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The Human Cell

Maths-in-action project at the University of Warwick, applying mathematical techniques to DNA Sequencing.
by

Anooj Dodhia

on 14 March 2016

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Transcript of The Human Cell

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day
dey
they
days
to
too
her
there
therm
ther
ter
wet
whe
wea
way
the
they
there
Let's ask about
?
The Human Cell
A mathematical insight into DNA Sequencing
Anooj Dodhia • 27 Feb 2013
The Human Cell
Anooj Dodhia • 27 Feb 2013
The Human Cell
Anooj Dodhia • 27 Feb 2013
"the weather today"
the | wea | ther | to | day
The Human Cell
Anooj Dodhia • 27 Feb 2013
Inference: If there were no Windows?
Rainy
Cold
Sunny
70%
25%
5%
45%
45%
10%
An introduction to DNA
vs
ATCG
AACC
GCAT
ATGC
AACG
Added information
Probability of a mutation between
C & G is much higher than any other
Uniform
prob.
Added
info.
Reality
But what if...
ATCGAATCGGTCTGAAGTCGATCGATTTGAC
TTAGAATCAGTATGAAGACGATCAATATGAG
TTGGTAACCGACAGTACTGGTTGGTTATCAG
WE NEED
A more
precise
PROBABILITY
framework
Markov Chains
"A memoryless random variable"
State Space
S = {A,T,C,G}
S = {codons}
Random Variable (the chain)
X(t)= {X(1), X(2), X(3), ... }
Transition Probabilities
Prob. of future depends only on the present, and not on the past:




Denote P[X(t+1) = b | X(t) = a] := P(a,b)
P[X(t+1) | X(1), X(2), ..., X(t)] = P[X(t+1) | X(t)]
Hidden Markov Model
Observed Markov
Chain
Underlying Markov
Chain
Probabilistic
Calculation
Sound-bites sent to Google's servers
Genetic code of interest
Google's interpretation of our voice
The sequence best matching our "observed" input
Umbrellas, coats & hats
The weather outside
But what can we do with this model?
The Forward-Backward Algorithm
The Viterbi Algorithm
25%
0%
75%
Big
Ideas
Probability
Inference
Markov Chain
Hidden Markov Models
Deoxyribonucleic acid
3,000,000,000 base pairs - 8 x dist(earth, sun)
e.g. heart cell structure or
digestive enzymes
We are interested in
a sequence of speech
which we split into
states
, or,
syllables (~3 letters)
And use
probability
to match
to the
closest sequence
in our database, made up of
{a-z, A-Z, 0-9, symbols}
We are interested in
a sequence of observed DNA
which we split into
states
, or,
codons (3 letters)
And use
probability
to match
to the
closest sequence
in our database, made up of
{A, T, C, G, '-'}
Any Questions?
Full transcript