Vorlesung Grundlagen der Bioinformatik Sommersemester Dozent: Prof. Daniel Huson

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1 Grundlagen der Bioinformatik, SS 08, D. Huson, April 21, Einführung Vorlesung Grundlagen der Bioinformatik Sommersemester 2008 Dozent: Prof. Daniel Huson Sprechstunde: Do und n.v. (Sand 14, C310a) 1.1 Ort and Zeitpunkt Vorlesungen: Montags 16ct-18h A301, Sand 1 Donnerstags 15ct-17h A301, Sand 1 Übungsgruppen: Tutor Tag Zeit Raum Robert Krug Mo 10 12h F118 (Sand 6/7) Verena Schattel Do 13 15h S6 (Gebäude C, 5.Ebene) Daniel Huson Juliane Damaris Klein huson at informatik.uni-tuebingen.de klein at informatik.uni-tuebingen.de Website: www-ab.informatik.uni-tuebingen.de/teaching/ss08/gbi 1.2 Prüfungsleistungen Diese Veranstaltung hat einen Umfang von 8 LP, Sie müssen also ca 8 30 = 240 Stunden Arbeitsaufwand einplanen. Teilnahme an den Übungen ist Pflicht. Übungsaufgaben können in Gruppen von bis zu zwei Personen bearbeitet und abgegeben werden. Die Anzahl der erreichten Punkte und die Mitarbeit in den Tutorien werden benotet. Diese Note macht 1 3 der Abschlussnote aus. Es gibt zwei Klausuren, die in der Mitte und am Ende der Vorlesung geschrieben werden. Diese machen jeweils 1 3 der Endnote aus. 1.3 Skript and Übungsblätter Es wird am Anfang jeder Vorlesung ein gedrucktes Skript herausgegeben, welches dann später, ggf. in leicht korregierter Form, als PDF auf der Vorlesungswebseite zur Verfügung gestellt wird. Übungsblätter werden in der Regel Montags in der Vorlesung ausgegeben und auf der Vorlesungswebseite als PDf zur Verfügung gestellt. Die Abgabe der Übungsblätter hat dann genau eine Woche später am Montag bis zum Anfang der Vorlesung zu erfolgen.

2 2 Grundlagen der Bioinformatik, SS 08, D. Huson, April 21, Bioinformatics The following characterization is from: Major advances in the field of molecular biology and advances in genomic technologies, have led to an explosive growth in the biological information generated by the scientific community. This huge amount of genomic information has led to an absolute requirement for computerized databases to store, organize, and index the data and for specialized tools to view and analyze the data. 1.5 What Is a Biological Database? A biological database is a large, organized body of persistent data, usually associated with computerized software designed to update, query, and retrieve components of the data stored within the system. A simple database might be a single file containing many records, each of which includes the same set of information. For example, a record associated with a nucleotide sequence database typically contains information such as contact name, the input sequence with a description of the type of molecule, the scientific name of the source organism from which it was isolated, and often, literature citations associated with the sequence. For researchers to benefit from the data stored in a database, two additional requirements must be met: easy access to the information a method for extracting only that information needed to answer a specific biological question NCBI (the National Center for Biotechnology Information) provides the largest publicly available database at: At NCBI, many of the databases are linked through a search and retrieval system called Entrez. Entrez allows a user to access and retrieve specific information from a single database and also to access integrated information from many NCBI databases. For example, the Entrez Protein database is cross-linked to the Entrez Taxonomy database. This allows a researcher to find taxonomic information (taxonomy is a division of the natural sciences that deals with the classification of animals and plants) for the species from which a protein sequence was derived. 1.6 What is Bioinformatics? Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is 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. At the beginning of the genomic revolution, a bioinformatics concern was the creation and maintenance of a database to store biological information, such as nucleotide and amino acid sequences. Development of this type of database involved not only design issues but the development of complex interfaces whereby researchers could both access existing data as well as submit new or revised data.

3 Grundlagen der Bioinformatik, SS 08, D. Huson (this part by K. Nieselt) April 21, Ultimately, however, all of this information must be combined to form a comprehensive picture of normal cellular activities so that researchers may study how these activities are altered in different disease states. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data, including nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, and use and management of, various types of information, and the development of new mathematical models, algorithms and statistics with which to assess relationships among members of large data sets, such as methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. 1.7 Overview: Part I: On sequences The first part of the course will include: Introduction Introduction to Molecular Biology, What is Bioinformatics, General Algorithms, Complexity Pairwise Alignment Edit Distance, Similarity Seqs, Dot plots, DP solutions, global=nw, Pairwise Alignment(local=SW), Heuristic: BLAST Multiple Alignments: DPs, progressive (Clustalw), Profiles (PSSM) Phylogeny: distance-based (UPGMA, NJ); character-based (small Maximum Parsimony problem)

4 4 Grundlagen der Bioinformatik, SS 08, D. Huson (this part by K. Nieselt) April 21, 2008 Markov Models and Hidden Markov Models:HMM algorithms (Forward, Viterbi), simple frequencybased training, application to CpG islands Gene prediction: Gene structure in prokaryotes and eukaryotes; Gene prediction in prokaryotes, simple ones for eukaryotes (such as ORF findings), GenScan E0+ E1+ E2+ I0+ I1+ I2+ Einit+ (initial exon) Eterm+ (terminal exon) F+ (5 UTR) Esngl+ (single exon gene) T+ (3 UTR) P+ (promoter) A+ (poly A signal) Forward (+) strand Reverse ( ) strand N (intergenic region) 1.8 Overview: Part II: On structures RNA secondary structure: Biological introduction, the RNA world, concepts, DP of Nussinov

5 Grundlagen der Bioinformatik, SS 08, D. Huson (this part by K. Nieselt) April 21, Proteins: biology and bioinformatics of proteins, Protein secondary structure: Chou-Fasman, DSSP Protein tertiary structure; structure classification (domains, folds, databases), threading

6 6 Grundlagen der Bioinformatik, SS 08, D. Huson (this part by K. Nieselt) April 21, Overview: Part III: On systems Databases in bioinformatics Basic concepts of databases, relational DBs, modelling, most important Bioinformatics Databases Microarrays; technology, normalization, expression matrix, differentially expressed genes, clustering

7 Grundlagen der Bioinformatik, SS 08, D. Huson (this part by K. Nieselt) April 21, 2008 Systems Biology networks, graph-based, simple algorithms on networks 7