Introduction to Bioinformatics234525-236523

Lecturer: Dr. Yael Mandel-Gutfreund

Teaching Assistance:

Martin Akerman

Sivan Bercovici

Course web site :http://webcourse.cs.technion.ac.il/234525

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What is Bioinformatics?

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Course Objectives

• To introduce the bioinfomatics discipline • To make the students familiar with the major

biological questions which can be addressed by bioinformatics tools

• To introduce the major tools used for sequence and structure analysis and explain in general how they work (limitation etc..)

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Course Structure and Requirements

1.Class Structure1. 2 hours Lecture 2. 1 hour tutorial

2. Home work• Homework projects will be given every second week• The homework will be done in pairs.• 5/5 homework projects submitted

2. A final project will be conducted and submitted in pairs

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Grading

• 30 % Homework assignments

• 70% final project

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Literature list• Gibas, C., Jambeck, P. Developing Bioinformatics

Computer Skills. O'Reilly, 2001. • Lesk, A. M. Introduction to Bioinformatics. Oxford

University Press, 2002.

• Mount, D.W. Bioinformatics: Sequence and Genome Analysis. 2nd ed.,Cold Spring Harbor Laboratory Press, 2004.

Advanced Reading

Jones N.C & Pevzner P.A. An introduction to Bioinformatics algorithms MIT Press, 2004

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What is Bioinformatics?

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“The field of science in which biology, computer science, and information technology merge to form a single discipline”

Ultimate goal: to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.

What is Bioinformatics?

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from purely lab-based science to an information science

BioinformaticsBio = Informatics

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Central Paradigm in Molecular Biology

mRNAGene (DNA) Protein

21ST centaury

Genome Transcriptome Proteome

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Genome

• Chromosomal DNA of an organism

• Coding and non-coding DNA

• Genome size and number of genes does not necessarily determine organism complexity

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Transcriptome

• Complete collection of all possible mRNAs (including splice variants) of an organism.

• Regions of an organism’s genome that get transcribed into messenger RNA.

• Transcriptome can be extended to include all transcribed elements, including non-coding RNAs used for structural and regulatory purposes.

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Proteome

• The complete collection of proteins that can be produced by an organism.

• Can be studied either as static (sum of all proteins possible) or dynamic (all proteins found at a specific time point) entity

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From DNA to Genome

Watson and Crick DNA model

First protein sequence1955

1960

1965

1970

1975

1980

1985

First protein structure

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1995

1990

2000 First human genome draft

First bacterial genome

Hemophilus Influenzae

Yeast genome

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Total 706 456

Eukaryotes 78 43

Bacteria 578 383

Archaea 50 29

Complete Genomes

2008 2007

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Comparison between the full drafts of the human and chimp genomesrevealed that they differ only by 1.23%

How humans are chimps?

Perhaps not surprising!!!

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The “post-genomics” eraThe “post-genomics” era

Goal:

to understand the living cell

Annotation Comparativegenomics

Structuralgenomics

Functionalgenomics

What’s Next ?

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CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG

CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA

CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC

AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA

AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA

TAT GGA CAA TTG GTT TCT TCT CTG AAT ......

.............. TGAAAAACGTA

Annotation

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Annotation

Identify the genes within a given sequence of DNA

Identify the sitesWhich regulate the gene

Predict the function

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CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG

CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA

CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC

AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA

AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA

TAT GGA CAA TTG GTT TCT TCT CTG

AAT .................................

.............. TGAAAAACGTA

TF binding sitepromoter

Ribosome binding SiteORF=Open Reading FrameCDS=Coding Sequence

Transcription

Start Site

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Comparativegenomics

Human ATAGCGGGGGGATGCGGGCCCTATACCCChimp ATAGGGG - - GGATGCGGGCCCTATACCCMouse ATAGCG - - - GGATGCGGCGC -TATACCA

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Researchers have learned a great deal about the function of human genes by examining their counterparts in simpler model organisms such as the mouse.

Conservation of the IGFALS (Insulin-like growth factor)Between human and mouse.

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Functionalgenomics

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Understanding the function of genes and other parts of the genome

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A large network of 8184 interactions among 4140 S. Cerevisiae proteins

A network of interactions can be built For all proteins in an organism

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Structural genomics

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Assigning the structures of all proteins

Protein-ligand complexes

Functional sites

fold Evolutionaryrelationship

Shape and electrostatics

Active sites

protein complexes

Biologic processes

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Resources and Databases

The different types of data are collected in database

– Sequence databases – Structural databases– Databases of Experimental Results

All databases are connected

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Sequence databases

• Gene database

• Genome database

• SNPs database

• Disease related mutation database

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Gene database

• Give information into gene functionality

• Alternative splicing of genes– Alternative pattern of exons included to create

gene product

• EST

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Genome Databases

• Data organized by species

• Clones assembled into contigous pieces ‘contigs’ or whole chromosomes

• Information on non-coding regions

• Relativity

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Genome Browsers

• Annotation adds value to sequence

• Easy “walk” through the genome

• Comparative genomics

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Genome Browsers

• UCSC Genome Browser http://genome.ucsc.edu/

• Ensembl Genome Browser (http://www.ensembl.org)

• WormBase: http://www.wormbase.org/

• AceDB: http://www.acedb.org/

• Comprehensive Microbial Resource: http://www.tigr.org/tigr-scripts/CMR2/CMRHomePage.spl

• FlyBase: http://flybase.bio.indiana.edu/

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SNP database

Single Nucleotide Polymorphisms (SNPs)

• Single base difference in a single position among two different individuals of the same species

• Play an important role in differentiation and disease

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Sickle Cell Anemia

• Due to 1 swapping an A for a T, causing inserted amino acid to be valine instead of glutamine in hemoglobin

Image source: http://www.cc.nih.gov/ccc/ccnews/nov99/

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Healthy Individual>gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA

ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA

GGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATGCTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGCTCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGATCCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCACCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCACTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACTGGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC

>gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens]

MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG

AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

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Diseased Individual>gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA

ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA

GGTGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATGCTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGCTCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGATCCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCACCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCACTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACTGGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC

>gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens]

MVHLTPVEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG

AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

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Disease Databases

• Genes are involved in disease

• Many diseases are well studied

• Description of diseases and what is known about them is stored

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Structure Databases

• 3-dimensional structures of proteins, nucleic acids, molecular complexes etc

• 3-d data is available due to techniques such as NMR and X-Ray crystallography

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Databases of Experimental Results

• Data such as experimental microarray images- expression data

• Proteomic data

• Metabolic pathways, protein-protein interaction data, regulatory networks

• ETC………….

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PubMed

• MEDLINE publication database– Over 17,000 journals– 15 million citations since 1950

Service of the National Library of Medicine

http://www.ncbi.nlm.nih.giv/PubMed

Literature Databases

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Putting it all Together

• Each Database contains specific information

• Like other biological systems also these databases are interrelated

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GENOMIC DATAGenBank

DDBJ

EMBL

ASSEMBLED GENOMES

GoldenPath

WormBase

TIGR

PROTEIN

PIR

SWISS-PROT

STRUCTUREPDB

MMDB

SCOP

LITERATURE

PubMed

PATHWAYKEGG

COG

DISEASE

LocusLink

OMIM

OMIA

GENESRefSeq

AllGenes

GDBSNPs

dbSNP

ESTs

dbEST

unigene

MOTIFS

BLOCKS

Pfam

Prosite

GENE EXPRESSION

Stanford MGDB

NetAffx

ArrayExpress