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Stuart M. Brown, Ph.D. Director: NYU Bioinformatics Core

Bioinformatics Data and Databases. Stuart M. Brown, Ph.D. Director: NYU Bioinformatics Core. Biologists Collect Lots of Data. Hundreds of thousands of species Millions of articles in scientific journals Genetic information: gene names phenotype of mutants

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Stuart M. Brown, Ph.D. Director: NYU Bioinformatics Core

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  1. Bioinformatics Data and Databases Stuart M. Brown, Ph.D. Director: NYU Bioinformatics Core

  2. Biologists Collect Lots of Data • Hundreds of thousands of species • Millions of articles in scientific journals • Genetic information: • gene names • phenotype of mutants • location of genes/mutations on chromosmes • linkage (distances between genes)

  3. High Throughput lab technology • PCR • Rapid inexpensive DNA sequencing • Many methods of collecting genotype data • Assays for specific polymorphisms • Genome-wide SNP chips • Must have data quality assessment prior to analysis

  4. What is a Database? • Organized data • Information is stored in "records" and "fields" • Fields are categories • Must contain contain data of the same type • Records contain data that is related to one object

  5. A Spreadsheet can be a Database • columnsare Fields • Rows are Records • Can search for a term within just one field • Or combine searches across several fields

  6. Data Formats • How to organize various types of genetic data? • Need standard formats • DNA sequence = GATC, but what about gaps, unknown letters, etc. • How many letters per line • ?? Spaces, numbers, headers, etc. • Store as a string, code as binary numbers, etc. • Use a completely different format for proteins?

  7. FASTA Format • In the process of writing a similarity searching program (in 1985), William Pearson designed a simple text format for DNA and protein sequences • The FASTA format is now universal for all databases and software that handles DNA and protein sequences One header line, starts with > with a [return] at end All other characters are part of sequence.Most software ignores spaces, carriage returns. Some ignores numbers >URO1 uro1.seq Length: 2018 November 9, 2000 11:50 Type: N Check: 3854 .. CGCAGAAAGAGGAGGCGCTTGCCTTCAGCTTGTGGGAAATCCCGAAGATGGCCAAAGACA ACTCAACTGTTCGTTGCTTCCAGGGCCTGCTGATTTTTGGAAATGTGATTATTGGTTGTT GCGGCATTGCCCTGACTGCGGAGTGCATCTTCTTTGTATCTGACCAACACAGCCTCTACC CACTGCTTGAAGCCACCGACAACGATGACATCTATGGGGCTGCCTGGATCGGCATATTTG TGGGCATCTGCCTCTTCTGCCTGTCTGTTCTAGGCATTGTAGGCATCATGAAGTCCAGCA GGAAAATTCTTCTGGCGTATTTCATTCTGATGTTTATAGTATATGCCTTTGAAGTGGCAT CTTGTATCACAGCAGCAACACAACAAGACTTTTTCACACCCAACCTCTTCCTGAAGCAGA TGCTAGAGAGGTACCAAAACAACAGCCCTCCAAACAATGATGACCAGTGGAAAAACAATG

