The Ensembl Database. Dott.ssa Inga Prokopenko. Corso di Genomica

Transcription

1 The Ensembl Database Dott.ssa Inga Prokopenko Corso di Genomica 1

2 Lecture 7.1 2

3 What is Ensembl? Public annotation of mammalian and other genomes Open source software Relational database system The future of genomic bioinformatics? Lecture 7.1 3

4 The Ensembl Project Ensembl is a joint project between EMBL European Bioinformatics Institute and the Sanger Institute to develop a software system which produces and maintains automatic annotation on eukaryotic genomes. Ensembl is primarily funded by the Wellcome Trust Lecture 7.1 4

5 The Ensembl Project The main aim of this campaign is to encourage scientists across the world - in academia, pharmaceutical companies, and the biotechnology and computer industries - to use this free information. - Dr. Mike Dexter, Director of the Wellcome Trust 5

6 Goal: An Accessible, Annotated Genome Diagram of contigview as what we want in the end Lecture 7.1 6

7 Ensembl Software System Uses extensively BioPerl ( The free mysql database Entire Ensembl code base is freely available under Apache open source license. Mainly written in Perl, extensions in C. Some viewers have been written in Java (e.g. Apollo). Lecture 7.1 7

8 Ensembl Genome Annotation Utilizes raw DNA sequence data from public sources Creates a tracking database (The Ensembl database ) Joins the sequences - based on a sequence scaffold or Golden Path Automatically finds genes and other features of the sequence Associates sequence and features with data from other sources Provides a publicly accessible web based interface to the database Lecture 7.1 8

9 The Genome Problem The problem with the genome (particularly human) is that it is large, complicated, and opaque to analysis (Ewan Birney, Ensembl) Genome features to identify include: Genes: protein coding, RNA, pseudogenes Regulatory elements SNPs, repeats, etc. Lecture 7.1 9

10 DNA sequence in Ensembl Sequences are determined in fragments (contigs) Features cross boundaries between fragments Entire sequence too large and changes too much (constantly updated and reassembled) to be stored as one long database entry Lecture

11 DNA sequence in Ensembl Core design feature is the virtual contig object Allows genome sequence to be accessed as a single large contiguous sequence even though it is stored as a collection of fragments VC object handles reading and writing features to the DNA sequence Lecture

12 Ensembl Gene Build System Three-part gene build system Best in genome matches for known genes Alignment of homologous genes Genes predicted on repeat-masked DNA All genes predicted based on experimental (available sequence) evidence Lecture

13 Best in genome predictions Find known proteins from SPTREMBL on genome using pmatch Incorporate cdnas using exonerate and EST_genome Align with gaps placed preferentially at splice consensus sites Allows prediction of 5 and 3 UTRs Refine predictions using genewise Lecture

14 Best in genome predictions Alignments shown in ContigView UTRs predicted Known gene (p53) ContigView of best in genome gene with associated evidence Proteins aligned Unigene clusters aligned cdnas aligned Lecture

15 Homology predictions Align homologous proteins using BLAST, genewise Paralogs (from same organism) Orthologs (from closely related organisms) Assemble novel genes Lecture

16 Ab initio gene predictions Use Genscan to identify novel exons Confirm exons by BLAST to known proteins, mrnas, UniGene clusters Based on ab initio predictions but require homology evidence ContigView of homology gene with associated evidence Unigene clusters aligned Proteins aligned GenScan predictions Novel gene Lecture

17 Pseudogenes Many pseudogenes also predicted Lecture

18 Ensembl Gene Build System Resulting Ensembl genes are highly accurate with low false positive rates Snapshot or stats on genes Ensembl human gene identifiers are 95% stable between builds Lecture

19 Ensembl EST genes ESTs not accurate enough to produce Ensembl genes, but important especially for identifying alternative transcripts Create an independent set of EST genes Known gene EST genes Unigene clusters aligned Lecture

20 Expressed sequence tag or EST short sub-sequence of a transcribed cdna sequence. may be used to identify gene transcripts, instrumental in gene discovery and gene sequence determination. The identification of ESTs has proceeded rapidly, with approximately 52 million ESTs available in public databases (e.g. GenBank 5/2008, all species). Lecture

21 ESTs An EST is produced by one-shot sequencing of a cloned mrna (i.e. sequencing several hundred base pairs from an end of a cdna clone taken from a cdna library). The resulting sequence is a relatively low quality fragment whose length is limited by current technology to approximately 500 to 800 nucleotides. Because these clones consist of DNA that is complementary to mrna, the ESTs represent portions of expressed genes. They may be present in the database as either cdna/mrna sequence or as the 21 reverse complement of the mrna, the template strand.

22 EST mapping ESTs can be mapped to specific chromosome locations using physical mapping techniques, such as radiation hybrid mapping or FISH. Alternatively, if the genome of the organism that originated the EST has been sequenced one can align the EST sequence to that genome. Lecture

23 ESTs and genes The current understanding of the human set of genes includes the existence of thousands of genes based solely on EST evidence. ESTs become a tool to refine the predicted transcripts for those genes, which leads to prediction of their protein products, and eventually of their function. The situation in which those ESTs are obtained (tissue, organ, disease state - e.g. cancer) gives information on the conditions in which the corresponding gene is acting. ESTs contain enough information to permit the design of precise probes for DNA microarrays that then can be used to determine the gene expression. 23

24 Ensembl EST genes Map ESTs to genome using Exonerate, BLAST, and EST2Genome Define transcripts by merging redundant ends, setting splice sites to common ends Finds splice sites and defines UTRs Alternative transcript predicted if at least one alternatively spliced EST exists Process transcripts with Genomewise to find longest ORF for each Lecture

25 Ensembl EST genes Evidence for genes shown (ExonView) Lecture