A comprehensive assessment of the transcriptome of cork oak (Quercus suber) through EST sequencing
- José B Pereira-Leal1Email author,
- Isabel A Abreu2, 3,
- Cláudia S Alabaça4,
- Maria Helena Almeida5,
- Paulo Almeida1,
- Tânia Almeida6, 7,
- Maria Isabel Amorim8,
- Susana Araújo9, 10, 11,
- Herlânder Azevedo12, 32,
- Aleix Badia13, 14,
- Dora Batista15,
- Andreas Bohn13, 14,
- Tiago Capote6, 7,
- Isabel Carrasquinho16,
- Inês Chaves17, 18, 19, 20,
- Ana Cristina Coelho21,
- Maria Manuela Ribeiro Costa12,
- Rita Costa16,
- Alfredo Cravador22,
- Conceição Egas23,
- Carlos Faro23,
- Ana M Fortes24,
- Ana S Fortunato25,
- Maria João Gaspar26, 27,
- Sónia Gonçalves6, 7,
- José Graça27,
- Marília Horta22,
- Vera Inácio28,
- José M Leitão4,
- Teresa Lino-Neto12,
- Liliana Marum19, 20,
- José Matos16,
- Diogo Mendonça16,
- Andreia Miguel19, 20,
- Célia M Miguel19, 20,
- Leonor Morais-Cecílio28,
- Isabel Neves1,
- Filomena Nóbrega16,
- Maria Margarida Oliveira2, 3,
- Rute Oliveira12,
- Maria Salomé Pais29,
- Jorge A Paiva9, 10, 30,
- Octávio S Paulo31,
- Miguel Pinheiro23,
- João AP Raimundo12,
- José C Ramalho25,
- Ana I Ribeiro25,
- Teresa Ribeiro6, 7, 28,
- Margarida Rocheta28,
- Ana Isabel Rodrigues5,
- José C Rodrigues30,
- Nelson JM Saibo2, 3,
- Tatiana E Santo4,
- Ana Margarida Santos1, 2, 3,
- Paula Sá-Pereira16,
- Mónica Sebastiana29,
- Fernanda Simões16,
- Rómulo S Sobral12,
- Rui Tavares12,
- Rita Teixeira5,
- Carolina Varela16,
- Maria Manuela Veloso16 and
- Cândido PP Ricardo17, 18
© Pereira-Leal et al.; licensee BioMed Central Ltd. 2014
Received: 14 March 2013
Accepted: 15 April 2014
Published: 15 May 2014
Cork oak (Quercus suber) is one of the rare trees with the ability to produce cork, a material widely used to make wine bottle stoppers, flooring and insulation materials, among many other uses. The molecular mechanisms of cork formation are still poorly understood, in great part due to the difficulty in studying a species with a long life-cycle and for which there is scarce molecular/genomic information. Cork oak forests are of great ecological importance and represent a major economic and social resource in Southern Europe and Northern Africa. However, global warming is threatening the cork oak forests by imposing thermal, hydric and many types of novel biotic stresses. Despite the economic and social value of the Q. suber species, few genomic resources have been developed, useful for biotechnological applications and improved forest management.
We generated in excess of 7 million sequence reads, by pyrosequencing 21 normalized cDNA libraries derived from multiple Q. suber tissues and organs, developmental stages and physiological conditions. We deployed a stringent sequence processing and assembly pipeline that resulted in the identification of ~159,000 unigenes. These were annotated according to their similarity to known plant genes, to known Interpro domains, GO classes and E.C. numbers. The phylogenetic extent of this ESTs set was investigated, and we found that cork oak revealed a significant new gene space that is not covered by other model species or EST sequencing projects. The raw data, as well as the full annotated assembly, are now available to the community in a dedicated web portal at http://www.corkoakdb.org.
This genomic resource represents the first trancriptome study in a cork producing species. It can be explored to develop new tools and approaches to understand stress responses and developmental processes in forest trees, as well as the molecular cascades underlying cork differentiation and disease response.
Oaks (Quercus spp.) are important trees of the Northern hemisphere. In Europe they form highly valuable widespread forests. Together with chestnut and beech, oaks belong to the Fagaceae, and are probably the best-known genus of the family. The evergreen cork oak (Q. suber) grows in the Western Mediterranean Basin, having as natural range Algeria, France, Italy, Morocco, Portugal, Spain and Tunisia, where it is managed under low-density anthropogenic open woodland forests. Quercus spp. are important for conservation of soil and water, biodiversity, natural landscape and climate, and for production of highly valuable materials, thus having high ecological, social and economic value.