  8. Multi-Sequence FASTA file >FBpp0074027 type=protein; loc=X:complement(16159413..16159860,16160061..16160497); ID=FBpp0074027; name=CG12507-PA; parent=FBgn0030729,FBtr0074248; dbxref=FlyBase:FBpp0074027,FlyBase_Annotation_IDs:CG12507 PA,GB_protein:AAF48569.1,GB_protein:AAF48569; MD5=123b97d79d04a06c66e12fa665e6d801; release=r5.1; species=Dmel; length=294; MRCLMPLLLANCIAANPSFEDPDRSLDMEAKDSSVVDTMGMGMGVLDPTQ PKQMNYQKPPLGYKDYDYYLGSRRMADPYGADNDLSASSAIKIHGEGNLA SLNRPVSGVAHKPLPWYGDYSGKLLASAPPMYPSRSYDPYIRRYDRYDEQ YHRNYPQYFEDMYMHRQRFDPYDSYSPRIPQYPEPYVMYPDRYPDAPPLR DYPKLRRGYIGEPMAPIDSYSSSKYVSSKQSDLSFPVRNERIVYYAHLPE IVRTPYDSGSPEDRNSAPYKLNKKKIKNIQRPLANNSTTYKMTL >FBpp0082232 type=protein; loc=3R:complement(9207109..9207225,9207285..9207431); ID=FBpp0082232; name=mRpS21-PA; parent=FBgn0044511,FBtr0082764; dbxref=FlyBase:FBpp0082232,FlyBase_Annotation_IDs:CG32854-PA,GB_protein:AAN13563.1,GB_protein:AAN13563; MD5=dcf91821f75ffab320491d124a0d816c; release=r5.1; species=Dmel; length=87; MRHVQFLARTVLVQNNNVEEACRLLNRVLGKEELLDQFRRTRFYEKPYQV RRRINFEKCKAIYNEDMNRKIQFVLRKNRAEPFPGCS >FBpp0091159 type=protein; loc=2R:complement(2511337..2511531,2511594..2511767,2511824..2511979,2512032..2512082); ID=FBpp0091159; name=CG33919-PA; parent=FBgn0053919,FBtr0091923; dbxref=FlyBase:FBpp0091159,FlyBase_Annotation_IDs:CG33919-PA,GB_protein:AAZ52801.1,GB_protein:AAZ52801; MD5=c91d880b654cd612d7292676f95038c5; release=r5.1; species=Dmel; length=191; MKLVLVVLLGCCFIGQLTNTQLVYKLKKIECLVNRTRVSNVSCHVKAINW NLAVVNMDCFMIVPLHNPIIRMQVFTKDYSNQYKPFLVDVKIRICEVIER RNFIPYGVIMWKLFKRYTNVNHSCPFSGHLIARDGFLDTSLLPPFPQGFY QVSLVVTDTNSTSTDYVGTMKFFLQAMEHIKSKKTHNLVHN >FBpp0070770 type=protein; loc=X:join(5584802..5585021,5585925..5586137,5586198..5586342,5586410..5586605); ID=FBpp0070770; name=cv-PA; parent=FBgn0000394,FBtr0070804; dbxref=FlyBase:FBpp0070770,FlyBase_Annotation_IDs:CG12410-PA,GB_protein:AAF46063.1,GB_protein:AAF46063; MD5=0626ee34a518f248bbdda11a211f9b14; release=r5.1; species=Dmel; length=257; MEIWRSLTVGTIVLLAIVCFYGTVESCNEVVCASIVSKCMLTQSCKCELK NCSCCKECLKCLGKNYEECCSCVELCPKPNDTRNSLSKKSHVEDFDGVPE LFNAVATPDEGDSFGYNWNVFTFQVDFDKYLKGPKLEKDGHYFLRTNDKN LDEAIQERDNIVTVNCTVIYLDQCVSWNKCRTSCQTTGASSTRWFHDGCC ECVGSTCINYGVNESRCRKCPESKGELGDELDDPMEEEMQDFGESMGPFD GPVNNNY …

  9. Other Standards? • Other types of important medical and genetic data may not have universal standards: • Genotype/haplotype • Clinical records • Gene expression • Protein structure • Alignments • Phylogenetic trees

  10. Reformatting Data Files • Much of the routine (yet annoying) work of bioinformatics involves messing around with data files to get them into formats that will work with various software • Then messing around with the results produced by that software to create a useful summary…

  11. Public Databases • In addition to your own experimental data, access to public data is essential for epidemiology • Complete genome sequences (human and pathogens/vectors) • SNPs • Genotypes • Population Sets • Supplemental data for specific Journal articles

  12. GenBank is a Database • Contains all DNA and protein sequences described in the scientific literature or collected in publicly funded research • Flatfile: Composed entirely of text • you could print the whole thing out • Each submitted sequence is a record • Had fields for Organism, Date, Author, etc. • Unique identifier for each sequence • Locus and Accession #

  13. Fields

  14. Accession Numbers!! • Databases are designed to be searched by accession numbers (and locus IDs) • These are guaranteed to be non-redundant, accurate, and not to change. • Searching by gene names and keywords is doomed to frustration and probable failure • Neither scientists nor computers can be trusted to accurately and consistently annotate database entries • If only scientists would refer to genes by accession numbers in all published work!

