Presentation on theme: "Albin Sandelin (University of Copenhagen) Jasmina Ponjavic (Oxford University) Boris Lenhard (Bergen University) David Hume (Queensland University) Piero."— Presentation transcript:
Albin Sandelin (University of Copenhagen) Jasmina Ponjavic (Oxford University) Boris Lenhard (Bergen University) David Hume (Queensland University) Piero Carninci (RIKEN Wako) Martin Frith (RIKEN Yokohama) Hideya Kawaji (NTT Software) Yoshihide Hayashizaki (RIKEN Yokohama) … >100 Japanese technicians New paradigms and resources for promoter studies
Aims Introduction of Cap Analysis of Gene Expression (CAGE) data and resources Insights on core promoter structure and transcriptional landscapes using CAGE (The JASPAR database) Main references: Carninci et al Nat Genet. 2006 Jun;38(6):626-35 Carninci et al Science 2005 Sep 2;309(5740):1559-63 Katayama et al Science 2005 Sep 2;309(5740):1564-6. Frith et al Genome Res 2006 Jun;16(6):713-22. Ponjavic et al Genome Biol 2006 Aug 17;7(8):R78
CAGE tags are the 20 first nucleotides of a full-length cDNA from a non-normalized cDNA library –Shiraki et al, PNAS 100:15776-81 (2003) Sequencing and mapping to the genome What is CAGE?
Advantages Large-scale sequencing with no cDNA normalization: –enables localization AND quantification of transcripts/promoters –Enables promoter localization with unprecedented sampling depth (sequence >1 million transcripts in one experiment…) Base-pair resolution, with strand information –Quite impressive validation rates even for single tags (86% true positives by RACE) Unbiased in terms of location: genome-wide Different RNA populations can be sequenced and compared
Takehome message II 1 gene – many promoters (what is a gene, anyway?) Many uncharacterized promoters await deeper study Many promoters and transcripts are at unexpected locations The genome has become a messy place to work in – transcripts everywhere
Brief examples of more detailed analyses using the same dataset: Evolutionary turnover of TSS –Frith et al 2006, Genome Res Dissection of TATA-containing core promoters –Ponjavic et al 2006, Genome Biol (There are some 10 more)
TSS turnover does exist …although this is not the default situation (We find about 1000 cases) When TSS turnover does occur, “phylogenetic footprinting” type TFBS search is problematic Can all functional elements that are active on genome level undergo turnover?