Transposable Elements (TE) in genomic sequence

1. Transposable Elements (TE) in genomic sequence Mina Rho

2. Contents • Definition • De novo identification of repeat families in large genomes (RepeatScout) Alkes L. Price, Neil C. Jones and Pavel A. Pevzner • Combined Evidence Annotation of Transposable Elements in Genome Sequences Hadi Quesneville, Casey M. Bergman, Olivier Andrieu, Delphine Autard, Danielle Nouaud, Michael Ashburner, Dominique Anxolabehere

3. Mobile element/Transposable element Transposon - a segment of DNA that can move around to different positions in the genome of a single cell. - cut out of its location and inserted into a new location. - consisting of DNA. Retrotransposon - copy and paste into a new location. - the copy is made of RNA and transcribed back into DNA using reverse transcriptase. - long terminal repeats (LTRs) at its ends. => expect to get information of evolution, mutation, changes of amount of DNA in the genome.

6. RepeatScout

7. Definition • Repeat family: a collection of similar sequences which appear many times in a genome. • the Alu repeat family has over 1 million approximate occurrences in the human genome • ~ 50% Human genome • l-mer: substring whose length is l

8. Backgroud • The current status on identification method of repeat families • Given an existing library of repeat families • RepeatMasker • De novo identification • REPuter (Kurtz et al., 2000) • RepeatFinder (Volfovsky et al., 2001) • RECON (Bao and Eddy, 2002) • RepeatGluer (Pevzner et al., 2004) • PILER (Edgar and Myers, 2005) • RepeatScout

9. Overview of RepeatScout • Method • Builds a table of high frequency l-mers as seeds • Extends each seed to a longer consensus sequence • Main advantage • an efficient method of similarity search which enables a rigorous definition of repeat boundaries.

10. How to create l-mer table Sequence i i+1 i+2 j k Hash table l-mer1 l-mer2 l-mer3 l-mer4 l-mer5 l-mer6 frequency Position of last occurrence

11. Output of l-mer table AAAAAAAAAAAGATA 8 2920943 AAAAAAAGGAAAGAA 5 2468525 AGGCTTGAACAATGG 3 1425014 AAAAAAAAGAAAGAA 62 3009663 GTTGGTTTCAAAGAA 7 2855871 AAAAAAAATTTTTTT 22 2992836 ATTCAAGTTAAATGG 4 1473342 ATTCAATGTAACCAC 3 1463008 ATGCATGCAATGCAT 9 1788944 ATGCATTTAAAAGAA 3 1464381 AAAAAACTCACTCCA 5 1489159

12. i i i i How to build all positions of repeats Sequence i i+1 i+2 j k Hash table l-mer1 l-mer2 l-mer3 l-mer4 l-mer5 l-mer6 j i i+2 k

13. S1 S2 S3 S4 Q1 S5 High frequency l-mer Q2 Q3 Q4 Query sequence (with l-mer1) S1 S2 S3 S4 S5 Extending Q maximizing objective function one nucleotide at a time

14. Objective Function |Q| : the length of Q C: minimum threshold on the number of repeat elements a(Q, Sk): a pairwise fit_preferred alignment score p: Incomplete-fit penalty

