Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms

Transcription

1 Course organzaton 1 Introducton Week 1-2) Course ntroducton A bref ntroducton to molecular bology A bref ntroducton to sequence comparson Part I: Algorthms for Sequence Analyss Week 3-8) Chapter 1-3 Models and theores» Probablty theory and Statstcs Week 3)» Algorthm complexty analyss Week 4)» Classc algorthms Week 5) Chapter 4. Sequence algnment week 6) Chapter 5. Hdden Markov Models week 7) Chapter 6. Multple sequence algnment week 8) Part II: Algorthms for Network Bology Week 9-16) Chapter 7. Omcs landscape week 9) Chapter 8. Mcroarrays Clusterng and Classfcaton week 10) Chapter 9. Computatonal Interpretaton of Proteomcs week 11) Chapter 10. Network and Pathways week 1213) Chapter 11. Introducton to Bayesan Analyss week 1415) Chapter 12. Bayesan networks week 16)

2 Introducton to Sequence Comparson Chaochun We 2

3 The smple but powerful dot plot A DNA dot plot of a human znc fnger transcrpton factor GenBank ID NM_002383) showng regonal self-smlarty 3

4 Sequence comparson algorthms Smple dentty as n C s strcmp)) Hashng Longest common substrng 4

5 Longest common substrng Smth and Waterman JMB

6 Analyss of algorthms and bg-o notaton Measure the Complexty of an algorthm: O) strcmp: On) longest common substrng: Onm) 6

7 Pattern matchng algorthms Brute force Knuth/Morrs/Pratt: a fnte state automata soluton Regular expressons and nondetermnstc fnte state automata 7

8 Dynamc programmng sequence algnment algorthms Needleman/Wunsch global algnment Smth/Waterman local algnment Lnear and affne gap penaltes 8

9 Two sequences X = x 1...x n and Y = y 1...y m Let ) be the optmal algnment score of X 1... of X up to x and Y 1... of Y up to Y 0 n 0 m) then we have Needleman/Wunsch global algnment 1970) 9 d d y x s max 0 00

10 Needleman/Wunsch global algnment 1970) -1-1) -1) S x y ) -d -1 ) ) -d 00 0 max sx y d d 10

11 3.5

12 Two sequences X = x 1...x n and Y = y 1...y m Let ) be the optmal algnment score of X 1... of X up to x and Y 1... of Y up to Y 0 n 0 m) then we have Smth/Waterman local algnment 1981) 12 d d y x s max 0 00

13 Lnear: wk) = k d Affne: wk) = d + k-1) e Let M) I x ) I y ) be the best scores up to ): M): x s algned to y ; I x ): x s algned to a gap; I y ) y s algned to a gap then we have Lnear and affne gap penaltes 13. 1) 1) max ) ; ) 1 ) 1 max ) ); 1) 1 ) 1) 1 ) 1) 1 max ) e I d M I e I d M I y x s I y x s I y x s M M y y x x y x

14 Readng materals Requred 1. A general method applcable to the search for smlartes n the amno acd sequence of two protens Needleman SB and Wunsch CD. J. Mol. Bol. 48: Identfcaton of Common Molecular Subsequences Smth T and Waterman MS. J. Mol. Bol. 147: The Smth/Waterman algorthm Other recommended background: 1. An mproved algorthm for matchng bologcal sequences Gotoh O. J. Mol. Bol. 162: The effcent form of the Needleman/Wunsch and Smth/Waterman algorthms. 2. Optmal algnment n lnear space Myers E. W. and Mller W. CABIOS 4: More advanced readng: a dvde and conquer method to reduce the memory cost from On^2) to On) 14

15 BLAT: Blast-Lke Algnment Tool Not BLAST Indexed on database BLAST ndexed on the query) Need ~1G memory for human genome Need some extra tme for database ntalzaton ndex) Can be 500 tmes faster than BLAST Can dsplay results n the UCSC genome browser 15

16 BLAT Desgned to quckly fnd DNA sequences of 95% and greater smlarty of length 25 bases or more. Proten sequences of 80% and greater smlarty of length 20 amno acds or more. In practce DNA BLAT works well on prmates and proten blat on land vertebrates 16

17 BLAT The BLAST-Lke Algnment Tool Tmng of BLAT vs.wu-tblastx on a Data Set of 1000 Mouse Reads aganst a RepeatMasked Human Chromosome 22 Method K N Matrx Tme WU- TBLASTX WU- TBLASTX / s 5 1 BLOSUM s BLAT / 1 61 s BLAT / 1 37 s K: the sze of the perfectly matchng as a seed for an algnment N: the number of hts n a gapless 100-aa wndow requred to trgger a detaled algnment. Matrx: column descrbes the match/msmatch scores or the substtuton score matrx used. 17

18 1 8 Comparson of sequencng platforms ) Platforms Sanger 454 HSeq X Ten * MSeq * NovaSeq * PacBo RS II** Nanopore Read length x150 Up to 60k Very long # of reads/run M B 12M 50M B ~ Up to 500 Error rate 10^-3 <10^-2 ~10^-3 ~10^-3 ~10^-3 ~10% Vares Cost$/Mbp) 5000 ~5 <0.01 ~0.5 <0.001 ~1.5 ~1 Tme/run ~3 hours ~7 hours <3 days 4-56 hours 19-40hr hours No fxed run tm Throughput 100Kb ~1Gb Tb 540Mb- 15Gb 167Gb- 6Tb 500M b-1gb Up to 1 Gb * **

19 Nature Botechnology ) 19

20 Latest progress of sequence algnment/mappng Algnng mappng) bllons of short reads Bowte SOAP BWA Tophat 20

21 Algorthms a) based on spaced-seed ndexng; b) based on Burrows-Wheeler transform Nature Botechnology ) 21