Genome Informatics conference 2008
Extracting biological knowledge from SVM models capableofdistinguishing alternative and constitutive splicing
T. Murlidharan Nair and Michael Gribskov

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Alternative and Constitutive splicing
Sequencing of the human genomes
revealed that it is merely the skeleton of
the underlying code of life

Many layers of annotation will berequired before it can be completelydeciphered

An important observation that came tolight was the small number of genes
identified from the genomeGIW2008

Alternative and Constitutive splicing
Alternative splicing of pre-mRNA is a major contributor to proteomic
diversity as well as control of gene expression levels.
Splicing is regulated developmentally as well as tissue specifically andaberrant splicing can lead to wide range of human diseases

GIW2008

Alternative and Constitutive splicing
Alternative splicing of pre-mRNA is a major contributor to proteomic
diversity as well as control of gene expression levels.

The long term goal is to understand the “splicing code” that will enable
the prediction of splicing patterns from primary mRNA transcript.

Core splicing signals are present in every intron
5’splice site
3’ splice site
Branch point

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Alternative and Constitutive splicing
Intron5’Exon
1

Exon 2GUA

Left (donor)
5’ splice site

Branch site
AG

Exon 1UAG

3’

5’

18-40 BPAG

3’Right(acceptor)
3’ splice
3’ siteExon 23’AG
5’A Intron lariat
G Following excision, the lariat
U
is rapidly degraded

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Alternative and Constitutive splicing
5’ss and 3’ss can be mapped precisely by aligning cDNA/ESTs to thegenome and there is a large corpus of data available that can be used tobuild models

Branch point sequence data sets are not as large

Exon definition is an important step in mammalian splicing regulation

Splicing process occurs with high fidelity despite the presence of pseudoexons

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Alternative and Constitutive splicing
Splicing regulatory elements
ESEs and ESSs promote or inhibit inclusion of exons that they are part ofISEs and ISSs promote or inhibit usage of adjacent splice sites or exons. They
are present in introns.

Can we identify distinguishing characteristics present in alternatively andconstitutively spliced junctions using learning methods?

Compare these with know regulatory elements and identify common
subsets.

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Alternative and Constitutive splicing
For building learning
requires good quality data

methods

Manually
annotatedalternatively
spliced events database (MAASE)(Zhang et. al., 2005) contains highly
curateddata
http://splice.bioinformatics.iusb.edu

The database contains informationon alternatively splice events and wasdeveloped to design primers for
splicing arrays.
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Alternative and Constitutive splicing
Support vector machines are kernel based classifiers used forclassification and regression.

They classify input data by transforming them into an n-dimensional
space and constructs a maximum margin hyperplane in that space
separating the two classes.

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Alternative and Constitutive splicing
One of the problems with using SVMs as compared to other probabilistic
methods, like weight matrices and Markov model, is that the resulting
decision function is not easily interpretable. Thus no relevant biologicalknowledge can be extracted from them.

Is it possible to understand the internal representation of an SVM modeland use that information to extract biologically important features?

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Alternative and Constitutive splicing
Algorithm

Interpreting the SVM classifier for biological relevanceInputs : S the set of test examples not used for training the SVM; f(i), set of SVM
models capable of optimally classifying S
Output: R, enumerated output reflecting the performance of the SVM classifier basedon the region where the noise was introduced.
W =Size of the window to introduce the noise
Begin
i =1
for m= 1 to 100 (the number of svm models)
Srand =Randomized subset set of test examples
while j ≤ number of Srandomized do
Srand [1 to W] = random sequence with the same probability distribution
end while
R[m]=f(Srandomized )end forend

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Alternative and Constitutive splicing
Separate Support vector machine (SVM) models were built to distinguishbetweenConstitutively spliced acceptor and alternatively spliced acceptor junctions
Constitutively spliced donor and alternatively splice donor junctions.

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Alternative and Constitutive splicing
Feature extraction algorithm was then used to identify learned information.

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Alternative and Constitutive splicing
Percentage position occurrence of hexamers (silences/enhancers)
around human alternatively spliced acceptor and constitutively spliced
acceptor junctions

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Alternative and Constitutive splicing
Percentage position occurrence of hexamers (silences/enhancers)
around human Alternatively spliced donor and constitutively spliceddonor junctions.

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Alternative and Constitutive splicing
Preliminary results point to the fact that features present are complex tocapture

The learning space for SVM needs to be increased which will improve theperformance of the model.

With the SVM model we hope to contribute to the parts list towards
derivation of the “splicing code”
Eventual goal is to develop a splicing simulation algorithm by integrating
the information derived with SVM models with those available in literatureby developing a probabilistic model.

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Alternative and Constitutive splicing
Acknowledgements
Prof. Xiang-Dong Fu UCSD
Dr. Christina Zhang
NSF for funding

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