Course: Bioinformatics for Biomedical Research (2014).
Session: 1.2- Storing and Accessing Information. Databases and Queries.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Cultivation of KODO MILLET . made by Ghanshyam pptx
Storing and Accessing Information. Databases and Queries (UEB-UAT Bioinformatics Course - Session 1.2 - VHIR, Barcelona)
1. 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
2. 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
213/05/2014
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
4. INFORMATION IN THE GENOMICS ERA
4
• 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
13/05/2014
5. DISTINCT DB USAGES
5
• 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?
13/05/2014
6. 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?
7. 7
Bioinformatics experiments:
BLAST searchSequence 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
13. The library of Congress
Google
Entrez
EnsEMBL
UCSC gemome browser
Databases
Information system
Query system
Storage System
Data
14. TO TAKE INTO ACCOUNT
1413/05/2014
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
15. MAIN RESOURCES PROVIDERS
1513/05/2014
• 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
17. TYPES OF DB
1713/05/2014
• 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
18. EMBL vs NCBI
1813/05/2014
• EMBL
– Bibliographic DB
– Taxonomic DB
– Nucleotide DB
– Genomic BD
– Protein BD
– Microarrays DB
…
• NCBI
– PubMed
– Entrez
– OMIM
– Books
– TaxBrowser
– Structure
…
19. BIBLIOGRAPHY DB
1913/05/2014
• Collection of papers published in
scientific journals
– Pubmed (NCBI)
– Medline (EBI)
– Biocatalog: papers organized by
concrete molecular biology topics
20. TAXONOMY DB
2013/05/2014
• 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
21. NUCLEOTIDE DB
2113/05/2014
• Sequences from experimental laboratories
• Daily updated
• Daily exchanging of its contents
– Genbank (NCBI)
– EMBL (EBI)
– KEGG (Genome net)
22. 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
23. GENOME DB
2313/05/2014
• Sequences and annotations of
whole genomes
– Ensembl (EBI)
– Genome viewer (NCBI)
– Goldenpath (UCSC)
• Specialized genomic resources
– Transfact
– EST
– UTRDB
– SpliceSitesDB
…
24. PROTEIN DB (I)
2413/05/2014
• Aminoacids primary
sequences
– Without human revision
• Trembl (EBI)
• NR (NCBI)
– With annotation’s curation
• Uniprot (EBI)
– Proteome DB
• Proteome analysis (EBI)
25. PROTEIN DB (II)
2513/05/2014
• 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.
26. PROTEIN DB (III)
2613/05/2014
• 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
29. 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
30. • 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
33. Another source of important Cancer Data:
:
http://www.sanger.ac.uk/genetics/CGP/cosmic/
34. 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.
36. LISTS OF BD
3613/05/2014
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
39. STRUCTURE OF THE DB
3913/05/2014
• 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
40. FORMATS OF THE DB
4013/05/2014
• 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.
41. SEQUENCE FASTA FORMAT
4113/05/2014
Identifier Additional info
sequence
1stline
>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
45. SUBMITTING DATA
4513/05/2014
• 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)
46. SUBMITTING SEQUENCES
4613/05/2014
How to submit your sequences to…
• EMBL
– http://www.ebi.ac.uk/embl/Submission/
• GeneBank
– http://www.nlm.nih.gov/pubs/factsheets/sdgenbk.html
47. SUBMITTING EXPRESSION DATA
4713/05/2014
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
49. ENTREZ
4913/05/2014
• 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.
54. 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.
Vall d'Hebron Institut de Recerca 21/06/2011
Ejemplos de búsqueda con Entrez
66. 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.
Vall d'Hebron Institut de Recerca 21/06/2011
Consulta (6.2) Alineamiento de secuencia y
estructura
67. Vall d'Hebron Institut de Recerca 21/06/2011
Consulta (7) Visualización en contexto
genómico