cap3 aula de algoritmo: cap3 felipe rodrigues da silva embrapa recursos genéticos e biotecnologia

CAP3

Aula de algoritmo: CAP3 Aula de algoritmo: CAP3

Felipe Rodrigues da Silva

Embrapa Recursos Genéticos e Biotecnologia

CAP3

Seqüenciamento

CAP3

Polimerização de DNA

ATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTTATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTT

TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||||||||||||||||||||||||||||||||

5’

5’3’

3’

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||

5’3’

ATGCTTCATGCTTC5’ 3’

AA

AAAA

AA

AA

AA

AAAA

AA

AA

TT TTTT TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGG

GG

GG

GG

GG

GG

CC

CC

CC

CC

CC

CC

CC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||||

5’3’


AA

AA

AA

AA

AAAA

AAAA

AA

AA

TTTTTT TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGGGG

GG

GG

GGGG

CC

CC

CC

CCCC

CC

CC

CC

CC

CC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||

5’3’

ATGCTTCATGCTTCTGTG5’ 3’

AA

AAAA

AA

AA AA

AAAA

AA

AA

TT TTTT

TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGG

GG

GG

GG

GG

GG

CC

CCCC

CC

CC

CC

CC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATCTTGGCAGATCT5’ 3’

AA

AAAA

AA

AAAA

AA

AA

AA AA

TT TTTT

TT

TTTTTT

TT

TT

TTTT GGGG

GGGG GG

GG

GG

GG GG

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CAP3



5’3’


AA

AAAA

AA

AAAA

AAAA

AA

AA

TTTT

TTTT

TTTTTT

TT

TT

TT TTGG

GG

GGGG

GG GG

GG

GG

GG

CC

CC

CC

CCCC

CCCC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACATGGCAGATCTGAACA5’ 3’

AA

AA

AAAA

AA

AA

AAAA

AA

AA

TTTTTT

TTTT

TTTTTT

TTTT

TT

GG

GGGG

GG

GGGG

GG

GG GG

CCCCCC

CC CCCC

CC

CCCC

CC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTTGGCAGATCTGAACAGTGTT5’ 3’

AA

AA

AA AAAA

AA

AAAA

AA

AATT

TTTT

TT

TTTTTTTT

TT TT

TTGG

GGGG

GG

GGGG

GG

GG GG

CC

CCCC

CCCC

CC

CC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||||||||||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGTGGCAGATCTGAACAGTGTTACTG5’ 3’

AA

AA

AAAA

AA

AA

AAAA

AA

AA TT

TTTT

TTTTTT

TTTT

TTTT

TTGG

GGGG

GG

GGGG

GG

GGGG

CC

CCCC

CCCC

CC

CC

CCCC

CC

CC

CAP3



5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATTGGCAGATCTGAACAGTGTTACTGAT5’ 3’

AA

AA

AAAA

AA

AA

AAAAAA

AATT

TTTT

TT

TT

TTTTTT

TT

TTTTGG

GG

GG

GG

GGGG

GG

GGGG

CC

CC CCCCCC

CCCC

CCCCCC

CC

CAP3



5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATTGGCAGATCTGAACAGTGTTACTGAT5’ 3’

AA AA

AA

AA

AAAA

AAAAAA

AA

TTTTTT

TTTT

TTTT TT

TTTT TT

GG

GG

GG

GG

GG

GG

GG

GG

GG CC CCCCCCCC

CC CC

CCCCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||||||||||||||||||||||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTTTGGCAGATCTGAACAGTGTTACTGATATTGCTT5’ 3’

AA AA

AA

AA

AA

AA AAAAAA

AATT

TTTT

TTTT

TT TTTT

TT

TT TTGG

GG

GG

GG

GGGG

GG

GGGGCC CCCC

CC CC

CCCC

CC CCCC

CC

CAP3



5’3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTTTGGCAGATCTGAACAGTGTTACTGATATTGCTT5’ 3’

AAAA

AA

AA

AA

AAAAAAAA

AA

TT

TTTT

TTTT

TTTTTT

TT

TTTTGG

GG

GG

GG

GGGG

GG

GGGG CCCC CC

CCCC

CCCC

CCCCCC

CC

CAP3

1972Walter Gilbert e Frederick Sanger

seqüenciamento de DNA

CAP3

Dideoxinucleotídeo

dideoxinucleotídeo

O

-

O-P-P-P-O-CH2

-O

-

O O O

BASE-O

-

-O

-

H

3' 2'

H

O

-

O-P-P-P-O-CH2

-O

-

O O O

BASE-O

-

-O

-

H

3' 2'

