De novo assembly and characterization of leaf transcriptome for the development of functional molecular markers of the extremophile multipurpose tree species Prosopis alba
- Susana L Torales†1Email author,
- Máximo Rivarola†2, 5,
- María F Pomponio1,
- Sergio Gonzalez2,
- Cintia V Acuña2,
- Paula Fernández2, 5,
- Diego L Lauenstein3,
- Aníbal R Verga3,
- H Esteban Hopp2, 4,
- Norma B Paniego2, 5 and
- Susana N Marcucci Poltri2
© Torales et al.; licensee BioMed Central Ltd. 2013
Received: 1 July 2013
Accepted: 7 October 2013
Published: 14 October 2013
Prosopis alba (Fabaceae) is an important native tree adapted to arid and semiarid regions of north-western Argentina which is of great value as multipurpose species. Despite its importance, the genomic resources currently available for the entire Prosopis genus are still limited. Here we describe the development of a leaf transcriptome and the identification of new molecular markers that could support functional genetic studies in natural and domesticated populations of this genus.
Next generation DNA pyrosequencing technology applied to P. alba transcripts produced a total of 1,103,231 raw reads with an average length of 421 bp. De novo assembling generated a set of 15,814 isotigs and 71,101 non-assembled sequences (singletons) with an average of 991 bp and 288 bp respectively. A total of 39,000 unique singletons were identified after clustering natural and artificial duplicates from pyrosequencing reads.
Regarding the non-redundant sequences or unigenes, 22,095 out of 54,814 were successfully annotated with Gene Ontology terms. Moreover, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) were searched, resulting in 5,992 and 6,236 markers, respectively, throughout the genome. For the validation of the the predicted SSR markers, a subset of 87 SSRs selected through functional annotation evidence was successfully amplified from six DNA samples of seedlings. From this analysis, 11 of these 87 SSRs were identified as polymorphic. Additionally, another set of 123 nuclear polymorphic SSRs were determined in silico, of which 50% have the probability of being effectively polymorphic.
This study generated a successful global analysis of the P. alba leaf transcriptome after bioinformatic and wet laboratory validations of RNA-Seq data.
The limited set of molecular markers currently available will be significantly increased with the thousands of new markers that were identified in this study. This information will strongly contribute to genomics resources for P. alba functional analysis and genetics. Finally, it will also potentially contribute to the development of population-based genome studies in the genera.
KeywordsProsopis alba Fabaceae Pyrosequencing Transcriptome assembly SSRs SNPs Functional annotation
The genus Prosopis Linnaeus emend Burkart, a member of the subfamily Mimosoideae within the family Fabaceae, comprises 44 species divided into 5 sections: Prosopis, Anonychium, Strombocarpa, Monilicarpa and Algarobia. This genus is spread around the world in arid and semiarid regions, including North and South America, North and Central Africa, Near East and the Caribbean region. The main center of diversity for Prosopis genus is located in Argentina  with 27 species. Of these species, 21 belonging to Algarobia section , which are distributed in the phytogeographic provinces of Chaco, Monte, and Espinal . They cover over one million square kilometers, which represents approximately one third of the total country area .
One of the most important features of this genus is its natural capacity to produce fertile interspecific hybrids [5–7]. This generates a syngameon complex integrated by species and subspecies which form a continuum . This complex includes six taxonomic species that play a significant role in Argentina: P. alba, P. hassleri, P. nigra, P. ruscifolia, P. chilensis and P. flexuosa.
The members of this complex develop deep roots that give these plants several advantaged. For instance, these deep roots reduce competition for water with herbaceous species, improve water balance of the system, provide nutrients to the subsurface layers and in some cases make the plants fairly independent of rainfall .
Their fruits are pods and may contain large amounts of sugar and protein which offer optimal energy for its use as fodder and for human consumption. They can also be used for firewood and charcoal, as well as for other products (honey, pollen, gums, etc.) . Also, “algarrobos” can be an alternative of livestock-forestry systems .
Within this group of “algarrobos”, P. alba known as "white algarrobo" displays the widest geographical distribution. This species grows in areas under average annual precipitations of 500 to 1200 mm, which are summer dominant, with extreme temperatures between 48°C maximal absolute, up to −10°C absolute minimum . P. alba comprises groups with different morphological characteristics, such as variations in leaves and fruits, and inhabits different ecological zones . Also, these morphological groups have distinct adaptation mechanisms to drought stress .
