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The complexity of the transcriptional landscape of the human genome Roderic Guig ó Center for Genomic Regulation, Barcel

The complexity of the transcriptional landscape of the human genome Roderic Guig ó Center for Genomic Regulation, Barcelona. genes and proteins. One gene, one enzyme Beadle and Tatum. The Central Dogma Francis Crick. The standard model of the eukaryotic gene.

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The complexity of the transcriptional landscape of the human genome Roderic Guig ó Center for Genomic Regulation, Barcel

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  1. The complexity of the transcriptional landscape of the human genomeRoderic GuigóCenter for Genomic Regulation, Barcelona International Conference on Bioinformatics HKUST, Hong Kong 2007

  2. genes and proteins • One gene, one enzymeBeadle and Tatum • The Central DogmaFrancis Crick International Conference on Bioinformatics HKUST, Hong Kong 2007

  3. The standard model of the eukaryotic gene most of the transcriptional output of the human genome is localized in well defined genomic loci, which encode mRNAs that, when exported into the cytosol, are translated into proteins International Conference on Bioinformatics HKUST, Hong Kong 2007

  4. GENSCAN HMM (Burge & Karlin). Slide from M. Alexandersson International Conference on Bioinformatics HKUST, Hong Kong 2007

  5. International Conference on Bioinformatics HKUST, Hong Kong 2007

  6. 1% of the genome. 44 regions • target selection. commitee to select sequence targets • manual targets – a lot of information • radom targets – stratified by non exonic conservation with mouse gene density International Conference on Bioinformatics HKUST, Hong Kong 2007

  7. DNase Hypersensitive Sites DNA Replication Epigenetic  Genes and Transcripts Cis-regulatory elements (promoters, transcription factor binding sites) Long-range regulatory elements (enhancers, repressors/silencers, insulators)

  8. International Conference on Bioinformatics HKUST, Hong Kong 2007

  9. gencode: encyclopedia of genes and gene variants identify all protein coding genes in the ENCODE regions: • identify one complete mRNA sequence for at least one splice isoform of each protein coding gene. • eventually, identify a number of additional alternative splice forms. • Roderic Guigó, IMIM-UPF-CRG • Stylianos Antonarakis, GeneveAlexandre Reymond • Ewan Birney, EBI • Michael Brent, WashU • Lior Pachter, Berkeley • Manolis Dermitzkakis, Sanger • Jennifer Ashurst, Tim Hubbard International Conference on Bioinformatics HKUST, Hong Kong 2007

  10. the gencode pipeline • mapping of known transcripts sequences (ESTs, cDNAs, proteins) into the human genome • manual curation to resolve conflicting evidence • additional computational predictions • experimental verification • FINAL ANNOTATION International Conference on Bioinformatics HKUST, Hong Kong 2007

  11. the gencode pipeline • mapping of known transcripts sequences (ESTs, cDNAs, proteins) into the human genome • manual curation to resolve conflicting evidence • additional computational predictions • experimental verification • FINAL ANNOTATION International Conference on Bioinformatics HKUST, Hong Kong 2007

  12. The gencode pipelinemanual curation: havana (sanger)experimental verification:genevabioinformatics: imim • 2608 transcripts in 487 loci • 137 transcripts in 53 non-coding loci • 1097 coding transcripts and 1374 non-coding transcripts in 434 protein coding loci • most of protein coding loci encode • a mixture of protein coding and • non-coding transcripts

  13. one gene - many proteinsvery complex transcription units International Conference on Bioinformatics HKUST, Hong Kong 2007

  14. Distribution of DNaseI HSs vs. TSS in Different Gene Annotation Sets from the ENCODE Chromatin and Replication Group, John Stamatoyannopoulos International Conference on Bioinformatics HKUST, Hong Kong 2007

  15. EGASP’05 • the complete annotation of 13 regions was released in january 30, 2005. • The annotation of the remaining 31 regions was being obtained, and it was withheld. • gene prediction groups were asked to submit predictions by april 15, 2005 in the remaining 31 regions. • 18 groups participated, submiting 30 prediction sets • predictions were compared to the annoations in an NHGRI sponsored workshop at the Wellcome Trust Sanger Institute, on may 6 and 7, 2005. International Conference on Bioinformatics HKUST, Hong Kong 2007

  16. EGASP’05 • two main goals: • to assess how automatic methods are able to reproduce the (costly) manual/computational/experimental gencode annotation • how complete is the gencode annotation. are there still genes consistenly predicted by computational methods International Conference on Bioinformatics HKUST, Hong Kong 2007

  17. accuracy measures International Conference on Bioinformatics HKUST, Hong Kong 2007

  18. accuracy at the coding exon level evidence-based dual genome “ab intio” International Conference on Bioinformatics HKUST, Hong Kong 2007

  19. EGASP’05 programs are quite good at calling the protein coding exons (accuracy at 80%) Not as good at calling the transcribed exons), butthe best of the programs predict correctly only 40% of the complete transcripts (considering only the coding fraction) International Conference on Bioinformatics HKUST, Hong Kong 2007

