Hospital Universitari Vall d’Hebron Institut de Recerca - VHIR

Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)

Bioinformàtica per la Recerca Biomèdica http://ueb.vhir.org/2014BRB

Alex Sánchez

alex.sanchez@vhir.org 13/05/2014

STORING AND ACCESSING INFORMATION DATABASES AND QUERIES

1. Data banks and databases ● Information in the genomics era ● Distinct DB usages ● To take into account ● Main resources providers

2. Types of databases ● EMBL vs NCBI ● Bibliography DB ● Taxonomy DB ● Nucleotide DB ● Genome DB ● Protein DB ● Microarray DB ● Other DB ● Lists of DB

PRESENTATION OUTLINE

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3. Structure and formats of the databases ● Structure of the DB ● Formats of the DB ● Sequence FASTA format ● GenBank entry example ● EMBL entry example

4. Submitting data

● Submitting sequences ● Submitting expression data

5. Tools for DB exploitation ● ENTREZ ● Cross-search tables ● Entrez queries ● Entrez fields ● Help system

Data banks and databases

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INFORMATION IN THE GENOMICS ERA

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• Genomics era: huge amount of data

• To be able to use this information, it should be properly stored

• The access to that info

– Must be quick

– Has to be done in a flexible way

• That is possible thanks to the

– Creation of databases

– It’s online availability

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DISTINCT DB USAGES

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• Information search – By keyword, accession number, authors…

• Homology search – Is there any sequence identical or similar to that mine?

• Pattern search – Has my sequence any known pattern?

• Predictions – Can I find proteins, with already known function, similar to

mine?

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Bioinformatics reagent: Databases

Organized array of information

Place where you put things in, and (if all is well) you should be able to get them out again.

Resource for other databases and tools.

Simplify the information space by specialization.

Bonus: Allows you to make discoveries.

Important question to ask: what is the data model?

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Bioinformatics experiments:

BLAST search Sequence Alignment

Reagents:

•Sequence •Databases

Method: •P-P BLASTP •N-P BLASTX •P-N TBLASTN •N-N BLASTN •N (P) – N (P) TBLASTX

Interpretation:

•Similarity •Hypothesis testing

Know your reagents

Know your methods

Do your controls

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Nature 409:452

Bioinformatics Citizenship: What it means, and what does it cost?

Databases

Information system

Query system

Storage System

Data

Databases

Information system

Query system

Storage System

Data

GenBank flat file COSMIC record Interaction Record Title of a book Book

Databases

Information system

Query system

Storage System

Data

Boxes

Oracle

MySQL

PC binary files

Unix text files

Bookshelves

Databases

Information system

Query system

Storage System

Data

A List you look at A catalogue indexed files SQL grep

The library of Congress Google Entrez EnsEMBL UCSC gemome browser

Databases

Information system

Query system

Storage System

Data

TO TAKE INTO ACCOUNT

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Information organization

Resources providers Databases Tools

Organizations or centers devoted to the offer and maintain the databases

To find/check/export information into/from DB

Diverse and very different information

MAIN RESOURCES PROVIDERS

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• The National Center for Biotechnology Information (NCBI) offers data banks, databases and tools at the USA

• The European Bioinformatics Institute (EBI) does a similar function in Europe

• GenomeNet gathers several databases from Japan

Types of databases

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TYPES OF DB

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• There are hundreds of BD, so it is not feasible to enumerate them (but they have tried here)

• We can classify them by multiple criteria

• The structural organization of the EMBL and the NCBI resources is radically different

EMBL vs NCBI

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• EMBL – Bibliographic DB

– Taxonomic DB

– Nucleotide DB

– Genomic BD

– Protein BD

– Microarrays DB

…

• NCBI – PubMed

– Entrez

– OMIM

– Books

– TaxBrowser

– Structure

…

BIBLIOGRAPHY DB

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• Collection of papers published in scientific journals

– Pubmed (NCBI)

– Medline (EBI)

– Biocatalog: papers organized by concrete molecular biology topics

TAXONOMY DB

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• Information on the classification of living things

– basically hierarchical

– and based on molecular evidences

• To classify any organism from which at least one nucleic acid sequence has been determined

• There is indeed some controversy in the scientific community

NUCLEOTIDE DB

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• Sequences from experimental laboratories

• Daily updated

• Daily exchanging of its contents

– Genbank (NCBI)

– EMBL (EBI)

– KEGG (Genome net)

Sequences NOT in NucleotideDB

• WGS: whole genome shotgun

• TPA: third party annotations

• SNPs

• SAGE tags (serial analysis of gene expression)

• RefSeq (Genomic, mRNA, or protein)

• Consensus sequences

GENOME DB

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• Sequences and annotations of whole genomes

– Ensembl (EBI)

– Genome viewer (NCBI)

– Goldenpath (UCSC)

