alon “alonzo” sade & harel “hipoptam” shein advisor: prof. michal linial (aka “m”)...
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Alon “Alonzo” Sade & Harel “Hipoptam” SheinAdvisor: Prof. Michal Linial (AKA “M”)
29.7.08
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Estimating amount of functional sequences in the genome
The ENCODE pilot project◦ Research on ncRNAs◦ Research on Alt.Splicing
Fun in the sun…
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Understanding how our genome encodes information
How that information underpins differences between individuals and species
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Currently estimated number of protein-coding genes:◦ Human: ∼ 20-25,000◦ Sea urchin: ∼ 23,000◦ Nematode worm: ∼ 19,000◦ Tetrahymena thermophila: ∼ 27,000 ( כי אין לנו
(שמרים We are complex, where is the information ? Protein coding sequences account for
<1.5% of the human genome What is the function of the remainder ?
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Alternative splicing ?
Non-protein-coding sequences contain large amounts of regulatory information ?
Recent discoveries say that the vast majority of the mammalian genome is transcribed◦ We’ll get back to that…
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Non-coding RNA An RNA that is not translated into a protein Many members in this family It was assumed that leftover RNA was
“junk”
2001 – Mattick claims: “more than 97% of RNA is ncRNA!”
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Ancient Repeats A CNE that was inserted into early
mammalian lineage Primarily transposon derived Has since become dormant Most are thought to be neutrally evolving
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Required for replication & structural integrity of the chromosome
Encode functional products Required for regulation or processing
◦ Includes sequences that may act as spacers
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At least 70% of the mammalian genome is transcribed
A lot of these are ncRNA shows cell-specific or developmental regulation
Functionality?
Noise, by-products for late evolvingBut all may also indicate functionality
!
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Recent evidence implicate ncRNAs in control of:◦ chromatin structure◦ epigenetic memory◦ Transcription◦ Translation◦ Splicing (possibly)
Most are evolving quickly but can maintain highly preserved regions in them
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∼5% of small segments in mouse & human are under selection(May range between 3%-8%)
Doesn’t include sequences that have diverged for other reasons than evolution
At the time we thought only ∼1.2% is protein-coding
5
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conservation is relative
Taken to be substitution rate measured under the assumption “ functional evolving ⇔ neutral rate”
Requires estimate of the “neutral rate of evolution”
Classes expected to be evolving free of constraint
Yes, everything is relative
5
שמירות
התפתחות טבעית
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classes chosen have included:
1. Mainly ARs
2. Lineage-specific nonexonic sequences3. Synonymous sites in codons
5
שמירות
התפתחות טבעית
מאפיינים3
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Estimates based on ARs may be biased:◦ The annotated and aligned ARs may comprise
mainly slowly evolving subset◦ ARs are under purifying selection
Lineage-specific & Nonexonic sequences Synonymous sites been found to be also biased
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The 5% study Conservation Netural rate classes chosen have included:
◦ Lineage-specific nonexonic sequences◦ Synonymous sites in codons◦ Ars
None of which is unbiased
5
שמירות
התפתחות טבעית
מאפיינים3
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Conclusions: Functionally RNAs illustrate:◦ Low conservation ↮ loss of functionally◦ Many functional transcripts have more relaxed
structure-function constraints
Many functional elements are unconstrained biologically active but provide no specific
benefit to the organism
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CFTR - cystic fibrosis transmembrane conductance regulator
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Figure 1. Conservation in the ENCODE CFTR locus
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Amount and function of the transcriptional output
Conservation Functionality estimates
Fractions of the genome under purifying selection may be have been underestimated
May get to 11.8%
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The ENCyclopedia Of DNA Elements
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GeneFrom Wikipedia, the free encyclopedia
”A gene is a locatable region of genomic sequence, corresponding to a unit of inheritance, which is associated with regulatory regions, transcribed regions and/or other functional sequence regions”
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A public research consortium Launched by US National Human Genome
Research Institute in September 2003 Goal: identify all functional elements in
the human genome sequence Top-down research Project Phases:
◦ Pilot Phase◦ Technological Phase◦ Production Phase
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• Goal:
Evaluate a variety of different Evaluate a variety of different methods for use in later stagesmethods for use in later stages
• Using a number of existing techniques to analyse a portion of the genome equal to about 1% (30mb)
• 35 groups provided more than 200 experimental & computational data
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• 50% were selected manually50% were selected randomly
• The two main criteria for manually : • The presence of well-studied
genes or other known sequence elements
• The existence of a substantial amount of comparative sequence data
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The randomly selected sequences• composed of 500kb regions • selected according to a stratified
random-sampling strategy based on • gene density –
#bases in genes/#other bases
• level of non-exonic conservation• 125 bases windows, base alignment with mouse
75%+, score (prediction), took the low score
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• The technology development phase is concurrent with the pilot phase
• Goal:Investigate and develop new, high throughput techniques and protocols
suitable for the production phase
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• One major challenge in the ENCODE project is annotating the large number of ncRNAs
• They are difficult to find in computational/experimental means
• Why ?
