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Sequence alignment

BI420 – Introduction to Bioinformatics. Sequence alignment. Gabor T. Marth. Department of Biology, Boston College marth@bc.edu. Biologically significant alignment. 1. Find two truly related sequences (subunits of human hemoglobin) in GenBank:. http://www.ncbi.nlm.nih.gov/gquery/gquery.fcgi.

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Sequence alignment

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  1. BI420 – Introduction to Bioinformatics Sequence alignment Gabor T. Marth Department of Biology, Boston College marth@bc.edu

  2. Biologically significant alignment 1. Find two truly related sequences (subunits of human hemoglobin) in GenBank: http://www.ncbi.nlm.nih.gov/gquery/gquery.fcgi hba_human hbb_human 2. Save sequences on the Desktop and rename: hba_human.fasta & hbb_human.fasta

  3. Biologically significant alignment 3. Visit a web-based pair-wise alignment program: http://artedi.ebc.uu.se/programs/pairwise.html 4. Upload our two proteins:

  4. Biologically significant alignment 5. Create a pair-wise alignment between the two protein sequences:

  5. Biologically plausible alignment Retrieve another sequence, leghemoglobin: Leg hemoglobin Create a pair-wise alignment with human hemoglobin A:

  6. Biologically plausible alignment http://en.wikipedia.org/wiki/Leghemoglobin

  7. Spurious alignment Retrieve the sequence of a human BRCA1 gene variant, clearly not related to hemoglobin: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Protein&cmd=search&term=NP_009225.1 Make the pair-wise alignment: Examples from: Biological sequence analysis. Durbin, Eddy, Krogh, Mitchison

  8. Alignment types How do we align the words: CRANE and FRAME? CRANE || | FRAME 3 matches, 2 mismatches How do we align words that are different in length? COELACANTH || ||| P-ELICAN-- COELACANTH || ||| -PELICAN-- 5 matches, 2 mismatches, 3 gaps In this case, if we assign +1 points for matches, and -1 for mismatches or gaps, we get 5 x 1 + 1 x (-1) + 3 x (-1) = 0. This is the alignment score. Examples from: BLAST. Korf, Yandell, Bedell

  9. Finding the “best” alignment COELACANTH | ||| PE-LICAN-- COELACANTH || P-EL-ICAN- COELACANTH PELICAN-- S=-6 S=-10 S=-2 COELACANTH || ||| P-ELICAN-- S=0

  10. Global vs. local alignment Aligning words: SHAKE and SPEARE 1. Global alignment: aligning the two sequences along their entire length (even if it means adding many “gaps”): SH-AKE | | | SPEARE SHAKE--- | | SP--EARE -OR- 1. Local alignment: aligning only a nicely matching section between the two sequences (possibly leaving the ends un-aligned): SHAKE | | SPEARE SHAKE SPEARE Example from: Higgs and Attwood

  11. Global alignment – Needleman-Wunsch + gap score g = -6 Pair-wise amino-acid scores S(ai,bi) (PAM250 scoring scheme) plus gap score g. Example from: Higgs and Attwood

  12. Global alignment – Needleman-Wunsch Recursion scheme to calculate scores from already known scores: { H(i-1,j-1) + S(ai,bi)  diagonal H(i,j) = best of: H(i-1,j) – g  vertical H(I,j-1) – g  horizontal Example from: Higgs and Attwood

  13. Global alignment – Needleman-Wunsch Initialization (filling the top row and left column from gap scores): Align the two sequences: AAGATTCAC and CCGCTCAA Example from: Higgs and Attwood

  14. Global alignment – Needleman-Wunsch Initialization (filling the top row and left column from gap scores): Example from: Higgs and Attwood

  15. Global alignment – Needleman-Wunsch Filling cell (1,1): Example from: Higgs and Attwood

  16. Global alignment – Needleman-Wunsch Filling the rest of the cells (i,j): Example from: Higgs and Attwood

  17. Global alignment – Needleman-Wunsch Tracing back to read out the alignment: Best global alignment: S-HAKE SPEARE Example from: Higgs and Attwood

  18. Local alignment – Smith-Waterman Recursion scheme changes: 1. if the best score for a cell is negative, we replace it by 0 (start over) 2. gaps at the boundary are ignored  they get 0 score { H(i-1,j-1) + S(ai,bi)  diagonal H(i,j) = best of: H(i-1,j) – g  vertical H(I,j-1) – g  horizontal 0  start over Example from: Higgs and Attwood

