Transcript Ensembl
Bioinformatics Biological Databases Revised 17/09/13 Database architecture What should be stored How should it be stored Database architecture Refers to the manner in the entries in a database are organized • for archiving • easy retrieval (queries) Relational database Data are stores in tables Relationships between records can be many to one or many to many. In the latter case an index is required. All records in a table have identical features A record is identified by its table and record identifier For each new feature we need a new table Navarro et al., 2003 Object oriented database Record is defined by the entire hierarchy eg pTyr Root/Proteins/Protein1/Modifications/Ptyr Relationships between records are of a parent/child type Easy to automatically update Navarro et al., 2003 Standardization Requires standardized data format • MIAMI (microarray data) • HAWK (sequence data) Requires intelligent knowledge bases Introduction • Repository databases – Redundant – High low quality – Cutting edge information • Curated databases – Manual & automatic curation – Organization of information important – But mainly annotated entries – An attempt to be nonredundant – Comprehensive in some cases Sequence databases Sequence Formats • A sequence file needs to be recognized by a computer program, • special formats have been invented – FastA – GenBank >gi|1071819|pir||B54759 ba-type ubiquinol oxidase (EC 1.10.3.-) chain I Paracoccus denitrificans MATFSNETTFLLGRLNWDAIPKEPIVWATFVVVAIGGIAALAALTKYRLWGWLWREWFTSVDHKKIGIMYIVLALIMFVR GFADAIMMRLQQVWAFGGSEGYLNSHHYDQIFTAHGVIMIFFVAMPFITGLMNYVVPLQIGARDVSFPFLNNFSFWMTVG GAVITMAS Sequence formats GenBank Sequence Repositories at Ncbi • http://www.ncbi.nih.gov/Database/index.html • GenBank uses a relational model • New sequences can be submitted by a submission page. • GenBank also accepts submission of sequences with a high error rate and provides curated databases (99% accuracy) • 200000 users a day, 4 million queries a day NCBI NCBI Repository databases Sequence retrieval at Ncbi through ENTREZ ENTREZ, a resource prepared by NCBI is used to retrieve a DNA or protein sequence or Medline from the databases at NCBI. Sequence Repositories at Ncbi: GenBank Redundant number of entries => need for a comprehensive database Limit search in Entrez, allows complex queries GenBank format: DNA sequence GenBank format: protein sequence Sequence Repositories at Ncbi: EST DNA +1 transcription mRNA translation protein protein EST EST represent first pass sequences with an error rate as high as 1 in 100, including incorrectly identified bases and insertions http://www.ncbi.nlm.nih.gov/dbEST/ EST Aid in gene prediction: extrinsic gene finding methods Fielden et al. 2002 Comprehensive databases Curated databases • Unigene (Ncbi): automatic partitioning of GenBank into a nonredundant set of gene-oriented clusters • RefSeq (Ncbi): • ENSEMBL/VEGA (Ebi): Integrate the information as such that for a locus in the genome a complete description is given that is no longer redundant Provide a comprehensive non redundant set of sequences including genomic DNA, transcript and protein products for major research organisms Comprehensive DB: UniGene UniGene Comprehensive DB: UniGene • UniGene is an experimental system for automatically partitioning GenBank sequences into a non-redundant set of gene-oriented clusters • Each UniGene cluster contains sequences that represent a unique gene as well as related information such as the tissue types in which the gene has been expressed and map location. • These clusters represent the same gene based on the alignment of EST sequences with each other and with the genome sequences of the organism. • no attempt has been made to produce contigs – splicing variants for a gene are put into the same set. – Moreover, EST-containing sets often contain 5' and 3' reads from the same cDNA clone, but these sequences do not always overlap. UniGene As more overlapping sequences are added the number of clusters for an organism decreases Comprehensive DB: UniGene Comprehensive DB: REfSeq • For a particular gene many independent redundant records might exist in GenBank • All this information is integrated as such that for a particular locus in the genome a complete description is given that is no longer redundant: the locuslink • Redundant GenBank entries e.g. representing distinct indications on the transcript of a gene (incomplete cDNA sequences, ESTs) are unified to a single refseq that represents the complete transcript • A Refseq sequence – protein (starting with NP_) – a genomic sequence (starting with NG_) – All RefSeq sequences that belong to the same locus on the genome receive the same locus link – Additional links to other interesting databases containing additional functional annotation or information are made (e.g to Gene Ontology, RefSeq Gene: RefSeq Comprehensive DB: Ensembl Comprehensive DB: Ensembl Human protein (Swiss Prot) Genewise Other proteins Blast cDNA exonerate EST exonerate Add UTR Ab initio gene prediction GeneScan Cluster merge Merge Add variants Genes M cluster merge (UniGene) EST genes Comprehensive DB: Ensembl Automatic pipeline of Ensembl Ensembl • Ab initio gene scan: doesn’t use protein/cDNA/EST evidence • More genomes available: gene predictions will improve • ENSEMBL: 70-75% genes annotated • EST genes used to help predicting UTR and splice variants • Problem automatic annotation: pseudogenes Processed (with poly A tail) pseudogene Unprocessed (rearrangement, duplication) Ensembl • ENSEMBL: automatic analysis flow • VEGA (vertebrate genome annotation database) database: manual curation • refSeq: best curated database for cDNAs (no integration with ESTs (<-> VEGA) AUTOMATIC • Weeks • Use draft sequence • No pseudogenes MANUAL • Months • Need finished sequence • Pseudogenes • Consult public databases/ literature Vega Other databases Expression Databases • Microarray database: – SMD (Stanford) – Miami express (Ebi) – GEO (Ncbi) • SAGE data base • EST based expression database • Proteome database SGD SGD SGD DDD • http://www.ncbi.nlm.nih.gov/UniGene/ddd.cgi?ORG=Hs Pathway database KEGG Ontologies Controlled vocabularies Tree structured Describe gene products and associated processes Species independent • Gene Ontology • Ecocyc Ontologies GO: gene ontology • Organize biological information about proteins classes and functions into a hierarchical classification using controlled vocabulary http://www.ensembl.org/Homo_sapiens/goview?query=GO%3A0003700 GO GO GO GO ORFs within functional category P-value (log10) MIPS functional category (top-level) Graphical representatio n of cluster Number of ORFs Cluster number GO 1 426 energy transport facilitation 47 40 10 5 3 196 cell growth, cell division and DNA synthesis 149 protein synthesis cellular organisation 48 5 4 71 50 107 19 5 159 cell rescue, defense, 20 cell death and ageing 4 6 171 cell growth, cell division and DNA synthesis 78 cell growth, cell division and DNA synthesis 11 metabolism 76 24 23 4 9 6 9 37 EcoCYC EcoCYC Databases with regulatory motifs DNA motifs • Transfac • RegulonDB Protein Motifs • PFAM • Prosite • http://www.ncbi.nlm.nih.gov/Tools/ ID accessionnumber of a genomic sequence in the nucleotide database • Many databases with sequences that give information on the same locus • Need for comprehensive databases • ENSEMBL • LocusLink/RefSeq (ncbi) Integration Integrated analysis (algorithmic level) • Different data sources “Meta-analysis” Sequence analysis How combining, integrating comparing data from different sources Gain global insight “systems biology” Expression analysis