  20. EGASP’05 many novel exons predicted:-8,634 unique exons predicted in intergenic regions- we ranked the exons according to the accuracy of te predicted programs- tested 238 exon pairs by RT-PCR in 24 tissues- only 7 (less than 3%) were confirmed positive International Conference on Bioinformatics HKUST, Hong Kong 2007

  21. International Conference on Bioinformatics HKUST, Hong Kong 2007

  22. DNase Hypersensitive Sites DNA Replication Epigenetic  Genes and Transcripts Cis-regulatory elements (promoters, transcription factor binding sites) Long-range regulatory elements (enhancers, repressors/silencers, insulators)

  23. Genome tiling arrays Slide from http://signal.salk.edu/msample.html Salk Institute Genomic Analysis Laboratory International Conference on Bioinformatics HKUST, Hong Kong 2007

  24. TRANSCRIPTION OF PROCESSED POLY A+ RNA based on a number of high throughput tecnologies International Conference on Bioinformatics HKUST, Hong Kong 2007

  25. Table 1: Summary of Transcriptional Coverage of ENCODE Regions.

  26. Table 1: Summary of Transcriptional Coverage of ENCODE Regions.

  27. Other recent studies Many individual studies suggest unanticipated complexity of the transcriptional map of the human genome: • Kapranov et al. (2007)RNA onto tiling arrays, novel RNA classes, hundreds of thousands of novel sites of transcription • Peters et al. (2007)LongSage, evidence for thousands of novel transcripts • Roma et al. (2007)gene trap sequence tags in mouse embryonic stem cells, thousands of novel transcripts • Unneberg and Claverie (2007)interchromosomal transcript chimerism • Denoeud et al. (2007)RACEarrays. Doubling the number of annotated exons in protein coding transcripts, widespread transcript chimerism International Conference on Bioinformatics HKUST, Hong Kong 2007

  28. tiling arrays reveal many novel sites of transcription TRANSCRIPTION MAP of HL-60 DEVELOPMENTAL TIME COURSE (data by Tom Gingeras, affymerix) International Conference on Bioinformatics HKUST, Hong Kong 2007

  29. characteristics of unannotated transfrags • short: 78bp on average compared with 121 for exonic transfrags • very gc-rich: 56% vs 42% in the background of unannotated regions • lack splice sites • no matches to protein or domain databases • lack of selective constraints HOWEVER: • reproducible across cell lines • support by independent evidence of transcription (mostly unspliced ESTs). • enriched for RNA structures. International Conference on Bioinformatics HKUST, Hong Kong 2007

  30. Rozowsky et al, 2007 Novel tar/transfrags are associated to known genes by identifying novel tars that are co-expressed with known genes across 11 cell lines and conditions International Conference on Bioinformatics HKUST, Hong Kong 2007

  31. Rozowsky et al., 2007 International Conference on Bioinformatics HKUST, Hong Kong 2007

  32. Denoeud et al, 2007 The ENCODE experiments • 5’ RACE on 12 tissues • primers in internal exons of 399 protein coding loci • RACE products hybridized into genome tiling arrays • 4573 race exons detected. 2324 novel International Conference on Bioinformatics HKUST, Hong Kong 2007

  33. 5’ RACE from TMEM15 Gene (region Enr232) identifies several tissue specific distal 5’ exons. Target gene International Conference on Bioinformatics HKUST, Hong Kong 2007

  34. International Conference on Bioinformatics HKUST, Hong Kong 2007

  35. more than 30% of RACEfrags more than 3Mb away from the index exon International Conference on Bioinformatics HKUST, Hong Kong 2007

  36. distal RACEfrags are associated to independently predictes sites of transcription initiation

  37. cloning and sequencing of RACEarray products International Conference on Bioinformatics HKUST, Hong Kong 2007

  38. cloning and sequencing of RACEarray products almost 30% of the sequenced products incorporate exons from upstream genes in chimeric structures International Conference on Bioinformatics HKUST, Hong Kong 2007

  39. RACEarrays: an strategy for normalization of RACE libraries, and exhaustive identification of alternative transcripts International Conference on Bioinformatics HKUST, Hong Kong 2007

  40. Array based normalization of RACE libraries If we select 40 clones at random from the RACE reaction, the probability of selecting a clone from the less abundant form is 0.01 (assuming a multinomial distribution) However, if the transcript forms could be segregated by RT-PCR, then by selecting again 40 random clones, 10 from each RT-PCR, the probability of selecting the less abundant form is now, 0.6 International Conference on Bioinformatics HKUST, Hong Kong 2007

  41. RT-PCR cloning and sequencing pilot (Kourosh Salehi-Ashtiani, DFCI) • 24novel RACEfrags tested by RT-PCR, including 6 cases previously confirmed in Denoeud et al. (2007) • Positive RT-PCR cloned, and 32 randomly selected clones sequenced. RESULTS 14 positive RT-PCR, 13 confirmed by sequencing. 42 novel transcript variants. Compared with the 52 previously know for the RT-PCR positive loci. Nearly all canonical splice boundaries Genomic extensions from 2.5 to 145Kb Difficulties in obtaining sequences for long cDNAs (which correlate with long genomic extensions)--but even with previously verified cases. Problems with RACEfrag assigntation to loci

