Bioinformatics Data and Databases Stuart M. Brown, Ph.D

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Bioinformatics Data and Databases

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

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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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• 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

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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

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A Spreadsheet can be a Database

• columns are Fields
• Rows are Records
• Can search for a term within just one field
• Or combine searches across several fields

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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?

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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 gt with a return at
end All other characters are part of
sequence.Most software ignores spaces, carriage
returns. Some ignores numbers
gtURO1 uro1.seq Length 2018 November 9, 2000
1150 Type N Check 3854 .. CGCAGAAAGAGGAGGCGC
TTGCCTTCAGCTTGTGGGAAATCCCGAAGATGGCCAAAGACA ACTCAAC
TGTTCGTTGCTTCCAGGGCCTGCTGATTTTTGGAAATGTGATTATTGGTT
GTT GCGGCATTGCCCTGACTGCGGAGTGCATCTTCTTTGTATCTGACCA
ACACAGCCTCTACC CACTGCTTGAAGCCACCGACAACGATGACATCTAT
GGGGCTGCCTGGATCGGCATATTTG TGGGCATCTGCCTCTTCTGCCTGT
CTGTTCTAGGCATTGTAGGCATCATGAAGTCCAGCA GGAAAATTCTTCT
GGCGTATTTCATTCTGATGTTTATAGTATATGCCTTTGAAGTGGCAT CT
TGTATCACAGCAGCAACACAACAAGACTTTTTCACACCCAACCTCTTCCT
GAAGCAGA TGCTAGAGAGGTACCAAAACAACAGCCCTCCAAACAATGAT
GACCAGTGGAAAAACAATG
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Multi-Sequence FASTA file

• gtFBpp0074027 typeprotein locXcomplement(161594
  13..16159860,16160061..16160497) IDFBpp0074027
  nameCG12507-PA parentFBgn0030729,FBtr0074248
  dbxrefFlyBaseFBpp0074027,FlyBase_Annotation_IDs
  CG12507 PA,GB_proteinAAF48569.1,GB_proteinAAF485
  69 MD5123b97d79d04a06c66e12fa665e6d801
  releaser5.1 speciesDmel length294
• MRCLMPLLLANCIAANPSFEDPDRSLDMEAKDSSVVDTMGMGMGVLDPTQ
  
• PKQMNYQKPPLGYKDYDYYLGSRRMADPYGADNDLSASSAIKIHGEGNLA
  
• SLNRPVSGVAHKPLPWYGDYSGKLLASAPPMYPSRSYDPYIRRYDRYDEQ
  
• YHRNYPQYFEDMYMHRQRFDPYDSYSPRIPQYPEPYVMYPDRYPDAPPLR
  
• DYPKLRRGYIGEPMAPIDSYSSSKYVSSKQSDLSFPVRNERIVYYAHLPE
  
• IVRTPYDSGSPEDRNSAPYKLNKKKIKNIQRPLANNSTTYKMTL
• gtFBpp0082232 typeprotein loc3Rcomplement(92071
  09..9207225,9207285..9207431) IDFBpp0082232
  namemRpS21-PA parentFBgn0044511,FBtr0082764
  dbxrefFlyBaseFBpp0082232,FlyBase_Annotation_IDs
  CG32854-PA,GB_proteinAAN13563.1,GB_proteinAAN135
  63 MD5dcf91821f75ffab320491d124a0d816c
  releaser5.1 speciesDmel length87
• MRHVQFLARTVLVQNNNVEEACRLLNRVLGKEELLDQFRRTRFYEKPYQV
  
• RRRINFEKCKAIYNEDMNRKIQFVLRKNRAEPFPGCS
• gtFBpp0091159 typeprotein loc2Rcomplement(25113
  37..2511531,2511594..2511767,2511824..2511979,2512
  032..2512082) IDFBpp0091159 nameCG33919-PA
  parentFBgn0053919,FBtr0091923
  dbxrefFlyBaseFBpp0091159,FlyBase_Annotation_IDs
  CG33919-PA,GB_proteinAAZ52801.1,GB_proteinAAZ528
  01 MD5c91d880b654cd612d7292676f95038c5
  releaser5.1 speciesDmel length191
• MKLVLVVLLGCCFIGQLTNTQLVYKLKKIECLVNRTRVSNVSCHVKAINW
  
• NLAVVNMDCFMIVPLHNPIIRMQVFTKDYSNQYKPFLVDVKIRICEVIER
  
• RNFIPYGVIMWKLFKRYTNVNHSCPFSGHLIARDGFLDTSLLPPFPQGFY
  
• QVSLVVTDTNSTSTDYVGTMKFFLQAMEHIKSKKTHNLVHN
• gtFBpp0070770 typeprotein locXjoin(5584802..558
  5021,5585925..5586137,5586198..5586342,5586410..55
  86605) IDFBpp0070770 namecv-PA
  parentFBgn0000394,FBtr0070804
  dbxrefFlyBaseFBpp0070770,FlyBase_Annotation_IDs
  CG12410-PA,GB_proteinAAF46063.1,GB_proteinAAF460
  63 MD50626ee34a518f248bbdda11a211f9b14
  releaser5.1 speciesDmel length257
• MEIWRSLTVGTIVLLAIVCFYGTVESCNEVVCASIVSKCMLTQSCKCELK
  
• NCSCCKECLKCLGKNYEECCSCVELCPKPNDTRNSLSKKSHVEDFDGVPE
  
• LFNAVATPDEGDSFGYNWNVFTFQVDFDKYLKGPKLEKDGHYFLRTNDKN
  

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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

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SNPStats
HapStat
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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

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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

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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

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Fields
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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!

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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 100 billion bases
• Scientists access GenBank directly over the Web
  at www.ncbi.nlm.nih.gov

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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.
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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)

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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)

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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'

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Web Query

• Most Scientific databases have a web-based query
  tool
• It may be simple

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or complex
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ENTREZ is the GenBank web query tool
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Advanced query interface
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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.

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NCBI Databases contain more than just DNA
protein sequences
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Other Important Databases

• Genomes
• Proteins
• Biochemical Regulatory Pathways
• Gene Expression
• Genetic Variation (mutants, SNPs)
• Protein-Protein Interactions
• Gene Ontology (Biological Function)

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UCSC Genome Browser
Search by gene name
or by sequence
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Lots of additional data can be added as optional
"tracks" - anything that can be mapped to
locations on the genome
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Ensembl at EBI/EMBL
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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

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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

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NCI BioCarta
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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

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BIND - The Biomolecular Interaction Network
Database
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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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Database Search Strategies

• General search principles - not limited to
  sequence (or to biology)
• 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!!

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Bioinformatics Paradigm

• Find the data
• Download the data
• Reformat the data

• Collect the samples
• Run molecular analysis
• Filter the data

• Run analysis software
• Collect and sort results
• Publish / Data sharing