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Bioinformatics Methods and Applications Dr. Hongyu Zhang Ceres Inc. Goals of the talk • The major battle fields in Bioinformatics research • The most popular weapons used in the battle History • Human genome project • Overlapping with other branches – Computational Biology – Biocomputing – Biostatistics – Cheminfomatics The Central Dogma of Molecular Biology Transcription DNA Translation RNA Protein Major battle fields in bioinformatics • DNA – Genome sequencing – Gene discovery • mRNA – Micro-array analysis – Sequencing • Protein – Structure modeling and prediction – Proteomics • … Major weapons • Computational algorithm – – – – • Probability and Statistical theory and methods – – – • Functions to describe the physical chemistry interactions in bio-molecules Molecular mechanics, Molecular dynamics algorithm Data storage and access – – • Bayesian theorem, Markov chain (HMM), Principle component Monte Carlo simulation Neural Network Physical chemistry – – • Hash method Dynamic algorithm String and Tree (binary, suffix) Clustering Database: Oracle, MySQL etc. Web interface Large-scale computing platform – – Hardware Software Genome sequencing: Celera shotgun assembly Venter et al. 2001 Gene discovery based on sequence comparison • Finding new genes based on their sequence similarity and evolution relationship with known genes • Methods – Hash-based database search method, like BLAST (PSI-BLAST), FASTA, BLAT etc. – Sequence alignment using Dynamic Programming algorithm BLAST database search (http://www.ncbi.nih.gov/BLAST/) Query sequence Database sequences Query database Sequence alignment • Example • Programs • CLUSTALW • DIALIGN BLAST ||| | BLA-T Dynamics algorithm Sequence A = (A1, A2, …, Ai, ..., Am) Sequence B = (B1, B2, …, Bj, …, An) H i, j H i 1, j 1 S Ai, Bj max H i , j 1 S , Bi H i 1, j S Ai, Ab initio gene prediction methods • Statistics based gene prediction – Nucleotides distribution frequencies in the coding regions – Exon/Intron boundary signal • Examples – GenScan, Burge and Karlin 1997 – Fgenesh, Solovyev and Salamov 1994 Hybrid gene prediction method • Example: Celera Otto program – BLAST against Refseq database – BLAST against EST database, other genomic sequences etc. – Genscan, Fgenesh Problems in Gene discovery • Example: Given a cDNA sequence, find its true location in the genome map among lots of alternatives 1 2 3 Query transcript/protein Genomic component 1’ 2’ 3’ Two-step solution 1. BLAST search of the cDNA sequence against the whole genome map 2. Using an LIS algorithm to find the correct genomic component hit l0 {hsp0 } l {max l , hsp }, if 0 s e Cutoff i j i i j 0 j i LIS max li 0i n Phylogenetic analysis • Goal: study the function and evolution relationship among a group of genes – Divide homologous genes into function families – Find the evolution relationship between the ortholog genes belonging to different species (e.g., the theory of Out of Africa) • Methods – Hierarchical Clustering – Neighbore-joining etc. • PHYLIP program, Univ. of Washington Micro-array analysis • Expression-genomics • Primary goals – Look for the genes with different expression levels between experiments, which are candidates of functional genes – Look for the group of genes that have correlated gene expression levels, which could suggest that they are in the same biological pathway • Methods – General probability and statistics methods – Dimension reduction • Principle components • Lowess – Clustering • Tools – S-plus, R Example • Herbicide – Plants was treated with herbicide to observe the gene expression profiles in a series of time steps. – The genes that appeared right before plant dies (12 hours) are the possible “death” genes – If we knock down the “death” genes in the normal plants, they could last longer time than the herbs. Protein structure prediction • Why is protein structure important? – The functions of a gene depend on its translated protein structure • Protein binding with its ligands • Protein-protein interactions – A protein molecule usually keeps one stable structure under normal physiological conditions (Anfinson, 1960es) – Drug design • Docking and high throughput drug screening. Sequence Bioinformatics Protein structure Function Protein structure prediction methods Homology modeling procedure Protein sequence Database search Select template structure Sequence alignment Build conserved regions first Loop modeling Build side-chains Optimizing Homology modeling programs • Academic software – MODELER, Sali A. – COMPOSER, Blundell T. – SWISS-MODEL – Rasmol (graphics) • Commercial software – QUANTA, MSI inc. – SYBYL, TRIPOS inc. Threading • Find the best fold candidates among a limited number of choices • Add 3D information to the score function of dynamic programming Ab initio protein structure principle • Threading programs – Topits, Eisenberg D. – Threader, Jones D. – ProSup, Sipple M – 123D, Alexandra N. • Ab initio programs – Rosetta, David Baker Current status in the protein structure prediction field • Moult J., CASP (Critical Assessment of Techniques for Protein Structure Prediction). • Homology modeling is very mature already • Threading and Ab initio method have been used in industry • Structure genomics Large scale computing platform • Hardware – Super-computers • Cray/SGI • DEC/Compaq • Intel – Linux clusters – Blade • Software – Parallel computing (MPP, PVM etc.) – Linux – Grid computing: the Globus Project Linux clusters Data storage and access • Bioinformatics is producing huge amount of data each day – How to organize and store data – How to access data • Database software – Commercial • Oracle, DB2, Sybase – Freeware • MySQL, PostgreSQL Data store and access • Bioinformatics is producing huge amount of data each day – How to organize and store data – How to access data • Database software – Commercial • Oracle, DB2, Sybase – Freeware • MySQL, PostgreSQL • Current popular database – DNA, protein sequence, like Genbank, SwisProt, PIR etc. – Protein structure, like PDB, Scop – DNA, mRNA, protein function, like GO, PFAM Database example: Gene Ontology (GO) Molecular function Cellular component Biological process Data access • Web interface – Protocol • CGI, JSP, ASP – Computer languages • Perl, Java, C/C++, Visual Basic, Visual C++ Forth looking • Where are the markets – Develop new programs – Assemble current programs to build more efficient data mining pipelines – Data storage and access – Integrate the current database to use them more effectively – Computing platform, including hardware, software support, consulting etc. • What we can offer – Multi-talents – Team work – Networking http://www.hongyu.org/paper/bioinformatics.ppt