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