Quercus suber shares with Phellodendron amurense (Amur cork tree) and Q. variabilis (Chinese cork oak) the odd ability of producing a continuous and renewable out-bark of cork, although only Q. suber cork has the fine physical and chemical properties for a highly profitable industrial use.
Portugal owns the credits of the world leading position on cork oak forest area (740,000 ha out of the world 2,200,000 ha), cork production (60% of the world exported cork volume), and cork processing (74% of world processed cork). In Portugal, in the past, oaks used to dominate the native forests but their area has rapidly decreased as a result of human activity. Still, cork oak forests are accounting for about 26% of the Portuguese forest .
However, cork oak (Q. suber) and holm oak (Q. ilex ssp. rotundifolia) decline reported in the Iberian Peninsula over the last 20 years has caused death of numerous trees, threatening the rural economy in this part of Europe [2–5]. It has been predicted that oak diseases in Europe could become more severe and expand to the North and East within the next few hundred years .
Nowadays, this species faces many other threats, such as drought, extreme temperature and pests, leading to a marked decline of cork oak stands, possibly related to the repeated successions of extremely dry and hot years with a significant reduction of springtime precipitation .
The relevance of Q. suber and the scarce information available on its genetics, biochemistry and physiology [8–14] fully justifies the generation of transcriptomics data that will allow a new insight on cork oak biology and genetics. These data are fundamental for designing selection programs and understanding the plant adaptation processes to both biotic and abiotic factors, plant’s plasticity, ecophysiological interactions, interspecific hybridization and gene flow.
For a species that has neither its genome sequenced, nor a physical map available, the information obtained from expressed sequence tags (ESTs) is a practical means for gene discovery and a way to start elucidating its physiology and functional genome. When this project started (in 2010) there were less than 300 ESTs available for Q. suber. Recently, this number has increased to almost 7,000 (http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html).
Other oak species have also been subjected to transcriptomic studies, namely two European white oak species (Q. petraea, sessile oak, and Q. robur, English oak) [15, 16], two American oak species (Q. alba, white oak, and Q. rubra, red oak) (reviewed in ). Ueno et al.  generated 222,671 non-redundant sequences (including alternative transcripts) from multiple cDNA libraries prepared from Q. petraea and Q. robur, which is a relevant resource for genomic studies and identification of genes of adaptive significance. In 2011, the same team produced another useful tool, a BAC library, for genome analysis in Q. robur. Another important tool to develop a physical map for a Fagaceae species was based on the work of Durand and co-workers , who produced a total of 256 oak EST-SSRs that were assigned to bins and their map position was further validated by linkage mapping (http://www.fagaceae.org). More recently,  generated the larger-to-date set of reads from the transcriptome of an oak species (Q. robur), combining 454 and Illumina sequencing.
Within a national initiative, Portugal organized a consortium to study cork oak ESTs (COEC – Cork oak ESTs Consortium, http://coec.fc.ul.pt/), where 12 projects were designed to obtain a deeper understanding of Q. suber functional genomics. Developmental aspects (gametophytes, fruit and embryo development, acorn germination, bud sprouting, vascular and leaf development), as well as cork formation and quality, and abiotic (oxidative stress, drought, heat, cold and salinity) and biotic interactions (including symbiosis and pathogenesis) were followed by 20 teams from all over the country. Two of these projects were fully dedicated to the bio-informatics analysis of the generated data and development of bioinformatics platforms, one of them further focusing on polymorphism detection and validation.
This paper presents the experiments conducted for large-scale sequencing of 21 cDNA libraries and construction of a cork oak transcriptome database containing 159,000 unigenes. Presently, this database constitutes one of the largest genomic resources available for oaks and was structured to accommodate future data on genomics and physiology of woody species. The tools that were generated are crucial to study cork oak biology and diversity, and to understand gene regulation and adaptation to a changing environment. Future developments will make possible the early detection of traits of interest. This initiative will contribute to genomic research in cork oak and the Fagaceae family, paving the way for further studies.
Results and discussion
Tissues and conditions used to produce the RNA libraries
Phloem (adult trees)
Xylem (adult trees)
Abiotic stress: control (leaves)
Abiotic stress: cold (leaves)
Abiotic stress: heat (leaves)
Embryos from fruits at 4 developmental stages
Whole fruits at 7 developmental stages
Biotic Stress: roots (germinated acorns) infected by Phytophthora cinnamomi.
Biotic Stress: roots (thin white roots from 18-month-old plants) infected by Phytophthora cinnamomi.
Mycorrhizal symbiosis (roots).
Annual stems from cork producing Quercus suber x cerris hybrid trees
Annual stems from cork non-producing Quercus suber x cerris hybrid trees
Bud sprouting (bud phases 1 and 2).
Bud sprouting (bud phases 3 and 4).