In Argentina, this native species is mainly used for saw timber (wood flooring and furniture) and the whole wood consumed comes from the native forests in “Parque Chaqueño” (Argentina) .
Besides, all “algarrobos”, including P. alba, may play a role on the recovery of degraded ecosystem ; hence re-population with these species generates favourable conditions for natural recovery of the whole ecosystem.
Up to date, few genomic data exist on Prosopis genus. A total of 1,467 expressed sequence tag (EST) from Prosopis juliflora has been deposited in the NCBI EST database . There are also a limited number of molecular markers published: six microsatellites isolated from Prosopis chilensis and 12 from a bulk sample of American algarrobos introduced to Australia .
To the best of our knowledge, here we report the largest contribution to sequence information of Prosopis spp. generated through new generation sequencing technologies. The results from the assembly and functional annotation of P. alba leaf transcriptome are presented, along with SSR and SNP motif mining. Nuclear and chloroplast SSR and SNP were discriminated in the analysis. Finally, this work generated a collection of 11 nuclear-SSR primer pairs validated for its application to diversity studies in P. alba and another set of 123 nuclear polymorphic SSRs determined in silico, of which 50% have the probability of being effectively polymorphic. The overall workflow of the project is represented in the Additional file 1.
Results and discussion
Transcriptome sequencing and assembly
Transcriptome functional annotation summary of P. alba
Number of sequences
Sequences with positive BLAST matches
Sequences annotated with Gene Ontology (GO) terms
Sequences without detectable BLAST matches
Sequences assigned to already known Enzyme Commission category
For assigning putative functions to the P. alba´s transcriptome, BLASTX searches  were performed aligning the assembled sequences to the 1,958,459 protein sequences from a custom-made Viridiplantae database. A total of 14,664 isotigs and 22,899 singletons showed significant BLASTX matches (with an expectation value<1e-10) (Table 1). A higher percentage of isotigs (93%) than singletons (59%) had BLASTX hits, probably due to the good quality of isotigs (68% longer than 600 bp), short lengths of singletons and the high e-value cut-off applied. Previous reports on de novo transcriptome assemblies of eukaryotes described lower percentage of isotigs, ranging from 20 to 40%, such as those described for lanville fritillary butterfly, a coral larval, lodgepole pine and microalgae [21–24]. In total, 37,563 unique sequences had at least one BLAST hit in the search, while the 17,251 remaining sequences (i.e. 32%) (Table 1) were orphans. However, these orphan sequences may still be informative for identifying putative biological functions which may be considered as P .alba specific.
After the analyses of seven completely sequenced genomes, the average number of genes encoded in a plant nuclear genome was estimated in approximately 30 thousands . Our annotated dataset with 12,610 isogroups, which can be used to estimate the number of gene locus, and 39,000 unique singletons most likely represent a good proportion of the P. alba gene catalogue.
Gene Ontology (GO) term annotation and metabolic pathway mapping
Using a full local installation of Blast2GO  and the InterProScan suite , we retrieved gene ontology (GO) terms and enzyme commission numbers (EC) for the P. alba unigenes (Additional file 2).
From the Blast2GO and InterProScan programs, a total of 43,389 GO terms were assigned to 22,095 unigenes (including 10,107 annotated isotigs and 11,988 annotated singletons). Among all the GO terms extracted, 14,422 (33%) belong to the Biological Process class, 19,077 (44%) fit the Molecular Function class and 9,890 (23%) belong to the Cellular Component class.
In terms of molecular function, the top three GO terms found were related to the following categories: binding 13,007 (46%), catalytic activities, 11,708 (41%) and transporters 1,312 (5%) (Figure 3B).
A detailed analysis (level 2) at the cellular component category sorted all transcripts from P. alba into 5 groups. Of these groups, the most representative categories were: cell (9,577), organelle (4,100) and macromolecular complex component (1,699) (Figure 3C).
Of the 22,095 sequences annotated with GO terms, 4,538 were assigned with EC numbers (2,191 isotigs and 2,347 singletons) (Table 1).
Top metabolic pathways in P. alba
٭Kegg metabolics patways
Number of unigenes
Starch and sucrose metabolism
Cyanoamino acid metabolism
Fatty acid biosynthesis
Regarding the analysis using KAAS, we found 485 transcripts involved in purine metabolism. This metabolic pathway is of fundamental importance in the growth and development of plants . For instance, purine is involved in building blocks for nucleic acid synthesis and is also an energy source, as well as a precursor for the synthesis of primary products and secondary products [36, 37]. Additionally, 476 genes associated with thiamine metabolism were detected. These genes are of particular interest to the Prosopis genus since the thiamine metabolism is involved in abiotic stress response through the Ca2+ salicylic acid and related signaling pathways .
