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Repeats in the Genome Lecture 11/2.

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Presentation on theme: "Repeats in the Genome Lecture 11/2."— Presentation transcript:

1 Repeats in the Genome Lecture 11/2

2 Repeats in the genome Interspersed repeats Tandem repeats
Microsatellites Minisatellites Satellites

3 http://mcb1. ims. abdn. ac. uk/djs/web/lectures/repeats1

4 Large repeats: Transposons
“Transposable elements” (TE’s) Sequences that get moved/copied into different loci in the genome P elements in Drosophila: genes piggybacked on transposons and inserted into the genome, in the lab “transgenic fruitflies”

5 Transposons

6 Transposons

7 Transposons

8 Retrotransposons: 2 examples
SINEs : Short Interspersed repeats bp; up to 1M copies; Non-autonomous Example : “Alu” repeats 13 % of human genome LINEs : Long Interspersed repeats Up to 7 Kbp long; ,000 copies Autonomous Examples: LINE1, LINE2, LINE3 21 % of human genome

9 Functions of interspersed repeats
May cause disruptions, disease Colorectal cancer Role in evolution of new genes Function of SINEs and LINEs not fully known Selfish DNA ? Parasitic elements akin to viruses

10 RepeatMasker Program to detect and mask interspersed repeats in a sequence Also finds low complexity sequences and masks them Can work with a library of known repeats

11 Tandem Repeats Satellites Mini- and micro-satellites
In centromeres and telomeres Repeating pattern 1bp s bp long Mini- and micro-satellites simple, small sequence repeats

12 Microsatellite 1-5bp repeating pattern
541 gagccactag tgcttcattc tctcgctcct actagaatga acccaagatt gcccaggccc 601 aggtgtgtgt gtgtgtgtgt gtgtgtgtgt gtgtgtgtgt gtatagcaga gatggtttcc 661 taaagtaggc agtcagtcaa cagtaagaac ttggtgccgg aggtttgggg tcctggccct 721 gccactggtt ggagagctga tccgcaagct gcaagacctc tctatgcttt ggttctctaa 781 ccgatcaaat aagcataagg tcttccaacc actagcattt ctgtcataaa atgagcactg 841 tcctatttcc aagctgtggg gtcttgagga gatcatttca ctggccggac cccatttcac a microsatellite in a dog (canis familiaris) gene

13 Microsatellites Copy numbers variable across individuals
Associated with human diseases Fragile X syndrome, Huntington’s disease, Myotonic dystrophy Can be used for genetic fingerprinting & paternity tests, due to high variability

14 Minisatellites 6-20 bp repeating pattern Consensus AGGATTTT
1 tgattggtct ctctgccacc gggagatttc cttatttgga ggtgatggag gatttcagga 61 tttgggggat tttaggatta taggattacg ggattttagg gttctaggat tttaggatta 121 tggtatttta ggatttactt gattttggga ttttaggatt gagggatttt agggtttcag 181 gatttcggga tttcaggatt ttaagttttc ttgattttat gattttaaga ttttaggatt 241 tacttgattt tgggatttta ggattacggg attttagggt ttcaggattt cgggatttca 301 ggattttaag ttttcttgat tttatgattt taagatttta ggatttactt gattttggga 361 ttttaggatt acgggatttt agggtgctca ctatttatag aactttcatg gtttaacata 421 ctgaatataa atgctctgct gctctcgctg atgtcattgt tctcataata cgttcctttg Consensus AGGATTTT

15 Minisatellites Highly polymorphic across individuals
Used for DNA fingerprinting Regulation of gene expression

16 Recognizing repeat sequences
“Dot plots” Self-similarity

17 Tandem repeat detection
Have to account for approximate tandem repeats Repeating unit may not be exactly same (mutations) May not be exactly in tandem (indels)

18 TRF (Benson) Assume > 80% sequence identity on average
Assume < 10% rate of indels Basic idea T A T A C G T C G A G A C T T A T C C A C G G A G A T A T T T A


20 Statistical criteria The candidate tandem repeat converted into a Bernoulli (head/tail) sequence Assess significance of this sequence, assuming a probabilistic model CCACAACC-CGTCAGGCAAGT CTGCACCATCGTCTGGGAAGT HTTHHTHTTHHHHTHHTHHHH

21 Statistical criteria Sequence of length 100, with pH = 0.75
>=95% of time, total number of heads is >=68 >=95% of time, total number of heads in runs of length 5 or more is >=26 We are counting only head-runs of length k or more This tells us what would would be a significant number of heads

22 Statistical criteria Due to indels, a repeating pattern of size d may induce exact-matching k-tuples separated by d,d1, d2 etc. Consider all such pairs, up to ddmax dmax calculated using an assumption about pI (the indel frequency) and a random-walk model

23 Statistical criteria Other criteria to
distinguish tandem repeats from non-tandem direct repeats matching k-tuples biased on one side pick tuple sizes

24 Mreps (another program)
Different algorithm to detect repeats Maximal run of k-mismatch tandem repeats, with period p: A maximal string such that any substring of length 2p is a tandem repeat with at most k mismatches All such maximal runs can be computed in time O(nk log(k)), where n is length of sequence

25 Mreps: Statistical criteria
Two reasons for insignificance Short length Reject runs of length < p+9 Too many mismatches Create “random” DNA sequences, and infer quality filter based on this

26 Gene Duplications If a region containing a gene is duplicated, a new copy of gene is created: paralogs Eases up the “selective pressure” on one of the copies free exploration of sequence space Cases of entire genomes being duplicated yeast, wheat

27 Pseudogenes Upon gene duplication, one of the two copies may gather a deleterious mutation Example: premature “stop codon” Once the gene “dies” in this fashion, no more selective pressure on it. Such a “dead” copy of a gene is a “pseudogene”

28 Pseudogenes Any sequence that appears to code for a gene product, but does not do so Origins of pseudogenes Gene duplication Change of environment, gene no longer needed portion of mRNA transcript reverse-transcribed and inserted into genome Create problems for genome study Mis-annotated as genes

29 Pseudogenes Pseudogenes mutate at “neutral” rate, free of any selective pressures Can be used for evolutionary analysis Example: In Drosophila, insertions:deletions in the ratio of 1:8, based on study of pseudogenes

30 Tandem Repeats and Binding Sites
Regulatory modules have 20-40% coverage by tandem repeats Based on a study on Drosophila Very significant statistically, if assuming low-order Markov background Relation between tandem repeats and binding sites ?

31 Tandem Repeats and Binding Sites
Possibility: Tandem repeats help in creating duplicates of binding sites Multiple copies of binding site helps exploring new binding sites helps fine-tune binding affinity Faster evolution ?

32 Implications for regulatory sequence analysis
Regulatory sequence modeled as a mixture of motif and non-motif “background” Background typically a Markov chain of fixed order Given last k bases, S[i..i+k-1], next base determined by a fixed probability distribution

33 Tandem Repeats in Model
Tandem repeats violate Markov assumption: previous k bases S[i..i+k-1] may provide a probability distribution on next base, OR we may have a tandem repeat of previous j <= k bases Similarly, a binding site or a part of a binding site may also be tandem repeated

34 Tandem Repeats in Model
Need to modify the probabilistic model to include tandem repeats Research topic

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