Ensembl workshop. Thomas Randall, PhD bioinformatics.unc.edu. handouts, papers, datasets

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1 Ensembl workshop Thomas Randall, PhD bioinformatics.unc.edu handouts, papers, datasets

2 Ensembl is a joint project between EMBL - EBI and the Sanger Institute to develop a free software system which produces and maintains annotation on selected eukaryotic genomes.

3 Ensembl Release 54 (May 2009) new release approx every 2 months improvements in assembly/annotation of older genome builds new genome builds Importation of genome databases (Drosophila recently) Improvements/changes to interface and or new precompiled annotations, etc. OR

4 Ensembl vs UCSC Browser Based on same genome builds easier to view older builds in UCSC Some annotation tracks overlap, some do not Complementary should know both UCSC easier to upload and display custom tracks Ensembl more comprehensive, customizable downloading (BioMart) Visual display of Ensembl better (my opinion) UCSC more stable, upgrades done transparently Track IDs in both are very confusing Both linked to Galaxy ( large scale file manipulation toolkit

5 Annotation Addition of positional, functional, regulatory and evolutionary datasets to a raw assembled genome. Genes, exon-intron boundaries, protein products, mirnas, alternative splicing, transcriptional start sites, expression, orthologs, paralogs, repeats, structural features, syntenic relationships, ChIP-chip data... Based on experimental data, computational predictions, and pipelines that are a mix of both are used. Displayed as tracks one track = one form of annotation Constantly changing!

6 One gene = 4.9 transcripts (in vivo complications) ENCODE: Comprehensive annotation of 1% of the human genome Nature 447: 799

7 Many pre-compiled bioinformatics analyses orthologs in all other Ensembl genomes multiple sequence alignments of above mapping of publicly available genome wide data ChIPchip etc. (features) Variety of gene prediction pipelines available (Havana, Vega, Ensembl) in addition to experimentally derived annotations Extensive links out to other databases Annotation is a mix of experimental and computationally derived sources Quality of annotation available for specific genomes depends largely on level of research on a genome: human/mouse good, opossum mediocre to non-existent

8 Ensembl architecture used by others

9 Annotation differs between groups Ensembl Gene ID - Ensembl stable gene identifiers are mapped between releases. In case a gene model changes dramatically, the old stable identifier may be retired and a new one assigned. Vega: Vega genes (Ashurst, et al., 2005) represent a set of manually annotated genes. Finished genomic sequence is analysed on a clone-by-clone basis using a combination of similarity searches against DNA and protein databases, as well as a series of ab initio gene predictions (GENSCAN, GENEWISE). Gene structures are annotated on the basis of human interpretation of the combined supportive evidence generated during sequence analysis. Havana: Annotation produced by the Human And Vertebrate Analysis and Annotation (Havana) group at Sanger Institute is publicly available from various websites such as Vega, Ensembl, and UCSC. Our annotation puts special emphasis on splice variants and pseudogenes, two areas still underdeveloped in automated annotation systems, and poly-adenylation features. Also, where other systems concentrate on or are limited to protein coding genes, many Havana transcripts are annotated without a protein coding region. These transcripts may function as noncoding RNAs or they may be incomplete gene fragments for which we cannot yet determine the CDS. We require that all annotated gene structures (transcripts) are supported by transcriptional evidence, either from cdna, EST or protein sequences, and as such not all annotated transcripts are necessarily complete. UCSC: The UCSC Genes track shows gene predictions based on data from RefSeq, Genbank, CCDS and UniProt. This is a moderately conservative set of predictions, requiring the support of one GenBank RNA sequence plus at least one additional line of evidence. The RefSeq RNAs are an exception to this, requiring no additional evidence. The track includes both protein-coding and putative non-coding transcripts. Some of these non-coding transcripts may actually code for protein, but the evidence for the associated protein is weak at best. Compared to RefSeq, this gene set has generally about 10% more protein-coding genes, approximately five times as many putative non-coding genes, and about twice as many splice variants.