H

deoxinucleotídeo

O

-

O-P-P-P-O-CH2

-O

-

O O O

BASE-O

-

-O

-

OH

3' 2'

H

O

-

O-P-P-P-O-CH2

-O

-

O O O

BASE-O

-

-O

-3' 2'

H

CAP3

Seqüenciamento de DNA

CAP3

Polimerização de DNAo dideoxi

TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||

5’3’


AA

AAAA

AA

AA

AA

AAAA

AA

AA

TT TTTT TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGG

GG

GG

GG

GG

GG

CC

CC

CC

CC

CC

CC

CC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||||

5’3’


AA

AA

AA

AA

AAAA

AAAA

AA

AA

TTTTTT TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGGGG

GG

GG

GGGG

CC

CC

CC

CCCC

CC

CC

CC

CC

CC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA||||||||||

5’3’

ATGCTTCATGCTTCTGTG5’ 3’

AA

AAAA

AA

AA AA

AAAA

AA

AA

TT TTTT

TT

TT TTTT

TT

TT

TTTT

GG

GG

GGGG

GG

GG

GG

GG

GG

CC

CCCC

CC

CC

CC

CC

CC

CCCC

CC

CAP3



5’3’


AA

AAAA

AA

AAAA

AA

AA

AA AA

TT TTTT

TT

TTTTTT

TT

TT

TTTT GGGG

GGGG GG

GG

GG

GG GG

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CAP3



5’3’


AA

AAAA

AA

AAAA

AAAA

AA

AA

TTTT

TTTT

TTTTTT

TT

TT

TT TTGG

GG

GGGG

GG GG

GG

GG

GG

CC

CC

CC

CCCC

CCCC

CC

CCCC

CC

CAP3


TACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAATACGAAGACCGTCTAGACTTGTCACAATGACTATAACGAA|||||||||||||||

5’3’

ATGCTTCATGCTTCTGGCAGATGGCAGATT5’ 3’

AA

AA

AAAA

AA

AA

AAAA

AA

AA

TTTTTT

TTTT

TTTTTT

TTTT

TT

GG

GGGG

GG

GGGG

GG

GG GG

CCCCCC

CC CCCC

CC

CCCC

CC

CC

CAP3



5’3’


AA

AA

AA AAAA

AA

AAAA

AA

AATT

TTTT

TT

TTTTTTTT

TT TT

TTGG

GGGG

GG

GGGG

GG

GG GG

CC

CCCC

CCCC

CC

CC

CC

CCCC

CC

CAP3



5’3’


AA

AA

AAAA

AA

AA

AAAA

AA

AA TT

TTTT

TTTTTT

TTTT

TTTT

TTGG

GGGG

GG

GGGG

GG

GGGG

CC

CCCC

CCCC

CC

CC

CCCC

CC

CC

CAP3



5’3’

ATGCTTCATGCTTCTGGCAGATGGCAGATT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATGGCAGATCTGAACAGTGTTACTGATT5’ 3’

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTGGCAGATCTGAACAGTGTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTTGGCAGATCTGAACAGTGTTACTGATATTGCTTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATGGCAGATCTGAACAGTGTTACTGATATT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGGCAGATCTGAACAGTGTTACTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGGCAGATCTGAACAGTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTGGCAGATCTGAACAGTGTTACTGATATTGCTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGGCAGATCTGAACAGTGTTACTGATATTT

ATGCTTCATGCTTCTT

ATGCTTCATGCTTCTGGCAGATCTGGCAGATCTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTTTGGCAGATCTGAACAGTGTTACTGATATTGCTT

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTGGCAGATCTGAACAGTGTTT

CAP3



ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATGGCAGATCTGAACAGTGTTACTGATT






ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTGGCAGATCTGAACAGTGTTACTGATATTGCTTATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGGCAGATCTGAACAGTGTTACTGATATTT

ATGCTTCATGCTTCTT





5’3’

CAP3









ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGCTGGCAGATCTGAACAGTGTTACTGATATTGCTTATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGGCAGATCTGAACAGTGTTACTGATATTT

ATGCTTCATGCTTCTT




CAP3


G A T C

• moldemolde• polimerasepolimerase• ddNNTPsTPs

•ddddGGTPsTPs •ddddAATPsTPs •ddddTTTPsTPs •ddddCCTPsTPs

CAP3











ATGCTTCATGCTTCTT




ATGCTTCATGCTTCTTGGATGCTTCATGCTTCTGTGGG

ATGCTTCATGCTTCTGGCATGGCAGG

ATGCTTCATGCTTCTGGCAGATCTTGGCAGATCTGG

ATGCTTCATGCTTCTGGCAGATCTGAACATGGCAGATCTGAACAGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTTGGCAGATCTGAACAGTGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTTGGCAGATCTGAACAGTGTTACTGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTTGGCAGATCTGAACAGTGTTACTGATATTGG

