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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 l.jpg

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

  • columnsare 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 > 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 ..









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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;







>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;



>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;





>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;







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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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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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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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  • 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

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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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ENTREZis the GenBank web query tool

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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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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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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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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