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Multiple Alignment

                    Dr Avril Coghlan
                   alc@sanger.ac.uk

Note: this talk contains animations which can only be seen by
downloading and using ‘View Slide show’ in Powerpoint
Pairwise versus Multiple Alignment
• So far we have considered the alignment of two
  sequences (‘pairwise alignment’)
           Q K E S G P S S S Y C
           |   | | |           |
         V Q Q E S G L V R T T C
• Alignment can be performed between three or more
  sequences (‘multiple alignment’)
           Q K E S G   P S S S Y C
           |   | | |             |
         V Q Q E S G   L V R T T C
           |   | |     | | |   | |
         V Q K E S L   L V R S T C
Multiple alignment
• Multiple alignments are useful for comparing many
  homologous sequences at once



 Multiple alignment of part of Eyeless from different animals
• Multiple alignments can be global or local
  The majority of widely used programs for making multiple alignments
        (eg. CLUSTAL, T-COFFEE) create global multiple alignments (not
        local multiple alignments)
  If the sequences share one stretch of high sequence similarity, it might
        make sense to make a multiple alignment of just that region of
        similarity eg. for Eyeless
  You can “cut out” the region of similarity from each sequence, & make a
        multiple alignment of that region eg. using CLUSTAL
Real data: Eyeless proteins




              Do you think it’s sensible to
              make a global multiple
              alignment of these
              sequences?
The alignment is not very
reliable in regions of low
similarity
for example look at the
alignment of fly Eyeless to
the other proteins here
•   Algorithms for aligning 2 sequences (eg. N-W, S-W) can be
    extended to multiple sequences
    For aligning 3 sequences using N-W, we fill in a table T that is a 3D cube,
    using the recurrence relation:
                       T(i-1,j-1,k-1) + σ(S1(i),S2(j)) + σ(S1(i),S3(k)) + σ(S2(j),S3(k))
    T(i, j, k) = max   T(i-1, j, k) + gap penalty + gap penalty
                       T(i, j-1, k) + gap penalty + gap penalty
                       T(i, j, k-1) + gap penalty + gap penalty
                       T(i-1, j, k-1) + σ(S1(i),S3(k)) + gap penalty + gap penalty
                       T(i, j-1, k-1) + σ(S2(j),S3(k)) + gap penalty + gap penalty
                       T(i-1, j-1, k) + σ(S1(i),S2(j)) + gap penalty + gap penalty
• The run-time increases exponentially with the
  number of sequences you want to align
  Aligning 4 sequences of 100 amino acids takes ~3 days!
• Heuristic algorithms for multiple alignment are
  generally used, as they are fast
  eg. CLUSTAL, T-COFFEE
  ‘Heuristic’ means they’re not guaranteed to find the best solution (best
  alignment here)
  (While N-W & S-W are proven to find the best alignment)
• A popular heuristic algorithm is CLUSTAL, by Des
  Higgins and Paul Sharp at Trinity College Dublin
  (1988)
  Uses a ‘progressive alignment’ approach ie. aligns the most similar 2
       sequences first; adds the next most similar sequence to that
  alignment; adds the next most similar sequence … etc.
CLUSTAL
• A popular heuristic algorithm is CLUSTAL, by Des
  Higgins and Paul Sharp at TCD (1988)
  Cited >37,000 times; D. Higgins is Ireland’s most cited scientist
• CLUSTAL makes a global multiple alignment using a
  ‘progressive alignment’ approach
• First computes all pairwise alignments and calculates
  sequence similarity between pairs
• These similarities are used to build a rough ‘guide
  tree’                           S1
                                              S2
                                              S3
                                              S4
•
1 Then aligns the most similar pair of sequences
  This gives us an alignment of 2 sequences (called a ‘profile’)
  eg. alignment of sequences S1 and S2

•
2 Aligns the next closest pair of sequences (or pair of
  profiles, or sequence and profile)
  eg. alignment of sequences S1 and S2

•
3 Aligns the next closest pair of seqs/profiles
  eg. alignment of profiles S1-S2 and S3-S4

                                                  MQTIF            S1
                               MQTIF
                               LH-IW          1
           MQTIF                                  LHIW        S2
           LH-IW
           LQS-W        3
                                                  LQSW
           L-S-F              LQSW                            S3
                                              2
                              L-SF
                                                  LSF    S4
• A property of this method is that gap creation is
  irreversible: ‘once a gap, always a gap’