15. Output of optimized Q >R=0 GGCCGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACTTGAGGTCAGGAGTTC GAGACCAGCCTGGCCAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAAATTAGCCGGGCGTGGTGGCGCGCGCCTG TAATCCCAGCTACTCGGGAGGCTGAGGCAGGAGAATCGCTTGAACCCGGGAGGCGGAGGTTGCAGTGAGCCGAGATCGCG CCACTGCACTCCAGCCTGGGCGACAGAGCGAGACTCCGTCTCAAAAAAAAAAAAAAAAAAAAAAAAAAA >R=1 AAAAGGCAGCAGAAACCTCTGCAGACTTAAATGTCCCTGTCTGACAGCTTTGAAGAGAGTAGTGGTTCTCCCAGCACGCA GCTGGAGATCTGAGAACGGACAGACTGCCTCCTCAAGTGGATCCCTGACCCCCGAGTAGCCTAACTGGGAGGCACCCCCC AGTAGGGGCAGACTGACACCTCACACGGCCAGGTACTCCTCTGAGAAAAAACTTCCAGAGGAACAATCAGGCAGCAACAT TTGCTGCTCACCAATATCCACTGTTCTGCAGCCTCTGCTGCTGATACCCAGGCAAACAGGGTCTGGAGTGGACCTCCAGC AAACTCCAACAGACCTGCAGCTGAGGGTCCTGTCTGTTAGAAGGAAAACTAACAAACAGAAAGGACATCCACACCAAAAA CCCATCTGTACGTCACCATCATCAAAGACCAAAAGTAGATAAAACCACAAAGATGGGGAAAAAACAGAGCAGAAAAACTG GAAACTCTAAAAAGCAGAGCGCCTCTCCTCCTCCAAAGGAACGCAGCTCCTCACCAGCAACGGAACAAAGCTGGACGGAG AATGACTTTGATGAGTTGAGAGAAGAAGGCTTCAGATGATCAAACTACTCCAAGCTAAAGGAGGAAATTCAAACCCATGG CAAAGAAGTTAAAAACCTTGAAAAAAAATTAGACGAATGGATAACTAGAATAACCAATGCAGAGAAGTCCTTAAAGGAGC TGATGGAGCTGAAAACCAAGGCTCGAGAACTACGTGAAGAATGCACAAGCCTCAGGAGCCGATGCGATCAACTGGAAGAA AGGGTATCAGTGATGGAAGATCAAATGAATGAAATGAAGTGAGAAGAGAAGTTTAGAGAAAAAAGAATAAAAAGAAATGA >R=2 TTTTTTTTTTTTTTTAGATGCGGGGTGTCACTGTGTTGCTCAGGCTGGTCTCAAACTCCTGGGCTCAAGTGATCCTCCCA CCTCAGCCTCTTTAATAGATGCGATTA >R=3 TTTTTATACATGCTGTAGACAATCAATTCACACCTGTACTTTTTTTTAAGGTTGTGTTATTGCACTTTTATACCTCTTGA CTGGTAGCTGATTTCCTTGAATACCTGTAAGGTAATCACCGGCTCACCAATGAATGTGGTTTTAACAATGGCTCACAGTG GCTTGGAAAGCCCTCATGGGAAGTATTTCTGAGGAAAAGTGGAGAGTGTGCAGGAATAGTTTTGAAAAACAGAGACAACC GATGTCCTCCTTCCCTCCCTTGCCTCTCCTCATGTGCCAGGTTTTCTGTTTTCTCCACTATTACAGAATCACCATGTTGT ATCCTGTGATGAAAAGTTTTTATCTCTTTAATCATCCCATTTCGTCCTCCAGACCTTTTTTTTTCTGGAAGGGTTGTAAG CAGAAGGGACGAAACATCTTCAGAAAAACACATTATGATATAAACTTAGTGAAAAGATTCATCATATTTAAGAAATGGAC AGGATGAAATCCTGAATTCATAAAAATTTTAAAAATCAGTTTACATAACATCCATCCCTTTTGTCTCTATCCCTTATCCA

16. Parameter setting and post processing • Parameter setting • Recommend the smallest l = 15 • For the arbitrary length L, • The length of Q up to 10,000bp on each side • Remove repeat families with Q < 50 • Postprocessing • Tandem Repeat finder, Nseg • Remove repeat families with >50% of their length annotated as low-complexity and tandem repeats • RepeatMasker • Mask the repeat families based on the library

17. Benchmark • C.briggsae genome (108Mb) • 7h on a single 0.5 GHz DEC Alpha processor

18. Combined evidence model of TE

19. Overview Query Sequences: Drosophila melanogaster (Fruit fly) Release 3, 4 Combined evidence model: pipeline of RepeatMasker, BLASTER, TBLASTX, all-by-all BLASTN, RECON, and TE-HMM - Methods for the annotation of known TE families - Methods for the annotation of anonymous TE families Benchmark : FlyBase Release 3.1 annotation Sensitivity and specificity, characteristics of boundary

20. Tools • Blaster • compares a query sequences against a subject databank. • Launches one of the BLAST (BLASTN, TBLASTN, BLASTX, TBLASTX). • Cut long sequences before launching BLAST and reassembles the results. • MATCHER • Maps match results onto query sequences by filtering overlapping hits. • Keeps the match results with E-value < 10-10 and length >20 • Chains the remaining matches by dynamic programming. • GROUPER • Gather similar sequences into groups

21. Measures For each nucleotide, • TP: correctly annotated as belonging to a TE • FP: falsely predicted as belonging to a TE • TN: correctly annotated as not belonging to a TE • FN: falsely predicted as not belonging to a TE

24. Method for the Annotation of known TE families • BLASTER using BLASTN and MATCHER (BLRn) • RepeatMasker (RM) • RepeatMasker with MATCHER (RMm)

25. Method for the Annotation of known TE families • BLASTER using BLASTN and MATCHER (BLRn) • RepeatMasker (RM) • RepeatMasker with MATCHER (RMm) • RepeatMasker-BLASTER (RMBLR) : combined hits from both BLRn and RM and give them to MATCHER

26. Method for the Annotation of anonymous TE families • all-by-all comparison with BLASTER using BLASTN, MATCHER, and GROUPER • RECON • BLASTER using TBLASTX and MATCHER • HMM

27. What they (we) learned • Overall, BLRn outperforms RM with respect to the precise determination of TE boundaries. • RM is more sensitive for the detection of small and divergent TE. • The difference between BLRn and RM make them complementary for TE annotation. • A combined-evidence framework can improve the quality and confidence of TE annotation.

28. Pipeline structure • TE detection software : BLASTER, RepeatMasker, TE-HMM, and RECON • Tandem repeat detection software : RepeatMasker, Tandem Repeat Finder (TRF), Mreps • Database: MySQL • Open Portable Batch System • Whole genomic sequence was segmented into chucks of 200kb overlapping by 10kb. • The results from different tool were stored in the database. • XML file is generated from the stored results and loaded into the Apollo genome annotation tool.

29. The Annotation Pipeline