Establishing gene models from the Pinus pinaster genome using gene capture and BAC sequencing
© Seoane-Zonjic et al. 2016
Received: 28 October 2015
Accepted: 17 February 2016
Published: 27 February 2016
In the era of DNA throughput sequencing, assembling and understanding gymnosperm mega-genomes remains a challenge. Although drafts of three conifer genomes have recently been published, this number is too low to understand the full complexity of conifer genomes. Using techniques focused on specific genes, gene models can be established that can aid in the assembly of gene-rich regions, and this information can be used to compare genomes and understand functional evolution.
In this study, gene capture technology combined with BAC isolation and sequencing was used as an experimental approach to establish de novo gene structures without a reference genome. Probes were designed for 866 maritime pine transcripts to sequence genes captured from genomic DNA. The gene models were constructed using GeneAssembler, a new bioinformatic pipeline, which reconstructed over 82 % of the gene structures, and a high proportion (85 %) of the captured gene models contained sequences from the promoter regulatory region. In a parallel experiment, the P. pinaster BAC library was screened to isolate clones containing genes whose cDNA sequence were already available. BAC clones containing the asparagine synthetase, sucrose synthase and xyloglucan endotransglycosylase gene sequences were isolated and used in this study. The gene models derived from the gene capture approach were compared with the genomic sequences derived from the BAC clones. This combined approach is a particularly efficient way to capture the genomic structures of gene families with a small number of members.
The experimental approach used in this study is a valuable combined technique to study genomic gene structures in species for which a reference genome is unavailable. It can be used to establish exon/intron boundaries in unknown gene structures, to reconstruct incomplete genes and to obtain promoter sequences that can be used for transcriptional studies. A bioinformatics algorithm (GeneAssembler) is also provided as a Ruby gem for this class of analyses.
KeywordsBAC Bioinformatic pipeline Gene capture Gene model construct Gene structure Maritime pine Promoter studies
Forests ecosystems play a fundamental role in the regulation of terrestrial carbon sinks and represent nearly 80 % of the world´s total plant biomass . Conifers dominate a large part of the forests in the northern hemisphere, and they are intensively exploited as the primary source of wood for industrial purposes . Conifers also exhibit unique characteristics among vascular plants, including: high genetic variability, long half-lives, seasonal survival, adaptation to secondary growth, and wood deposition among others . Despite their economic and ecological importance, genomic studies of conifers have been hampered by the large size of their genomes, which range from 20 to 40 Gb, approximately 200 times the size of the Arabidopsis genome and approximately seven times the size of human genome . However, recent technical advances in genomic sequencing have enabled the assembly of the Norway spruce , white spruce  and loblolly pine  genomes, and the sequencing of a number of additional species is underway [4, 8]. Although these assemblies represent landmark in conifer genomics, technological challenges continue to face the assembly and annotation of conifer genomes; they are characterized by a proliferation of retrotransposons, highly diverged repetitive sequences, accumulation of non-coding regions and extensive gene duplication [4, 8]. Also, large families of transposons and retrotransposons have been reported to occupy long stretches of the sequences in Pinus genomes [8, 9].
The analysis of BAC clones has been the most common approach used for genome characterization and in hierarchical sequencing projects, such as the human genome  or other genomes without available references . The screening of BAC libraries has been used to target gene-rich regions in white spruce, but the approach has proven to be very laborious because most clones contain the non-coding regions of the genes, which is expected due to the large size of conifer genomes .
An alternative to obtaining the gene sequences of large and complex genomes is to perform an enrichment step to isolate the genomic DNA sequences of interest that contain the coding regions of genes by massive parallel sequencing and use them for further analysis . This system named “gene capture”, uses rapid selective hybridization technique to obtain sequences of interest much more efficiently . “-Gene capture-” has been widely used as a diagnostic tool for human whole exome analyses [15–17] but the use of the technique in plants has been much more limited [18, 19].
In this work, we used “-gene capture-” to elucidate the target, gene-rich regions in the genome of the maritime pine (Pinus pinaster L. Aiton), a conifer species of great ecological and economic importance in Europe and for which whole-transcriptome resources are available [20, 21]. To achieve this goal, 120-mer probes were designed from 866 tentative maritime pine transcripts, which include the probes for three characterized BAC clones as a control. These BAC clones were isolated by screening a maritime pine BAC library using specific cDNA probes  and then used as a reference for gene capture assays.
In this approach, megagametophyte calli haploid DNA from maritime pine was isolated, fractioned and bounded by a series of specific adapters for 454 sequencing. The captured genomic sequences were sequenced in an FLX-Titanium platform, and the reads were assembled and analyzed using the GeneAssembler bioinformatic pipeline to recover the gene models. This experimental approach also provided sequences for the proximal promoter region of the targeted genes. This can be used as initial information for genome walking to thoroughly characterize the cis elements contained in the regulatory region of these genes.