• Specialized genomic resources

– Transfact

– EST

– UTRDB

– SpliceSitesDB

…

PROTEIN DB (I)

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• Aminoacids primary sequences

– Without human revision

• Trembl (EBI)

• NR (NCBI)

– With annotation’s curation

• Uniprot (EBI)

– Proteome DB

• Proteome analysis (EBI)

PROTEIN DB (II)

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• Secondary structures or protein domains

• They depend on the protein source and the analysis perfomed on them

– PROSITE: Regular Expressions over Swiss-Prot

– PRINTS: Set of motifs that define a family over Swiss-Prot/TrEMBL

– BLOCKS: Aligned motifs from PROSITE/PRINTS

– PFAM: Markov Modelos over Swiss-Prot

– INTERPRO: Integrates information from several domain-focused data bases.

PROTEIN DB (III)

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• 3D structures with coordinates of each atom

– PDB: Reference protein 3D

structure (x-ray, NMR) database

– CATH: Classification of the PDB in different functional and structural groups

– MMDB: subset de PDB maintained by the NCBI

– MSD: subset of the PDB maintained by the EBI

MICROARRAY DB

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• Expression arrays results

– ArrayExpress

– caArray

– Gene Expression Omnibus

OTHER DB (1)

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• Biological Annotations

– Gene Ontology

– KEGG

– Gene Cards

• Therapeutic targets

– Therapeutic targets database

– PharmGKB

…

Historical perspective on the Human Genome Data

Human Expressed Seq Tags (mRNA) sequencing

Human genome mapping and sequencing

Population analysis and polymorphism measurements

Genome Wide Association Studies

<the Homer paper>

The Cancer Genome Atlas pilot

The 1000 genome project

The Cancer Genome Atlas

The International Cancer Genome Consortium

• Detailed Phenotype and Outcome data • Region of residence • Risk factors • Examination • Surgery • Drugs • Radiation • Sample • Slide • Specific histological features • Analyte • Aliquot • Donor notes

• Gene Expression (probe-level data) • Raw genotype calls • Gene-sample identifier links • Genome sequence files

ICGC Controlled Access Datasets

• Cancer Pathology Histologic type or subtype Histologic nuclear grade

• Patient/Person Gender Age range

• Gene Expression (normalized) • DNA methylation • Genotype frequencies • Computed Copy Number and Loss of Heterozygosity • Newly discovered somatic variants

ICGC OA Datasets

http://goo.gl/w4mrV

Main source of Cancer Data: ICGC

http://dcc.icgc.org/

Module 2a bioinformatics.ca

Another source of important Cancer Data:

:

http://www.sanger.ac.uk/genetics/CGP/cosmic/

Module 2a bioinformatics.ca

What is Cancer Data? Structured Clinical Data about the patient

Structured Clinical Data about the treatment

Structured Clinical Data about the tumor

Associated with a number of positions (hundreds, if not thousands) of nucleotide coordinate system on one reference genome.

ICGC is implementing NCBI’s bioprojects http://www.ncbi.nlm.nih.gov/bioproject

LISTS OF BD

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Nucleic Acids Research Database Listing

– Annual Database issue http://www.oxfordjournals.org/nar/database/c/

– Suplement that comes with each year’s January issue

– 2009 2013 describes 179 1512 databases, sorted into 14 categories and 41 subcategories.

– They ara added to the list of Nucleic Acids Research online Molecular Biology Database Collection

– Good starting point for selecting the appropriate DB

LISTS OF BD

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Structure and formats of the DB

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STRUCTURE OF THE DB

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• The way of organizing data in any DB depends mainly in the model or architecture in which it is based on

• There are multiple models

Relational, Hierarchical, Network-based…

but the most usual relational

– Several tables, that could have relationships between them

– The relationships are done through key fields

FORMATS OF THE DB

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• To work with relational DB implies the use of plane data formats

– Text files

– Some kind of labels to specify the contents of every line or region of the file

• There are multiple formats, so a good program or application should be able to recognize (and even interchange) them.

