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Transcript of Introduction to Bioinformatics 234525-236523 Lecturer: Dr. Yael Mandel-Gutfreund Teaching...
![Page 1: Introduction to Bioinformatics 234525-236523 Lecturer: Dr. Yael Mandel-Gutfreund Teaching Assistance: Martin Akerman Sivan Bercovici Course web site :](https://reader035.fdocuments.us/reader035/viewer/2022062804/5697bf821a28abf838c85964/html5/thumbnails/1.jpg)
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