                                              MQTIF            S1
                            MQTIF
                            LH-IW      1
          MQTIF                               LHIW        S2
          LH-IW
          LQS-W       3
                                              LQSW
          L-S-F             LQSW                          S3
                                       2
                            L-SF
                                              LSF    S4

• This is a ‘heuristic algorithm’, ie. is not guaranteed to
  give the best alignment
  However, is very fast & works well in most cases
Software for making alignments
• For multiple alignment (heuristic programs)
  CLUSTAL http://www.ebi.ac.uk/Tools/msa/clustalw2/
  T-COFFEE http://tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee_cgi/index.cgi
  MUSCLE http://www.ebi.ac.uk/Tools/msa/muscle/
  MAFFT http://mafft.cbrc.jp/alignment/software/
Further Reading
•   Chapter 3 in Introduction to Computational Genomics Cristianini & Hahn
•   Chapter 6 in Deonier et al book Computational Genome Analysis
•   Practical on multiple alignment in R in the Little Book of R for
    Bioinformatics:
    https://a-little-book-of-r-for-
    bioinformatics.readthedocs.org/en/latest/src/chapter5.html

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Multiple alignment

  • 1. Multiple Alignment Dr Avril Coghlan [email protected] Note: this talk contains animations which can only be seen by downloading and using ‘View Slide show’ in Powerpoint
  • 2. Pairwise versus Multiple Alignment • So far we have considered the alignment of two sequences (‘pairwise alignment’) Q K E S G P S S S Y C | | | | | V Q Q E S G L V R T T C • Alignment can be performed between three or more sequences (‘multiple alignment’) Q K E S G P S S S Y C | | | | | V Q Q E S G L V R T T C | | | | | | | | V Q K E S L L V R S T C
  • 3. Multiple alignment • Multiple alignments are useful for comparing many homologous sequences at once Multiple alignment of part of Eyeless from different animals • Multiple alignments can be global or local The majority of widely used programs for making multiple alignments (eg. CLUSTAL, T-COFFEE) create global multiple alignments (not local multiple alignments) If the sequences share one stretch of high sequence similarity, it might make sense to make a multiple alignment of just that region of similarity eg. for Eyeless You can “cut out” the region of similarity from each sequence, & make a multiple alignment of that region eg. using CLUSTAL
  • 4. Real data: Eyeless proteins Do you think it’s sensible to make a global multiple alignment of these sequences?
  • 5. The alignment is not very reliable in regions of low similarity for example look at the alignment of fly Eyeless to the other proteins here
  • 6. Algorithms for aligning 2 sequences (eg. N-W, S-W) can be extended to multiple sequences For aligning 3 sequences using N-W, we fill in a table T that is a 3D cube, using the recurrence relation: T(i-1,j-1,k-1) + σ(S1(i),S2(j)) + σ(S1(i),S3(k)) + σ(S2(j),S3(k)) T(i, j, k) = max T(i-1, j, k) + gap penalty + gap penalty T(i, j-1, k) + gap penalty + gap penalty T(i, j, k-1) + gap penalty + gap penalty T(i-1, j, k-1) + σ(S1(i),S3(k)) + gap penalty + gap penalty T(i, j-1, k-1) + σ(S2(j),S3(k)) + gap penalty + gap penalty T(i-1, j-1, k) + σ(S1(i),S2(j)) + gap penalty + gap penalty
  • 7. • The run-time increases exponentially with the number of sequences you want to align Aligning 4 sequences of 100 amino acids takes ~3 days! • Heuristic algorithms for multiple alignment are generally used, as they are fast eg. CLUSTAL, T-COFFEE ‘Heuristic’ means they’re not guaranteed to find the best solution (best alignment here) (While N-W & S-W are proven to find the best alignment) • A popular heuristic algorithm is CLUSTAL, by Des Higgins and Paul Sharp at Trinity College Dublin (1988) Uses a ‘progressive alignment’ approach ie. aligns the most similar 2 sequences first; adds the next most similar sequence to that alignment; adds the next most similar sequence … etc.
  • 8. CLUSTAL • A popular heuristic algorithm is CLUSTAL, by Des Higgins and Paul Sharp at TCD (1988) Cited >37,000 times; D. Higgins is Ireland’s most cited scientist • CLUSTAL makes a global multiple alignment using a ‘progressive alignment’ approach • First computes all pairwise alignments and calculates sequence similarity between pairs • These similarities are used to build a rough ‘guide tree’ S1 S2 S3 S4
  • 9. • 1 Then aligns the most similar pair of sequences This gives us an alignment of 2 sequences (called a ‘profile’) eg. alignment of sequences S1 and S2 • 2 Aligns the next closest pair of sequences (or pair of profiles, or sequence and profile) eg. alignment of sequences S1 and S2 • 3 Aligns the next closest pair of seqs/profiles eg. alignment of profiles S1-S2 and S3-S4 MQTIF S1 MQTIF LH-IW 1 MQTIF LHIW S2 LH-IW LQS-W 3 LQSW L-S-F LQSW S3 2 L-SF LSF S4
  • 10. • A property of this method is that gap creation is irreversible: ‘once a gap, always a gap’ MQTIF S1 MQTIF LH-IW 1 MQTIF LHIW S2 LH-IW LQS-W 3 LQSW L-S-F LQSW S3 2 L-SF LSF S4 • This is a ‘heuristic algorithm’, ie. is not guaranteed to give the best alignment However, is very fast & works well in most cases
  • 11. Software for making alignments • For multiple alignment (heuristic programs) CLUSTAL http://www.ebi.ac.uk/Tools/msa/clustalw2/ T-COFFEE http://tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee_cgi/index.cgi MUSCLE http://www.ebi.ac.uk/Tools/msa/muscle/ MAFFT http://mafft.cbrc.jp/alignment/software/
  • 12. Further Reading • Chapter 3 in Introduction to Computational Genomics Cristianini & Hahn • Chapter 6 in Deonier et al book Computational Genome Analysis • Practical on multiple alignment in R in the Little Book of R for Bioinformatics: https://a-little-book-of-r-for- bioinformatics.readthedocs.org/en/latest/src/chapter5.html

