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- 1. Barbados Workshop on the Computational Identification and Analysis of Transposable Elements April 18th - 25th, 2014 Florian Maumus with Hadi Quesneville (URGI-INRA, Versailles, France)
- 3. REPET package Genome TEdenovo TEannot Repeat annotation Hadi Quesneville
- 4. De novo repeatome detection Deep repeatome annotation Repeat annotation in large genomes
- 5. De novo repeatome detection Deep repeatome annotation Repeat annotation in large genomes
- 7. Repeat complement = Repeatome The Repeatome includes: Transposable elements Endogenous viruses Tandem repeats Ribozymes Genes … 7 = What you get with repeat-finders!
- 9. Dark matter, the genomic humus « Repeats » Old repeats Dark matter Detected Detectable? Background Noise Burst Decay Melt
- 10. Complexity of the repeatome Turnover ++ Recent activity +++ Turnover - Recent activity - young old
- 11. Different history, different challenges Maize 2.3 Gb genome About 85% repeats Human 3.2 Gb genome About 50% repeats
- 12. LECA: Core eukaryotic genes + Copia, Gypsy, LINEs, DNA transposons… TEs have been jumping around genes over evolutionary times
- 14. Contents include: Professional Tool Roll Archaeology Margin Trowel Battiferro Leaf & Square Battiferro forged ornamental tools lance Battiferro Trowel and Square Aluminium scale rulers Small Tools Set Hand Shovel Small Brush Mason Line* Line Pegs Line Level Plumb Bob Retractable Hi-Viz Grip Knife Battiferro Trowel* *Optional. Archeology toolbox
- 16. Repeatome toolbox K-mer strict : Tallymer, DSK K-mer based : RepeatScout, P-clouds Similarity, e.g Recon Combined RepeatModeler (RepeatScout + Recon) TEdenovo (Recon + Piler + Grouper; + RepeatScout in v2.2)
- 17. REPET: TEdenovo TEdenovo pipeline Consensus library + RepeatScout (v2.2) REPET Classification utility
- 18. REPET Classification tool Consensus library TR search Tandem Repeat Finder BLASTx tBLASTx Repbase Pfam hmm GyDB hmm rDNA tRNA Host genes Consensus 1: termLTRs 0,12% TR Bx: AtGypsy; Btx: none profiles: IN, RT LTR retro Consensus 2: none 0,32% TR Bx: none; Btx: none profiles: LRR Host gene Consensus 3: none 0,23% TR Bx: none; Btx: none profiles: none Unclassified Summary of evidences Proposed Classification
- 19. REPET: TEannot TEannot pipeline genome annotation
- 20. TEdenovo Performance, Complementarity ? RepeatScout RepeatModeler
- 21. Experimental model Arabidopsis thaliana 120 Mb
- 23. Sensitivity & Specificity 0 10 20 30 40 50 Mb Genome coverage
- 24. All TEdenovo+RS+RM RepeatScout RepeatModeler TEdenovo TRF Tallymer Sensitivity Percent reference coverage 0 10 20 30 40 50 60 70 80 90 100
- 25. All TEdenovo+RS+RM RepeatScout RepeatModeler TEdenovo Biological Sensitivity Percent 24-nt sRNA coverage (Lister et al., 2008) 0 10 20 30 40 50 60 70 80 90 100
- 26. TEdenovo RepeatModeler RepeatScout 35 30 25 20 15 10 5 0 Genome coverage increase (%) REPET, RepeatScout, and RepeatModeler employ complementary computational methods that together enable to better represent repeatome complexity.