  42. A very efficient strategy for targeted large scale transcript discovery • RACEarray normalization448 atempted clone sequences  42 novel transcripts • 1 novel transcript per 10 clones sequenced. • Carnici et al. (Genome Research 2003, 13:1273-1289)1,989,385 ESTs  70,214 transcripts (mouse) • 1 transcript after 30 sequenced ESTs. (and the majority of transcripts already known) International Conference on Bioinformatics HKUST, Hong Kong 2007

  43. CONCLUSIONS • there is substantial amount of transcription which does not appear to be associated to protein coding loci • only a fraction of the transcript diversity of protein coding loci appears to have been surveyed so far. • in particular, protein coding loci appear to have tissue specific distal alternative transcriptional start sites • RACEarrays are an effective normalization strategy for identifciation of rear transcripts • ENCODE transcriptional landscape: network of overlapping coding and non-coding transcripts, resulting in a continuum of transcription (more than 90% of the ENCODE regions are transcribed in at least one strand) International Conference on Bioinformatics HKUST, Hong Kong 2007

  44. PROVING THE FUNCTIONALITY OF NOVEL TRANSCRIPTS International Conference on Bioinformatics HKUST, Hong Kong 2007

  45. The GENCODE annotation • 487 loci. 2608 transcripts • 53 non-coding loci. 137 transcripts • 434 protein coding loci. • 1097 coding transcripts • 1374 non-coding transcripts • 5.7 transcripts per protein coding locus • 2.5 coding transcripts per locus • 1.7 proteins per locus International Conference on Bioinformatics HKUST, Hong Kong 2007

  46. the combined analysis of BioSapiens, Kellis and Goldman identified 184 annotated protein coding transcripts which challenged (from the structural, functional and/or evolutionary standpoint) our current view of proteins. Footnote: removing these 184 proteins from the set of 738 GENCODE proteins, will leave 554 proteins for 434 loci; barely 1,3 proteins per locus International Conference on Bioinformatics HKUST, Hong Kong 2007

  47. Structural Effects of Pepsinogen C Alternative Splice Variant Locus RP11-298J23.1 codes for pepsinogen C. The structure of pepsinogen C is 1htrA. Isoform -003 is missing 80 residues with respect to pepsinogen C. Here the missing section of -003 is in light green. The missing section in this isoform would remove the core from both subdomains of the structure. Both the N-terminal sub-domain (on the left) and the C-terminal sub-domain would have to refold. This is the view from above looking down into the active cleft of the proteinase. Active site aspartates are shown in ball and chain. One of the two active site residues is in the missing section. The symmetry apparent in this isoform suggests that although it will have to refold it may very well be able to reform into a single subdomain. Michael Tress & Alfonso Valencia CNB, Madrid

  48. Expression levelsalternative vs constitutive • Q-PCR in three cell lines: • SKNAS • GM06990 • HelaS3 International Conference on Bioinformatics HKUST, Hong Kong 2007

  49. Polysomal associationalternative vs constitutive International Conference on Bioinformatics HKUST, Hong Kong 2007

  50. ACKNOWLEDGEMNTS Jan Korbel (Yale) Julien Lagarde (IMIM) Jeff Long (Affx) Todd Lowe (UCSC) G. Madhavan (Affx) Anton Nekrutenko (Penn State) David Nix (Affx) Jakob Pedersen (UCSC) Alex Reymond Geneva) Joel Rozowsky (Yale) Yijun Runan (GIS) Albin Sandelin (RIKEN) Mike Snyder (Yale) Peter F. Stadler (U. Vienna) Kevin Struhl (Harvard) Hari Tammana (Affx) Scott Tennenbaun (SUNY, Albany) Chia Lin Wei (GIS) Matt Weirauch (UCSC) Deyou Zheng (Yale) Addam Frankish(Sanger) Tom Gingeras (Affymetrix) Roderic Guigó (CRG) ENCODE GT GROUP Stilyanos Antonarakis (Geneva) Robert Baertsch (UCSC) Ian Bell (Affx) Ewan Birney (EBI) Robert Castelo (IMIM) Jill Cheng (Affx) Evelyn Cheung (Affx) Hiram Clawson (UCSC) France Denoeud (IMIM) Sam Deustch(Geneva) Sujit Dike (Affymetrix) Jorg Drenkow (Affymetrix) Olof.Emanuelsson (Yale) Paul Flicek (Sanger) Mark Gerstein (Yale) Srinka Ghosh (Affx) Jenn Harrow (Sanger) Greg Helt (Afffx) Ivo Hofacker (U. Vienna) Tim Hubbard (Sanger) Phil Kapranov (Affx) Damian Keefe (EBI)

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