Editor's Notes

  • #4: Mouse sequence from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSMUST00000111083.1 Chicken from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSGALT00000019805.3 Seasquirt from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSCINT00000013350.2 Human Eyeless (PAX6) from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENST00000379111.1 D. Melanogaster Eyeless from: http://www.treefam.org/cgi-bin/TFseq.pl?id=FBtr0100396.5 Aligned using clustalw. Viewed in Jalview. Saved as humanflyothers_clustal.png
  • #5: Mouse sequence from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSMUST00000111083.1 Chicken from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSGALT00000019805.3 Seasquirt from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSCINT00000013350.2 Human Eyeless (PAX6) from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENST00000379111.1 D. Melanogaster Eyeless from: http://www.treefam.org/cgi-bin/TFseq.pl?id=FBtr0100396.5 Aligned using clustalw. Viewed in Jalview. Saved as humanflyothers_clustal.png
  • #6: Mouse sequence from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSMUST00000111083.1 Chicken from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSGALT00000019805.3 Seasquirt from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENSCINT00000013350.2 Human Eyeless (PAX6) from: http://www.treefam.org/cgi-bin/TFseq.pl?id=ENST00000379111.1 D. Melanogaster Eyeless from: http://www.treefam.org/cgi-bin/TFseq.pl?id=FBtr0100396.5 Aligned using clustalw. Viewed in Jalview. Saved as humanflyothers_clustal.png
  • #7: Image from www.cs.iastate.edu/~cs544/.../Multiple_Sequence_Alignment.ppt slide 12 For recurrence relation, see page 189 in Jones & Pevzner ‘An introduction to bioinformatics algorithms’
  • #9: Image credit (Des Higgins): http://www.idaireland.com/_internal/cimg!0/52302eob2zw6kiy4ed60bl5ugmuau17 Image credit (Paul Sharp): http://www.biology.ed.ac.uk/people/homepages/images/pmsharp.jpg
  • #10: Image credit: http://www.biomedcentral.com/content/figures/1471-2105-5-113-1-l.jpg
  • #11: Image credit: http://www.biomedcentral.com/content/figures/1471-2105-5-113-1-l.jpg