SEQUENCE FASTA FORMAT

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Identifier Additional info

se

qu

ence

1st lin

e

>gi|15341523|gb|AF405321.1| Human echovirus 29 strain JV-10 5' UTR, partial

sequence CAAGCACTTCTGTTTCCCCGGACTGAGTATCAATAGACTGCTCACGCGGTTGAAGGAGAAAACGTTCGTT

ATCCGGCCAACTACTTCGAGAAACCTAGTAACGCCATGGAAGTTGTGGAGTGTTTCGCTCAGCACTACCC

CAGTGTAGATCAGGTTGATGAGTCACCGCATTCCCCACGGGTGACCGTGGCGGTGGCTGCGTTGGCGGCC

TGCCCATGGGGAAACCCATGGGACGCTCTTATACAGACATGGTGCGAAGAGTCTATTGAGCTAGTTGGTA

GTCCTCCGGCCCCTGAATGCGGCTAATCCCAACTGCGGAGCATACACTCTCAAGCCAGAGGGTAGTGTGT

CGTAATGGGCAACTCTGCAGCGGAACCGACTACTTTGGGT

>gi|15341527|gb|AF405325.1| Human echovirus 6 strain D' Amori 5' UTR, partial

sequence

CAAGCACTTCTGTTTCCCCGGACCGAGTATCAATAAGCTGCTCACGCGGCTGAAGGAGAAAGTGTTCGTT

ACCCGGCTAGTTACTTCGAGAAACCTAGTACCACCATGAAGGTTGCGCAGCGTTTCGCTCCGCACAACCC

CAGTGTAGATCAGGTCGATGAGTCACCGCGTTCCCCACGGGCGACCGTGGCGGTGGCTGCGTTGGCGGCC

TGCCCATGGGGCAACCCATGGGACGCTTCAATACTGACATGGTGCGAAGAGTCTATTGAGCTAACTAGTA

GTCCTCCGGCCCCTGAATGCGGATAATCTTAACTGCGGAGCAGGTGCTCACAATCCAGTGGGTGGCCTGT

CGTAACGGGCAACTCTGCAGCGGAACCGACTACTTTGGGT

GENBANK ENTRY EXAMPLE

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EMBL ENTRY EXAMPLE

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Submitting data

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SUBMITTING DATA

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• Several biological databases are public, so any (properly identified) user can contribute uploading new data

• There are multiple types of data to upload, but the most usual are

– Sequencies

– Expression data (from microarrays)

SUBMITTING SEQUENCES

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How to submit your sequences to…

• EMBL

– http://www.ebi.ac.uk/embl/Submission/

• GeneBank

– http://www.nlm.nih.gov/pubs/factsheets/sdgenbk.html

SUBMITTING EXPRESSION DATA

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And your expression data to…

• ArrayExpress (EBI)

– http://www.ebi.ac.uk/microarray/submissions.html

• Gene Expression Omnibus (NCBI)

– https://www.ncbi.nlm.nih.gov/geo/info/faq.html

Tools for DB exploitation

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ENTREZ

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• It is the NCBI’s searching system

• Great power and versatility, but less intuitive than SRS

• It doesn’t provide forms for each field

• Usually used in a “Top Bottom” manner

– Perform a first query

– Refine the results until reaching what you are looking for.

CROSS-SEARCH TABLES

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ENTREZ QUERIES

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• Boolean operators: AND, OR, NOT, “”, *

• AND applied by default

• Query by Accession Numbers (AC) in

– Genbank / EMBL / DDBJ:

• 1 char. + 5 nums. (U12345)

• 2 char. + 6 nums. (AF123456)

– SwissProt / PIR:

• 1 char. + 5 nums. (P12345)

• Refine queries with the reserved word LIMITS

• Combine queries with HISTORY

ENTREZ AVAILABLE FIELDS

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HELP AND INFORMATION SYSTEM

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Estamos interesados en el gen MLH1 humano, implicado en el cáncer de colon

– Separar el grano de la paja: identificar una secuencia de mRNA representativa y bien anotada del gen MLH1.

– Obtener literatura asociada y su secuencia protéica.

– Identificar proteínas similares.

– Identificar dominios conservados dentro de la proteína.

– Identificar mutaciones conocidas en el gen o la proteína.

– Encontrar la estructura tridimensional de la proteína, si esta es conocida, o si no es así, identificar estructuras de secuencia homóloga.

– Ver el contexto genómico del gen y descargar la región que lo contiene.

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Ejemplos de búsqueda con Entrez

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Consulta directa (1.1)

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Consulta directa (1.2) Límites

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Consulta directa (1.3) Filtros

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Consulta directa (1.4) Registro

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Consulta (2) Enlaces a otras BD

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Consulta (3) Secuencias

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Consulta (4) Proteína

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Consulta (5.1) Mutaciones

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Consulta (5.2) SNPs

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Consulta (5.3) OMIM

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Consulta (6.1) Estructuras

Mouse over the residues of NP_000240 until the grey footer bar shows ‘gi 4557757, loc 67’ (Glycine). Click on the corresponding Glycine residue in 1H7U_A (loc 74) to highlight it.

In the structure window use the left mouse button to spin the 3D structure until you can clearly see and identify the highlighted residue. Is it possibly in the active site? For example, is it within 5 Ä of the ATPS molecule?

Double click on the Mg-complexed ATPS to highlight it. Then use the menu bar option called ‘Show/Hide|Select By Distance|Residues Only’ to highlight all residues within 5 Ä of the ATPS. Indeed, the Glycine at position #74 is within 5 Ä and is likely part of the active site for this energy-producing domain. This hints at the possible problems a Gly Trp mutation might cause at that position.

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Consulta (6.2) Alineamiento de secuencia y estructura

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Consulta (7) Visualización en contexto genómico