BAC clone isolation and characterization
Exon length comparisons among complete AS genes from P. pinaster and AS from two angiosperm plants
Exon length (nt)
Pinus pinaster AS1/AS3/AS5
Arabidopsis thaliana ASN3 (At5g10240)
Arabidopsis thaliana ASN1 (At3g47340)
Populus trichocarpa AS (Potri.009G072900)
Populus trichocarpa AS (Potri.005G075700)
Intron length comparisons among complete AS genes from P. pinaster and AS from two angiosperm plants
Intron length (nt)
Pinus pinaster AS1
Pinus pinaster AS3
Pinus pinaster AS5
Arabidopsis thaliana ASN3 (At5g10240)
Arabidopsis thaliana ASN1 (At3g47340)
Populus trichocarpa AS (Potri.009G072900)
Populus trichocarpa AS (Potri.005G075700)
A similar analysis of the structure of the SuSy and XET genes was performed. The SuSy BAC clone is represented in a single scaffold of 59,327 bp (Fig. 1b), and the gene sequence is organized into 14 exons encoding a protein with 769 amino acids. The comparison to the SuSy gene models in other plants indicated that the 5´end of the gene is missing in the pine BAC clone obtained for this study. The lengths and composition of the exon and intron are presented in Additional file 2: Table S1 and Additional file 3: Table S2, respectively, and a comparative study showed a high degree of conservation in the number and size of the exons in the maritime pine BAC model compared with the SuSy2 gene from Arabidopsis and the POPTRDRAFT_830445 gene from P. trichocarpa. In contrast, substantial differences in the number and lengths of the exons were found when compared with SuSy3 from Arabidopsis and POPTRDRAFT_826368 from Populus.
The XET BAC clone is represented in a single, 64,767-bp scaffold (Fig. 1c), and the sequence renders a model organized into 4 exons and 3 introns encoding a protein with 287 amino acids. The lengths and distribution of the exons and introns of the gene contained in the BAC clone are represented in Additional file 4: Table S3 and Additional file 5: Table S4, respectively. Two Arabidopsis gene models, XET 9 (At4g032210) and XET 3 (At3g25050), were closest to the pine gene sequence, but it was impossible to determine which is more similar to the gene contained in the pine BAC.
Content and type of repeats present in SuSy and AS1 BACs from P. glauca and P. pinaster
Susy P. glauca
Susy P. pinaster
AS P. glauca
AS P. pinaster
Number of elements/percentage of sequence
10259 bp (7.49 %)
5173 bp (8.71 %)
7548 bp (5.80 %)
595 bp (1.29 %)
7 (6,78 %)
7 (7,52 %)
9 (4,65 %)
1 (0,70 %)
2 (0,16 %)
1 (0,70 %)
2 (0,16 %)
7 (6,78 %)
6 (6,82 %)
7 (4,49 %)
1 (1,09 %)
1 (0,24 %)
1 (0,55 %)
6 (5,60 %)
5 (6,58 %)
6 (3,94 %)
2 (0,10 %)
18 (0,67 %)
17 (0,94 %)
18 (0,69 %)
11 (0,98 %)
1 (0,03 %)
3 (0,25 %)
7 (0,36 %)
2 (0,31 %)
Genomic DNA capture and gene model generation
At the same time as the BAC library screening, we conducted a gene capture procedure using maritime pine haploid DNA . Genomic DNA captured obtained using the SureSelect kit was sequenced in 454/Roche, and a total of 2,036,142 captured raw reads with an average size of 769 nt were cleaned with SeqTrimNext, producing 1,942,057 useful reads. These reads were assembled with MIRA3, yielding a total of 144,707 contigs and 305,396 “debris” reads. The contigs served to reduce the sequence space and to provide longer consensus sequences to facilitate the building of the gene model.
The best gene model is as follows: (i) the one that recovers more full-length seeding sequences; (ii) the one with the lowest overlap percentage of contigs at the exon level; and (iii) the one with the lowest fragmentation index, that is, the one obtained from the fewest number of contigs. The GeneAssembler gene model for each full-length protein was saved in a gff3 file, which included the contigs. Additionally, a FASTA file was generated with the gene model sequences and an index relating each contig with its full-length protein.
Genomic sequences were recovered for the 866 maritime pine genes selected to design the probes, and this recovery was independent of the use of a reference genome for building the gene model. Accordingly, the reconstruction that was done without a reference genome was selected as it represents the highest mean recovery of each protein, approximately 82 %, and the lowest sequence redundancy. Most of the gene models with a high recovery percentage had between one to five exons, and particularly for those with one and two exons, there were many gene models with a recovery rate above 50 %. In addition, there were 20 gene models with a recovery rate over 100 %, but these were considered to be incorrect due to the presence of sequence repeats, which the algorithm was unable to handle. All of the generated contigs and models are available in the Pine Gene Capture database (PGC, http://www.scbi.uma.es/pgc/).
Comparison of BAC and gene capture approaches to defining maritime pine gene models
To further explore the quality of the genomic sequences derived from the gene capture approach, the structures of the AS, SuSy and XET genes were compared to those derived by BAC clone sequencing.
