends with a
stop codon.• 5’ splice sites (exon to intron) are usually GT; • 3’ splice sites (intron to exon) are usually AG.• The distribution of nucleotides and dinucleotides is usually
different in introns and exons.
Gene Prediction and Motifs
• Upstream regions of genes often contain motifs that can be used for gene prediction
-10
STOP
0 10-35
ATG
TATACTPribnow Box
TTCCAA GGAGGRibosomal binding site
Transcription start site
Positional dependence
• In this data, every time a “G” appears in position 1, an “A” appears in position 3.
• Conversely, an “A” in position 1 always occurs with a “T” in position 3.
ACTG
ACTT
GCAC
ACTT
ACTA
GCAT
ACTA
ACTT
Example of (Positional) Weight Matrix
• Computed by measuring the frequency of every element of every position of the site (weight)
• Score for any putative site is the sum of the matrix values (converted in probabilities) for that sequence (log-likelihood score)
• Disadvantages: – cut-off value required– assumes independence between adjacent bases
TACGAT
TATAAT
TATAAT
GATACT
TATGAT
TATGTT
1 2 3 4 5 6
A 0 6 0 3 4 0C 0 0 1 0 1 0G 1 0 0 3 0 0T 5 0 5 0 1 6
Conditional probability
• What is the probability of observing an “A” at position 2, given that we observed a “C” at the previous position?
GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG
Conditional probability
• What is the probability of observing an “A” at position 2, given that we observed a “C” at the previous position?
• Answer: total number of CA’s divided by total number of C’s in position 1.
• 3/11 = 27%• Probability of observing CA = 3/18 = 17%.
GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG
Conditional probability
• What is the probability of observing a “G” at position 3, given that we observed a “C” at the previous position?
GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG
Conditional probability
• What is the probability of observing a “G” at position 3, given that we observed a “C” at the previous position?
• Answer: 9/12 = 75%.
GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG
Promoter Structure in Prokaryotes (E.Coli)
Transcription starts at offset 0.
• Pribnow Box (-10)
• Gilbert Box (-30)
• Ribosomal Binding Site (+10)
• Detect potential coding regions by looking at ORFs– A genome of length n is comprised of (n/3) codons– Stop codons break genome into segments between
consecutive Stop codons– The subsegments of these that start from the Start codon
(ATG) are ORFs• ORFs in different frames may overlap
3
n
3
n
Genomic Sequence
Open reading frame
ATG TGA
Open Reading Frames (ORFs)
• Long open reading frames may be a gene. At random, we should expect one stop codon every (64/3) ~= 21 codons. However, genes are usually much longer than this
• A basic approach is to scan for ORFs whose length exceeds certain threshold. This is naïve because some genes (e.g. some neural and immune system genes) are relatively short
Long vs.Short ORFs
Testing ORFs: Codon Usage
• Create a 64-element hash table and count the frequencies of codons in an ORF
• Amino acids typically have more than one codon, but in nature certain codons are more in use
• Uneven use of the codons may characterize a real gene• This compensate for pitfalls of the ORF length test
Open Reading Frames in Bacteria
• Without introns, look for long open reading frame (start codon ATG, … , stop codon TAA, TAG, TGA)
• Short genes are missed (<300 nucleotides)• Shadow genes (overlapping open reading frames on opposite
DNA strands) are hard to detect• Some genes start with UUG, AUA, UUA and CUG for start
codon• Some genes use TGA to create selenocysteine and it is not a
stop codon
Coding Statistics
• Unequal usage of codons in the coding regions is a universal feature of the genomes
– uneven usage of amino acids in existing proteins– uneven usage of synonymous codons (correlates with the
abundance of corresponding tRNAs)
• We can use this feature to differentiate between coding and non-coding regions of the genome
• Coding statistics - a function that for a given DNA sequence computes a likelihood that the sequence is coding for a protein
Coding Statistics
• Many different ones
– codon usage– hexamer usage– GC content– compositional bias between codon positions– nucleotide periodicity– …
Codon Usage in Human Genome
AA codon /1000 frac Ser TCG 4.31 0.05Ser TCA 11.44 0.14Ser TCT 15.70 0.19Ser TCC 17.92 0.22Ser AGT 12.25 0.15Ser AGC 19.54 0.24
Pro CCG 6.33 0.11Pro CCA 17.10 0.28Pro CCT 18.31 0.30Pro CCC 18.42 0.31
AA codon /1000 frac Leu CTG 39.95 0.40Leu CTA 7.89 0.08Leu CTT 12.97 0.13Leu CTC 20.04 0.20
Ala GCG 6.72 0.10Ala GCA 15.80 0.23Ala GCT 20.12 0.29Ala GCC 26.51 0.38
Gln CAG 34.18 0.75Gln CAA 11.51 0.25
Codon Usage in Mouse Genome
Codon Usage and Likelihood Ratio
• An ORF is more “believable” than another if it has more “likely” codons
• Do sliding window calculations to find ORFs that have the “likely” codon usage
• Allows for higher precision in identifying true ORFs; much better than merely testing for length.
