ivan g. costa filho [email protected] centro de informática universidade federal de pernambuco

Biologia In Silico - Centro de Informática - UFPE

Ivan G. Costa [email protected]

Centro de InformáticaUniversidade Federal de Pernambuco

Processamento de Cadeias de Caracteres


Tópicos

• Cadeias de Caracteres Biológicas• Problemas Básicos

– alinhamento par/múltiplo– busca de motifs– modelagem de famílias de proteínas

• Métodos– Algoritmos dinâmicos– cadeias escondidas de Markov– métodos probabilísticos


Disciplina

• Aulas – Marco/Abril– introdução de conceitos/métodos básicos– Aulas práticas

• Seminários - Abril/Maio– apresentação de tópicos da disciplina

• Individual - pós• duplas – graduação

• Projeto Maio a Junho– analise de dados reais (de artigos

discutidos) em grupo


Avaliação

• 40% - apresentação dos seminários– avaliação pelos companheiros de

classe e presença • 20% - listas de exercícios• 40% - projeto em grupo

– nota individual - cada grupo é responsável por descrever a participação


Bibliografia

• R Durbin, Sean R Eddy, A Krogh, Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press.

• An Introduction to Bioinformatics Algorithms, Neil Jones e Pavel Pevzner, MIT Press, 2004

• Ver pagina para literatura especifica de cada aula …

– www.cin.ufpe.br/~igcf


Biologia Molecular


Entender a vida a nível celular

• Como a informação genética é herdada

• Como a informação genética influencia processos celulares

• Como genes trabalham juntos para realizar uma função celular


Informação Genética - DNA

• DNA (ácido desoxirribonucleico) – Cadeia de

nucleotídeos – 4 tipos: A;C;G;T– forma fita dupla a

partir da complementaridade.

• A = T e C = G


Dogma Central - Transcrição

• Transcrição – DNA para RNA

• RNA (acido ribonucléico)– fita simples.– 4 tipos: A;C;G;U– Moléculas instáveis– Transporte de

informação do núcleo ao citoplasma


Dogma Central - Transcrição

• Transcrição – copia seqüência de bases do DNA para o RNA (com U ao invéss de T).


Dogma Central - Tradução

• Tradução– RNA -> Proteínas– realizada pelo ribossomo– Código genético

• Proteínas– cadeia de aminoácidos– 20 tipos diferentes– adquire uma estrutura tri-

dimensional– entidades funcionais da

célula


Tradução - Código Genético

• Combinações de códons (3 bases) codificam um dos 20 aminoácidos.


Dogma Central

• Dogma: fluxo de informação

DNA mRNA Proteína• Gene: segmento de DNA

codificando uma proteína.• Transcrito: segmento de

RNA transcrito de uma gene.

• Um gene corresponde a uma proteína e uma função celular.


Controle da Expressão Gênica

• Como se da o controle da expressão gênica?

• Certas proteínas, fatores de transcrição, se ligam ao DNA e são responsáveis por iniciar a transcrição.


Controle da Regulação Gênica


• Manage molecular biological data– Store in databases, organise, formalise, describe...

• Compare molecular biological data• Find patterns in molecular biological data

– phylogenies– correlations (sequence / structure / expression / function

/ disease)

Goals:• characterise biological patterns & processes• predict biological properties

– low level data ⇒ high level properties (eg., sequence ⇒ function)

Bioinformatics


Bioinformatics: neighbour disciplines

• Computational biology– Broader concept: includes computational

ecology, physiology, neurology etc...

• -omics:– Genomics– Transcriptomics– Proteomics

• Systems biology– Putting it all together...– Building models, identify control & regulation


Molecular biology data...

