Introduction to Bioinformatics 20120

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Introduction to Bioinformatics20120

• Gianluca Pollastri
• office CS A1.07
• email gianluca.pollastri_at_ucd.ie

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Credits

• Richard Lathrop and Pierre Baldis Bioinformatics
  courses at University of California _at_ Irvine.

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Course overview

• Context DNA, RNA, proteins
• Resources GenBank, PDB, etc.
• Algorithms for sequence comparison.
• Phylogenetics.
• Structural bioinformatics protein structure
  prediction.

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Lecture notes

• http//gruyere.ucd.ie/2007_courses/20120/
• confidential..

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Recommended/useful readings

• No book is actually required
• Introduction to Bioinformatics
• Lesk
• Introduction to Computational Molecular Biology
• Setubal, Meidanis
• Bioinformatics the Machine Learning approach
• Baldi, Brunak

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• CS 20120, Introduction to Bioinformatics
• Assignment 1, 29 January 2007
• 10 of the overall mark
• To hand in by midnight of February 12
• 1. identify your favourite pet
• 2. get the protein sequence for one of its genes
  on
• a. http//www.ncbi.nlm.nih.gov/entrez/
• 3. BLAST your sequence against UniProt at
• a. http//www.ebi.ac.uk/blast2/index.html?UniProt
• 4. If you get less than 6 results from 6
  different organisms, go back to 2 and choose
  another protein
• 5. Select 6 sequences returned by BLAST, from 6
  different organisms (ticking the appropriate
  boxes and downloading them in fasta format will
  give you the right input format for the next
  step)
• 6. Run clustalW on them using the page (be
  patient, might take time)
• a. http//www.ebi.ac.uk/clustalw/index.html
• 7. Draw a phylogenetic tree for your guide tree
  (.dnd) using an online viewer, e.g.
• a. http//bioweb.pasteur.fr/seqanal/interfaces/dra
  wtree.html
• 8. email me (gianluca.pollastri_at_ucd.ie)
• a. your protein sequence UniProt record

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Public tools summary

• DNA/RNA databases GenBANK/EMBL - 3-way
  consortium
• Protein sequences UniProt - SWISS-PROT, TrEMBL,
  etc.
• Protein structures Protein Data Bank (PDB)
• A massive amount of portals, servers, boutique
  databases, etc., some good, some a bit less.
• To sort out the above, a lot of servers
  benchmarking other servers (e.g. EVA, LiveBench,
  etc.)

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Some goals of Bioinformatics

• Understand biology based on sequences
• Interrelate sequence, structure, expression
  (presence/absence), function understand the
  system
• Use current sequence data to travel back in
  time..
• Use all this knowledge to produce technologies
  (for health, agriculture, etc.)

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More specific goals

• Given a sequence, find sequences in a database
  that are similar to it (sequence comparison).
• Given a structure, find structures in a database
  that are similar to it (structure comparison).
• Given a sequence, find its structure (protein
  structure prediction).
• Given a structure, find a sequence whose
  structure is similar to it (protein synthesis)

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Data size

• 1011 letters in DNA repositories a decent-sized
  hard disk
• 6 complete years of issues of the NY Times (which
  has notoriously large weekend supplements..)
• A formidable increase rate..

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Computer Science needed

• Given the size and nature of molecular biology
  data, a set of specific computer science
  technologies are especially crucial for
  bioinformatics
• Fast algorithms for comparing strings, 3D
  structures
• Efficient data structures
• Data mining/machine learning

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Sequence Comparison
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Sequence comparison

• Most important primitive operation in
  computational biology. Almost everything else we
  can do relies on it.
• Similarity between two sequences (DNA, protein)
  how much they look alike (are they likely to be
  evolutionarily related?).
• Alignment how we place a sequence vs another to
  maximise matches between the two, often to
  measure similarity.

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Sequence comparison (2)

• Similarity between two sequences
• of identical letters in the same positions
• OK, but this works only if all letters are
  equally dissimilar..
• evolutionary distance
• great, but how do we compute that?
• something in between?
• any ideas?
• DADLAKKNNCIACHQVETKVVGPALKDIAAKYADKDDAATYLAGKIKGGS
  SGVWGQIPMPPNVNVSDADAKALADWILTLK
• ...LYAEKACAGCHSTDSRLVGPSYKGLFGSTRGVIADENYIRKSILQPT
  AQVVKGYPMPSQGQLSDDEINALIEYIKTLK

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Scenario 1

• 2 sequences over the same alphabet (e.g. both
  proteins, or both DNA), roughly the same length.
  Small differences. We want to find them

ACCTGGGCTACGTGACTTA-AACT ACCTG-GCTACGAGACTTATAACT
d i
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Scenario 1 (2)

• E.g. multiple labs sequencing the same gene, or
  protein. There may be small differences due to
  natural variations and sequencing errors.