• However, average vertebrate exon length is 130 nucleotides, which is often too small to produce reliable peaks in the likelihood ratio
• Further improvement: in-frame hexamer count (frequencies of pairs of consecutive codons)
Splicing Signals
• Try to recognize location of splicing signals at exon-intron junctions. This has yielded a weakly conserved donor splice site and acceptor splice site
• Profiles for sites are still weak, and lends the problem to the Hidden Markov Model (HMM) approaches, which capture the statistical dependencies between sites
Donor and Acceptor Sites: GT and AG dinucleotides
• The beginning and end of exons are signaled by donor and acceptor sites that usually have GT and AC dinucleotides
• Detecting these sites is difficult, because GT and AC appear very often
exon 1 exon 2GT AC
AcceptorSite
DonorSite
A more realistic (and complex) HMM model for Gene
Prediction (Genie)
Assessing performance: Sensitivity & Specificity
• Testing of predictions is performed on sequences where the gene structure is known
• Sensitivity is the fraction of known genes (or bases or exons) correctly predicted– “Am I finding the things that I’m supposed to find”
• Specificity is the fraction of predicted genes (or bases or exons) that correspond to true genes– “What fraction of my predictions are true?”
• In general, increasing one decreases the other
Measures of Prediction Accuracy, Part 1
Nucleotide level accuracy
Sensitivity =
Specificity =
TN FPFN TN TNTPFNTP FN
REALITY
PREDICTION
number of correct exonsnumber of actual exons
number of correct exonsnumber of predicted exons
Measures of Prediction Accuracy, Part 2
Exon level accuracy
REALITY
PREDICTION
WRONGEXON
CORRECTEXON
MISSINGEXON
Graphic View of Specificity and Sensitivity
iveFalseNegatveTruePositi
veTruePositi
AllTrue
veTruePositiSn
+==
iveFalsePositveTruePositi
veTruePositi
eAllPositiv
veTruePositiSp
+==
Quantifying the tradeoff: Correlation Coefficient
( )( ) ( )( )[ ]( )( )( )( )
FNTNPNFPTPPP
FNTPAPFPTNAN
PNAPPPAN
FNFPTNTPCC
+=+=
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Examples of Gene FindersFGENES – linear DF for content and signal sensors and DP for
finding optimal combination of exons
GeneMark – HMMs enhanced with ribosomal binding site recognition
Genie – neural networks for splicing, HMMs for coding sensors, overall structure modeled by HMM
Genscan – WM, WA and decision trees as signal sensors, HMMs for content sensors, overall HMM
HMMgene – HMM trained using conditional maximum likelihood
Morgan – decision trees for exon classification, also Markov Models
MZEF – quadratic DF, predict only internal exons
Ab initio Gene Finding is Difficult
• Genes are separated by large intergenic regions
• Genes are not continuous, but split in a number of (small) coding exons, separated by (larger) non-coding introns
– in humans coding sequence comprise only a few percents of the genome and an average of 5% of each gene
• Sequence signals that are essential for elucidation of a gene structure are degenerate and highly unspecific
• Alternative splicing
• Repeat elements (>50% in humans) – some contain coding regions
Problems with Ab initio Gene Finding
• No biological evidence
• In long genomic sequences many false positive predictions
• Prediction accuracy high, but not sufficient
Integrated Approaches for Gene Finding
• Programs that integrate results of similarity searches with ab initio techniques (GenomeScan, FGENESH+, Procrustes)
• Programs that use synteny between organisms (ROSETTA, SLAM)
• Integration of programs predicting different elements of a gene (EuGène)
• Combining predictions from several gene finding programs (combination of experts)
Combining Programs’ Predictions
• Set of methods used and they way they are integrated differs between individual programs
• Different programs often predict different elements of an actual gene
they could complement each other yielding better prediction
Related Work
• This approach was suggested by several authors
• Burset and Guigó (1996)
– Investigated correlation between 9 gene-finding programs – 99% of exons predicted by all programs were correct– 1% of exons completely missed by all programs
• Murakami and Tagaki (1998)
– Five methods for combining the prediction by 4 gene-finding programs
– Nucleotide level accuracy measures improved by 3-5% in comparison with the best single
AND and OR Methodsexon 1
exon 2
union
intersection
Combining Genscan and HMMgene
• High prediction accuracy as well as reliability of their exon probability made them the best candidates for our study
• Genscan predicted 77% of exons correctly, HMMgene 75%, both 87%
111 624 91Genscan HMMgene