6922 Nucleic Acids Research, 2011, Vol. 39, No. 16 85% identity or more. For each annotated feature, the cumulated sequence length of probes identifying a positive methylation signal was normalized by total length of probes covering the feature. siRNA density Small RNAs deep-sequencing data obtained from Arabidopsis Whole-aerial tissues were downloaded from GEO (accession: GSE14696) (24) and used to calculate the 24-nt siRNA density for all TE sequences with deﬁned DNA methylation patterns. As there was a high correl- ation between different replicates of this library, we merged them together to achieve a 6 million read library. Reads were mapped to the A. thaliana genome using MUMmer v3 and 24-nt siRNA density was calculated as follows: ND ¼ TNR P NRi NMi Region length 108: Where NRi is the number of reads corresponding to match Mi and NMi is the total number of matches for the sequence across the genome. TNR is the total number of reads in library and Region length is the length of TE sequence for which density is being calculated. Densities are expressed as number of reads per kilobite of the sequence per hundred thousand of the library reads. The R package (http://cran.r-project.org) and Perl (http://www.perl.org) were used for the statistical and DNA methylation analyses, respectively. RESULTS Improved annotation of TE sequences Identiﬁcation of TE sequences within genomes by homology searches is challenging because many of these sequences are highly degenerate derivatives of functional TEs or occur as nested insertions. Previously, we described a method that substantially improves TE sequence detec- tion (7). This method relies on the use of multiple sets of TE reference sequences, speciﬁcally designed to reﬂect diverse aspects of TE structure and evolution on the one hand, and on a TE annotation pipeline which combines several sequence-similarity search programs on the other (7). The annotation pipeline has been further reﬁned, in particular to allow for the detection of nested insertions through a ‘long join procedure’. Brieﬂy, two or more TE sequences separated by <5 kb in the genome are joined together in the ﬁnal annotation if they align in the same order and orientation within <500 bp from each other on the corresponding TE reference sequence and if they diverge from it to a similar extent (Figure 1; ‘Materials and Methods’ section). Using this improved version of the TE-annotation pipeline, we now identify a total of 31 245 TE sequences, which cover 25 Mb (21%) of the 119-Mb genome sequence available. As initially reported (4), retroelements, which transpose through an RNA intermediate, represent the largest fraction of TE sequences (10 Mb), followed by helitrons (8 Mb) and DNA transposons (7 Mb), which transpose through rolling circle and cut and paste processes, respectively. A detailed description of the de- tected TE sequence models is provided in File 1 in Supplementary Data and the new annotation can be found at TAIR, starting with release 8. Of note, 85% and 2.5%, respectively, of sequences annotated in the TAIR release 7 as pseudogenes (3315/3897) and genes (790/31 726) show at least 75% overlap with our TE an- notation (File 2 in Supplementary Data), indicating that they are in all likelihood TEs. Deﬁning a robust DNA methylation dataset Two studies have combined bisulphite treatment of genomic DNA, which converts unmethylated cytosines to uracils but leaves methylated cytosines intact, with next-generation sequencing to provide single-nucleotide resolution DNA methylation maps of the Arabidopsis genome (5,6). The two studies produced essentially iden- tical results and although no extensive analysis of TE se- quences was carried out, it was concluded in both cases that repeat elements including TEs are typically methyl- ated at CG, CHG and CHH sites. Moreover, these two studies reported that 30% of genes are methylated, but almost exclusively at CG sites and within part of the transcribed region only. In order to explore the genome-wide patterns of DNA methylation associated with TE sequences more systemat- ically and in greater detail, the DNA methylation data of Cokus et al. (5) were ﬁrst reassessed. Although the average sequencing coverage was 20-fold (5), large variations were observed, with 66.7% and 1.3% of sequenced cyto- sines covered by >10 or <50 reads, respectively (Figure 2). To avoid any potential problems resulting from this uneven coverage, only those cytosines with read depths between 10 and 50 were considered for our analysis, which amounted to 32% of uniquely mapped cytosines. Although CG sites are usually either unmethylated or methylated on over 80% of the molecules sequenced (5,6), a signiﬁcant number (15% of total) have intermediate methylation levels (File 3 in Supplementary Data). Thus, among CG sites with at least one read indicative of methy- lation, only 53% in genes and 66% in TE sequences have methylation levels above 80% (Supplementary Figure S1). Given the possible involvement of transcription in gene body methylation (22,25), lower levels of methylation of CG sites within genes could reﬂect tissue-speciﬁc differ- ences in expression. In the case of TE sequences, which are not transcribed in most cell types, intermediate levels of methylation at CG sites may rather indicate active demethylation or preferential action of the de novo, RNA-directed DNA methylation (RdDM) pathway over the so-called maintenance DNA methylation at these sites (3). Our analysis also conﬁrmed that in contrast to CG methylation, CHG and CHH methylation, which are re- stricted almost exclusively to TE sequences and other repeat elements, rarely reach levels greater than 80% (5,6). Based on these observations, sites were declared as methylated if at least 10% of the reads for CG sites or at least one read for CHG and CHH sites indicate methy- lation. Using these criteria, 82, 74 and 31% of CG, CHG