>alpha-DATGCTGACCGACTCTGACAAGAAGCTGGTCCTGCAGGTGTGGGAGAAGGTGATCCGCCACCCAGACTGTGGAGCCGAGGCCCTGGAGAGGTGCGGGCTGAGCTTGGGGAAACCATGGGCAAGGGGGGCGACTGGGTGGGAGCCCTACAGGGCTGCTGGGGGTTGTTCGGCTGGGGGTCAGCACTGACCATCCCGCTCCCGCAGCTGTTCACCACCTACCCCCAGACCAAGACCTACTTCCCCCACTTCGACTTGCACCATGGCTCCGACCAGGTCCGCAACCACGGCAAGAAGGTGTTGGCCGCCTTGGGCAACGCTGTCAAGAGCCTGGGCAACCTCAGCCAAGCCCTGTCTGACCTCAGCGACCTGCATGCCTACAACCTGCGTGTCGACCCTGTCAACTTCAAGGCAGGCGGGGGACGGGGGTCAGGGGCCGGGGAGTTGGGGGCCAGGGACCTGGTTGGGGATCCGGGGCCATGCCGGCGGTACTGAGCCCTGTTTTGCCTTGCAGCTGCTGGCGCAGTGCTTCCACGTGGTGCTGGCCACACACCTGGGCAACGACTACACCCCGGAGGCACATGCTGCCTTCGACAAGTTCCTGTCGGCTGTGTGCACCGTGCTGGCCGAGAAGTACAGATAA>alpha-AATGGTGCTGTCTGCCAACGACAAGAGCAACGTGAAGGCCGTCTTCGGCAAAATCGGCGGCCAGGCCGGTGACTTGGGTGGTGAAGCCCTGGAGAGGTATGTGGTCATCCGTCATTACCCCATCTCTTGTCTGTCTGTGACTCCATCCCATCTGCCCCCATACTCTCCCCATCCATAACTGTCCCTGTTCTATGTGGCCCTGGCTCTGTCTCATCTGTCCCCAACTGTCCCTGATTGCCTCTGTCCCCCAGGTTGTTCATCACCTACCCCCAGACCAAGACCTACTTCCCCCACTTCGACCTGTCACATGGCTCCGCTCAGATCAAGGGGCACGGCAAGAAGGTGGCGGAGGCACTGGTTGAGGCTGCCAACCACATCGATGACATCGCTGGTGCCCTCTCCAAGCTGAGCGACCTCCACGCCCAAAAGCTCCGTGTGGACCCCGTCAACTTCAAAGTGAGCATCTGGGAAGGGGTGACCAGTCTGGCTCCCCTCCTGCACACACCTCTGGCTACCCCCTCACCTCACCCCCTTGCTCACCATCTCCTTTTGCCTTTCAGCTGCTGGGTCACTGCTTCCTGGTGGTCGTGGCCGTCCACTTCCCCTCTCTCCTGACCCCGGAGGTCCATGCTTCCCTGGACAAGTTCGTGTGTGCCGTGGGCACCGTCCTTACTGCCAAGTACCGTTAA

• DNA sequences


Molecular biology data...

• Amino acid sequences

• Protein structure:– X-ray crystallography

– NMR


Cell biology & proteomics data...

• Subcellular localization


• Homology / Alignment• Simple pattern (“word”) recognition • Statistical methods

– Weight matrices: calculate amino acid probabilities– Other examples: Regression, variance analysis,

clustering

• Machine learning– Like statistical methods, but parameters are estimated

by iterative training rather than direct calculation– Examples: Neural Networks (NN), Hidden Markov Models

(HMM), Support Vector Machines (SVM)

• Combinations

Prediction Methods


Similarity between sequencesIf two sequences look similar, the explanation

may be:• Homology (common descent)• Convergent evolution (common function → common selective pressure)• Chance!


Sequences are related

• Darwin: all organisms are related through descent with modification• => Sequences are related through descent with modification• => Similar molecules have similar functions in different organisms

Phylogenetic tree based on ribosomal RNA: three domains of life


Sequences are related II

Phylogenetic tree of globin-type proteins found in humans


Why compare sequences?

• Determination of evolutionary relationships

• Prediction of protein function and structure (database searches).

Protein 1: binds oxygen

Sequence similarity

Protein 2: binds oxygen ?


Biological Databases

• Vast biological and sequence data is freely available through online databases

• Use computational algorithms to efficiently store large amounts of biological data

Examples

• NCBI GeneBank http://ncbi.nih.gov Huge collection of databases, the most prominent being the nucleotide sequence database

• Protein Data Bank http://www.pdb.org

Database of protein tertiary structures

• SWISSPROT http://www.expasy.org/sprot/ • Database of annotated protein sequences

• PROSITE http://kr.expasy.org/prositeDatabase of protein active site motifs


Alinhamento de Sequencias


BLAST

• A computational tool that allows us to compare query sequences with entries in current biological databases.

• A great tool for predicting functions of a unknown sequence based on alignment similarities to known genes.


BLAST


Some Early Roles of Bioinformatics• Sequence comparison• Searches in sequence databases


Biological Sequence Comparison• Needleman-

Wunsch, 1970– Dynamic

programming algorithm to align sequences


Busca de Sinais de Localização


Protein sorting in eukaryotes

• Proteins belong in different organelles of the cell – and some even have their function outside the cell

• Günter Blobel was in 1999 awarded The Nobel Prize in Physiology or Medicine for the discovery that "proteins have intrinsic signals that govern their transport and localization in the cell"


Secretory proteins have a signal peptide

Initially, they are transported across the ER membrane

Protein sorting: secretory pathway / ER


Signal peptides

A signal peptide is an N-terminal part of the amino acid chain, containing a hydrophobic region.