Abiotic Stress: drought, salt and oxidative stresses (roots and shoots)
Leaves (from 8 locations for polymorphism detection)
High quality cork
Low quality cork
Assembly metrics of this project compared with those of two large oak transcriptome sequencing projects
Q. suber(this study)
Q. petraea/Q. robur
454 + Sanger
454 + Illumina
14 (454) + 20 (Sanger)
16 (454) + 8 (Illumina)
1,578,192 (454) + 145,827 (Sanger)
821,534 (454) + 255,237,702 (Illumina)
Contigs & single reads
Coverage and depth
The large number of libraries used, together with the choice of a two-step assembly, resulted in a high redundancy. Most of the nearly 5 million filtered ESTs were assembled into a large number of unigenes (~159 K). We obtained an average coverage depth of 3.9 (number of times each nucleotide was sequenced), with a maximum depth of 429 (25% percentile = 1; 75% percentile = 5). This is higher than other recent tree EST projects using the same sequencing platform (e.g. ), likely due to the extensive number of libraries sequenced in this project, prepared from multiple tissues, developmental stages and stress conditions. After the two rounds of assembly, 61,687 high quality reads remained unassembled and were treated as singletons. Thus, 65% of our unigenes derive from contigs, higher than other recent comparable projects (see Table nine in ).
In the absence of a complete genome sequence, it is impossible to know the true coverage of the cork oak gene space offered by this project. However, when we queried the proteomes of Arabidopsis thaliana and Populus trichocarpa using BLASTp to determine the potential number of unique genes detected, using a cut off of e < 10-5, we found that 65% of cork oak unigenes hit 23,482 out of 27,379 predicted proteins in A. thaliana (85%), and 30,318 out of 45,555 in P. trichocarpa (67%) . These numbers represent a rough estimate of the upper (85%) and lower (67%) boundaries one can expect from the Q. suber transcriptome coverage. This figure doesn’t change significantly if we use a more lenient cut off of e < 10-2, where we hit 24,093 (79%) and 30,719 (67%), respectively. A high degree of redundancy in our unigenes is suggested, as multiple unigenes hit the same target genes in either species. The remaining 55,921 unigenes cannot find any hit in either A. thaliana or P. trichocarpa, representing about 35% of the cork oak transcriptome. These include small unigenes that would not achieve significance in BLASTp comparisons (see Figure 2A), as well as potential novel genes not present in these two genomes. This number could be eventually overestimated, if we consider some under-assembly in our libraries.
We performed a serial clustering at increasing levels of identity in order to evaluate the degree of redundancy in our assembly (Figure 2C). We found that at the protein level, there was a sharp decrease in the number of clusters at 95% identity, indicating that approximately 8000 predicted peptides show a high identity between each other, comparable to that found in other oak species . This could indicate a recent event of polyploidization giving rise to many highly similar genes. Alternatively, and probably most likely, this could be accounted by the high genetic diversity among the multiple unrelated trees used to prepare the libraries . Sequencing errors not fully resolved due to the relatively low coverage of many unigenes could also be responsible for this result. In the first scenario our decision to filter off redundancies at the cDNA level at 98% could have been excessive, leading to the underestimation of the predicted number of unigenes. In contrast, the second and third scenarios would suggest that 95% is insufficient and we are overestimating the number of unigenes that may be closer to 151,000. We do not have enough data to favour any of these scenarios, in particular because all three may co-exist. We have thus chosen the 98% cDNA clustering as a conservative parameter that we hope does not over-cluster paralogues. With future data accumulation, it will be easier to fuse unigenes than to resolve incorrectly clustered paralogues.
Unigene naming criteria are as follows
Database and interface
We have developed the first large-scale library for the cork oak, an important economic resource in Southern Europe and North of Africa. We carried out a preliminary analysis of its gene content and functional annotation, and built a public platform for data sharing. Nineteen different libraries were sequenced, covering genes expressed in multiple tissues, developmental stages and stress conditions. Our results suggest that we covered a large fraction of the cork oak gene space. Many of its unigenes are dissimilar to any other plant genes. These likely represent incomplete assemblies due to library biases, but may also include several true cork-oak specific genes, which once identified will represent a promising avenue to understand the molecular basis of the response leading to cork formation. We believe that this sequencing effort will enable the community to explore the molecular basis of the cork oak physiology, as well as its responses to the multiple abiotic and biotic challenges that the cork oak forest is currently experiencing.