- 27. Conclusions I TEdenovo outcompetes RepeatModeler and RepeatScout Greater coverage with Less consensus Larger consensus Larger copies Complementarity of TEdenovo, RepeatModeler and RepeatScout Comprehensive annotation of complex repeatomes
- 28. De novo repeatome detection Deep repeatome annotation Repeat annotation in large genomes
- 29. Arabidopsis 120 Mb Experimental model CDS Repeatome Dark matter 0% 100% Three strategies with REPET: Annotate genome with genomic copies Use relaxed parameters for HSP detection Use P-clouds to detect short repeat fragments
- 30. Iterative annotation Annotate genome with genomic copies (Expand the knowledge)
- 31. Iterative annotation Annotate genome with genomic copies (Expand the knowledge)
- 32. Iterative annotation Annotate genome with genomic copies (Expand the knowledge)
- 33. Iterative annotation Annotate genome with genomic copies Genome TEdenovo Consensus TEannot Genomic copies TEannot Genomic copies TEannot Genomic copies TEannot Genomic copies
- 34. Genome Reference 24-nt sRNA Tallymer RepeatScout RepeatModeler 0 10 20 30 40 50 60 70 80 90 100 TEdenovo_1 TEdenovo_2 TEdenovo_3 TEdenovo_4 Iterative annotation Annotate genome with genomic copies
- 35. AA AC AG AT CA CC CG CT GA GC GG TA TC GT TG TT 0,15 0,05 -0,05 CDS TEdenovo delta_2vs1 delta_3vs2 delta_4vs3 Dinucleotide composition
- 36. Relevance Genome annotation using the delta_2vs1 copies masks as much as 23 Mb (19.5%) of the genome Covers 66% of the reference annotation and 56% of the TEdenovo annotation The supplementary annotations from TEdenovo_2 are highly representative of the A. thaliana repeatome.
- 39. Relaxed (parameters) annotation Default : Identity > 90%, Evalue<1e-300 Cool : Identity > 85%, Evalue < 1e-50 Soft : Identity > 80%, Evalue < 1e-20 Consensus size
- 40. Relaxed (parameters) annotation 24 nt sRNA Tallymer Reference RepeatScout RepeatModeler 0 10 20 30 40 50 60 70 80 90 100 TEdenovo_1 TEdenovo_cool TEdenovo_soft TEdenovo_soft_2
- 41. Copy/consensus identity along chr1 TEdenovo Cool Soft ()
- 42. Deep annotation of the A. thaliana repeatome RepeatScout RepeatModeler TEdenovo Repbase (+Buisine et al.) Remove redundancy Bundle library TEannot Consensus size
- 43. Deep annotation of the A. thaliana repeatome selected not selected TEannot P-clouds Complete bundle annotation
- 44. Copies Consensus P-clouds In-cloud k-mers De Koning et al.
- 46. • TEdenovo
- 47. • Bundle
- 48. • Bundle + P-clouds => Repeated and repeat-derived sequences contribute at least 30% to the A. thaliana genome Enhanced repeat detection in gene-rich regions
- 49. Arabidopsis repeats browser Genes Buisine et al. RepeatScout RepeatModeler REPET Deep annotations 24-nt sRNA
- 50. Conclusions II Innovative approaches for deep repeatome annotation About one third of the A. thaliana genome of repetitive origin (vs 24%) Increased sensitivity and detection of old repeat remnants Improved genome evolution and epigenetic analyses Continuum between repeatome and genomic dark matter Time
- 51. De novo repeatome detection Deep repeatome annotation Repeat annotation in large genomes
- 52. All genomes should benefit the greater quality of TEdenovo Adapted from Nina V. Fedoroff (2012) and Steven M. Carr
- 53. Limitations with REPET All-by-all genome comparison => LOTS (Gb) of high scoring pairs (HSPs) HSP files > 1 Gb are not handled by Piler Grouper can last for weeks Impossible to run TEdenovo on whole large and/or highly repeated genomes until recently
- 54. Solutions Use a sample of whole genome as input for TEdenovo (e.g. 300Mb) (As recommended for RepeatModeler)
- 55. Tomato genomes S. pennellii : 942 Mb S. lycopersicum : 782 Mb
- 56. 0 0.5 1 Gb TEdenovo (n HSP >= 5) 320 Mb input Consensus library TEannot
- 57. Mb 82% of the Solanum pennellii ATGC space masked
- 58. Conclusions III Efficient annotation of large plant genomes with REPET Still quite a long process !
- 59. De novo repeat annotation in large genomes Future developments Parallelize Grouper Parallelize the “Long join” procedure Establish phyla-specific approaches Develop strategies to annotate genomes with different composition old, complex repeatomes as compared to large plant genomes
- 60. De novo repeat annotation in large genomes Future challenges & perspectives Propose TEdenovo and TEannot pipelines on GALAXY Deliver REPET compilation for use on a cloud
- 61. Véronique Jamilloux Tina Alaeitabar Timothée Chaumier Olivier Inizan Mark Moissette Hadi Quesneville THANK YOU !