ATGCTTCATGCTTCTGGTGGCC

ATGCTTCATGCTTCTGGCAGATTGGCAGATCC

ATGCTTCATGCTTCTGGCAGATCTGAATGGCAGATCTGAACC

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTATGGCAGATCTGAACAGTGTTACC

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGTGGCAGATCTGAACAGTGTTACTGATATTGCC

ATGCTTCATGCTTCTGGCTGGCAA

ATGCTTCATGCTTCTGGCAGTGGCAGAA

ATGCTTCATGCTTCTGGCAGATCTGTGGCAGATCTGAAATGCTTCATGCTTCTGGCAGATCTGATGGCAGATCTGAAA

ATGCTTCATGCTTCTGGCAGATCTGAACTGGCAGATCTGAACAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTTGGCAGATCTGAACAGTGTTAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGTGGCAGATCTGAACAGTGTTACTGAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATTGGCAGATCTGAACAGTGTTACTGATAA

G A T C

CAP3











ATGCTTCATGCTTCTT




ATGCTTCATGCTTCTTGGATGCTTCATGCTTCTGTGGG

ATGCTTCATGCTTCTGGCATGGCAGG

ATGCTTCATGCTTCTGGCAGATCTTGGCAGATCTGG

ATGCTTCATGCTTCTGGCAGATCTGAACATGGCAGATCTGAACAGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTTGGCAGATCTGAACAGTGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTTGGCAGATCTGAACAGTGTTACTGG

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTTGGCAGATCTGAACAGTGTTACTGATATTGG

ATGCTTCATGCTTCTGGTGGCC

ATGCTTCATGCTTCTGGCAGATTGGCAGATCC

ATGCTTCATGCTTCTGGCAGATCTGAATGGCAGATCTGAACC

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTATGGCAGATCTGAACAGTGTTACC

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATATTGTGGCAGATCTGAACAGTGTTACTGATATTGCC

ATGCTTCATGCTTCTGGCTGGCAA

ATGCTTCATGCTTCTGGCAGTGGCAGAA

ATGCTTCATGCTTCTGGCAGATCTGTGGCAGATCTGAAATGCTTCATGCTTCTGGCAGATCTGATGGCAGATCTGAAA

ATGCTTCATGCTTCTGGCAGATCTGAACTGGCAGATCTGAACAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTTGGCAGATCTGAACAGTGTTAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGTGGCAGATCTGAACAGTGTTACTGAA

ATGCTTCATGCTTCTGGCAGATCTGAACAGTGTTACTGATTGGCAGATCTGAACAGTGTTACTGATAA

CAP3

Gel inteiro

CAP3

Gel e cromatograma

CAP3


CAP3

Montagem

CAP3

Shotgun

• Amostrar fragmentos da seqüência-alvo da maneira

mais aleatória possível.

• Determinar a maior porção possível das seqüências

das extremidades destes fragmentos

Sanger F, Coulson AR, Hong GF, Hill DF, Petersen GB. (1982) Nucleotide

sequence of bacteriophage lambda DNA. J Mol Biol 162(4): 729-73.

CAP3

Montagem shotgun

DNA original

CAP3

Maior problema computacional

Determinar a disposição das seqüências

dos fragmentos que seja mais consistente

com as sobreposições encontradas

Este é um problema NP-completo!

CAP3

Para complicar...

1. Há uma certa porcentagem de erros nas leituras

2. A leitura pode ser proveniente de qualquer uma

das duas fitas

3. Há desvio de representatividade

4. Existem “falsas” sobreposições

CAP3

CAP3

Um programa para montagem de seqüências de DNA

Xiaoqiu Huang1 and Anup Madan2

1 Department of Computer Science, Michigan Technological University,

Houghton, Michigan;