Samples, collection and preparation
Within this initiative, in order to guarantee high transcript coverage and to increase gene diversity, total RNA was isolated from Quercus suber biological samples obtained from different organs and tissues at varying developmental stages (roots, leaves, buds, flowers, fruits, phellogen, vascular tissue, good and bad quality cork), as well as from plants that had been exposed to infection with Phytophthora cinnamomi, symbiosis with Pisolithus tinctorius mycorrhizal fungus and different abiotic stresses (cold, heat, drought, salinity and oxidative stress). Furthermore, total RNA was also isolated, at two distinct dates (May and September), from annual shoots of 30 years old Quercus suber x cerris hybrid trees that either produce or don’t produce cork, in order to cover different developmental stages of the phellogen meristem. No approval or licenses were required for sample collection. In each library, plant material from half-siblings (e.g. abiotic and biotic stress libraries) or from several unrelated trees was used. All the plant material used was from Portuguese trees except for those trees used to detect polymorphism, which were from different Mediterranean countries . The detailed conditions applied in each situation are described in http://www.corkoakdb.org/libraries. The full set of libraries is described in Table 1.
cDNA preparation, library normalization and pyrosequencing
Total RNA from each tissue/condition was used as the source of starting material for cDNA synthesis and production of normalized cDNA libraries intended for 454 sequencing. Briefly, the total RNA quality was verified on Agilent 2100 Bioanalyzer with the RNA 6000 Pico kit (Agilent Technologies, Waldbronn, Germany) and the quantity assessed by fluorimetry with the Quant-iT RiboGreen RNA kit (Invitrogen, CA, USA). A fraction of 1–2 μg of total RNA was used for cDNA synthesis with the MINT cDNA synthesis kit (Evrogen, Moscow, Russia), a strategy based on the SMART double-stranded cDNA synthesis methodology using a modified template-switching approach that allows the introduction of known adapter sequences to both ends of the first-strand cDNA. Amplified cDNA was then normalized with TRIMMER cDNA Normalization kit (Evrogen, Moscow, Russia) using the Duplex-Specific Nuclease-technology [20, 29].
Normalized cDNA was quantified by fluorescence and sequenced in 454 GS FLX Titanium according to the standard manufacturer’s instructions (Roche-454 Life Sciences, Brandford, CT, USA) at Biocant (Cantanhede, Portugal).
Sequence processing and assembly
The implemented sequence analysis strategy included an initial pre-processing stage, performed on each library, where contaminant, low quality, redundant and repeat-full sequences were removed and each library assembled. This was followed by a multilibrary assembly (described below, and summarized in Figure 1). Initially, each read, respective quality scores and ancillary information, were extracted from the sequencing machine output (.sff), using open source software sff_extract (http://bioinf.comav.upv.es/sff_extract/). Reads of each sample were selected using a Python pipeline that screens the reads for primer sequences, classifying them by sample origin and allocating them in different files. For each sample we generated a file with the sequences (.fasta) and the corresponding file with the quality scores (.qual). At this stage we removed adaptors and reads smaller than 40 bp. Thereafter, artificial duplicates associated with pyrosequencing were removed using cd-hit-454  at a threshold of 98%, and Seq-trim  was used to remove small sequences (length < 100 bp) or sequences with low quality (QV > 20, quality window = 10), as well as poly-A or poly-T tails, and adaptors.
In the following step, contaminant sequences were removed. For this, a database of possible types of contaminants was prepared (ContaminantsDB - see supplementary material for details) and queried with the Q. suber reads using BLASTn (5, -E 3 -e 1e-09 -q -5 -b 1 -G 3). Reads that found a match in this database, were subsequently blasted against a database of plant proteins (PlantDB - see supplementary material for details) using the same parameters as before. If the hit (match) e-value in ContaminantsDB was smaller than hit (match) e-value in Plant DB, the read was considered as a contaminant and removed from the pipeline. The remaining reads continued in the pipeline to be screened for repetitive elements, using the program RepeatMasker 3.2.9 (http://www.repeatmasker.org) against PlantRepeatsDB . Whenever sequences were masked in more than 90% of their length they were discarded.
The final step of the preprocessing stage was the classification of all the trimmed reads into potential mitochondrial, chloroplastidial or nuclear sequences. For this, a BLASTn (-e = 0.001) was first performed against a database containing coding region sequences from complete plant mitochondrial genomes (from Arabidopsis thaliana, Medicago truncatula and Populus tricocharpa). The sequences that presented a hit were considered potential mitochondrial sequences and were kept in a FASTA file reserved for this organelle sequences. A similar process was then applied against a database of coding region sequences of plant complete plastidial genomes (same organisms).
