Barbados Workshop on the Computational Identification and Analysis of Transposable Elements

April 18th - 25th, 2014

Florian Maumus with Hadi Quesneville (URGI-INRA, Versailles, France)

REPET package

TEdenovo TEannotGenome Repeat annotation

HadiQuesneville

De novo repeatome detectionDeep repeatome annotation

Repeat annotation in large genomes

De novo repeatome detectionDeep repeatome annotation

Repeat annotation in large genomes

7

Repeat complement = Repeatome

The Repeatome includes:Transposable elements

Endogenous virusesTandem repeats

RibozymesGenes

…

= What you get with repeat-finders!

Burst and Decay

« Repeats » Old repeats Dark matter

Dark matter, the genomic humus

Detected Detectable? Background Noise

Burst Decay Melt

Turnover ++Recent activity +++

Turnover -Recent activity -

Complexity of the repeatome

old

young

Maize2.3 Gb genomeAbout 85% repeats

Human3.2 Gb genomeAbout 50% repeats

Different history, different challenges

LECA:Core eukaryotic genes +Copia, Gypsy, LINEs, DNA transposons…

TEs have been jumping around genes over evolutionary times

Contents include: Professional Tool Roll

Archaeology Margin TrowelBattiferro Leaf & Square

Battiferro forged ornamental tools lanceBattiferro Trowel and Square

Aluminium scale rulersSmall Tools Set

Hand ShovelSmall BrushMason Line*

Line PegsLine LevelPlumb BobRetractable

Hi-Viz Grip KnifeBattiferro Trowel*

*Optional.

Archeology toolbox

Repeatome toolbox

K-mer strict : Tallymer, DSK

K-mer based : RepeatScout, P-clouds

Similarity, e.g Recon

CombinedRepeatModeler (RepeatScout + Recon)

TEdenovo (Recon + Piler + Grouper; + RepeatScout in v2.2)

REPET: TEdenovo

TEdenovo pipeline Consensus library

+ RepeatScout (v2.2)REPET Classification utility

REPET Classification tool

Consensus library

TR searchTandem Repeat Finder

BLASTxtBLASTxRepbase

Pfam hmmGyDB hmm

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

rDNAtRNA

Host genes

Summary of evidences Proposed Classification

TEannot pipeline genome annotation

REPET: TEannot

TEdenovo

RepeatScout

RepeatModeler

Performance, Complementarity ?

Experimental model

Arabidopsis thaliana120 Mb

Consensus sequences

0 10 20 30 40 50 Mb

Genome coverage

Sensitivity & Specificity

Tallymer

TRF

TEdenovo

RepeatModeler

RepeatScout

TEdenovo+RS+RM

All

0 10 20 30 40 50 60 70 80 90 100

Percent reference coverage

Sensitivity

TEdenovo

RepeatModeler

RepeatScout

TEdenovo+RS+RM

All

0 10 20 30 40 50 60 70 80 90 100

Percent 24-nt sRNA coverage (Lister et al., 2008)

Biological Sensitivity

TEdenovo RepeatModeler RepeatScout0

5

10

15

20

25

30

35

Gen

ome

cove

rage

incr

ease

(%

)

REPET, RepeatScout, and RepeatModeler employ complementary computational methods that together enable to better represent repeatome complexity.

Conclusions I

TEdenovo outcompetes RepeatModeler and RepeatScout Greater coverage with

Less consensus Larger consensusLarger copies

Complementarity of TEdenovo, RepeatModeler and RepeatScoutComprehensive annotation of complex repeatomes

De novo repeatome detectionDeep repeatome annotation

Repeat annotation in large genomes

Arabidopsis120 Mb

CDS Repeatome Dark matter

0% 100%

Experimental model

Three strategies with REPET:Annotate genome with genomic copies

Use relaxed parameters for HSP detection

Use P-clouds to detect short repeat fragments

Iterative annotationAnnotate genome with genomic copies

(Expand the knowledge)

Iterative annotationAnnotate genome with genomic copies

(Expand the knowledge)

Iterative annotationAnnotate genome with genomic copies

(Expand the knowledge)

Genome

Consensus

Genomic copies

Genomic copies

Genomic copies

Genomic copies

TEannot

TEannot

TEannot

TEdenovo

TEannot

Iterative annotationAnnotate genome with genomic copies

RepeatModeler

RepeatScout

Tallymer

24-nt sRNA

Reference

Genome

0 10 20 30 40 50 60 70 80 90 100

TEdenovo_1

TEdenovo_2

TEdenovo_3

TEdenovo_4

Iterative annotationAnnotate genome with genomic copies

AAAC

AG

AT

CA

CC

CG

CTGA

GC

GG

GT

TA

TC

TG

TT

-0,05

0,05

0,15

CDS

TEdenovo

delta_2vs1

delta_3vs2

delta_4vs3

Dinucleotide composition

Relevance

Genome annotation using the delta_2vs1 copies

masks as much as 23 Mb (19.5%) of the genome

Covers 66% of the reference annotationand 56% of the TEdenovo annotation

The supplementary annotations from TEdenovo_2 are highly representative of the A. thaliana repeatome.

Relaxed (parameters) annotation

Relaxed (parameters) annotation

Relaxed (parameters) annotation

Default : Identity > 90%, Evalue<1e-300Cool : Identity > 85%, Evalue < 1e-50Soft : Identity > 80%, Evalue < 1e-20

Consensus size

RepeatModeler

RepeatScout

Reference

Tallymer

24 nt sRNA

0 10 20 30 40 50 60 70 80 90 100

TEdenovo_1

TEdenovo_cool

TEdenovo_soft

TEdenovo_soft_2

Relaxed (parameters) annotation

TEdenovo

Cool

Soft

Copy/consensus identity along chr1

()

Deep annotation of the A. thaliana repeatome

RepeatScout

RepeatModeler

TEdenovo

Repbase(+Buisine et al.)

Remove redundancy

Bundle libraryTEannot

Consensus size

selectednot

selected

Deep annotation of the A. thaliana repeatome

TEannot

P-clouds

Complete bundle

annotation

P-cloudsCopies Consensus

In-cloud k-mers

De Koning et al.

• TEdenovo

• Bundle

=> Repeated and repeat-derived sequences contribute at least 30% to the A. thaliana genome

Enhanced repeat detection in gene-rich regions

• Bundle + P-clouds

Arabidopsis repeats browser

Deep annotations

REPET

RepeatModeler

RepeatScout

Buisine et al.

24-nt sRNA

Genes

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

De novo repeatome detectionDeep repeatome annotation

Repeat annotation in large genomes

All genomes should benefit the greater quality of TEdenovo

Adapted from Nina V. Fedoroff (2012) and Steven M. Carr

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

Solutions

Use a sample of whole genome as input for TEdenovo (e.g. 300Mb)

(As recommended for RepeatModeler)

Tomato genomes

S. pennellii : 942 Mb

S. lycopersicum : 782 Mb

TEdenovo (n HSP >= 5)

0 0.5 1 Gb

Consensus library

320 Mb input

TEannot

Mb

82% of the Solanum pennellii ATGC space masked

Conclusions III

Efficient annotation of large plant genomes with REPET

Still quite a long process !

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

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

Véronique Jamilloux

Tina Alaeitabar

TimothéeChaumier

Mark Moissette

Olivier Inizan

HadiQuesneville

THANK YOU !