Course organization. Part II: Algorithms for Network Biology (Week 12-16)

Transcription

1 Course organzaton 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 3-11) Chapter 1-3 Models and theores» Probablty theory and Statstcs Week 3)» Algorthm complexty analyss Week 4)» Classc algorthms Week 5)» Lab: Lnux and Perl Chapter 4 Sequence algnment week 6) Chapter 5 Hdden Markov Models week 8) Chapter 6. Multple sequence algnment week 10) Chapter 7. Motf fndng week 11) Chapter 8. Sequence bnnng week 11) 1 Part II: Algorthms for Network Bology Week 12-16)

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) 3 showng regonal self-smlarty

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 1 1 sx y 1 1 d d 10

11 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) 11 d d y x s max 0 00

12 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 12. 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

13 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) 13

14 BLAT: Blast-Lke Algnment Not BLAST Tool 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 Kent WJ 2002) BLAT the BLAST-lke algnment tool Genome Research 124): Blat AQ:

15 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 15

16 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. 16 Matrx: column descrbes the match/msmatch scores or the substtuton score matrx used.

17 Comparson of NGSs vs. tradtonal technology Platforms Sanger 454 Solexa SOLD ead Lengthbps) Capacty reads/run) Error Rate 10^-3 <10^-2 ~10^-2 ~10^-2 Cost$/Mbp) 5000 ~5 ~0.6 ~0.2 Tme/run ~3h ~7h 2-10d 3-14d Throughput 100Kb ~1Gb ~600Gb Gb 17

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19 Latest progress of sequence algnment/mappng Algnng mappng) bllons of short reads Bowte SOAP BWA Tophat 19

20 Algorthms a) based on spaced-seed ndexng; b) based on Burrows-Wheeler transform 20