ENCODE: understanding our genome
Ewan BirneyThe ENCODE Project Consortium
Biosapiens Network of Excellence
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ENCODE experiments
Area Assay GroupsProteins Manual annotation,
RT-PCRGuigo, Harrow+Hubbard, Reymond
Transcripts Tiling Arrays Gingeras, SnyderTranscripts Tag seq. Yijun, RikenGeneral Chromatin Marks
Tiling Arrays, ChIP Dunham, Reng
Sequence sp. Factors
Tiling Arrays, ChIP Snyder, Gingeras, Farnham, Dunham
DNaseI sens. PCR, Tiling arrays Stam. , CrawfordReplication Tiling arrays DuttaConservation Comparative
sequenceGreen, Sidow, Miller
DNA structure Hydroxyl radical LeibPromoter Reporter assays Myers
ENCODE Pilot• Considered too expensive and
too risky to decide on winning technologies (started in 2004)
• 1% of the genome (30MB) chosen - all experiments on the same 1%
• Pilot phase ended– Analysis and publication– Scale up to genome wide now
funded
A lot of Chip/Chip
Nowdays, a lot of Chip/seq
Transcription
Transcription• Lots of it
– And not all of it genes– And even when it is inside a gene,
not all of it with open reading frames
– And even when it has an open reading frame, not all of it making sense! (evolutionary or structurally)
• Not technical false positives
Protein coding loci are far more complex than we think
• On average 5 transcripts per locus
• Many do not encode proteins (as far as we can see)
• Even the ones which do encode proteins, many of these proteins look “weird”
Unplausible structures
Many effects on potential function
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Signal peptides, TM Helices• 1097 protein transcripts from
487 loci– 219 have signal peptides (107 loci)– 12 loci have an isoform without the
signal peptide– 41 transcripts have a gain or loss
of a tansmembrane helix (sometimes up to 8!)
a inactive, "stressed"
(d) (e)
b active (beta inserted)(c)
(f)
The Clade B Serpins PotentialMissing fragments
Transcription Start Sites
Technologies on TSS
Gencode
Manual Ann.UnbiasedTxFrag
Ditag data
Cage data
Histone mod.Dnase I sens
Sequence spFactors (eg Myc)
Integration Strategy
Anchor on 5’ endsGenCode 5’ and CAGE/DiTag
Categorise and assess usingTranscript based evidence
Exons, TxFrags, CpG islands
Assess categories withHistone and TF data
16,051 unique TSS
8,587 TSS “tight clusters”
5 different classesFirst 4 low-Pvalues
First 4 categories haveBiological signals:4,491 TSS
TSS CategoriesCategory Number
(non-redundant)P-value of overlap
GenCode 5’ 1730 2e-70
Exon(sense) 1437 6e-39
Exon(anti) 521 3e-8
TxFrag 639 7e-63
CpG 164 4e-90
No support 2666
GenCode 5’ ends
Unsupported tags
Novel TSSs
Conclusion• There are 4,418 TSS with
multiple lines of evidence supporting them
• This is ~10 fold more than the number of Genes
• Only 38% would be traditionally classified as TSS (less if one took Ensembl or RefSeq)
Implications of many more TSSs
• Consistent with considerable diversity of transcripts
• Independently integrating Chip/Chip data suggested ~1,000 “Regulatory Clusters”– 25% proximal considering
Ensembl/Refseq– 65% when this TSS catalog is
considered
More subtle conclusions• Sequence specific factors are
distributed symmetrically around the TSS– Should we only be taking upstream
regions for reporter genes?• Histone information is highly
correlated with gene on/off status– Generalising many locus specific
studies
Gene On/Off
Gene status prediction
Distal sites
Finding distal sites• Chip/Chip not “great”
– Most look close to one of these new TSSs
– Factor bias?• DNaseI Hypersenstive Sites
– All factors give a DHS signal– 55% of DHSs are distal to any TSS
Distal DHS
Most surveyed factors are proximal
Replication
H3K27me3 is correlated
Evolutionary conservation and ENCODE
Evolutionary conservation
…but not everything is constrained
Why is there a discrepancy?• False positives in the experiments
– But experiments validate at >80% and cross-validate each other
• False negatives in the constraint detection– But can detect up to 8bp elements, and within
“neutral” zone of alignability• Neutral turnover model
Neutral biochemical events
Time
Lineage specific
Time
“Functional” conservation
HumanMouse
Special case: Transcription
GeneRegulatory Information
Constrained sequence
Constrained sequence
Pre-miRNAs
What should we learn from ENCODE
• “whacky” transcription is real (but god knows what it does)– Unconventional Transcript
• Lots more TSSs than we understand– Many “distal” regions are actually close
to promoters• Broad specificity marks are more
useful– DNaseI sites, Histone marks
Neutral model for biochemical events on the genome
• Because things happen reproducibly in multiple tissues does not imply selection
• (this is not the same as experimental variance)
• Could imply “functional” conservation outside of orthologous bases– Comparative genomics sequencing not
enough (but a great starting point!)– Comparative functional investigation
Consortia work• ENCODE
– Experimentally lead consortia– Needs a lot of computational
collaboration• Biosapiens
– Computationally lead consortia– Needs experimental collaboration
(!)• DNA: ENCODE• Protein: Biosapiens
What happens next?
Ensembl Regulatory Build
elements
Chr 14,5677077-567896
Status
GM06990Cells, Myc bound
Initial Regulatory Build• DNaseI Hypersenstive sites, 6 histone
modifications, CTCF binding • ~110,000 elements, ~2MB of DNA• 6,000 “promoter associated” by
inherent pattern (DNaseI + H3K36me3)
• Available now
• This year: Mouse, More classification
Regulatory build
Ensembl - at your service• Web browser www.ensembl.org• MySQL DB access• BioMart
• “Geek for a week”– You send someone to use for a
week• Xose for a day
– We send someone to you for a day
The ENCODE Project ConsortiumDamian Keefe, Yutao Fu, Zhiping Weng, Mike Snyder, Elliott Marguilles, John Stam., Manolis Dermitzakis, Tom Gingeras, Roderic Guigo, Ian Dunham, Christophe Koch, Anindya Dutta Paul Flicek and 293 others…
The Biosapiens Network of Excellence
Michael Tress, Alfonso Valencia, Janet Thornton, Roderic Guigo, Soren Brunak, David Jones, Martin Vingron, Anna Tramontano, Jacques van Helden and 57 others…