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• We must consider secondary structure as well as nucleotide sequence
• Structure can be detected more reliably from a set of related sequences
• RNA secondary structure is imperative when searching for structured ncRNAs
• So RNA search algorithms are expensive…
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אתה מתחיל הכי חזק
שאתה יכולולאט לאט, אתה מגביר !
• In 1985 Sankoff suggested to perform sequence alignment and minimal free energy folding simultaneously
• For two sequences of length n it’s O(n6)• Exponential in the number of sequences• Given the high cost, for many years it
rested in oblivion...
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• Several approximation attempts have been developed• FOLDALIGN• Dynalign• Stemloc• Consan
• All trying to increase performance w/o sacrificing accuracy
• They still remain relatively expensive
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First align then fold More attractive nowadays RNAz & EvoFold use
existing alignments◦ Thousands of new potential
structured ncRNAs◦ restricted to highly
conserved segments
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X As sequence similarity drops, frequent compensating base changes causes misalignments
X Assumes RNA structure is present in all sequences in the alignment
X Global alignments within fixed-width sliding window
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CMFinder◦ Search set of orthologous, unaligned seq. for
conservation◦ Doesn’t use external alignments (\orthology)◦ Doesn’t use sliding windows
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We scanned 2*56,017 block from UCSC MULTIZ multiple alignment files
We restricted analysis to blocks that don’t overlap exons or conserved elements
8.68 Mb (of 30), 3.87Mb repetitive sequences (RepeatMasker)
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10,106 predicted motifs meeting cutoff◦ Composite score > 5◦ Free energy < -5
Estimated false-positive of 50%
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Some predicted motifs overlap Sense/antisense to each other Considering as single candidates we have
6587 candidate regions◦ Average region length – 80 nt◦ Covering 6.1% of input◦ More dense in nonrepetative regions (7.9%
against 3.9%)
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ENCODE regions are poor in known ncRNAs Only one known ncRNA fully overlapped our
input (has-miR-483) It received a high score (8.6, -31.4) Also scored high as miRNA by RNAmicro
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GENCODE annotations aim to identify all human protein-coding genes in the ENCODE regions
40% of our candidates are intergenic 60% overlap some non exonic part of a
coding gene
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Elfar Torarinsson et al. Genome Res. 2008; 18: 242-251
Figure 3. Average pairwise sequence similarity of the predicted motifs versus the fraction that has been realigned compared to the
original alignments
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To explore the biological relevance of our prediction methods, we selected 11 high-confidence candidates◦ score>9, energy<-15, length>60, base change>5
We tested expression of these 11 candidates and found that 8 of 11 candidates could be detected in human RNA by RT-PCR
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Expression of predicted ncRNA candidates by RT-PCR analysis
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Expression of predicted ncRNA candidates by RT-PCR analysis
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ncRNAs are receiving increasing attention First large-scale search for structured
ncRNA using local structure motif finiding algorithm
One can benefit from realignment consider sequence and structure
Identified several thousand new ncRNA cadidates
Need for high-throughput methods to identify potential functions for the results
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2.53 protein coding variants per locus Key to understanding how human
complexity can be encoded by so few genes
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Alt. Spilicing has to be demonstrated at the protein level
Many of the alternative isoforms are not likely to add functionality
So what is the advantage ?
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Prof. Michal Linial for the guidance You for listening
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Pheasant and Mattick (2007), Raising the estimate of functional human sequences, Genome Res., 17: 1245-1253.
The ENCODE Project Consortium (2007), Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project, Nature, 447: 799-816.
Torarinsson et al. (2008), Comparative genomics beyond sequence-based alignments: RNA structures in the ENCODE regions, Genome Res., 18:242-251.
Tress et al. (2007), The implications of alternative splicing in the ENCODE protein complement, Proc. Natl Acad. Sci. USA, 104: 5495–5500.
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