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Biological Databases

Biological Databases. 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).

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Biological Databases

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  1. Biological Databases

  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 technology • 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. Structured Data • Repository of information • managed and accessed differently • Flat-file (text) • Relational (key) • “talk” to each other

  7. Standard Data Formats • DNA sequence = ACGT, 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? Need standard formats!!

  8. FASTA Format • William Pearson (1985) • 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. >URO1 uro1.seq Length: 2018 November 9, 2000 11:50 Type: N Check: 3854 .. CGCAGAAAGAGGAGGCGCTTGCCTTCAGCTTGTGGGAAATCCCGAAGATGGCCAAAGACA ACTCAACTGTTCGTTGCTTCCAGGGCCTGCTGATTTTTGGAAATGTGATTATTGGTTGTT GCGGCATTGCCCTGACTGCGGAGTGCATCTTCTTTGTATCTGACCAACACAGCCTCTACC CACTGCTTGAAGCCACCGACAACGATGACATCTATGGGGCTGCCTGGATCGGCATATTTG TGGGCATCTGCCTCTTCTGCCTGTCTGTTCTAGGCATTGTAGGCATCATGAAGTCCAGCA GGAAAATTCTTCTGGCGTATTTCATTCTGATGTTTATAGTATATGCCTTTGAAGTGGCAT CTTGTATCACAGCAGCAACACAACAAGACTTTTTCACACCCAACCTCTTCCTGAAGCAGA TGCTAGAGAGGTACCAAAACAACAGCCCTCCAAACAATGATGACCAGTGGAAAAACAATG

  9. 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 …

  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 Sequence Databases • Three major repositories: • NCBI (www.ncbi.nlm.nih.gov) • EBI (www.ebi.ac.uk) • DDBJ (www.dbj.nig.ac.jp) • Same sequence information in all three, but different tools for searching and retrieval

  12. GenBank • Contains all DNA and protein sequences described in the scientific literature or collected in publicly funded research • Flatfile: Composed entirely of text • 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!!

  15. http://www.ncbi.nlm.nih.gov/Genbank • Once upon a time, GenBank mailed out sequences on CD-ROM disks a few times per year. • At least doubles in size every 18 months • There are approximately 106,533,156,756 bases in 108,431,692 sequence records in the traditional GenBank divisions and 148,165,117,763 bases in 48,443,067 sequence records in the WGS division as of August 2009.

  16. A few words about RefSeq • Many sequences in GenBank correspond to the same gene • genomic clones, full length mRNA, various kinds of ESTs, submitted by different investigators • RefSeq is the “Reference Sequence” for a gene - as determined by GenBank curators • best guess given the current evidence, can change • usually based on the longest mRNA • usually has both 5’ and 3’ UTR • Not necessarily reliable • A lot is not yet known… eg, alternative splicing

  17. Many Datasets 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)

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

  19. … or complex

  20. ENTREZis the GenBank web query tool

  21. Advanced query interface:

  22. 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.

  23. NAR Database Issue • Online collection of biological databases: http://www3.oup.co.uk/nar/database/c/

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

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

  26. Ensembl at EBI/EMBL

  27. 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

  28. Genome Ontology • Biology is a messy science • Assortment of names, mutants, odd phenotypes • “sonic hedgehog” • Genome Ontology • Molecular function (specific tasks) • Biological process (broad biological goal) • Cellular component (location)

  29. Database Searching A database can only be searched in ways that it was designed to be searched 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'

  30. Strategies • Use accession numbers whenever possible • Start with broad keywords and narrow the search using more specific terms • Try variants of spelling, numbers, etc. • Search all relevant databases • Be persistent!!

  31. Golden Rules • Use published databases and methods • Supported, maintained, trusted by community • Document what you have done !!! • Sequence identification numbers • Server, database, program VERSION • Program parameters • Assess reliability of results

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