Vorlesung Grundlagen der Bioinformatik .

Vorlesung

Grundlagen der Bioinformatik

http://gobics.de/lectures/ss07/grundlagen

Information from a SingleSequenceAlone

Sequence alignment in molecular data analysis:

Information from a SingleSequenceAlone

Multi-OrganismHigh QualitySequences

Sequence alignment in molecular data analysis:

(M. Brudno)

Tools for multiple sequence alignment

seq1 T Y I M R E A Q Y E

seq2 T C I V M R E A Y E

seq3 Y I M Q E V Q Q E

seq4 Y I A M R E Q Y E


seq1 T Y I - M R E A Q Y E

seq2 T C I V M R E A - Y E

seq3 Y - I - M Q E V Q Q E

seq4 Y – I A M R E - Q Y E






Functionally important regions more conserved than non-functional regions






Functionally important regions more conserved than non-functional regions

Local sequence conservation indicates functionality!




seq3 - Y I - M Q E V Q Q E


Astronomical Number of possible alignments!



seq2 T C I V - M R E A Y E



Astronomical Number of possible alignments!






Which one is the best ???


Questions in development of alignment programs:

(1) What is a good alignment?

→ objective function (`score’)

(2) How to find a good alignment?

→ optimization algorithm

First question far more important !


Most important scoring scheme for multiple alignment:

Sum-of-pairs score for global alignment.

Divide-and-Conquer Alignment (DCA)

J. Stoye, A. Dress (Bielefeld)

Approximate optimal global multiple alignment

Divide sequences into small sub-sequences Use MSA to calculate optimal alignment for sub-

sequences Concatenate sub-alignments

Divide-and-Conquer Alignment (DCA)


Problems with traditional approach:

Results depend on gap penalty

Heuristic guide tree determines alignment; alignment used for phylogeny reconstruction

Algorithm produces global alignments.

First step in sequence comparison: alignment

global alignment (Needleman and Wunsch, 1970; Clustal W)

atctaatagttaatactcgtccaagtat atctgtattactaaacaactggtgctacta



atc--taatagttaat--actcgtccaagtat||| || || | || ||| || | | ||atctgtattact-aaacaactggtgctacta-




local alignment (Smith and Waterman, 1983)

atctaatagttaatactcgtccaagtat gcgtgtattactaaacggttcaatctaacat




local alignment (Smith and Waterman, 1983)

atc--taatagttaatactcgtccaagtat || || | || gcgtgtattact-aaacggttcaatctaacat

New question: sequence families with multiple local similarities

Neither local nor global methods appliccable

New question: sequence families with multiple local similarities

Alignment possible if order conserved

The DIALIGN approach

Morgenstern, Dress, Werner (1996),PNAS 93, 12098-12103

Combination of global and local methods

Assemble multiple alignment from gap-free local pair-wise alignments (,,fragments“)


atctaatagttaaactcccccgtgcttag

cagtgcgtgtattactaacggttcaatcgcg

caaagagtatcacccctgaattgaataa


atc------taatagttaaactcccccgtgcttag






caaa--gagtatcacccctgaattgaataa




caaa--gagtatcacc----------cctgaattgaataa


atc------taatagttaaactcccccgtgc-ttag

cagtgcgtgtattactaac----------gg-ttcaatcgcg






Consistency!


atc------TAATAGTTAaactccccCGTGC-TTag

cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg

caaa--GAGTATCAcc----------CCTGaaTTGAATaa


Score of an alignment:

Define score of fragment f:

l(f) = length of fs(f) = sum of matches (similarity values)

P(f) = probability to find a fragment with length l(f) and at least s(f) matches in random sequences that have the same length as the input sequences.

Score w(f) = -ln P(f)



Define score of fragment f:

Define score of alignment as

sum of scores of involved fragments

No gap penalty!



Goal in fragment-based alignment approach: find

Consistent collection of fragments with maximum sum of weight scores


atctaatagttaaaccccctcgtgcttagagatccaaaccagtgcgtgtattactaacggttcaatcgcgcacatccgc

Pair-wise alignment:


atctaatagttaaaccccctcgtgcttagagatccaaaccagtgcgtgtattactaacggttcaatcgcgcacatccgc


recursive algorithm finds optimal chain of

fragments.


------atctaatagttaaaccccctcgtgcttag-------agatccaaaccagtgcgtgtattactaac----------ggttcaatcgcgcacatccgc--


recursive algorithm finds optimal chain of

fragments.