The gene capture model for the SuSy gene agreed well with the structure of the BAC clone. The maritime pine model is closer to the SuSy2 gene from Arabidopsis and Populus with very well conserved number, length and exon position (Additional file 2: Table S1). Only exons 7 and 15 displayed small differences of 6 and 27 nt, respectively. The combination of BAC sequencing and gene capture allowed us to complete the first exon sequence, which can be considered an additional advantage of combining the two strategies.
The BAC clone and the gene capture model displayed very similar intron sizes; even intron 8, which contained a gap in the BAC assembly, was completed using the gene capture data (Additional file 3: Table S2). Although the distribution and length of exons was well conserved, an increase in the variability of the intron size was observed, which was similar to what was found for the AS gene family. In this case, the average size of the introns of the maritime pine gene was higher than for those genes of the other two species included in the comparison.
In the third part of this study, building the XET models, we did not include poplar genes, as we did for the AS1 and SuSy genes. The XET-related gene family is very large in plants, and as the poplar genome recently underwent a whole genome-wide duplication , the number of XET genes to compare to find the closest model to the pine gene would be very large. Therefore, considering the larger size of the gene family, we could not obtain a single model through gene capture. Instead, the two potential gene models that were generated are listed in Additional file 4: Table S3.
Gene capture of regulatory regions
An additional aim of this study was to test if the gene capture methodology could be extended to recover unknown sequences of the 5´end of the genes so that these sequences could be used: i) for a genome walking approach to obtain the promoter sequence or ii) directly for functional studies using the proximal promoter region of the genes.
To validate these results, the 5´upstream sequences of three genes included in our gene capture study were compared to the corresponding gene promoter sequences that had been previously cloned and functionally characterized [30, 31]. For the glutamine synthetase (GS1a) gene, 518 nucleotides of the sp_v1.1_unigene26377 gene capture model overlapped with the GenBank promoter sequence [GenBank:AJ225121] (Additional file 6: Figure S2); 907 nucleotides for the phenylalanine ammonia lyase (PAL) gene promoter [GenBank:HE866754] overlapped with the sp_v1.1_unigene15094 model (Additional file 7: Figure S3); and 630 nucleotides for the prephenate aminotransferase (PAT) gene promoter [GenBank:HE866755] matched the unigene all_rep_c3941_PAT gene capture model (Additional file 8: Figure S4).
In this study, we present a strategy to generate maritime pine gene models, confirmed by the structure of available BAC clones, which demonstrates that gene capture is a powerful technology for establishing gene structures in species without reference genomes, such as the maritime pine.
Maritime pine gene structure
Generally, it is well accepted that a positive relationship exits between genome size and intron lengths in eukaryotes. In conifers, which have large genomes ranging from 18 to 35 Gbp, longer introns can contribute to large genome sizes . In this study, we performed a deep comparison of exon/intron distribution for three pine genes against the Arabidopsis and poplar sequences from the databases.
The second feature contributing to the large conifer genomes is retrotransposon expansion, which contrasts with what has been described for large angiosperm genomes, where gene duplications and, polyploidization, as well as retrotransposons expansion, are the most common features contributing to genome size [32, 33]. Thus, retrotransposon expansion can be of primary importance in explaining genome size in Pinus species . We analyzed the presence of retroelements in the BAC clones included in this study by comparing two available BACs containing the AS1 and SuSy genes in P. glauca.
In terms of number/percentage, the LTR and Gypsy retrotransposons are most abundant in the SuSy and AS1 BACs from P. glauca and P. pinaster (Table 3). These findings suggest that retrotransposon expansion is a reasonable hypothesis to explain the large genome size in P. pinaster, as has been proposed for other conifers [34, 35]. However, much more data on the retrotransposon families of maritime pine are needed to confirm this hypothesis.
Gene capture results are consistent with BAC clone structures; this outcome can be extended to other frames
The algorithm developed for building gene models in this work has been used with plants as target organisms, but it is not restricted to them, its use with gene capture data derived from other organisms is possible.
A total of 866 gene models were obtained with our in-house bioinformatics pipeline (GeneAssembler), with a median recovery percentage of 82 % from the original full-length sequence. However, there were several constraints, such as complex gene families, the presence of pseudogenes and the lack of a reference genome, that limited the final results. The algorithm designed in this study can address these problems, correctly resolving the gene models. It is also necessary to note is that the guided recovery using genes from model organisms, such as A. thaliana, P. trichocarpa, O. sativa or P. patens, could be useful when the coding sequence is incomplete. However, if enough transcript sequence information is available, the model can be built without any reference, which can be justified by taking into account that the contigs used to build the gene model come from the pool of single-hsp contigs. This pool has no defined exon-intron contig structure, and the contigs can be either exon fragments or putative pseudogenes.
The pipeline was also able to provide sequences for the 5´upstream region of most genes (85 %), and furthermore, 55 % of the gene models have at least one contig containing 500 to 1000 bp of the regulatory region of the gene. These findings indicate that the pipeline can be tuned to find a substantial portion of the gene promoter regions. These results validate the use of gene capture as a technology to provide information about the promoter region of genes in species where reference genomes are not available. This genomic information can be used in silico studies or to obtain the promoter region for functional studies of a significant number of maritime pine genes.
