Compression of Protein Sequences

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Compression of Protein Sequences

• EE-591 Information Theory
• FEI NAN, SUMIT SHARMA
• May 3, 2003

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GENERAL OVER VIEW OF THE PROJECT

• DNA and Protein form the basic structure of the
  life and they can be represented by any normal
  text file.
• When it comes to compression, we see that they
  dont show the good result from the normal
  compression techniques.
• In this project we have used a new technique
  called CP (Compression Scheme) for their
  compression and analysis it how far it is good in
  Protein Compression.

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THE NEED OF THE COMPRESSION

• There are two different motivation for the
  compression
• Compression enables efficient use of the
  resource such as storage and bandwidth
• From scientific perspective it provides a way of
  capturing and quantifying structure in the
  sequence
• We here have put stress on the second one as we
  are here dealing with the biological sequence

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• A good model for compression will contain a few
  symbols with high probability (and preferably one
  dominant symbol), thus allowing very compact
  coding of those probable symbols.

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• We see that DNA is the genetic material of the
  life it transmits information from one generation
  to another
• We can represent DNA as sequence of the symbol
  represented by four symbol alphabet of
  nucleotides
• A adenine
• C cytosine
• G guanine
• T thymine
• A will only bond with T and G will only bond
  with C

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DNA STRUCTURE
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DNA STRUCTURE
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Protein Structure

• Since we know that Protein sequence is a very
  large one as it is composed of 20 amino acid so
  they have a high level of redundancy since we
  can represent the sequence in a normal text file.
  
• But when we talk abut the compression we have to
  take care of the fact they are biological
  sequence and protein are subjected to mutation
  that destroy repetition.

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3-D Protein Structure
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PROTEIN STRCTURE
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• The redundancy in protein majorly comes through
  two sources
• New genes arise through duplication
• Mistake made while copying DNA and other cellular
  process

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• In compression of the protein we take into
  account a distance metric this distance reflect
  their mutation probabilities that is symbol that
  are close together are derived from the same
  symbol by mutation and if far apart other wise .
• In our scheme we have taken distance to combine
  the prediction made by different context ,we sum
  up over all possible context up to a certain
  length weighted by their similarity to the
  current context.

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• In our project we have make use of the following
  concepts
• PPM
• Adaptive coding

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PROBABILITIC PREDICTION METHOD(PPM)

• The basic idea of the PPM is to used last few
  character to predict the probabilities of the
  coming one .
• Example we have a sequence abcd that end with
  aba so PPM will calculate the previous
  occurrence of the aba and tallies with the symbol
  that will occur next

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CP COMPRESSION SCHEME

• In this scheme we take into consideration that
  biological sequence constantly undergo into
  mutation and as long as the new sequence has
  similar properties the mutation will be accepted
  ,thus exact repletion is overlaid with mutation
  which is modeled by the distance function ,thus
  it is desirable to take into account in
  compression scheme
• In general this is given by the formula

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• We see in the equation that weight frequent
  context more highly this has some merits as it
  has less variance in distribution thus to improve
  this more we take the weight context equally ,we
  do that by converting f

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Explanation of the program

• Calculation of the first order
• Calculation of the second order
• Calculation of the third order

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Major Function

• There are major functions in the code.
• Function to read the mutation matrix
• Function to read the target sequence
• Function to compute the n-order value
• Function to compute the dynamic probability based
  on the CP algorithm (a major part distinct from
  some other existing algorithm of the 1987 CACM
  article by Witten, Neal, and Cleary. )
• Main function

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Modules in the Code

• There is a function to calculate the dynamic
  probability of each symbol. i.e. When we read a
  new symbol, we increase the occurrence of that
  symbol by 1 and increase the total number of
  symbols we have read by 1 and use this function
  module to compute the probability dynamically.

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ADAPTIVE CODING

• Adaptive coding doesnt require the probabilities
  to be transmitted with the encoded data
• Require only one pass through the data coding
• Doesnt use the fixed symbol probabilities

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Time of Execution of Each Text
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Time of Execution of Each Text

• As we can see from the diagram in the last slide,
  the time for the successful execution of n-order
  CP is increased with the n by a factor of 20.
• So we cannot make n too large although we might
  receive a good compression ratio.
• Craig G, Nevill-Manning, Ian H. Witten Protein
  is incompressible

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Improvements

• Improvement of the execution time.
• Improvement of the float precision.
• Solution
• By addition of high-performance hardware
• By Optimization of software algorithm

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Thanks

• ?