------atctaatagttaaaccccctcgtgcttag-------agatccaaaccagtgcgtgtattactaac----------ggttcaatcgcgcacatccgc--

Optimal pairwise alignment: chain of fragments with maximum sum of weights found by dynamic programming:

Standard fragment-chaining algorithm

Space-efficient algorithm


Multiple alignment:





Multiple alignment:



caaccctgaattgaagagtatcacataa

(1) Calculate all optimal pair-wise alignments


Multiple alignment:




(1) Calculate all optimal pair-wise alignments


Fragments from optimal pair-wise alignments might be inconsistent


Fragments from optimal pair-wise alignments might be inconsistent

1. Sort fragments according to scores

2. Include them one-by-one into growing multiple alignment – as long as they are consistent

(greedy algorithm, comparable to rucksack problem)





Consistency problem





Upper and lower bounds for alignable positions


atc------taatagt taaactcccccgtgcttag

Cagtgcgtgtattact aacggttcaatcgcg




atc------taata-----gttaaactcccccgtgcttag

Cagtgcgtgtatta-----ctaacggttcaatcgcg








site x = [i,p] (sequence i, position p)






Calculate upper bound bl(x,i) and lower bound bu(x,i) for each x and sequence i






bl(x,i) and bu(x,i) updated for each new fragment in alignment


Consistency bounds are to be updated for each new fragment that is included in to the growing Alignment

Efficient algorithm

(Abdeddaim and Morgenstern, 2002)


Advantages of segment-based approach:

Program can produce global and local alignments!

Sequence families alignable that cannot be aligned with standard methods

Program input

Program usage:

> dialign2-2 [options] <input_file>

<input_file> = multi-sequence file in FASTA-format

Program output

DIALIGN 2.2.1 ************* Program code written by Burkhard Morgenstern and Said Abdeddaim e-mail contact: [email protected] Published research assisted by DIALIGN 2 should cite: Burkhard Morgenstern (1999). DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment. Bioinformatics 15, 211 - 218.

For more information, please visit the DIALIGN home page at

http://bibiserv.techfak.uni-bielefeld.de/dialign/

program call: ./dialign2-2 -nt -anc s

Aligned sequences: length: ================== ======= 1) dog_il4 300 2) bla 200 3) blu 200

Average seq. length: 233.3

Please note that only upper-case letters are considered to be aligned.

Program output

Alignment (DIALIGN format): =========================== dog_il4 1 cagg------ ----GTTTGA atctgataca ttgc------ ---------- bla 1 ctga------ ---------- ---------- --------GC CAAGTGGGAA blu 1 ttttgatatg agaaGTGTGA aacaagctat cctatattGC TAAGTGGCAG 0000000000 0000000000 0000000000 0000000011 1111111111 dog_il4 25 ---------- --ATGGCACT GGGGTGAATG AGGCAGGCAG CAGAATGATC bla 17 ggtgtgaata catgggtttc cagtaccttc tgaggtccag agtacc---- blu 51 ccctggcttt ctATGTGCAC AGAATGGGAG GAAAGTGCCT GCTAGTGAGC 0000000000 0000000000 0000000000 0000000000 0000000000 dog_il4 63 GTACTGCAGC CCTGAGCTTC CACTGGCCCA TGTTGGTATC CTTGTATTTT bla 63 ---------- ---------- ---TTTCCCA TGTGCTCCAT GGTGGAATGG blu 101 CAGGGACTCA GAGAGAATGG AGTATAGGGG TCAGGGCat- ---------- 0000000000 0000000000 0009999999 9999999888 8888888888 dog_il4 113 TCCGCCCCTT CCCAGCACca gcattatcct ---GGGATTG GAGAAGGGGG bla 90 ACCACTCCTT CTCAGCACaa caaagcccaa gaaGGTGTTG CGTTCTAGAC blu 140 ---------- ---------- ---------- ---GGGGTGG CCTTAGGCTC 8888888888 8888888800 0000000000 0007777777 7777777777



cagtgcgtgtattactaac----------ggttcaatcgcg



atc------TAATAGTTAaactccccCGTGC-TTag------

cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg

caaa--GAGTATCAcc----------CCTGaaTTGAATaa--

Alignment of large genomic sequences

Fragment-based alignment approach useful for alignment of genomic sequences.

Possible applications: Detection of regulatory elements Identification of pathogenic microorganisms Gene prediction

DIALIGN alignment of human and murine genomic sequences

DIALIGN alignment of tomato and Thaliana genomic sequences


Gene-regulatory sites identified by mulitple sequence alignment (phylogenetic footprinting)

Performance of long-range alignment programs for exon discovery (human - mouse comparison)

Performance of long-range alignment programs for exon discovery (thaliana - tomato comparison)

Vorlesung Grundlagen der Bioinformatik .

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