Exploring the organization of maritime pine gene families
Phylogenetically, the angiosperm AS genes can be grouped into two classes: ASI and ASII [37, 38]. The AS genes belonging to ASII class generally have 13 introns while the ASI class lacks intron five . A phylogenetic tree was generated using the deduced amino acid sequences of the five AS pine genes from P. pinaster and P. taeda in addition to the AS sequences from Physcomitrella patens and different angiosperms used in previous AS phylogenetic analysis  (Fig. 7). Until now, the gymnosperm AS genes were considered to belong to the AS class II group . But it can be concluded from our phylogenetic analysis that the conifer AS proteins do not belong to either AS class I or class II, which had been previously described for angiosperms. As shown in Fig. 7, the pine AS proteins group together separately from either of the ASI or ASII angiosperm classes. The models that are phylogenetically closer to angiosperms are the class II ASN3 gene from Arabidopsis and the class II Potri.005G075700 gene from poplar, which were included in our comparative study of exon/intron AS gene organization. It has previously been suggested that the angiosperm AS class II genes are closer to the ancestral AS gene , and our results support this hypothesis because all conifer AS genes conserve the 13 introns in their structure. Moreover, the conifer AS genes are closer to the bryophyte P. patens at sequence level.
The AS BAC clone sequenced in this work contains the genomic sequence of the AS1 gene, but due to the close similarity among the AS1, AS3 and AS5 pine genes (Tables 1 and 2, Fig. 4, Additional file 9: Figure S5 and Additional file 10: Figure S6), the model we obtained using the designed algorithm was predominantly AS3. A thorough examination of the contigs recovered by gene capture is included in Additional file 11: Table S6, and it illustrates that numerous contigs could be included in the AS3 instead of the AS1 model in this case. There were mainly truncated sequences, or intron-lacking sequences, that can be considered to be pseudogenes of AS3, and this indicates that the presence of numerous pseudogenes derived from a single gene can bias the results of this type of gene capture experiment. In the early stages of our study, we included this gene model because we thought it would be a good example of a pine gene belonging to a family with relatively few members to the XET gene and its family composition. In addition, the AS gene has a relatively large number of exons compared to the structure of the XET gene, which has fewer exons that can be used to detect differences in sequence recovery. However, we have found that this additional information related to the numerous pseudogenes in the AS family highlights the complexity of the maritime pine genome.
The comparison of the gene models produced by both approaches revealed that (i) processing the gene capture data with GeneAssembler was highly successful with 82 % of the gene models recovered even when a reference genome was not available. (ii) Successful and useful gene models can be obtained using probes designed from cDNAs; inference from of a single gene model would be limited in complex gene families with many members for which supplementary information would be required. (iii) Gene capture can serve to fill gaps in the gene structure established by BAC sequencing, as is the case with the SuSy gene model included in this study. (iv) Studying the gene models of different members belonging to the same family (e.g., AS) creates new possibilities for facilitating intra- and inter- comparative studies aimed at understanding function in the light of evolution.
PCR-based screening of a pooled BAC library
A 0.8x coverage pooled BAC library was previously prepared  as 83 glycerol stocks of E. coli pools, and each pool contained approximately 4000 distinct clones with an average size of 107 kbp. DNA of BAC pool was prepared by a modified alkaline lysis method using a previously described protocol .
For the primary PCR screening, specific genomic sequences of asparagine synthetase (AS), sucrose synthase (SuSy) genes or xyloglucan endotransglycosylase (XET) were amplified using primers designed against the available cDNA sequence (Additional file 12: Table S5). The PCR profile used for screening the pool library was as follows: 20 s denaturing at 94 °C, 45 s annealing at 60 °C and 45 s elongation at 72 °C. This temperature profile was repeated for 35 cycles, and putative positive PCR product pools were on sequenced a CEQ 8000 automated capillary sequencer (Beckman Coulter, Barcelona Spain). After this, the original cell pools were titrated to determine the appropriate dilution to obtain ~7000 colonies per Qtrays Genetix plate (24.2 × 24.2 cm), and the different clones were then individualized in 36 × 384-well plates using a QPIX2 ® (Genetix).
For the secondary screening, high-density filters were prepared on 22.2 × 22.2-cm nylon membranes using a 96-pin gridding head on the Genetix QPIX2 ® robot. Each BAC filter was gridded to 6 rows × 4 columns in a single spot and distributed in six different fields. Membranes were incubated overnight at 37 °C with the colony side facing up on LB/chloramphenicol (12.5 μg/ml) plates, and processing and hybridization of the filters was done as recommended in the QPix manual. [32P]-labeled specific genomic sequences were used as probes. The hybridized membranes were exposed to a phosphorimaging screen (Fuji Imaging plate BAS-MS 2040) for 24 h at room temperature and scanned using an FLA-7000 Imaging System (Fujifilm). Using a grid map, the pattern of hybridization allowed us to identify the 384-well plate(s) and the plate address(es) of the positive clone(s).
