BMC Genomics 2007, 8:427 http://www.biomedcentral.com/1471-2164/8/427 young leaves by grinding 5 g of the tissue in liquid nitro- gen, followed by 5 min incubation in 35 ml of ice-cold H buffer [47]. The homogenate was filtered through 48 μm nylon mesh, adjusted to 35 ml volume with 1 × H buffer, and centrifuged at 200 × g for 15 min at 4°C. Pelleted nuclei were resuspended and centrifuged using the same conditions once in 35 ml of H buffer, and once in 15 ml of TC buffer (50 mM TRIS-HCl pH 7.5, 75 mM NaCl, 6 mM MgCl2, 0.1 mM CaCl2). The final centrifugation was performed for 5 min only and the nuclei were resus- pended in 2 ml of TC. DNA was released by incubating the nuclei with 40 mM EDTA, 0.2% SDS and 0.25 μg/μl of proteinase K for 4 hours at 37°C and purified by phenol extraction and ethanol precipitation. The sequencing was performed by 454 Life Sciences (Branford, CT, USA) using the GS-20 instrument and yielded 322,396 quality filtered sequence reads with the average length of 104 bp. The reads were deposited into NCBI Short Read Archive [48]. Processing and initial analysis of 454 reads The reads were screened for their similarity to adaptors and primers used in 454 sequencing using BLAST [49] and 595 reads producing blastn hits with expectation (E) value < 1e-15 were removed from the set. The same procedure was repeated to detect contamination with organellar sequences, removing 2,322 reads with similarity to plant chloroplast genomes and 77 reads similar to mitochon- drial DNA. The remaining 319,402 reads were used for all subsequent analyses. These reads included a total of 33.3 Mb, corresponding to 1/129 of the pea haploid genome size (1C = 4,300 Mb [18]). Thus, the calculated repeat copy numbers were multiplied by 129 in order to com- pensate for the actual genome coverage. To determine the representation of previously character- ized pea repeats in 454 sequences, these repeats were used as queries in blastn searches against a database of the 454 reads, using E-value cutoff of 1e-10. BLAST outputs were parsed using a BioPerl [50] script determining the number of similarity hits at all positions along the sequences, cal- culating their average numbers and the corresponding estimates of genomic copy numbers. The filtered set of 454 reads used for this analysis is available for BLAST searches at our web site [51]. Reconstruction of repetitive sequences Reconstruction of genomic repeats as well as other com- puter analysis were performed on an IBM xSeries 226 dual processor server with 4 GB RAM running under the Gen- too Linux operating system. In addition to the programs listed below, several scripts written in Perl were used for data processing and analysis. These scripts are available upon request from authors. The repeat reconstruction was done using TIGR Gene Indices clustering tools (TGICL [25]) employing the following parameters, which were optimized for our dataset by evaluating number and size of resulting clusters, the length of the assembled contigs and the presence of chimeric (misassembled) contig sequences. All-versus-all pairwise similarity scores of the reads were produced by mgblast and parsed using tclust, performing transitive-closure clustering of the read pairs sharing a minimum of 90% similarity over at least 70% of the shorter sequence. The reads within individual clusters were assembled into contigs using tgicl run with the -O '- p 80 -o 40' parameters, specifying overlap percent identity and length cutoff for cap3 assembler. Although most clus- ters included reads derived from only a single family of genomic repeats, the largest cluster (CL1) was composed from a large number of reads corresponding to several unrelated families of mobile elements. This problem was probably caused by the presence of "hybrid" reads includ- ing sequences of two different elements (spanning their insertion sites or recombination breakpoints, see also Dis- cussion). To separate these unrelated sequences, the con- tigs assembled from CL1 were further grouped into sub- clusters using sclust (parameters HEAVY = 3010 SCORE = 150), which performs weighted seeded clustering based on mutual similarities of the contigs. The programs mgblast, tclust, tgicl, cap3 and sclust are included in the TGICL package and the detailed description of parameters can be obtained from the program help. The assembled contigs were labeled based on the cluster they originated from (CL [number]Contig [number]); in case of CL1 the name also included sub-cluster number (SCL [number]_CL1Contig [number]). The contig sequences were characterized by calculating their average read depth (RD) and genome representation (GR = RD × contig length). The genome representation was also calcu- lated for individual clusters by summing GR values of the corresponding contigs. Contigs from the clusters repre- senting at least 0.01% of the pea genome (GR > 3,300) were subjected to sequence similarity searches (blastn, blastx) against GenBank database and to detection of con- served protein domains using RPS-BLAST [52] in order to identify the type and family of the repeat they originated from. Satellite repeats were identified by detection of tan- dem subrepeats within the contig sequences using dotter [53]. Total proportion of identified repeats in the pea genome was calculated by summing the GR of the clusters assigned to the same type of repeat. Detailed information about cluster characterization is available from Additional file 3. All contig sequences with their RD and GR values can be downloaded from our website; alternatively, the contigs can be selected by sequence similarity searches against user-provided queries [51]. Sequence analysis Phylogenetic analysis of retroelements was done using multiple sequence alignment of reverse transcriptase Page 12 of 16 (page number not for citation purposes)