gene feature recognition

NUS-KI Course on Bioinformatics, Nov 2005

For written notes on this lecture, please read Chapters 4 and 7 of The Practical Bioinformatician

Gene Feature Recognition

Limsoon Wong


Recognition of Splice Sites

A simple example to start the day

NUS-KI Course on Bioinformatics, Nov 2005 Copyright 2005 © Limsoon Wong

Splice Sites

AcceptorDonor


Acceptor Site (Human Genome)

• If we align all known acceptor sites (with their splice junction site aligned), we have the following nucleotide distribution

• Acceptor site: CAG | TAG | coding regionImage credit: Xu


Donor Site (Human Genome)

• If we align all known donor sites (with their splice junction site aligned), we have the following nucleotide distribution

• Donor site: coding region | GTImage credit: Xu


What Positions Have “High” Information Content?

• For a weight matrix, information content of each column is calculated as

– X{A,C,G,T} Prob(X)*log (Prob(X)/0.25)

• When a column has evenly distributed nucleotides, its information content is lowest

• Only need to look at positions having high information content


Information Content Around Donor Sites in Human Genome

• Information content column –3 = – .34*log (.34/.25) – .363*log

(.363/.25) – .183* log (.183/.25) – .114* log (.114/.25) = 0.04

column –1 = – .092*log (.92/.25) – .03*log (.033/.25) – .803* log (.803/.25) – .073* log (.73/.25) = 0.30

Image credit: Xu


Weight Matrix Model for Splice Sites

• Weight matrix model – build a weight matrix for donor, acceptor,

translation start site, respectively– use positions of high information content

Image credit: Xu


Splice Site Prediction: A Procedure

• Add up freq of corr letter in corr positions:

• Make prediction on splice site based on some threshold

AAGGTAAGT: .34 + .60 + .80 +1.0 + 1.0 + .52 + .71 + .81 + .46 = 6.24

TGTGTCTCA: .11 + .12 + .03 +1.0 + 1.0 + .02 + .07 + .05 + .16 = 2.56

Image credit: Xu


Recognition of Translation Initiation Sites

An introduction to the World’s simplest TIS recognition system

A simple approach to accuracy and understandability


Translation Initiation Site


A Sample cDNA

• What makes the second ATG the TIS?

299 HSU27655.1 CAT U27655 Homo sapiensCGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT............................................................ 80................................iEEEEEEEEEEEEEEEEEEEEEEEEEEE 160EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE 240EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE


Approach

• Training data gathering• Signal generation

– k-grams, distance, domain know-how, ...• Signal selection

– Entropy, 2, CFS, t-test, domain know-how...• Signal integration

– SVM, ANN, PCL, CART, C4.5, kNN, ...


Training & Testing Data

• Vertebrate dataset of Pedersen & Nielsen [ISMB’97]

• 3312 sequences• 13503 ATG sites• 3312 (24.5%) are TIS• 10191 (75.5%) are non-TIS• Use for 3-fold x-validation expts


Signal Generation

• K-grams (ie., k consecutive letters)– K = 1, 2, 3, 4, 5, …– Window size vs. fixed position– Up-stream, downstream vs. any where in window– In-frame vs. any frame

0

0.5

1

1.5

2

2.5

3

A C G T

seq1

seq2

seq3


Signal Generation: An Example

• Window = 100 bases• In-frame, downstream

– GCT = 1, TTT = 1, ATG = 1…• Any-frame, downstream

– GCT = 3, TTT = 2, ATG = 2…• In-frame, upstream

– GCT = 2, TTT = 0, ATG = 0, ...

299 HSU27655.1 CAT U27655 Homo sapiensCGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT


An Example File Resulting From Feature

Generation


Too Many Signals

• For each value of k, there are 4k * 3 * 2 k-grams

• If we use k = 1, 2, 3, 4, 5, we have 24 + 96 + 384 + 1536 + 6144 = 8184 features!

• This is too many for most machine learning algorithms


Signal Selection (Basic Idea)

• Choose a signal w/ low intra-class distance• Choose a signal w/ high inter-class distance


Signal Selection (eg., t-statistics)


Signal Selection (eg., 2)


Signal Selection (eg., CFS)

• Instead of scoring individual signals, how about scoring a group of signals as a whole?

