Genomics. Ernst W. Mayr Fakultät für Informatik TU München

Transkript

1 Genomics Ernst W. Mayr Fakultät für Informatik TU München

2 A. Biologische Hintergründe nde 1. Gene und Phänotypisches

3 1.1. Beobachtungen nach Mendel

4 1.2. Eukaryotische Zelle

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6 1.3. DNA-Modell nach Watson/Crick (1953)

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9 2. Genexpression

10 2.1. Vereinfachte Darstellung des Ablaufs

11 2.2. Der universelle genetische Code

12 2.3. Steuerung der Genexpression

13 B. Genomsequenzierung

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15 1. Sequenzierung kurzer DNA-Abschnitte Abschnitte

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17 2. Whole-Genome Shotgun Sequencing ABI 3730xl automated DNA analyzers for shotgun sequencing

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19 3. Bedeutende Sequenzierungsprojekte Jahr Organismus Basenpaare Sequenziert Gene (Vorhersage) 1977 Bacteriophage Haemophilus influenzae Mycoplasma genitalium Saccharomices cerevisiae Methanococcus jannaschii Escherichia coli Helicobacter pylori Mycobacterium tubercolosis Caenorhabditis elegans Deinococcus radiodurans Homo sapiens Chr Homo sapiens Chr Drosophila melanogaster Vibrio cholerae Arabidopsis thaliana Mycobacterium leprae Homo sapiens (public) Homo sapiens (Celera) Fugu rubripes Mus musculus Anopheles gambiae Plasmodium falciparum Plasmodium yoelii yoelii Rattus norvegicus Dictyostelium discoideum

20 4. Datenmengen durch Genomsequenzierung

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22 C. Bioinformatik 1. Hauptgebiete: i. Sequenzanalyse, Genom-Annotation, Analyse evolutionärer Ereignisse (Mutationen, etc.) ii. iii. Phylogenie Genexpressionsanalyse und Vorhersage iv. Analyse regulatorischer Netzwerke v. Proteinstruktur: Vorhersage, Vergleich, Docking, etc. vi. Modellierung und Simulation biologischer Systeme vii. etc.

23 2. Biologische Datenbanken Nucleotide Sequence Databases RNA sequence databases Protein sequence databases Structure Databases Genomics Databases (non-vertebrate) Metabolic and Signaling Pathways Human and other Vertebrate Genomes Human Genes and Diseases Microarray Data and other Gene Expression Databases Proteomics Resources Other Molecular Biology Databases Organelle databases Plant databases Immunological databases

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25 3. Suchen in Sequenzdatenbanken 1 i i+m-1 n t: CGATTACT AACTGGCATA CAACTGGTGTGCCATGATTACGGATA CAATGACCTGTTAC.. TACTTAGTCT s: CAACTGGTGTGCCATGATTACGGATA 1 m If there exists at least one occurrence of s within t then the position i of the first occurrence should be returned.

26 Naïve String Searching Scan s and t left-to-right as long as the corresponding nucleotides match. Upon a mismatch shift s to the right. t: 1 n A A A C T G A s: A A A C A G C A 1 m t: 1 n A A A C T G A s: A A A C A G C A 1 m An occurrence of s in t has been found once the (successful) scanning of s has reached its right boundary.

27 D. Comparative Genomics 1. Aufgabe der vergleichenden Genomik Analyse und Vergleich von genetischem Material verschiedener Spezies zum Studium der Evolution, Genefunktion und vererblicher Krankheiten Vergleich von Gene location Gene structure Exon number Exon lengths Intron lengths Sequence similarity Gene characteristics Splice sites Codon usage Conserved synteny

28 2. Beispiel: Mensch vs. Maus Figure 1 Regions of the human and mouse homologous genes: Coding exons (white), noncoding exons (gray}, introns (dark gray), and intergenic regions (black). Corresponding strong (white) and weak (gray) alignment regions of GLASS are shown connected with arrows. Dark lines connecting the alignment regions denote very weak or no alignment. The predicted coding regions of ROSETTA in human, and the corresponding regions in mouse, are shown (white) between the genes and the alignment regions.

29 Comparison of mouse chromosome 16 and the human genome Mural et al., Science, 2002, 296:1661 Celera group Synteny with human chr. s 3,8,12,16,21,22 and rat chr. s 10,11 Q: Why more breakpoints in mousehuman than in mouse-rat? Q: Why more conserved genes in human than in rat?

30 This also can occur between chromosomes The longer the divergence time between 2 species, the more recombination has occurred 100 million years since human-mouse divergence 40 million years since rat-mouse divergence

31 E. Functional Genomics 1. Genexpressionsanalyse

32 1.1. Genexpressionsanalyse mit DNAMicroarrays

33 1.2. In-Situ Hybridisierung

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35 1.3. Zweifarben-Hybridisierung

36 1.4. Photolithographische Gene Chip Arrays

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38 1.5. Typischer Work Flow in der Cluster Analyse Expression Profiles Normalization Similarity Measure Gene Expression Data Clustering

39 2. Analyse biologischer Netze 2.1. Gen-regulatorische Netzwerke

40 2.2. Metabolische Netzwerke

41 2.3. Signalling Netzwerke

42 2.4. Proteininteraktionsnetze

43 2.5. Algorithmische Probleme 1. Suchen in Netzwerken (Teilgraphisomorphismen) 2. Vergleichen von Netzwerken 3. Centrality: Bestimmung zentraler Komponenten 4. Datenintegration: Consensus-Netzwerke 5. Clustering: Bestimmung dichter Teilnetze 6. Analyse hierarchischer Netzstrukturen 7. Layout: Graphische Darstellung 8. etc.

44 UAA Vielen Dank!