Data Mining-mining sequence patterns in biological data

1. Mining Sequence Patterns
in Biological data
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2. Bioinformatics
 Applies Computer Technology in Molecular biology
 Develops algorithms and methods to manage and analyze biological
data
 Effective methods are needed to compare and align biological
sequences and discover sequential patterns
 Type of data
 DNA: helix-shaped molecule whose constituents are two parallel strands
of nucleotides : Adenine (A), Cytosine (C), Guanine (G), Thymine (T)
 Proteins: Composed of 20 amino acids
 Produced from DNA using 3 operations or transformations: transcription, splicing and translation
 Gene : Sequence of hundreds of individual nucleotides arranged in a
particular order
 Genome : Complete set of genes of an organism
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3. Alignment of Biological Sequences
 Alignment – given two or more input biological sequences, identify similar
sequences with long conserved sub-sequences
 Pair-wise Sequence alignment
 Multiple Sequence Alignment
 In nucleotides – two symbols align if they are identical
 In amino acids – they align if identical / or one can be derived from the other
 Local Alignment Vs Global Alignment
 Substitution matrix – represent probability of substitution
 Alignment score can be calculated
 Need for alignment
 Two sequences are homologous if they share the same ancestor
 Degree of similarity – helps to determine degree of homology
 Helps to construct evolution tree or phylogenetic tree
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4. Pairwise Alignment
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A E G H W
A 5 -1 0 -2 -3
E -1 6 -3 0 -3
H -2 0 -2 10 -3
P -1 -1 -2 -2 -4
W -3 -3 -3 -3 15
Gap penalty: -8
Gap extension: -8
HEAGAWGHE-E
P-A--W-HEAE
HEAGAWGHE-E
--P-AW-HEAE
(-8) + (-8) + (-1) + (-8) + 5 + 15 + (-8)
+ 10 + 6 + (-8) + 6 = 1
(-2) + (-8) + (5) + (-8) + (-8) + (15) + (-8)
+ 10 + 6 + (-8) + 6 = 0
20 x 20 triangular matrices – Available

5. Pairwise Alignment
 Needleman-Wunsch Algorithm
 Smith-Waterman Algorithm
 Build up Optimal Sequences
 Use Dynamic Programming
 O(n2
) Time Complexity
 Dot matrix plot
 Uses boolean matrices to represent alignments that can be detected visually
 O(n2
) Time Complexity
 Heuristic Algorithms
 BLAST – Basic Local Alignment Search Tool
 FASTA – Fast Alignment Tool
 First locate high-scoring short stretches and extend them
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6. BLAST Local Alignment Algorithm
 Finds regions of local similarity between bio-sequences
 Matches nucleotide / protein sequences to sequence databases and
calculates statistical significance of matches
 Breaks the sequences to be compared into sequences of fragments (words)
and seeks matches between words
 DNA – word size – 11 bases
 Amino Acids – 3 amino acids
 Creates a hash table of matching words
 Moves from exact matches to neighborhood words
 Due to hashing – O(n)
 Variants : MEGABLAST (long alignments), Discontinuous MEGABLAST
(gapped alignments- similar not identical), BLASTN (Adjustable word size),
BLASTP…
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7. Multiple Sequence Alignment Methods
 Goal – To find common patterns among all considered sequences
 Applications
 To build gene / protein families
 Identify amino acids which are essential sites for structure and function
 More complex than Pair wise alignment
 Multi-dimensional alignment / Approximate alignment
 Methods
 Series of pair-wise alignments
 Feng-Doolittle alignment
 Computes all possible pair wise alignments by dynamic programming
 Constructs a Guide tree – by clustering and progressive alignment
 Multiple Sequence alignment
 Hidden Markov Models
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8. HMM for Biological Sequence Analysis
 Finding CpG Islands
 Methylation process – converts C in CpG to T
 CpG occurrence – rare
 Methylation is suppressed around start regions of genes
 Areas with high concentration – CpG Islands
 Given a short sequence is it from a CpG island
 Given a long sequence – can all CpG islands be
found
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9. Markov Chain
 Probability of a symbol depends only on previous symbol
 Markov Chain model – states and transitions (probability)
 Probability of a sequence x = x1x2…xL
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−
−−−
=
=
L
i
ii
LLLL
xxx
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2
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112211
)|Pr()Pr(
)Pr()|Pr()...|Pr()/Pr()Pr(
Markov model can be used for classification
- To distinguish CpG islands from others using the
training data construct two models + and -. Classify a
given sequence based on P(x|+) and P(x|-)
- Probability values are estimated from training
sequences

10. Hidden Markov Model
 Used to find all CpG islands in a long DNA Sequence
 Merge two Markov chains and add transition probabilities between the two
states
 Hidden Markov Model: states, transitions, emission probabilities (probability
of producing a symbol at a state)
 Hidden because the states visited in generating a sequence are not known
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11. Hidden Markov Models
 Tasks
 Evaluation: Given a sequence x determine probability P(x) –
Forward Algorithm
 Decoding: Given a sequence, determine most probable path
through the model – Viterbi Algorithm
 Learning: Given a model and training sequences, find the model
parameters – Baum Welch Algorithm
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