• CFS– Correlation-based Feature Selection– A good group contains signals that are highly

correlated with the class, and yet uncorrelated with each other


Sample k-grams Selected by CFS

• Position –3• in-frame upstream ATG• in-frame downstream

– TAA, TAG, TGA, – CTG, GAC, GAG, and GCC

Kozak consensusLeaky scanning

Stop codon

Codon bias?


Signal Integration

• kNN– Given a test sample, find the k training samples

that are most similar to it. Let the majority class win

• SVM– Given a group of training samples from two

classes, determine a separating plane that maximises the margin of error

• Naïve Bayes, ANN, C4.5, ...


Neighborhood

5 of class

3 of class

=

Illustration of kNN (k=8)

Image credit: ZakiTypical “distance” measure =


Using WEKA for TIS Prediction


Results (3-fold x-validation)

TP/(TP + FN) TN/(TN + FP) TP/(TP + FP) Accuracy

Naïve Bayes 84.3% 86.1% 66.3% 85.7%

SVM 73.9% 93.2% 77.9% 88.5%

Neural Network 77.6% 93.2% 78.8% 89.4%

Decision Tree 74.0% 94.4% 81.1% 89.4%

3-NN* 73.2% 92.9% 77.2% 88.0%

* Using top 20 2-selected features from amino-acid features


Validation Results (on Chr X and Chr 21)

• Using top 100 features selected by entropy and trained on Pedersen & Nielsen’s

ATGpr

Ourmethod


Technique Comparisons

• Pedersen&Nielsen [ISMB’97]

– 85% accuracy– Neural network– No explicit features

• Zien [Bioinformatics’00]

– 88% accuracy– SVM+kernel engineering– No explicit features

• Hatzigeorgiou [Bioinformatics’02]

– 94% accuracy (with scanning rule)

– Multiple neural networks– No explicit features

• Our approach– 89% accuracy (94% with

scanning rule)– Explicit feature

generation– Explicit feature selection– Use any machine

learning method w/o any form of complicated tuning


Recognition of Transcription Start Sites

An introduction to the World’s best TSS recognition system

A heavy tuning approach


Transcription Start Site


Structure of Dragon Promoter Finder

-200 to +50window size

Model selected based on desired sensitivity


Each model has two submodels based on GC content

GC-rich submodel

GC-poor submodel

(C+G) =#C + #GWindow Size


Data Analysis Within Submodel

K-gram (k = 5) positional weight matrix

p

e

i


Promoter, Exon, Intron Sensors

• These sensors are positional weight matrices of k-grams, k = 5 (aka pentamers)

• They are calculated as s below using promoter, exon, intron data respectively Pentamer at ith

position in input

jth pentamer atith position in training window

Frequency of jthpentamer at ith positionin training window

Window size


Data Preprocessing & ANNTuning parameters

tanh(x) =ex e-x

ex e-x

sIE

sI

sEtanh(net)

Simple feedforward ANN trained by the Bayesian regularisation method

wi

net = si * wi

Tunedthreshold


Accuracy Comparisons

without C+G submodels

with C+G submodels


Notes


References (TIS Recognition)

• A. G. Pedersen, H. Nielsen, “Neural network prediction of translation initiation sites in eukaryotes”, ISMB 5:226--233, 1997

• H.Liu, L. Wong, “Data Mining Tools for Biological Sequences”, Journal of Bioinformatics and Computational Biology, 1(1):139--168, 2003

• A. Zien et al., “Engineering support vector machine kernels that recognize translation initiation sites”, Bioinformatics 16:799--807, 2000

• A. G. Hatzigeorgiou, “Translation initiation start prediction in human cDNAs with high accuracy”, Bioinformatics 18:343--350, 2002


References (TSS Recognition)

• V. B. Bajic et al., “Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates”, J. Mol. Graph. & Mod. 21:323--332, 2003

• J. W. Fickett, A. G. Hatzigeorgiou, “Eukaryotic promoter recognition”, Gen. Res. 7:861--878, 1997

• A. G. Pedersen et al., “The biology of eukaryotic promoter prediction---a review”, Computer & Chemistry 23:191--207, 1999

• M. Scherf et al., “Highly specific localisation of promoter regions in large genome sequences by PromoterInspector”, JMB 297:599--606, 2000

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