Signal peptides differ between proteins, and can be hard to recognize.


Simple pattern (“word”) recognition

Example: PROSITE entry PS00014, ER_TARGET:

Endoplasmic reticulum targeting sequence (”KDEL-signal”).

Pattern: [KRHQSA]-[DENQ]-E-L

NB: only yes/no answers!


ALAKAAAAMALAKAAAANALAKAAAARALAKAAAATALAKAAAAVGMNERPILTGILGFVFTMTLNAWVKVVKLNEPVLLLAVVPFIVSV

• Estimate probabilities for nucleotides / amino acids• Information content in sequences; logos; Position- Weight

Matrices.• Quantitative answers.

Statistical Methods


Busca de Motifs


Random Sample

atgaccgggatactgataccgtatttggcctaggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatactgggcataaggtaca

tgagtatccctgggatgacttttgggaacactatagtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgaccttgtaagtgttttccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatggcccacttagtccacttatag

gtcaatcatgttcttgtgaatggatttttaactgagggcatagaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtactgatggaaactttcaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttggtttcgaaaatgctctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatttcaacgtatgccgaaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttctgggtactgatagca


Implanting Motif AAAAAAAGGGGGGG

atgaccgggatactgatAAAAAAAAGGGGGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaataAAAAAAAAGGGGGGGa

tgagtatccctgggatgacttAAAAAAAAGGGGGGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgAAAAAAAAGGGGGGGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAAAAAAAAGGGGGGGcttatag

gtcaatcatgttcttgtgaatggatttAAAAAAAAGGGGGGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtAAAAAAAAGGGGGGGcaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttAAAAAAAAGGGGGGGctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatAAAAAAAAGGGGGGGaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttAAAAAAAAGGGGGGGa


Where is the Implanted Motif?

atgaccgggatactgataaaaaaaagggggggggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaataaaaaaaaaggggggga

tgagtatccctgggatgacttaaaaaaaagggggggtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgaaaaaaaagggggggtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaataaaaaaaagggggggcttatag

gtcaatcatgttcttgtgaatggatttaaaaaaaaggggggggaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtaaaaaaaagggggggcaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttaaaaaaaagggggggctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcataaaaaaaagggggggaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttaaaaaaaaggggggga


Implanting Motif AAAAAAGGGGGGG

with Four MutationsatgaccgggatactgatAgAAgAAAGGttGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatacAAtAAAAcGGcGGGa

tgagtatccctgggatgacttAAAAtAAtGGaGtGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgcAAAAAAAGGGattGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAtAAtAAAGGaaGGGcttatag

gtcaatcatgttcttgtgaatggatttAAcAAtAAGGGctGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtAtAAAcAAGGaGGGccaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttAAAAAAtAGGGaGccctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatActAAAAAGGaGcGGaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttActAAAAAGGaGcGGa


Where is the Motif???

atgaccgggatactgatagaagaaaggttgggggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatacaataaaacggcggga

tgagtatccctgggatgacttaaaataatggagtggtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgcaaaaaaagggattgtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatataataaaggaagggcttatag

gtcaatcatgttcttgtgaatggatttaacaataagggctgggaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtataaacaaggagggccaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttaaaaaatagggagccctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatactaaaaaggagcggaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttactaaaaaggagcgga


Why Finding (15,4) Motif is Difficult?

atgaccgggatactgatAgAAgAAAGGttGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccg

acccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatacAAtAAAAcGGcGGGa

tgagtatccctgggatgacttAAAAtAAtGGaGtGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccga

gctgagaattggatgcAAAAAAAGGGattGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggaga

tcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAtAAtAAAGGaaGGGcttatag

gtcaatcatgttcttgtgaatggatttAAcAAtAAGGGctGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaa

cggttttggcccttgttagaggcccccgtAtAAAcAAGGaGGGccaattatgagagagctaatctatcgcgtgcgtgttcat

aacttgagttAAAAAAtAGGGaGccctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgta

ttggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatActAAAAAGGaGcGGaccgaaagggaag

ctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttActAAAAAGGaGcGGa

AgAAgAAAGGttGGG

cAAtAAAAcGGcGGG

..|..|||.|..|||


Próxima Aula

• Ler capitulo 1 do Durbin • Introdução a algoritmos

dinâmicos (10/08)


Agradecimentos

• Alguns slides extraidos de – Biological Sequence Analysis

course, CBS, Universidade Tecnica da Dinamarca

– Neil Jones, University of California at San Diego

ivan g. costa filho [email protected] centro de informática universidade federal de pernambuco

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