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faculty.ucr/~tgirke/Teaching/Gen240B_2003

Web-based/Open-source Tools for Bioinformatics and Genome Analysis. http://www.faculty.ucr.edu/~tgirke/Teaching/Gen240B_2003.ppt. Bioinformatics Areas. A. Traditional Bioinformatics Sequence analysis Gene expression analysis Proteomics Metabolic profiling Phenotypes Networks

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faculty.ucr/~tgirke/Teaching/Gen240B_2003

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  1. Web-based/Open-source Tools for Bioinformatics and Genome Analysis http://www.faculty.ucr.edu/~tgirke/Teaching/Gen240B_2003.ppt

  2. Bioinformatics Areas A. Traditional Bioinformatics • Sequence analysis • Gene expression analysis • Proteomics • Metabolic profiling • Phenotypes • Networks B. Structural Bioinformatics • Molecular modeling • Drug design C. Biological Databases Systems Biology

  3. Focus of this Seminar 1. Sequences2. Structure3. Expression4. Functional Groups Bio* Projects and Databases

  4. 1. Some Analysis Steps • Fragment Assembly: ESTs and genes • Mapping • Annotation • Gene predictions • ORFs, UTRs, introns, exons, promoters • Lots of errors in eukaryote genomes!! • Similarity searches • BLAST, FASTA, Smith-Waterman • Gene families • Domain databases • Multiple alignments • Structure/Function • 2D, 3D structure (availability?)

  5. Important Sequence Databases Selection • NCBI • Entrez: http://www.ncbi.nlm.nih.gov/ • Batch Entrez: http://www.ncbi.nlm.nih.gov/entrez/batchentrez.cgi • Downloads: ftp://ftp.ncbi.nih.gov/blast/db/ • EMBL-EBI • General: http://www.ebi.ac.uk/ • Downloads: http://www.ebi.ac.uk/FTP/ • Swiss-Prot • General: http://us.expasy.org/ • Downloads: http://us.expasy.org/expasy_urls.html • TIGR • General: http://www.tigr.org/ • Downloads: ftp://ftp.tigr.org/pub/data/ • Protein Data Bank (PDB) • General: http://www.rcsb.org/pdb/ • Downloads: ftp://ftp.rcsb.org/pub/pdb/data

  6. Example: NCBI

  7. Sequence Database Searches Important search algorithms • Swiss-Waterman, FASTA, BLAST BLAST Flavors: http://www.ncbi.nlm.nih.gov/Sitemap/index.html#BLAST • BLAST: BLASN, BLASTP, TBLASTN, TBLASTX • Psi-BLAST: Position-Specific Iterated BLAST • RPS-BLAST: Reverse Position-Specific BLAST • Phi-BLAST: Pattern Hit Initiated BLAST • Mega-BLAST: 10 faster than BLASTN • BLAST2: pairwise comparisons • WU-BLAST: Washington University BLAST Download of NCBI BLAST tools: ftp://ftp.ncbi.nih.gov/toolbox/

  8. Homework AssignmentFinish only one assignment! • Go to http://www.ncbi.nlm.nih.gov/, select protein DB, run query: P450 & hydroxylase & human [organism], select under ‘Limits’ SwissProt • report final query syntax from ‘Details’ page. • Save GIs from this final query to file (select ‘GI List’ format under display) • report how many GIs you retrieved • Retrieve the corresponding sequences through Batch-Entrez (http://www.ncbi.nlm.nih.gov/entrez/batchentrez.cgi) using GI list file as query input -> save sequences in FASTA format • Generate multiple alignment and tree of these sequences using Multalign (http://prodes.toulouse.inra.fr/multalin/multalin.html) • save multiple alignment and tree to file • identify putative heme binding cysteine • Open corresponding SwissProt page (http://us.expasy.org/sprot/) for first P450 sequence in your list • Compare putative heme binding cysteine and compare with consensus pattern from Prosite database • Report corresponding Pfam ID • How many mouse (Mus musculus) sequences are in this family (use ‘species tree’ on Pfam db) • BLASTP against nr database (use again first P450 in your list), select on “See Conserved Domains from CDD” (this runs RPS-BLAST), click on red P450 domain. • Compare resulting alignment with result from MultAlin • View 3D structure in Cn3D, save structure (screen shot) and highlight heme binding cysteine

  9. Remote Homology Detection • Psi-BLAST/RPS-BLAST • HMMs: HMMER, SAM • Domain databases • Fold recognition approaches (Meta Servers)

  10. Protein Domain DatabasesSelection • PFAM • http://pfam.wustl.edu/ • PROSITE • http://us.expasy.org/prosite/ • ProDom • http://prodes.toulouse.inra.fr/prodom/2002.1/html/home.php • InterPro • http://www.ebi.ac.uk/interpro/

  11. Selection of Tools for Promoter Analysis • Verbumculus, UC Riverside • http://www.cs.ucr.edu/%7Estelo/Verbumculus/ • AlignACE & ScanACE • http://arep.med.harvard.edu/mrnadata/mrnasoft.html • MEME and META-MEME, San Diego Super Computer Center: • http://www.sdsc.edu/Research/biology/ • Regulatory Sequence Analysis Tools (RSA) • http://rsat.ulb.ac.be/rsat/ • Gibbs Motif Sampler, Coldspring Harbor: • http://argon.cshl.org/ioschikz/gibbsDNA/mgibbsDNA-form.html • Motif Sampler, searches for over-represented motifs • http://www.esat.kuleuven.ac.be/~thijs/Work/MotifSampler.html • Stanford, motif finding in upstream sequences • http://genome-www4.stanford.edu/cgi-bin/ewing/oligoAnalysis.pl

