1. Tri-I Bioinformatics Workshop: Public data and tool repositories Alex Lash & Maureen Higgins Bioinformatics Core Memorial Sloan-Kettering Cancer Center

2. Workshop sections 1.Retrieving data from public resources public databases at NCBI, EBI, Ensembl locate and utilize some of the myriad of publicly available bioinformatics tools common data formats 2.Genome Browsers genome build process, ongoing and complete genome projects genome browsers of Ensembl, UCSC and NCBI Mapviewer broad survey of analysis tools and tutorials available on the Web for use directly and after download

3. Public data and tool repositories Section 1 Retrieving data from public resources

4. Goals A.Understand the scope and organization of the major public databases: NCBI, EBI/ Ensembl. B.Understand the importance of a unique identifiers, database fields, logical operators and wildcards. C.Be able to query, retrieve and display publications and sequences. D.Be able to visualize/analyze protein structure

5. Amyloid Precursor Protein (APP) ß-secretase  -secretase G-protein coupled receptor that binds heparin and laminin Controls nerve cell growth interacts with protein-synthesis machinery amyloid fibril amyloid plaque

6. NCBI Strengths are data storage, annotation and BLAST: 1.PubMed: Biomedical publications 2.Heritable diseases and syndromes 3.GenBank: Nucleotide and protein sequences 4.BLAST: Pairwise sequence comparison 5.Curated gene-centric data, including reference sequences 6.Genome builds 7.Nucleotide sequence traces Ex: Finding Entrez Gene record for APP

7. Indexing and logical operators Query: app[Gene Name] AND homo sapiens[Organism] 1 2 3 4 5 6 7 8… 0 1 1 0 0 0 0 0… … 0 1 0 0 0 1 0 0… … 1 0 0 0 1 1 0 0… … 0 1 0 0 0 0 1 0… aardvark … app … homo sapiens … mus musculus 0 0 0 0 0 1 0 0…AND 0 0 0 0 0 1 0 0… 1 0 0 0 1 1 0 0…

8. An Entrez Query 1.Query parsed: terms, fields and operators organized in a tree (if syntax incorrect generate error or warning) 2.Unfielded terms matched to synonyms, and extra terms, fields and operators added as needed 3.For each database: a)According to order of operations: i.Term found in appropriate index (if term not found, then generate warning) ii.Bit map pulled and uncompressed iii.Pairwise operations performed with previous result (if zero result, then stop) b)Number of results generated 4.If Global Query, display results summary and stop 5.List of UIDs generated from final result 6.UIDs sorted by user preference 7.Records pulled and displayed by user preference

9. Gene-centric questions 1.Where is a gene located? 2.What’s its genomic sequence? 3.What variations are associated with it? 4.What’s its exon-intron structure? 5.What are the mRNA sequences of its alternate transcripts? 6.What are the protein sequences of its isoforms? 7.What post-translational modification is possible? 8.What regulates its transcription? 9.What are its co-regulated partners? 10.What’s its normal function? 11.What’s its function in disease? 12.How does it fit into the larger cellular context? May depend upon cellular “state” Ex: Looking over the Entrez Gene record for APP

10. Common id and record formats 2.Formats a)Flat i.GenBank and GenPeptGenPept ii.FASTAFASTA iii.Multiple FASTA iv.AlignmentAlignment v.Multiple alignment vi.Tab-delimited b)Hierarchical i.ASN.1ASN.1 ii.XMLXML iii.HTML 1.Ids a)GenBank accession i.Nucleotide i.BI559391,Y00264BI559391,Y00264 ii.Protein i.AAB23646AAB23646 iii.RefSeqRefSeq b)Ensembl c)UniGene i.Hs.651215Hs.651215 d)PDB Structures i.1iyt1iyt e)HUGO Gene Names i.APP

11. NCBI’s RefSeq project 1.Is a project to create curated sequence records for the biopolymers of the Central Dogma: DNA, mRNA and protein 2.First release 2003 3.4,079 organisms, 3,234,358 proteins 4.Goals 1.non-redundancy 2.explicitly linked nucleotide and protein sequences 3.updates to reflect current knowledge of sequence data and biology 4.data validation and format consistency distinct accession series 5.ongoing curation by NCBI staff and collaborators, with reviewed records indicated 5.What’s its relationship to BLAST database called “nr”?

12. UniGene versus Entrez Gene 1.UniGene 1.Automated process that compares and clusters transcript-source sequences (no assembly) 2.Gene discovery tool: predates Entrez Gene, genome assemblies 3.Based primarily on EST sequences 4.ID turn-over and retirement is common 5.Currently 76 taxa and 1,299,304 clusters 2.Entrez Gene 1.Curated clearinghouse of gene-centric information 2.Grew out of LocusLink (eukaryote model organisms) and Entrez Genome (bacteria, viruses, organelles) 3.ID turn-over and retirement happens, but is less common since it is based primarily on sequenced genomes 4.Currently 3882 taxa and 2,479,759 genes 3.Hs: 85,793 UniGene clusters compared to 38,604 Entrez Gene records

13. EBI/Ensembl Strengths are data storage and analysis software: 1.Biomedical publications 2.Nucleotide and protein sequences 3.Protein domains/signatures 4.Sequence comparison 5.Sequence analysis 6.Structure analysis 7.Protein function analysis 8.Ensembl genome browser Ex: Looking at the APP gene in the EBI/Ensembl resources

14. Ensembl ids 1.Human 1.ENSG: gene 2.ENST: transcript 3.ENSE: exon 4.ENSP: protein 2.Other organisms 1.ENS{species 3-letter code}{G|T|P}{11 digits} 2.RNO=rat 3.MUS=mouse

15. Amyloid Precursor Protein (APP) ß-secretase  -secretase G-protein coupled receptor that binds heparin and laminin amyloid fibril amyloid plaque Ex: Viewing the structure of an amyloid fibril DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA

16. Other structure tools 1.Structure visualization. Free applications: a)RasMol b)Cn3D c)VMD 2.Structure prediction servers/applications a)CASP: Critical Assessment of Techniques for Protein Structure Prediction b)General method: i.Sequence similarity search to identify closest homolog with known structure ii.Fit to homolog’s known structure, minimizing some constraint

17. Problems 1.Query Entrez Gene with the following two queries separately and then explain the differences between the two results using a logical NOT operation: a)tyrosine kinase[Gene Ontology] AND human[Organism] b)cd00192[Domain] AND human[Organism] 2.Retrieve the APP gene record from NCBI and use the Display dropdown menu to display Conserved Domain Links. Use the ids of the listed domains to query Entrez Gene for records with the same domains. 3.Use the SNP Geneview link at NCBI to identify coding SNPs in the APP gene. Which SNP is missing from this display which was present in the Ensembl APP protein record? 4.Use the Homologene link at NCBI to identify possible functional orthologs for human APP. How does this list compare to the Ensembl list of orthologs that we reviewed previously?