We chose MIRA 3.2.0  to assemble the resulting sequences, as this has been shown to have higher coverage than other assemblers . For each library, we obtained contigs and singletons with the following parameters: --job = denovo, est, accurate, 454; --GE:not = 20; --SK:not = 20; 454_SETTINGS -LR:mxti = no, -CL:qc = no:cpat = no:mbc = yes, --AL:egp = no:mrs = 85, -OUT:sssip = yes, -AS:mrpc = 1. Following this step, all the contigs and singlets resulting from the assembly of each library were then clustered to remove redundancy using CD-HiT , and the resulting non-redundant sequence collection was re-assembled using the same parameters as before. The resulting sequences were considered to be Unigenes, and at this point they were given an unigene accession number. Libraries L20 and L21 were not used in the analysis presented in this manuscript, but are available in the full assembly on the CorkOakDB.
In order to be able to translate the nucleotide sequences to protein sequences, the pipeline first performs a Blast search (blastx) against a RNA database , to remove non-protein coding unigenes. It then queries all Viridiplantae protein sequences existing in the Uniprot database . The program Prot4EST  then takes the outputs of these BLAST searches and translates the sequences into putative peptide sequences. Those unigenes without significant hits are translated using the program ESTscan , and for the remaining untranslated sequences, the longest ORF of the 6 frames is selected.
In order to assign names to the genes/proteins found, putative peptides were used to query, using BLASTp at a cut off of e < 10-5, a database of Uniprot sequences from A. thaliana and P. tricocharpa. Whenever a putative peptide does not have a hit, it is considered “Predicted hypothetical protein”. If a similar hit is detected, then the protein name is assigned to the putative peptide in Q. suber together with a label that describes the level of confidence of the annotation (see Table 4).
In order to obtain domains and functional sites of putative peptides, an Interpro search was executed . The Interpro database  integrates different classification methods based on amino-acid patterns and profiles, protein family fingerprints, protein sequences and structural domains, as well as functional information. The Interpro database 28.0 was downloaded and searches were run locally. Afterwards, a BLAST (BLASTp) search against non-redundant protein database was executed and results entered the program Blast2GO . We used the pipeline version of the B2G called B2g4pipe, obtaining GO-terms and E.C. Numbers. The same pipeline was used to assign Interpro domains for the transcriptomes analysed in Figure 5.
Accession numbers and unigene naming
Accession numbers on the corkoakDB have the following format QS_000000, for unigenes, and QS_P_000000 for putative peptides. Whenever the sequences are putative mitochondrial or potential chloroplast sequences they start with QSm or QSc, respectively.
Comparisons to other organisms were made using predicted proteomes obtained from the superfamily database  release 1.75. We used BLASTp for the comparisons, always filtering for low complexity regions and using the cut offs indicated in the text. We used the standard NCBI’s taxonomic tree as a reference for Figure 6. Red oak libraries were obtained from the Fagaceae genomics web (http://www.fagaceae.org/node/87455) and processed using our own pipeline, resulting in 38,346 predicted unigenes. We then used BLASTp with a cut off at e = 0.01 to determine how many unigenes from the cork oak were similar to at least one unigene in the red oak.
Availability of supporting data
All sequenced ESTs were submitted to the sequence read archive (http://www.ncbi.nlm.nih.gov/sra) with the accession number ERP001762, and accession name “Cork Oak”.
JBPL, ACC, AC, CF, MF, SG, MH, JML, JM, CMM, LMC, MMO, JAPP, OSP, MMV, CPPR- Fund raising, consortium planning and organization. JBPL, IAA, MHA, TA, HA, ABohn, ICarrasquinho, IChaves, ACC, MMRC, RC, AC, CF, SG, MH, TLN, JM, CMM, LMC, FN, MMO, MSP, JAPP, OSP, NJMS, MS, FS, RTavares, RTeixeira, CV, MMV, CPPR- Project organization and writing. IAA, CSA, TA, MIA, SA, HA, DB, TC, ICarrasquinho, IChaves, ACC, MMRC, RC, ASF, MJG, SG, JG, MH, JML, TLN, LM, DM, AM, CMM, FN, MMO, RO, JAPP, OSP, JAPR, JCRamalho, AIRibeiro, TR, AIRodrigues, JCRodrigues, NJMS, TES, MS, FS, RSS, RTavares, CPPR- Preparation of the plant material and assays. CSA, TA, MIA, SA, HA, DB, TC, IChaves, ACC, MMRC, RC, ASF, SG, MH, VI, TLN, DM, AM, FN, JAPP, JCRamalho, AIRibeiro, MR, TES, PSP, MS, FS, RSS, RTavares- RNA preparation. CE, CF, MP- Transcriptome sequencing and analyses. JBPL, PA, ABadia, ABohn, IN, MP, AMS- Bioinformatics. JBPL, IAA, PA, HA, DB, ABohn, ICarrasquinho, IChaves, ACC, MMRC, RC, AC, CE, CF, MF, ASF, SG, MH, JML, TLN, LM, JM, AM, CMM, LMC, FN, MMO, JAPP, OSP, MP, JCRamalho, AIRibeiro, NJMS, AMS, MS, FS, RTavares, RTeixeira, CV, CPPR- Paper writing and discussion. All authors read and approved the final manuscript.