  12. Example: RSA

  13. Promoter DatabasesSelection • Regulatory Sequence Analysis Tools (RSA) • http://rsat.ulb.ac.be/rsat/ • Eukaryotic Promoter Database • http://www.epd.isb-sib.ch/ • Human Promoter Database • http://zlab.bu.edu/%7Emfrith/HPD.html • Arabidopsis • http://exon.cshl.org/cgi-bin/atprobe/atprobe.pl

  14. Alternative HomeworkDo only one assignment! • Work through tutorial of Regulatory Sequence Analysis Tools (http://rsat.ulb.ac.be/rsat/). • Provide short summary for different tools

  15. 2. Protein Modeling • Tool collection: http://faculty.ucr.edu/~tgirke/Links.htm • Databases: • Protein Data Bank: • General: http://www.rcsb.org/pdb/ • Downloads: ftp://ftp.rcsb.org/pub/pdb/data • More databases: http://faculty.ucr.edu/~tgirke/Links.htm#Databases

  16. 3. Microarrays and Chips Definition: Hybridization-based technique that allows simultaneous analysis of thousands of samples on a solid substrate. Applications: Examples  Transcriptional Profiling  Gene copy number  Resequencing  Genotyping  Single-nucleotide polymorphism  DNA-protein interaction  Insertional library screening  Identification of new cell lines  Etc. Developing Areas:  Protein arrays  Chemical arrays

  17. Why Microarrays? Input Samples Outputs WT Prognosis Mutants Transgenics DNA Arrays gene expression Diagnosis Treatments biotic, abiotic, chemicals Target identification Simultaneous analysis of over 50,000 genes  Signaling and Metabolic Networks Regulatory genes  First step in discovery of gene function  Prediction of limiting factors in biological processes  Rapid analysis of mutants and transgenics  Reduce time of costly clinical studies and field trials

  18. Basic Analysis Steps • Image analysis • Filtering, background correction • Standardization, scaling and normalization • Significance analysis (replicates) • Cluster analysis (time series) • Integration with sequence and functional information

  19. WTt1 WTt1 WTt2 WTt3 WTt4 WTt5 Planning Steps of Transcriptional Profiling Experiments 1. Biological question(s), e.g.: - Which genes are up or down-regulated in a mutant/transgenic line? - Which genes cycle during a series of treatments? 2. Selection of best biological samples - Minimize variability in sample collection. 3. Develop validation and follow-up strategy for expected expression hits - e.g. real-time PCR and analysis of transgenics or mutants 4. Choose type of experiment - pairwise: e.g.WT vs. Mutant/Transgenic - series of time points or treatments  allows cluster analysis 5. Choose Reference - sample with maximum number of expressed genes (maxim. biolog.information) - pooled RNA of all points: less variability from reference, saves chips WTt1 WTt2 MTt1 MTt2

  20. Planning Steps of Transcriptional Profiling Experiments 6. How many replicates? - biological replicate: starts with sample collection - technical replicate: starts usually with same RNA isolation - dye-swaps: (1) WT-Cy3:MT-Cy5, (2) WT-Cy5:MT-Cy3 7. Management of sample collection and RNA isolation - Define a “realistic” volume - RNA quality tests!!!! 8. cDNA/cRNA labeling - Which labeling technique? RNA amplification, reliability, sensitivity, etc. 9. Array hybridizations and post-processing 10. Array scanning

  21. Important Pattern Recognition (clustering) Methods • Hierarchical clustering • single, average (UPGMA) and complete linkage • Non-hierarchical clustering • Self Organizing Maps (SOM) • k-means • Dimension Reduction Analysis • Principal Component Analysis • Neural Networks & Machine Learning

  22. Tools for Microarray Analysis • Image analysis: ScanAlyze • Normalization: SNOMAD, R projects • Mining/clustering: J-Express, R projects • Much more: http://faculty.ucr.edu/%7Etgirke/Links.htm#Profiling

  23. Example of an Integrated Clustering Tool: J-Express

  24. Microarray DatabasesSelection • Stanford Microarray Database (SMD) http://genome-www5.stanford.edu/MicroArray/SMD/ • Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo/

  25. Alternative Homework Assignment Do only one assignment! - Go to the SNOMAD page (Standardization and Normalization of Microarray Data): http://pevsnerlab.kennedykrieger.org/snomadinput.html - Select “Use an Example dataset to see how SNOMAD works” and chose either option #2 (Incyte dataset) or #3 (Affymetrix dataset). If you prefer you can use your own or other public data instead. A good resource to download public data is the Stanford site: http://genome-www5.stanford.edu/cgi-bin/SMD/publicData.pl - Select all possible transformations and graphs and submit the data for processing. - Report: Give a short description (one or two sentences) for each graph/transformation of the returned results.

  26. 4. Functional GroupsAssigning “Biological Meaning” to Profiling Data • Protein Families • COGs (43 genomes, NCBI): http://www.ncbi.nlm.nih.gov/COG/ • Protein Domain Databases (PFAM) • Gene Ontology Consortium Df: controlled vocabulary for all organisms http://www.geneontology.org/ • Pathways • KEGG Metabolic Pathways http://www.genome.ad.jp/kegg/kegg2.html • WIT Database (39 genomes) http://wit.mcs.anl.gov/WIT2/

  27. Toolboxes for Bioinformaticians Popular scripting languages Perl: http://www.perl.com/ Python: http://www.python.org/ Bio* modules for processing data from databases and applications BioPerl: http://bio.perl.org/ BioPython: http://biopython.org/ BioJava: http://www.biojava.org/ BioRuby: http://bioruby.org/ Statistics R: http://www.R-project.org BioConductor (Microarray): http://www.bioconductor.org/ Database systems MySQL: http://www.mysql.com/ PostgreSQL: http://www.postgresql.org/

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