When the metabolic pathways from P. alba was compared with other tree species (N. nervosa), we were able to observe 45 shared pathways. From these pathways, 8 were differential from Prosopis and 10 from Nothofagus (data not shown).
Assessment of leaf transcriptome assembly
Again, chromosome 6 of M. truncatula had fewer mRNA sequences homologous to G. max as well as with P. alba. When comparing M. truncatula genome with Lotus japonica, similar results were obtained. These findings demonstrate the lack of marker-based synteny with pea  and the abundance of nucleotide-binding site-Leu-rich repeat genes . The unusual high proportion of heterochromatin in this chromosome as it was previously reported  may explain why we found less homologous mRNA sequences in chromosome 6 of M. truncatula.
Single Nucleotide Polymorphisms (SNPs) were identified through the analysis of the multiple alignments produced during the assembly process. The criterion for this analysis was reducing the probability of false positive identification (see Materials and Methods).
Single nucleotide polymorphism (SNPs) statistics
Single sequence repeats (SSRs) detection
Using the SSR webserver from the Genome Database for Rosaceae (GDR), we identified and characterized several SSRs (microsatellites) motifs as potential molecular markers in the Prosopis putative unigenes collection generated in this work.
The criterion used for the SSR selection was based on the minimum number of repeats (see Materials and Methods). These settings resulted in the identification of 5,956 nuclear SSRs within 54,768 unigenes, i.e. SSR frequency of 11% taking into account multiple repeat occurrences in a same unique locus. This frequency was comparable to that reported in Nothofagus (15%)  and lower than in oak (19 and 24%) [45, 47]. A total of 4,990 (9%) nuclear unigenes contained at least one SSR, suggesting that they are distributed throughout the whole leaf transcriptome. Additionally, 4,593 SSRs (77 %) had sufficient flanking sequences to allow the design of appropriate unique primers to generate PCR products within the range of 100 to 300 bp. Detailed information of the SSRs that were discovered in this research is described in Additional file 3.
Seventy two percent of the sequences had only one SSR (72%) and 20 % had two. Of the unique SSR, 44% were of trimeric motif followed by 28% of di- and 19% of tetranucleotide motifs. The SSRs were highly distributed over the sequences; which provides a useful tool for different genetic studies (Figure 6).
The topography of SSR distribution was analyzed for the presence of SSR within predicted UTRs and coding sequence regions (See Materials and Methods). About 44% of the SSR sequences were inside ORF sequences, being most of them trinucleotide and hexanucleotides repeats (58%). In the UTRs, the dinucleotides motifs were more frequent (35%) comparable to those reported in other trees such as oak (27%) , Nothofagus (40%)  and pines (65-75%) .
Eighty one percent of the repeated sequences found in ORF had a combination of length motif and repeat number multiple of three, (i.e., (TC)9, (GA)9, (CTCC)3, (CGCCC)3) which did not modify the reading frame, (i.e. nine motifs of two nucleotides =18 bp = 6 codons). This could be explained on the basis of the selective disadvantage of non-trimeric SSR variants in coding regions, possibly causing frame-shift mutations .
Finally, we also identified SSRs belonging to gene families associated with the production of cellulose, the lignin biosynthetic pathway  and with transcription factors. As for the production of cellulose, we detected genes such as cellulose synthases (CesA), glycosyl transferases and sucrose synthase. The identified genes related to the lignin biosynthetic pathway  were cinnamoyl alcohol dehydrogenase as well as cinnamoyl reductase-like protein. From the genes associated with transcription factors, zinc finger proteins and some anti-oxidants (for example, gdp-mannose pyrophosphorylase) were identified. Stress related sequences such as heat shock proteins and zeaxanthin epoxidase were also identified in the reference P. alba transcriptome, Interestingly, zeaxanthin epoxidase is a precursor of abscisic acid (ABA) that is involved in response to abiotic stress, including tolerance to heavy metals.