For the subsequent steps, the cells of the positive clones were recovered and plated on LB agar with 12.5 μg/ml chloramphenicol, 90 μg/ml IPTG and 90 μg/ml X-GAL. After a second round of PCR and sequencing, the presence of a specific genomic sequence was confirmed. The detailed workflow protocol is available in Additional file 1: Figure S1.
BAC DNA Sequencing library preparation and 454 parallel sequencing
The BAC insert size was estimated by pulse field gel electrophoresis (FIGE, Bio-rad Lab. Inc.). The 454 sequencing library was prepared with 6 μg of the BAC plasmid DNA using shotgun and standard long, paired-end sequencing kits, according to the specifications of Roche, the manufacturer. Sequencing libraries were quantified with 2100 BioAnalyzer (Agilent), processed by emulsion PCR and sequenced on a 454/Roche GS FLX as described in the manuals (Roche Diagnostics). The 24 L12 (AS), 25 M3 (XET) BACs libraries, were respectively sequenced in paired-end and shotgun pools using MIDs loaded in a 1/2 region, and BAC 26C15 library (SuSy) was sequenced in paired-end pools in a 1/4 region of 70 × 75 Picotiterplate (PTP). The pair-end BAC reads, which had been preprocessed by SeqTrimNext, were assembled using Newbler version 2.3 with the default parameters.
Capture array gene selection
The 91,086 full-length unigenes predicted from the P. pinaster transcriptome assembly from SustainPineDB  were used to select the genes for the DNA capture array. The full-length unigenes were filtered based on their GO terms , and the KEGG pathways  annotations mainly focused in metabolism. Because some unigenes contained annotations from several functions, this filter resulted in 1462 unigenes. Additionally, to reduce redundancy, the unigenes sharing the same ortholog from UniProt  were filtered to get only one gene per ortholog, reducing the selected number of unique unigenes to 1026. BLAST  best hits were used to assign the orthologous genes.
After the unigene selection was done, a better assembly of P. pinaster transcriptome was performed and published , so we searched for the genes from the 1026 unigenes in the SustainPine assembly version 1.1 (http://www.scbi.uma.es/sustainpinedb/) using BLAST. Finally, 866 unigene sequences were selected to be included in the DNA capture array. As described previously, we added 3 genomic sequences from cloned BACs as control sequences, the asparagine synthetase [BAC at GenBank: KP172187] from 5’ UTR to the stop codon, a clone containing the sequence of a gene encoding a sucrose synthase [BAC at GenBank: KP172194] and a clone containing the xyloglucan endotransglycosylase [BAC at GenBank: KP172185]. The distribution of the unigenes used for the capture array in functional categories is shown in Additional file 13: Figure S7.
DNA capture array design
A Ruby custom script was used to design the probes for an Agilent SureSelect DNA Capture Array, and it was optimized to fit the 866 genes selected (see Capture array gene selection). In total, 56,667 probes of 120 nt in length were designed, and the probe density was increased in the first third of the transcript sequences (including the 5’UTR). In this way, all of the genes were represented in the array with 6x coverage in the first third of the gene sequence (using a tilling distance of 19 nt) and 4x coverage in the other two thirds (using a tilling distance of 29 nt). On average, 65 overlapping probes represented each gene, and they were uploaded to the Agilent eArray website (https://earray.chem.agilent.com/earray/) for the production of the array.
Haploid genome DNA preparation
Maritime pine cones were collected from Oria 6, a genotype of P. pinaster Aiton from the natural population Sierra de Oria (Almería, Spain), selected based on its response to extreme drought conditions. Cones were surface sterilized with 96 % ethanol for 20 min and air-dried in a laminar flow cabinet before seed isolation. Haploid megagametophytes were isolated from sterilized seeds for tissue culture establishment . For sequence capture, the P. pinaster A5 callus haploid line derived from the megagametophyte tissue was used. The DNA was extracted from the calli using a DNeasy Plant Mini Kit (Qiagen) and it was quantified with a Quant-it PicoGreen dsDNA Assay Kit (Invitrogen). The quality was assessed with an Agilent 2100 Bioanalyzer.
Targeted capture and 454 parallel sequencing
Haploid genomic DNA from P. pinaster was captured using the Agilent SureSelect Target Enrichment System following the manufacturer´s protocols with minor modifications. Two micrograms of this DNA were fragmented to 1.5 kb in size and purified by gel extraction using a MinElute Gel extraction kit (Qiagen), and the quality of fragmentation and purification was assessed with an Agilent 2100 Bioanalyzer. Fragment ends were repaired, and RL adaptors (Roche) were ligated to the fragments, and the resulting adapter-ligated sample was purified using Agencourt AMPure XP beads (Beckmann Coulter). The DNA library was amplified by PCR and captured by hybridization at 65 oC for 24 h with the biotinylated RNA library “bait” (Agilent). Bound genomic DNA was purified with streptavidin-coated magnetic Dynabeads (Invitrogen) and re-amplified. Stratagene Herculase II enzyme (Agilent) was used for both PCR reactions, and the resulting captured library was purified using Agencourt AMPure XP beads (Beckmann Coulter) and was assessed with the Agilent 2100 Bioanalyzer. Finally the captured library was sequenced on Roche GS-FLX+ using a two-region gasket according to the manufacturer´s protocols.