2 Department of Molecular Biotechnology, University of Washington, School

of Medicine, Seattle, Washington

Genome Research 9: 868-877

CAP3

Três fases do algoritmo de montagem Remoção das

extremidades de baixa qualidade

Cálculo de sobreposição de

reads

Remoção das “falsas” sobreposições

Construção dos contigs

Alinhamento múltiplo e geração do

consenso

CAP3

Três fases do algoritmo de montagem Remoção das

extremidades de baixa qualidade

Cálculo de sobreposição de

reads

Remoção das “falsas”

sobreposições

Construção dos contigs

Alinhamento múltiplo e geração

do consenso

CAP3

Identificação de sobreposição

• Concatena todos os reads

• Encontram-se segmentos de alta pontuação

– Caracter separador

• Busca binária na lista ordenada de posição dos

reads na seqüência combinada

• Não são analisadas porções anteriores ao read

atual

CAP3

Cálculo das posições de corte de read

corte 5’

corte 3’

Read h

Read f

Read g

CAP3

Smith-Waterman ponderado

• Match = m * min(q1,q2)

• Mismatch = n * min(q1,q2)

• Gap = -q * min(q1,q2)

CAP3

Cálculo da sobreposição de reads

CAP3

Cálculo da sobreposição de reads

• Feito por alinhamento global

• Banda de busca 2x maior que no alinhamento

local

• Avaliação

– comprimento

– identidade

– pontuação de similaridade

– HQDs (max [0, min(q1, q2)-b], d=soma

– taxa de discrepância (r1+r2+e)

CAP3

Uso de limitadores (constraints)

1. Layout preeliminar

2. Checagem de qualidade

3. Corrige os ruins

• com mais que u problemas

4. Liga contigs

• com mais que v limitações satisfeitas

CAP3

Uso de limitadores (constraints)

CAP3

Alinhamento e consenso

• Alinha, em ordem crescente de posição,

read com o consenso já montado

• Soma ponderada de qualidade

– base consenso = maior soma

– qualidade consenso = soma base w – soma

base x – soma base y ...

CAP3

Qualidade Média

CAP3

Cálculo de pontuação

CAP3

Resultados do CAP3

Data set

GenBank accession

no.No. of reads

Average length of

reads

Length of provided sequence

Running time (min)

No. of large

contigsLength of CAP3

sequenceNo. of

differences

203 AC004669 1812 598 89,779 37 1 90,292 0

216 AC004638 2353 614 124,645 154 1 132,057 17

322F16 AF111103 4297 1011 159,179 127 1 157,982 11

526N18 AF123462 3221 965 180,182 73 2 180,128 10

CAP3

Construção de Scaffolds

Data set

Length of answer

sequenceNo. of reads

per kb

Ability to make

scaffold with CAP3

Ability to make

scaffold with PHRAP

188A7 112,773 10.6 yes yes

201G24 184,666 10.8 yes yes

213L3 135,545 10.3 yes yes

257P13 184,998 10.1 yes yes

488C13 187,237 11.1 yes yes

501I4 231,464 11.7 yes no

CAP3

Program Data set Longest Contig # large contigs length of gaps internal errors

CAP3 3XA 6189 57 52,885 443

PHRAP 3XA 6396 54 38,146 529

CAP3 3XB 12,368 44 71,761 71

PHRAP 3XB 13,116 47 60,436 228

CAP3 3XC 10,709 49 54,229 227

PHRAP 3XC 11,406 45 34,727 332

CAP3 3XD 11,408 43 67,586 115

PHRAP 3XD 11,350 49 60,312 240

CAP3 5XA 10,582 42 27,965 249

PHRAP 5XA 18,268 31 14,396 252

CAP3 5XB 26,034 17 10,405 100

PHRAP 5XB 33,693 18 7,322 115

CAP3 5XC 20,939 29 20,520 172

PHRAP 5XC 20,912 27 16,617 261

CAP3 5XD 14,219 35 23,635 46

PHRAP 5XD 14,696 33 17,113 129

CAP3 8XA 71,025 12 4,681 83

PHRAP 8XA 71,395 8 1,061 80

CAP3 8XB 53,127 8 883 59

PHRAP 8XB 53,078 7 542 36

CAP3 8XC 52,134 8 752 4

PHRAP 8XC 76,922 6 774 6

CAP3 8XD 72,690 7 1,241 35

PHRAP 8XD 102,523 6 648 60

CAP3 10XA 91,380 4 0 28

PHRAP 10XA 91,329 3 0 11

CAP3 10XB 167,655 1 0 5

PHRAP 10XB 138,551 2 0 7

CAP3 10XC 106,631 5 321 44

PHRAP 10XC 77,747 4 330 12

CAP3 10XD 79,900 4 468 2

PHRAP 10XD 79,978 3 346 2

CAP3

Excelente revisão de “montadores”

• http://students.cec.wustl.edu/~cs547/Literature/

cap3 aula de algoritmo: cap3 felipe rodrigues da silva embrapa recursos genéticos e biotecnologia

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