This project was funded by “Fundação para a Ciência e a Tecnologia” (FCT) within a National Consortium (COEC – Cork Oak ESTs Consortium) that supported 12 sub-projects (SOBREIRO/033, 035, 014, 034, 015, 017, 038, 019, 029, 039, 030, 036/2009). The authors further wish to acknowledge FCT for ten doctoral (BD) and post-doctoral (BPD) fellowships (Tânia Almeida: SFRH/BD/44410/2008, Tiago Capote:SFRH/BD/69785/2010, Inês Chaves: SFRH/BPD/20833/2004, Ana S. Fortunato: SFRH/BPD/47563/2008, Marília Horta: SFRH/BPD/63213/2009, Liliana Marum: "SFRH/BPD/47679/2008, Andreia Miguel: SFRH/BD/44474/2008, Margarida Rocheta: SFRH/BPD/64905/2009, Tatiana E. Santo: SFRH/BD/47450/2008, Mónica Sebastiana: SFRH/BPD/25661/2005). Andreas Bohn, Nelson J.M. Saibo, Rita Teixeira were supported by the Programa Ciência 2007, financed by POPH (QREN) and Isabel A. Abreu, Susana Araujo, Dora Batista, A. Margarida Fortes, Jorge A.P. Paiva, Sónia Gonçalves by Programa Ciência 2008, also funded by POPH (QREN). A Margarida Santos was funded through iBET (PEst-OE/EQB/LA0004/2011). Maintenance of the CorkOakDB is supported by the Instituto Gulbenkian de Ciência.
- de Gestão Florestal DN: Inventário Florestal Nacional- Portugal Continental. IFN 2005–2006. 2010, Autoridade Florestal Nacional: LisbonGoogle Scholar
- Brasier MD, Robredo F, Ferraz J: Evidence for Phytophthora cinnamomi involvement in Iberian oak decline. Plant Pathol. 1993, 42: 140-145. 10.1111/j.1365-3059.1993.tb01482.x.View ArticleGoogle Scholar
- Sanchez ME, Caetano P, Ferraz J, Trapero A: Phytophthora disease of Quercus ilex in south-western Spain. Forest Pathol. 2002, 32: 5-18. 10.1046/j.1439-0329.2002.00261.x.View ArticleGoogle Scholar
- Moreira AC, Martins J: Influence of site factors on the impact of Phytophthora cinnamomi in cork oak stands in Portugal. Forest Pathol. 2005, 35: 145-162. 10.1111/j.1439-0329.2005.00397.x.View ArticleGoogle Scholar
- de Sousa E, Santos M, Varela MC, Henriques J: Perda de vigor dos montados de sobro e azinho: Análise da situação e perspectivas. 2007Google Scholar
- Bergot M, Cloppet E, Pérarnaud V: Simulation of potential range expansion of oak disease caused by Phytophthora cinnamomi under climate change. Glob Change Biol. 2004, 10: 1539-1552. 10.1111/j.1365-2486.2004.00824.x.View ArticleGoogle Scholar
- Pereira JS, Kurz-Besson C: Coping with drought. Cork Oak Woodlands on the Edge – Ecology, Adaptive Management and Restoration. 2009, Washington: Island Press, 73-80. 1Google Scholar
- Marum L, Miguel A, Ricardo CP, Miguel C: Reference gene selection for quantitative real-time PCR normalization in Quercus suber. PLoS ONE. 2012, 7: e35113-10.1371/journal.pone.0035113.PubMed CentralPubMedView ArticleGoogle Scholar
- Coelho AC, Lima MB, Neves D, Cravador A: Genetic diversity of two evergreen oaks (Quercus suber L. and Q (ilex) rotundifolia Lam.) in Portugal using AFLP markers. Silvae Genetica. 2006, 55: 105-118.Google Scholar
- Chaves I, Passarinho JAP, Capitão C, Chaves MM, Fevereiro P, Ricardo CPP: Temperature stress effects in Quercus suber leaf metabolism. J Plant Physiol. 2011, 168: 1729-1734. 10.1016/j.jplph.2011.05.013.PubMedView ArticleGoogle Scholar
- Graça J, Santos S: Suberin: a biopolyester of plants’ skin. Macromol Biosci. 2007, 7: 128-135. 10.1002/mabi.200600218.PubMedView ArticleGoogle Scholar
- Soler M, Serra O, Molinas M, Huguet G, Fluch S, Figueras M: A genomic approach to suberin biosynthesis and cork differentiation. Plant Physiol. 2007, 144: 419-431. 10.1104/pp.106.094227.PubMed CentralPubMedView ArticleGoogle Scholar
- Vaz M, Pereira JS, Gazarini LC, David TS, David JS, Rodrigues A, Maroco J, Chaves MM: Drought-induced photosynthetic inhibition and autumn recovery in two Mediterranean oak species (Quercus ilex and Quercus suber). Tree Physiol. 2010, 30: 946-956. 10.1093/treephys/tpq044.PubMedView ArticleGoogle Scholar