Roche 454 data processing
The reads were preprocessed using the SeqTrimNext pipeline (http://www.scbi.uma.es/seqtrimnext) , which is available from the Plataforma Andaluza de Bioinformatica (University of Malaga, Spain). Low quality sequences, linkers, adaptors, vector fragments, organelle DNA, and contaminated sequences were removed, and the longest informative part of the read was retained, discarding sequences below 40 bp.
Data assembly and gene recovery criteria
Useful reads were assembled by the MIRA assembler, version 3 , to obtain the contigs for building the gene models. Full-length transcripts and the protein sequences were predicted using Full-Lengther Next  based on SustainPine 1.1 database  information.
The captured fragments were identified, and the gene models were generated using our own bioinformatic pipeline (GeneAssembler), which can be downloaded and installed on any Unix/Linux-based computer as a Ruby gem: https://rubygems.org/gems/gene_assembler (for a detailed description of the GeneAssembler pipeline see Additional file 14).
The gene sequences from Arabidopsis thaliana, Populus trichocarpa, Oryza sativa and Physcomitrella patens were downloaded from Phytozome 9.1  to improve gene recovery, and an ortholog search was performed to enhance the gene model building strategy. A Blast X (by default parameters)  with the full-length proteins was performed against each set of genes. All gene matches were considered to be putative orthologs, which means that for each match, the exon-intron coordinate was retrieved.
Sequence alignment and phylogenetic analysis
The sequences used for alignments and phylogenetic trees were obtained in Phytozome database (http://phytozome.jgi.doe.gov) except for P. taeda that were obtained from Congenie database (http://congenie.org/). P. pinaster AS1 [GenBank:ADU02856]; and AS2 [GenBank:ADK13052] protein sequences were obtained from GenBank at the NCBI. For P. pinaster AS3 [PGC:geneCapture_all_rep_c7631] and AS5 [PGC:geneCapture_all_rep_c8956/geneCapture_all_rep_c10521 we used sequences obtained in the course of this work and deposited in the Pine Gene Capture database (PGC, http://www.scbi.uma.es/pgc/). Finally for P. pinaster AS4 [SPDB: sp_v3.0_unigene97582/sp_v3.0_unigene8248] we used the sequence obtained from our transcriptomic database SustainPineDB  (http://www.scbi.uma.es/sustainpinedb/).
The CLUSTALW program was used for sequence alignments . The phylogenetic tree was constructed with full-length AS amino acid sequences using the neighbor-joining method  with 1000 bootstrap replications. The evolutionary distances were computed using the JTT matrix-based method  and are in the units of the number of amino acid substitutions per site. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). All ambiguous positions were removed for each sequence pair. The positions not presented in all the sequences were eliminated. Finally there were a total of 509 positions in the final dataset. All of these analyses were conducted in MEGA6 .
Availability of supporting data
The bioinformatic pipeline (GeneAssembler) used for generating gene models, can be downloaded at: https://rubygems.org/gems/gene_assembler. All of the generated contigs and models are available in the Pine Gene Capture database (PGC, http://www.scbi.uma.es/pgc/). The phylogenetic data can be found at http://purl.org/phylo/treebase/phylows/study/TB2:S18787. Other supporting data of this article are included as additional files
This work has been funded by grants from the European Commission Seventh Framework grant PROCOGEN (FP7-KBBE-2011-5) and the Spanish Ministerio de Economía y Competitividad (BIO2012-33797). PSZ was supported by funds from Junta de Andalucía, P10-CVI-6075.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Olson JS, Watts JA, Allison LJ. Carbon in live vegetation of major world ecosystems. In: Report ORNL. Oak Ridge: Oak Ridge National Laboratory; 1983. p. 5862.Google Scholar
- Farjon A. A Handbook of the World´s Conifers. Leiden: Brill Academic Publishers; 2010.View ArticleGoogle Scholar
- Hamrick JL, Godt MJW, Sherman-Broyles SL. Factors influencing levels of genetic diversity in woody plant species. New For. 1992;6:95–124.View ArticleGoogle Scholar
- Mackay J, Dean JFD, Plomion C, Peterson DG, Cánovas FM, Pavy N, et al. Towards decoding the conifer giga-genome. Plant Mol Biol. 2012;80:555–69.View ArticlePubMedGoogle Scholar
- Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin Y-C, Scofield DG, et al. The Norway spruce genome sequence and conifer genome evolution. Nature. 2013;497:579–84.View ArticlePubMedGoogle Scholar
