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SVMs Learn a Function to Distinguish between Positive and Negative based on the statistics of the features in the training examples. A Novel Approach To Diploid Base Calling. Aaron R. Quinlan ([email protected]) and Gabor T. Marth ([email protected]), - PowerPoint PPT Presentation
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Aaron R. Quinlan ([email protected]) and Gabor T. Marth ([email protected]), Department of Biology, Boston College, Chestnut Hill, MA 02467
http://bioinformatics.bc.edu/marthlab/
Our SNP detection method, detects SNPs across clonal reads basedon base composition and quality.
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3
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C/T
P(TT|R) =.9991
P(CT|R) =.96
Method for Diploid Base Calling (Support Vector Machine - based)
Collect Heterozygous and Homozygous
Training Examples
Calculate indicative featuresthat separate heterozygotes
from homozygotes.
SNP 1 SNP 2 SNP N…
Trained SVM Can Separate Unseen
Homozygotes and HeterozygotesMake Diploid Base Calls on Unseen Alignments.
P(CT|R) = .34
P(CT|R) = .01
P(AC|R) = .999
P(AT|R) = .001
P(S1S2|R) = Probability of allelic combination given the read
SVM
SVMs Learn a Function to Distinguish between Positive and Negative based on the statistics of the features in the training examples.
We are integrating diploid base calling (heterozygote detection) into
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)SNP(P
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Base Call/Quality Polymorphism Rate
Base Composition Depth of Alignment
Probability of polymorphism
Assessing the Accuracy of the Initial Prototype:
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“Unseen” Alignments
SNP (A/G) FoundAcross MultipleClonal Reads
PCR-basedsequences of diploid individualsCalls = (CC, CT, TT)
P(CC|R) =.9995
P(CT|R) =.003
Summary:1.We built a diploid base
calling prototype from the ground up. The initial prototype’s performance is similar to Polyphred 5.
2.We are currently compiling a larger example set to improve accuracy.
3.Our method incorporates information from multiple reads for a given individual in a statistically-rigorous fashion.
4.This prototype represents the first major expansion of .
5.We are currently working to expand the prototype to a production-ready application
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1Probability of each possible diploid base call (AA,CC,GG,TT,AC,AG,AT,CG,CT,GT)
Each Possible Diploid Base Call/Probability
Prior Probability of Each Diploid Genotype
Depth of Alignment
Observed Diploid Variations/Probabilities
SVM Score+ is Het
- is Hom
P(H
et)
+0
-
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Utilizing multiple reads per individual, we can make an individual genotype call.
Forward ReadReverse Read
P(GT | Read) = .98
P(GT | Read) = .87
Individual Genotype Call: P(GT) = .993
Prior(GT Frequency) = .34?
Rationale: The accuracy of the consensus diploid base call for an individual increases with the number of reads available for that individual.
Polyphred 5 was tested with the following settings: quality = 21, score = 99, source, ref_comp
0
Convert SVM Score to P(Het)
Assessing the Genotyping Accuracy of the Initial Prototype
Objective: To enhance with an accurate diploid base calling algorithm
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0-.1 .1-.2 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .7-.8 .8-.9 .9-.95 .95-.99 .99-1
P(Het) Range
Tru
e P
osi
tive
Rat
e
Accuracy
A Novel Approach To Diploid Base Calling
P(CT) = .9
P(CC) = .045
P(TT) = .045P(Others) = .01
Probability of Each Genotype
From a diploid base call
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<.1 <.2 <.3 <.4 <.5 <.6 <.7 <.8 <.9 <.95 <.99 <1
P(Het) Limit
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ctio
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f C
alla
ble
Het
s F
ou
nd
SensitivityNumber of Calls within Posterior Probability Range
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P(Het) Range
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er o
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alls
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21851 Data Accuracy by P(Het) Score
Number of Alignments Analyzed: 993Total Number of Read Positions: 231874Total Number of Heterozygotes: 31411Total Number of Homozygotes: 143370
Note: Polyphred was tested on alignments created by PolyBayes. This allowed Polyphred to analyze a larger fraction of reads, asCompared to Phrap Alignments.