- Almeida T, Menéndez E, Capote T, Ribeiro T, Santos C, Gonçalves S: Molecular characterization of Quercus suber MYB1, a transcription factor up-regulated in cork tissues. J Plant Physiol. 2013, 170: 172-178. 10.1016/j.jplph.2012.08.023.PubMedView ArticleGoogle Scholar
- Ueno S, Provost GL, Léger V, Klopp C, Noirot C, Frigerio J-M, Salin F, Salse J, Abrouk M, Murat F, Brendel O, Derory J, Abadie P, Léger P, Cabane C, Barré A, de Daruvar A, Couloux A, Wincker P, Reviron M-P, Kremer A, Plomion C: Bioinformatic analysis of ESTs collected by Sanger and pyrosequencing methods for a keystone forest tree species: oak. BMC Genomics. 2010, 11: 650-10.1186/1471-2164-11-650.PubMed CentralPubMedView ArticleGoogle Scholar
- Tarkka MT, Herrmann S, Wubet T, Feldhahn L, Recht S, Kurth F, Mailänder S, Bönn M, Neef M, Angay O, Bacht M, Graf M, Maboreke H, Fleischmann F, Grams TEE, Ruess L, Schädler M, Brandl R, Scheu S, Schrey SD, Grosse I, Buscot F: OakContigDF159.1, a reference library for studying differential gene expression in Quercus robur during controlled biotic interactions: use for quantitative transcriptomic profiling of oak roots in ectomycorrhizal symbiosis. New Phytol. 2013, 199: 529-540. 10.1111/nph.12317.PubMedView ArticleGoogle Scholar
- Kremer A, Abbott AG, Carlson JE, Manos PS, Plomion C, Sisco P, Staton ME, Ueno S, Vendramin GG: Genomics of Fagaceae. Tree Genetics & Genomes. 2012, 8: 583-610. 10.1007/s11295-012-0498-3.View ArticleGoogle Scholar
- Rampant PF, Lesur I, Boussardon C, Bitton F, Martin-Magniette M-L, Bodénès C, Le Provost G, Bergès H, Fluch S, Kremer A, Plomion C: Analysis of BAC end sequences in oak, a keystone forest tree species, providing insight into the composition of its genome. BMC Genomics. 2011, 12: 292-10.1186/1471-2164-12-292.View ArticleGoogle Scholar
- Durand J, Bodénès C, Chancerel E, Frigerio J-M, Vendramin G, Sebastiani F, Buonamici A, Gailing O, Koelewijn H-P, Villani F, Mattioni C, Cherubini M, Goicoechea PG, Herrán A, Ikaran Z, Cabane C, Ueno S, Alberto F, Dumoulin P-Y, Guichoux E, de Daruvar A, Kremer A, Plomion C: A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study. BMC Genomics. 2010, 11: 570-10.1186/1471-2164-11-570.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhulidov PA, Bogdanova EA, Shcheglov AS, Shagina IA, Wagner LL, Khazpekov GL, Kozhemyako VV, Lukyanov SA, Shagin DA: A method for the preparation of normalized cDNA libraries enriched with full-length sequences. Russ J Bioorg Chem. 2005, 31: 170-177. 10.1007/s11171-005-0023-7.View ArticleGoogle Scholar
- Timme RE, Delwiche CF: Uncovering the evolutionary origin of plant molecular processes: comparison of Coleochaete (Coleochaetales) and Spirogyra (Zygnematales) transcriptomes. BMC Plant Biol. 2010, 10: 96-10.1186/1471-2229-10-96.PubMed CentralPubMedView ArticleGoogle Scholar
- Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. BMC Genomics. 2010, 11: 1-16. 10.1186/1471-2164-11-1.View ArticleGoogle Scholar
- Tuskan GA, DiFazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam N, Ralph S, Rombauts S, Salamov A, Schein J, Sterck L, Aerts A, Bhalerao RR, Bhalerao RP, Blaudez D, Boerjan W, Brun A, Brunner A, Busov V, Campbell M, Carlson J, Chalot M, Chapman J, Chen GL, Cooper D, Coutinho PM, Couturier J, Covert S, Cronk Q, et al: The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray). Science. 2006, 313: 1596-1604. 10.1126/science.1128691.PubMedView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhi-Liang H, Bao J: CateGOrizer: a web-based program to batch analyze Gene Ontology Classification Categories. Online J Bioinformatics. 2008, 9 (2): 108-112.Google Scholar