- Birol I, Raymond A, Jackman SD, Pleasance S, Coope R, Taylor GA, et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics. 2013;29:1492–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Wegrzyn JL, Lin BY, Zieve JJ, Dougherty WM, Martinez-Garcia PJ, Koriabine M, et al. Insights into the Loblolly Pine Genome: Characterization of BAC and Fosmid Sequences. PLoS One. 2013;8:e72439.PubMed CentralView ArticlePubMedGoogle Scholar
- de la Torre AR, Birol I, Bousquet J, Ingvarsson PK, Jansson S, Jones SJM, et al. Insights into conifer Giga-Genomes. Plant Physiol. 2014;166:1724–32.View ArticleGoogle Scholar
- Kovach A, Wegrzyn JL, Parra G, Holt C, Bruening GE, Loopstra CA, et al. The Pinus taeda genome is characterized by diverse and highly diverged repetitive sequences. BMC Genomics. 2010;11:420.PubMed CentralView ArticlePubMedGoogle Scholar
- Lander ES, Consortium IHGS, Linton LM, Birren B, Nusbaum C, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.View ArticlePubMedGoogle Scholar
- Rampant PF, Lesur I, Boussardon C, Bitton F, Martin-Magniette ML, Bodenes C, et al. Analysis of BAC end sequences in oak, a keystone forest tree species, providing insight into the composition of its genome. BMC Genomics. 2011;12:292.View ArticleGoogle Scholar
- Sena JS, Giguère I, Boyle B, Rigault P, Birol I, Zuccolo A, et al. Evolution of gene structure in the conifer Picea glauca: a comparative analysis of the impact of intron size. BMC Plant Biology. 2014;14:95.View ArticleGoogle Scholar
- Mamanova L, Coffey AJ, Scott CE, Kozarewa I, Turner EH, Kumar A, et al. Target-enrichment strategies for next-generation sequencing. Nat Methods. 2010;7:111–8.View ArticlePubMedGoogle Scholar
- Lin X, Tang W, Ahmad S, Lu J, Colby CC, Zhu J, et al. Applications of targeted gene capture and next-generation sequencing technologies in studies of human deafness and other genetic disabilities. Heart Research. 2012;288:67–76.View ArticleGoogle Scholar
- Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR, Zumbo P, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. PNAS. 2009;106:19096–101.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12:745–55.View ArticlePubMedGoogle Scholar
- Neves LG, Davis JM, Barbazuk WB, Kirst M. Whole-exome targeted sequencing of the uncharacterized pine genome. Plant J. 2013;75:146–56.View ArticlePubMedGoogle Scholar
- Mascher M, Richmond TA, Gerhardt DJ, Himmelbach A, Clissold L, Sampath D, et al. Barley whole exome capture: a tool for genomic research in the genus Hordeum and beyond. Plant J. 2013;76:494–505.PubMed CentralView ArticlePubMedGoogle Scholar
- Canales J, Bautista R, Label P, Gomez-Maldonado J, Lesur I, Fernandez-Pozo N, et al. De novo assembly of maritime pine transcriptome implications for forest breeding and biotechnology. Plant Biotechnol J. 2014;12(3):286–99.View ArticlePubMedGoogle Scholar
- Cañas RA, Canales J, Gómez-Maldonado J, Ávila C, Cánovas FM. Transcriptome analysis in maritime pine using laser capture microdissection and 454 pyrosequencing. Tree Physiol. 2014;34:1278–88.View ArticlePubMedGoogle Scholar
- Bautista R, Villalobos DP, Díaz-Moreno S, Cantón FR, Canovas FM, Claros MG. Toward a Pinus pinaster bacterial artificial chromosome library. Ann Forest Sci. 2007;64:855–64.View ArticleGoogle Scholar
- Fernández-Pozo N, Guerrero-Fernández D, Bautista R, Gómez-Maldonado J, Avila C, Cánovas FM, et al. GENote v.β.: A web tool prototype for annotation of unfinished sequences in non-model Eukaryotes. In: Freitas AT, Navarro A, editors. Lecture Notes in Bioinformatics. Berlin: Springer; 2012. p. 66–71.Google Scholar
- Canales J, Rueda-López M, Craven-Bartle B, Avila C, Cánovas FM. Novel insights into regulation of asparagine synthetase in conifers. Frontiers Plant Sci. 2012;3:100.View ArticleGoogle Scholar
- Arrillaga I, Guevara MA, Muñoz-Bertomeu, Lázaro-Gimeno D, Sáez-Laguna E, Díaz LM, et al. Selection of haploid cell lines from megagametophyte cultures of maritime pine as a DNA source for massive sequencing of the species. Plant Cell Tiss Organ Cult. 2014;118:147–55.View ArticleGoogle Scholar
- Petrov DA. Evolution of genome size: new approaches to an old problem. Trends Genet. 2001;17:23–8.View ArticlePubMedGoogle Scholar
- Wendel JF, Cronn RC, Alvarez I, Liu B, Small RL, Senchina DS. Intron size and Genome size in plants. Mol Biol Evol. 2002;19:2346–52.View ArticlePubMedGoogle Scholar