ACCTGGGCTACGTGACTTA-AACT ACCTG-GCTACGAGACTTATAACT
d i
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Scenario 2

• 2 sequences over the same alphabet. We want to
  figure out if the suffix of one is similar to a
  prefix of another

..ACCCGACCTGGGCTACGTGACTTA-AA
ACCTG-GCTACGAGACTTATAACTTCAA
...
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Scenario 3

• Same as 2 (compare the end of one sequence vs the
  beginning of the other) but now we have hundreds
  of sequences.
• We also know that many of these sequences are
  likely to be unrelated, i.e. many/most pairs of
  sequences dont match. Two problems in one
  finding which sequences match, and
  quantify/qualify the match.
• E.g. genome sequencing. DNA cut in different
  places by enzymes, sequenced, and then
  reassembled.

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Scenario 4

• Find substrings of two sequences that are similar
  to each other. All the surrounding stuff in both
  sequences can be different.

..ACCCGACCTGGGCTACGTGACTTA-AAGGACGC
TTTGTACCTG-GCTACGAGACTTATAACTTCAA
-----...------
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Scenario 4

• E.g. finding a common motif in two regions,
  finding a common domain (functional unit) between
  two different proteins.

..ACCCGACCTGGGCTACGTGACTTA-AAGGACGC
TTTGTACCTG-GCTACGAGACTTATAACTTCAA
-----...------
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Scenario 5

• Same as 4, but now we have 1 sequence A vs
  thousands of sequences. Most of these sequences
  are NOT similar to A. Two problems in one find
  which sequences are similar, and gauge their
  similarity to A.
• We have a motif and want to find all the
  sequences that include it
• We have a protein domain and want to find all the
  proteins that include it
• We have a gene and ...
• In general when we are trying to find biological
  similarity we are in scenario 4 or 5

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Global sequence comparison

• The two sequences below look similar. We are
  looking for an algorithm to detect this, and
  align the sequences optimally (including gaps).

ACCTGGGCTACGTGACTTA-AACT ACCTG-GCTACGAGACTTATAACT
d i
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Global sequence comparison (2)

• We want to compare whole sequences we are not
  interested in small regions of local similarity
  in the middle of irrelevant/random/unrelated
  information.
• E.g. we have a gene A, and want to compare it to
  a list of genes L. We are sure that A and all
  sequences in L are actual genes.
• We may not know much or anything about A, but we
  have information about the elements of L. If A
  looks like an element B of L then some of what we
  know about B may be transferred to A.

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Alignments

• We want to insert arbitrary spaces (gaps) in both
  sequences so that we end up with the max number
  of matches (same letter in the same position)
• random deletions and insertions are common during
  evolution - without allowing gaps we wouldnt be
  able to detect many evolutionary relationships
• Spaces at the end of the sequences are OK
• perhaps a sequence is longer than the other one,
  and insertions at the end of a sequence tend to
  be more neutral that insertions in the middle
• Only illegal (silly) thing two spaces in the
  same position.

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OK
---TGGGCTACGTGACTTA-AACT ACCTG-GCTACGAGACTTATAACT
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NO! (silly)
---TGGGCTA-CGTGACTTA-AACT ACCTG-GCTA-CGAGACTTATAAC
T
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Visualising sequence similarity using dotplots
example

• ACCTGGGCCACGT
• ACCAGGCTACGA

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Score

• How do we score an alignment?
• For instance
• 1 match
• -1 mismatch
• -2 gap
• (Not at all the only way, e.g. for proteins there
  are much better ways, as we will see)

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Score
ACCTGGGCTACGTGACTTA-AACT ACCAG-GCTACGAGACTTATAGCT
--- 19 matches, 3
mismatches, 2 gaps -gt score 19x1 3x(-1)
2x(-2) 12
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Score (2)
...TGGGCTACGTGACTTA-AACT ACCAG-GCTACGAGACTTATAGCT
...--- 16 matches, 3
mismatches, 2 gaps, 3 gaps at the beginning of a
seq -gt score 16x1 3x(-1) 2x(-2) 3x0 9
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Algorithms for sequence comparison

• Generating all possible alignments and picking
  the best one impossibly slow.
• Dynamic programming (here programming has
  nothing to do with computers) solving a problem
  by splitting it dynamically into subparts.
• We build up a solution based on similarity
  between prefixes of the two sequences..