  19. Local alignment – Smith-Waterman Initialization Example from: Higgs and Attwood

  20. Local alignment – Smith-Waterman Align the two sequences: AAGATTCAC and CCGCTCAA Initialization Example from: Higgs and Attwood

  21. Local alignment – Smith-Waterman Filling the cells: Example from: Higgs and Attwood

  22. Local alignment – Smith-Waterman Trace-back: Best local alignment: SHAKE SPEARE Example from: Higgs and Attwood

  23. Visualizing pair-wise alignments Visit a web server running a dot-plotter: http://bioweb.pasteur.fr/seqanal/interfaces/dotmatcher.html Upload hba_human and hbb_human, and create dot-plot:

  24. Scoring schemes Match-mismatch-gap penalties: e.g. Match = 1 Mismatch = -5 Gap = -10 Scoring matrices

  25. Multiple alignments Fetch HXK (hexokinase) sequences from NCBI; save as hxk.fasta on the Desktop

  26. Multiple alignments Visit a web-hosted clustalW site (e.g.: http://artedi.ebc.uu.se/programs/clustalw.html) and upload the HXK sequences

  27. Multiple alignments The multiple alignment of 24 hexokinese protein sequences from various species

  28. Anchored multiple alignment

  29. Similarity searching vs. alignment Alignment Similarity search query database

  30. The BLAST algorithms

  31. BLAST report http://www.ncbi.nlm.nih.gov/BLAST/ gi|7428631

  32. BLAST report

  33. The BLAST algorithm Sequence alignment takes place in a 2-dimensional space where diagonal lines represent regions of similarity. Gaps in an alignment appear as broken diagonals. The search space is sometimes considered as 2 sequences and somtimes as query x database. • Global alignment vs. local alignment • BLAST is local • Maximum scoring pair (MSP) vs. High-scoring pair (HSP) • BLAST finds HSPs (usually the MSP too) • Gapped vs. ungapped • BLAST can do both

  34. BLOSUM62 neighborhood of RGD The BLAST algorithm RGD 17 KGD 14 QGD 13 RGE 13 EGD 12 HGD 12 NGD 12 RGN 12 AGD 11 MGD 11 RAD 11 RGQ 11 RGS 11 RND 11 RSD 11 SGD 11 TGD 11 • Speed gained by minimizing search space • Alignments require word hits • Neighborhood words • W and T modulate speed and sensitivity T=12

  35. Word length

  36. 2-hit seeding • Alignments tend to have multiple word hits. • Isolated word hits are frequently false leads. • Most alignments have large ungapped regions. • Requiring 2 word hits on the same diagonal (of 40 aa for example), greatly increases speed at a slight cost in sensitivity.

  37. Extension of the seed alignments • Alignments are extended from seeds in each direction. • Extension is terminated when the maximum score drops below X. The quick brown fox jumps over the lazy dog. The quiet brown cat purrs when she sees him. Text example match +1 mismatch -1 no gaps

  38. BLAST statistics >gi|23098447|ref|NP_691913.1| (NC_004193) 3-oxoacyl-(acyl carrier protein) reductase [Oceanobacillus iheyensis] Length = 253 Score = 38.9 bits (89), Expect = 3e-05 Identities = 17/40 (42%), Positives = 26/40 (64%) Frame = -1Query: 4146 VTGAGHGLGRAISLELAKKGCHIAVVDINVSGAEDTVKQI 4027 VTGA G+G+AI+ A +G + V D+N GA+ V++ISbjct: 10 VTGAASGMGKAIATLYASEGAKVIVADLNEEGAQSVVEEI 49 How significant is this similarity?

  39. Scoring the alignment Query: 4146 VTGAGHGLGRAISLELAKKGCHIAVVDINVSGAEDTVKQI 4027 VTGA G+G+AI+ A +G + V D+N GA+ V++ISbjct: 10 VTGAASGMGKAIATLYASEGAKVIVADLNEEGAQSVVEEI 49 4 -1 4 S (score)

  40. The Karlin-Altschul equation The “Expect” or “E-value” Scaling factor A minor constant Normalized score Expected number of alignments Raw score Length of query Length of database Search space The “P-value”

  41. The sum-statistics Sum statistics increases the significance (decreases the E-value) for groups of consistent alignments.

  42. The sum-statistics The sum score is not reported by BLAST!

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