10 Gene Identifiers (in silico complications)

11 ID chaos

12 Genome Builds All genomes are in different states of completion Gaps are being filled, corrections are being made New techniques or ideas for improvements to assemblies Human Feb 2009 assembly = hg19 = GRCh37 Mar 2006 assembly = hg18 = NCBI36 May 2004 assembly = hg17 = NCBI35 July 2003 assembly = hg16 = NCBI34 NM_ (CFTR) hg18: chr hg17: chr hg16: chr Mouse July 2007 = mm9 = NCBI37 Feb 2006 = mm8 = NCBI36.. May 2004 = mm5 = NCBI33 NM_ (PEMT) hg18: chr hg17: chr hg16: chr

Genome Builds Chromosome specific change Between build 35&36, Human Most recent human release - Spring 2009 Most recent mouse release - Sumer 2009 chr1 chr2 chr3 chr4 chr5 chr6 chr7

13 Genome Builds Chromosome specific change Between build 35&36, Human Most recent human release - Spring 2009 Most recent mouse release - Sumer 2009 chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrx chry yes yes yes yes no yes yes no yes yes no yes no no no no no no no no no yes yes yes

14 Overview Navigation of site gene, location, variations Customizing annotations BLAT Exporting data Custom Tracks (importing data) BioMart (brief)

15 Help and Documentation page

16 Location tab Gene tab Navigation of Ensembl Many individual summaries Currently displayed summary

17 Each summary of data Is customizable Configure this page customize region based on annotations of interest

18 Customization of view based on different categories of annotation

19 Multi BlastView Blast and Blat Ensembl provides access to the BLAST and BLAT sequence similarity search algorithms via a single interface. It allows for simultaneous searches with up to 30 query sequences against multiple target species. BLAT Blast like alignment tool Both are faster than BLAST, good for short sequence alignments, near identity

BLAT Blast Like Alignment ~500 X faster than BLAST Tool Default alignment tool in Ensembl Manages the sequence database differently than BLAST Good for aligning small sequences, oligos Best for

20 BLAT Blast Like Alignment ~500 X faster than BLAST Tool Default alignment tool in Ensembl Manages the sequence database differently than BLAST Good for aligning small sequences, oligos Best for alignments of near identity used in resequencing projects to compare against a reference genome Both UCSC and Ensembl do BLAT Ensembl output links to underlying genomes Ensembl better Kent, 2002 Genome Res. 12: 656 Ensembl BLAST is slow

21 Exporting Data from Ensembl Use Export Gene Data function from the Gene tab Use Export information about region function from Location tab Use BioMart for exporting larger custom datasets Use Ensembl FTP download to grab raw genome wide data

22 Exporting from Gene tab Most useful for downloading sequence of entire gene with flanking region and desired features

23 Export options Choose type of sequence to export

24 There are several options for saving the file. One is to use Save page as. When the Save As Window appears, type in a filename and an extension such as.txt. The resulting file that is saved to your desktop can then be directly dragged into Vector NTI. Also, copy and paste into Word/Notepad

25 Exporting from Location tab

26 There are several options for saving the file. One is to use Save page as. When the Save As Window appears, type in the filename using.gb as extension. The resulting file that is saved to your desktop can then be directly dragged into Vector NTI with features.

27 Importing data Add/manage custom data Start at any Location tab Click on Add/manage Choose upload Close window to display as a track

28 BioMart example: Download coding regions for all genes on human chr22 Database: Ensembl 52, Homo sapiens genes Filters Region: chr22 Gene, ID list limit: HGNC symbol Attributes Data type: Sequences Header: turn off all Ensembl Attributes, Associated gene name Sequences: choose coding sequence Result: 955 ORFs in concatenated FASTA format

29 No tool is an island In genome queries, multiple tools required Interaction between tools is often the limiting factor ??? see PLOS Comp Biol 4 e121 for discussion on the interaction of these tools

ab initio and Evidence-Based Gene Finding

ab initio and Evidence-Based Gene Finding A basic introduction to annotation Outline What is annotation? ab initio gene finding Genome databases on the web Basics of the UCSC browser Evidence-based gene