- Cañas RA, de la Torre F, Cánovas FM, Cantón FR. High levels of asparagine synthetase in hypocotyls of pine seedlings suggest an essential role of the enzyme in re-allocation of seed-stored nitrogen. Planta. 2006;224:83–95.View ArticlePubMedGoogle Scholar
- Tuskan GA, Difazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science. 2006;313:1596–604.View ArticlePubMedGoogle Scholar
- Gómez-Maldonado J, Avila C, Barnestein P, Crespillo R, Cánovas FM. Interaction of cis-acting elements in the expression of a gene encoding cytosolic glutamine synthetase in pine seedlings. Physiol Plant. 2004;121:537–45.View ArticleGoogle Scholar
- Craven-Bartle B, Pascual MB, Cánovas FM, Avila C. A Myb transcription factor regulates genes of the phenylalanine pathway in maritime pine. Plant J. 2013;74:755–66.View ArticlePubMedGoogle Scholar
- Bennetzen JL. Mechanisms and rates of genome expansion and contraction in flowering plants. Genetica. 2002;115:29–36.View ArticlePubMedGoogle Scholar
- Walbot V, Petrov DA. Gene galaxies in the maize genome. Proc Natl Acad Sci. 2001;98:8163–4.PubMed CentralView ArticlePubMedGoogle Scholar
- Morse AM, Peterson DG, Islam-Faridi MN, Smith KE, Magbanua Z, Garcia SA, et al. Evolution of Genome size and complexity in Pinus. PloS One. 2009;4:e4332.PubMed CentralView ArticlePubMedGoogle Scholar
- Magbanua ZV, Ozkan S, Bartlett BD, Chouvarine P, Saski CA, Liston A, et al. Adventures in the Enormous: A 1.8 Million Clone BAC library for the 21.7 Gb Genome of loblolly Pine. PloS One. 2011;6:e16214.PubMed CentralView ArticlePubMedGoogle Scholar
- Cánovas FM, Avila C, Cantón FR, Cañas RA, de la Torre F. Ammonium assimilation and amino acid metabolism in conifers. J Exp Bot. 2007;58:2307–18.View ArticlePubMedGoogle Scholar
- Herrera-Rodríguez MB, Carrasco-Ballesteros S, Maldonado JM, Pineda M, Aguilar M, Pérez-Vicente R. Three genes showing distinct regulatory patterns encode the asparagine synthetase of sunflower (Helianthus annus). New Phytol. 2002;155:33–45.View ArticleGoogle Scholar
- Cañas RA, Quilleré I, Christ A, Hirel B. Nitrogen metabolism in the developing ear of maize (Zea mays): analysis of two lines contrasting in their mode of nitrogen management. New Phytol. 2009;184:340–52.View ArticlePubMedGoogle Scholar
- Gaufichon L, Reisdorf-Cren M, Rothstein SJ, Chardon F, Suzuki A. Biological functions of asparagine synthetase in plants. Plant Sci. 2010;179:141–53.View ArticleGoogle Scholar
- Villalobos D, Bautista R, Cánovas FM, Claros MG. Isolation of Bacterial Artificial Chromosome DNA by Means of Improved Alkaline Lysis and Double Potassium Acetate Precipitation. Plant Mol Biol Rep. 2004;22:1–7.View ArticleGoogle Scholar
- Barrell D, Dimmer E, Huntley RP, Binns D, O'Donovan C, Apweiler R. The GOA database in 2009--an integrated Gene Ontology Annotation resource. Nucleic Acids Res. 2009;37(Database issue):D396–403.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42(Database issue):D199–205.PubMed CentralView ArticlePubMedGoogle Scholar
- UniProt Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2014;42(Database issue):D191–8.Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.View ArticlePubMedGoogle Scholar
- Falgueras J, Lara AJ, Fernandez-Pozo N, Canton FR, Perez-Trabado G, Claros MG. SeqTrim: a high-throughput pipeline for pre-processing any type of sequence read. BMC Bioinformatics. 2010;11:38.PubMed CentralView ArticlePubMedGoogle Scholar
- Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WE, Wetter T, et al. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004;14(6):1147–59.PubMed CentralView ArticlePubMedGoogle Scholar
- Lara A, Pérez-Trabado G, Villalobos D, Díaz-Moreno S, Cantón F, Claros MG. A Web Tool to Discover Full-Length Sequences: Full-Lengther. In: Corchado E, Corchado JM, Abraham A, editors. Innovations in Hybrid Intelligent Systems. Berlin: Springer; 2007. p. 361–8.View ArticleGoogle Scholar
- Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40(Database issue):D1178–86.PubMed CentralView ArticlePubMedGoogle Scholar
- Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. doi:10.1186/1471-2105-10-421.PubMed CentralView ArticlePubMedGoogle Scholar
- Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22:4673–80.PubMed CentralView ArticlePubMedGoogle Scholar
- Saitou N, Nei M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–25.PubMedGoogle Scholar
- Jones DT, Taylor WR, Thornton JM. The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci. 1992;8:275–82.PubMedGoogle Scholar
- Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.PubMed CentralView ArticlePubMedGoogle Scholar