- Barakat A, DiLoreto DS, Zhang Y, Smith C, Baier K, Powell W, Wheeler N, Se deroff R, Carlson JE: Comparison of transcriptome from cankers and healthy stems in American chestnut (Castanea dentata) and Chinese chestnut (Castanea mollissima). BMC Plant Biol. 2009, 9: 51-62. 10.1186/1471-2229-9-51.PubMed CentralPubMedView ArticleGoogle Scholar
- Altenhoff AM, Dessimoz C: Phylogenetic and functional assessment of orthologs inference projects and methods. PLoS Comp Biol. 2009, 5: e1000262-10.1371/journal.pcbi.1000262.View ArticleGoogle Scholar
- Varela MC: Handbook of the EU Concerted Action on cork oak: FAIR 1 CT 95-0202; European network for the evaluation of genetic resources of cork oak for appropriate use in breeding and gene conservation strategies. 2003, Lisboa (Portugal): INIAGoogle Scholar
- Shcheglov AS, Zhulidov PA, Bogdanova EA, Shagin DA: Nucleic Acids Hybridization Modern Applications. 2007, Dordrecht: Springer Netherlands, 97-124.View ArticleGoogle Scholar
- Niu B, Fu L, Sun S, Li W: Artificial and natural duplicates in pyrosequencing reads of metagenomic data. BMC Bioinformatics. 2010, 11: 187-10.1186/1471-2105-11-187.PubMed CentralPubMedView ArticleGoogle Scholar
- Falgueras J, Lara AJ, Fernandez-Pozo N, Canton FR, Perez-Trabado G, Claros MG: SeqTrim: a high-throughput pipeline for preprocessing any type of sequence reads. BMC Bioinformatics. 2010, 11: 38-10.1186/1471-2105-11-38.PubMed CentralPubMedView ArticleGoogle Scholar
- Ouyang S: The TIGR Plant Repeat Databases: a collective resource for the identification of repetitive sequences in plants. Nucleic Acids Res. 2004, 32: 360D-363D. 10.1093/nar/gkh099.View ArticleGoogle Scholar
- Chevreux B, Pfisterer T, Wetter T: Assembly of Genomic Sequences Assisted by Automatic Finishing. German Conf Bioinformatics. 1999, 183-184.Google Scholar
- Papanicolaou A, Stierli R, ffrench-Constant RH, Heckel DG: Next generation transcriptomes for next generation genomes using est2assembly. BMC Bioinformatics. 2009, 10: 447-10.1186/1471-2105-10-447.PubMed CentralPubMedView ArticleGoogle Scholar
- Li W, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006, 22: 1658-1659. 10.1093/bioinformatics/btl158.PubMedView ArticleGoogle Scholar
- Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, Kulam-Syed-Mohideen AS, McGarrell DM, Marsh T, Garrity GM, Tiedje JM: The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucl. Acids Res. 37 (suppl 1): D141-D145.
- Apweiler R: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 2004, 32: 115D-119D. 10.1093/nar/gkh131.View ArticleGoogle Scholar
- Wasmuth JD, Blaxter ML: prot4EST: translating expressed sequence tags from neglected genomes. BMC Bioinformatics. 2004, 5: 187-10.1186/1471-2105-5-187.PubMed CentralPubMedView ArticleGoogle Scholar
- Iseli C, Jongeneel C, Bucher P: ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. Proc Int Conf Intell Syst Mol Bio. 1999, 138-148.Google Scholar
- Zdobnov EM, Apweiler R: InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001, 17: 847-848. 10.1093/bioinformatics/17.9.847.PubMedView ArticleGoogle Scholar
- Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, Finn RD, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Laugraud A, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, Mistry J, Mitchell A, Mulder N, Natale D, Orengo C, Quinn AF, et al: InterPro: the integrative protein signature database. Nucleic Acids Res. 2009, 37 (Database issue): D211-D215.PubMed CentralPubMedView ArticleGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21: 3674-3676. 10.1093/bioinformatics/bti610.PubMedView ArticleGoogle Scholar
- Gough J, Chothia C: SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments and genome assignments. Nucleic Acids Res. 2002, 30: 268-272. 10.1093/nar/30.1.268.PubMed CentralPubMedView ArticleGoogle Scholar
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