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MEPI group meeting, 2017
Mukarram Hossain
University of Cambridge
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Alignment-free subtyping of HIV sequences



Mukarram Hossain

Department of Veterinary Medicine
University of Cambridge

MEPI group meeting, March 2017

 

Subtype classification

  • Viruses are often grouped into subtypes.
  • Subtypes have wide implications on the following studies of viruses:
    • clinical
    • epidemiological
    • structural
    • functional
  • Existing classification techniques mostly rely on alignments followed by phylogenetic and/or statistical algorithms.

Alignment uncertainty

 

Alignment-free classification

  • Lossless compression techniques have shown promising results for biological sequence classification:
    • Protein family prediction (Begleiter et al., 2004)
    • Protein structure prediction (Ferragina et al., 2007)



 

COMET

  • COMET is an ultrafast alignment free subtyping tool
  • Uses Prediction by Partial Matching (PPM)
  • Initially designed for HIV-1
  • COMET was tested on both synthetic (1090698) and clinical (10625) HIV datasets
  • Sensitivity and specificity were comparable to or higher than:
    • REGA (de Oliveira et al., 2005) and
    • SCUEAL (Pond et al., 2009)
  • Detected and identified new recombinant forms

COMET algorithm

  • Builds variable-order Markov models for each reference sequence
  • Given a query, COMET calculates log likelihood of observing a base at each positions
  • This results in a matrix of likelihood values
  • Subtype call is done using a decision tree

The decision tree

 

Classification using Neural Networks

  • Neural networks are computational system mimicking biological brain
  • Consists of a cluster of neural units organised in layers



 

ANN: design

  • The input layer consists of 32 neurons:
    • gets values from the fixed context
  • Hidden layer consists of N neurons
    • processes inputs coming from the input layer using wights and biases
  • Output layer consists of 4 neurons
    • uses softmax funnction to generate probabilities for the Nucleotide bases A, C, G, T

ANN : implementation

  • Based on the example code from the book 'Neural networks and deep learning' by Michael Nielsen
  • Written in Python3

ANN: training

  • We use the reference sequence set used in COMET to train the ANN
  • Cross-validation is done using randomly removing one sequence from the training set
  • Cross-entropy cost function is used to update network weights and biases

ANN: subtyping

  • For each nucleotide positions in the query sequence:
    • ANNs from each subtype generates probabilities of seeing the nucleotide given previous context
  • The decision tree used in COMET is used to predict the subtype of the query sequence

Cross-validation

 

Test datasets

  • Reference
  • Vanderbilt
  • PR-RT

Accuracy comparison

 

Future direction

  • Optimise neural network parameters
  • Larger context size?
  • Recursive Neural Networks (RNN)?
  • Report breakpoints for potential novel recombinants
  • Implement using TensorFlow