  15. http://www.ncbi.nlm.nih.gov/Genbank • GenBank is managed by the National Center for Biotechnology Information (NCBI) at the NIH (part of the U.S. National Library of Medicine) • Once upon a time, GenBank mailed out sequences on CD-ROM disks a few times per year. • Now GenBank is over 100billion bases • Scientists access GenBank directly over the Web at www.ncbi.nlm.nih.gov

  16. What is GenBank? GenBank is the NIH genetic sequence database, an annotated collection of all publicly available DNA sequences (Nucleic Acids Research 2007 Jan ;35(Database issue):D21-5). There are approximately 65,369,091,950 bases in 61,132,599 sequence records in the traditional GenBank divisions and 80,369,977,826 bases in 17,960,667 sequence records in the WGS division as of August 2006.

  17. Relational Databases • Databases can be more complex than a single spreadsheet • GenBank has proteins and SNPs as well as DNA • Some fields (i.e. phosphorylation sites) apply to protein, but not DNA • Better to create a separate spreadsheet format for Protein records • Each different spreadsheet is called a Table • Different Tables are linked by key fields • (i.e. DNA and protein for same gene)

  18. Many Tables at NCBI • The NCBI hosts a huge interconnected database system that, in addition to DNA and protein, includes: • Journal Articles (PubMed) • Genetic Diseases (OMIM) • Polymorphisms (dbSNP) • Cytogenetics (CGH/SKY/FISH & CGAP) • Gene Expression (GEO) • Taxonomy • Chemistry (PubChem)

  19. Database Design A database can only be searched in ways that it was designed to be searched You can search within a specific Field in a specific Table - and sometimes can combine searches from different Fields and/or Tables (Boolean: "AND" and "OR" searches) Bad to search for "human hemoglobin" in a 'Description' field Much better to search for "homo sapiens in 'Organism' AND "HBB" in 'gene name'

  20. Web Query • Most Scientific databases have a web-based query tool • It may be simple…

  21. … or complex

  22. ENTREZis the GenBank web query tool

  23. Advanced query interface:

  24. ENTREZ has pre-computed links between Tables • Relationships between sequences are computed with BLAST • Relationships between articles are computed with "MESH" terms (shared keywords) • Relationships between DNA and protein sequences rely on accession numbers • Relationships between sequences and PubMed articles rely on both shared keywords and the mention of accession numbers in the articles.

  25. NCBI Databases contain more than just DNA & protein sequences

  26. Other Important Databases • Genomes • Proteins • Biochemical & Regulatory Pathways • Gene Expression • Genetic Variation (mutants, SNPs) • Protein-Protein Interactions • Gene Ontology (Biological Function)

  27. UCSC Genome Browser Search by gene name: or by sequence:

  28. Lots of additional data can be added as optional "tracks" - anything that can be mapped to locations on the genome

  29. Ensembl at EBI/EMBL

  30. SNPs (Single Nucleotide Polymorphisms) • Genetic variation • Can be alleles of genes • also differences in non-coding regions collected from genome sequencing of different individuals • dbSNP at the NCBI - all public SNP data • SNP Consortium at CSHL - high quality set

  31. KEGG: Kyoto Encylopedia of Genes and Genomes • Enzymatic and regulatory pathways • Mapped out by EC number and cross-referenced to genes in all known organisms (wherever sequence information exits) • Parallel maps of regulatory pathways

  32. NCI BioCarta

  33. Protein-Protein Interactions • Metabolic and regulatory pathways • Transcription factors • Co-expression • Biochemical data • crosslinking • yeast 2-hybrid • affinity tagging • Useful feedback to genome annotation/protein function and gene expression

  34. BIND - The Biomolecular Interaction Network Database

  35. Genome Ontology • Genetics is a messy science • Scientists have been working in isolation on individual species for many years - naming genes, mutants, odd phenotypes • “sonic hedgehog” • Now that we have complete genome sequences, how to reconcile the names across all species? • Genome Ontology uses a single 3 part system • Molecular function (specific tasks) • Biological process (broad biologial goals - e.g cell division) • Cellular component (location)

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