Transcript Molecular & Genetic Epidemiology

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)
• 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
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
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
SNP ID
SNPSeq
ID
Gene
+primer
-primer
Hap A
Hap B
Hap
C
D1Mit160_1
10.MMHAP6
7FLD1.seq
lymphocyte
antigen 84
AAGGTAAAA
GGCAATCAG
CACAGCC
TCAACCTGG
AGTCAGAGG
CT
C
—
A
M-05554_1
12.MMHAP3
1FLD3.seq
procollagen,
type III, alpha
TGCGCAGAA
GCTGAAGTC
TA
TTTTGAGGT
GTTAATGGTT
CT
C
—
A
M-05554_2
X60184
complement
component
factor i
ACTTCCAGC
CCTGGCTCT
ATATGCCAC
CAAGAAGCA
A
C
—
M-09947_3
AF067835
caspase 8
TCACAGAGG
GAAACATGA
AG
CTCCACATT
GAACCAAAG
CA
G
C
T
M-11415_1
U02023
insulin-like
growth factor
binding protein
GGGAAAAGC
CTGAAAGAA
GC
AGCTGAAAC
CGGACATCA
AT
T
G
—
D1Mit284_
3
J05234
nucleolin
TGTTGGAAC
CGACTTCTT
CA
AAGAGTCAA
AGAATTTATG
GAATGA
G
T
T
Structured Data
• Repository of
information
• managed and
accessed differently
• Flat-file (text)
• Relational (key)
• “talk” to each other
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!!
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
..
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:CG33919PA,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
…
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…
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
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 #
Fields
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!!
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.
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
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)
Web Query
• Most databases have a web-based query
tool
• It may be simple…
… or
complex
ENTREZ is the GenBank
web query tool
Advanced
query
interface:
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.
NAR Database Issue
• Online collection of biological databases:
http://www3.oup.co.uk/nar/database/c/
UCSC Genome Browser
Search by gene name:
or by sequence:
Lots of additional
data can be
added as
optional "tracks"
- anything that
can be mapped
to locations on
the genome
Ensembl at EBI/EMBL
KEGG: Kyoto Encylopedia of
Genes and Genomes
• Enzymatic and regulatory pathways
• Mapped out by EC number and crossreferenced to genes in all known organisms
(wherever sequence information exits)
• Parallel maps of regulatory pathways
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)
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'
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!!
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