Validation of the predicted microsatellite markers
Eighty seven microsatellites were selected according to their sequence length, GC content and functional annotation. As for functional annotation, we selected those related to the following categories: stress, calcium metabolism, peroxidases, myb and zinc finger proteins, among other putative functions (51% were located in predicted ORFs). The 87 loci were tested for PCR amplification in six individuals. All of them (100%) were effectively amplified validating the quality of the assembly and the utility of the SSRs herein identified. Similar results were obtained in Nothofagus by applying the same strategy for the assembly and the in silico searches for SSRs. Similar research carried out using Illumina sequencing technology in sesame showed that about 90% primer pairs successfully amplified DNA fragments . However, the rate of SSR validation was lower (64.9%) when the marker mining was done using EST produced by Sanger technology , possibly because of the low-quality of the EST sequences. The lower rate could also be explained by the use of primer sequences derived from chimerical cDNA clones.
Polymorphic SSR primer pairs derived from P. alba unigenes
Marker ID name gene bank accession no.
Primer sequence 5´-3´
Zinc finger protein magpie-like
Zinc finger protein magpie-like
Zinc finger protein magpie-like
Uncharacterized protein loc100815794
Myb transcription factor myb138
zf-hd homeobox protein
Pentatricopeptide repeat-containing protein
Calcium-binding mitochondrial carrier protein s −1
Polymorphic SSR predicted
From the 2,352 nuclear SSRs detected in contigs/isotigs (which means they have several reads that allow to determine putative polymorphism), a total of 1,995 (85%) had defined primers for PCR reactions, belonging to 1,622 different isogroups (unigenes).
In order to predict nuclear polymorphic SSRs, we carried out in silico PCR for each of the sequences from the different contigs/isotigs. For this purpose, all 1,063,520 high quality reads were used. To achieve a higher success rate, another set of primers was also were designed closer to the SSR motifs in order to capture short length reads included in contigs.
At least 123 nuclear polymorphic SSRs were detected by PCR in silico; which only includes isotigs integrated by three or more reads and whose product size generated by in silico PCR differed in at least two base length (Additional file 3). An apparent underestimation of nuclear polymorphisms in silico was observed when considering that from the 9 clear polymorphic SSRs coming from isotigs (Table 4), of which, only two of them (22%) resulted polymorphic under the criteria used in silico. However, from the 87 SSRs that were amplified in vitro, 69 belonged to isotigs and only six had enough reads to be considered in this in silico analysis; which resulted in only three effectively polymorphic SSRs in silico (50%). Therefore, it can be predicted that 52 SSRs out of the 123 new SSRs that were detected in silico will be effectively polymorphic in vitro. This result could be an interesting survey of potential useful SSRs and could contribute significantly to the SSRs available in other reports [18, 19].
A total of 135 GO terms were allocated to the 116 nuclear isogroups containing the polymorphic SSRs that were identified in silico in this research. They were assigned under the categories “Biological Process” (39 terms), “Molecular Function” (55 terms), and “Cellular Component” (41 terms). The most represented subcategories assigned under “Biological Process” at third-level terms were: “primary metabolic process” (16.4%), “cellular metabolic process” (15.6%) and “macromolecule metabolic process” (14%). In addition, many of the terms that classified as “Molecular Function” were represented by genes in the following subcategories: “hydrolase activity” (20%), “nucleic binding” (18%), “ion binding” (16%) and “protein binding” (13%). In addition, seven metabolic pathways were represented by at least one sequence, with its corresponding EC number. This makes these functional markers especially useful for population and evolution analyses of P. alb a.
We detected 44,079 chloroplast reads through alignment analyses to related chloroplast (cp) sequences. After an alignment analysis with the legume Vigna radiata chloroplast genome, 56 contigs composed of 59,040 bp were generated, spanning a total of 129,208 bp that belong to the Prosopis cp genome. The chloroplast reads of P. alba with 59,040 bp represented ~40 % of the total cp genome of V. radiata (151,271 bp) . There were 55 intra scaffold gaps in P. alba cp genome with a mean sequence gap size of 1252 bp.
A total of 14 isogroups harboring 36 SSRs were also found: 18 with designed primers, 17 with different BLASTX hits related to chloroplast metabolism (oxidoreductase, ribosomal, etc.) and four polymorphic in silico (Additional file 3). Chloroplast SSRs were previously described in several plants such as Pinus radiata, Oryza sativa, Nicotiana[55–57]. More recently several other organisms were also characterized for these SSRs: Eucalyptus globulus, G. max, V. radiata, M. truncatula, V. vinifera among others. All of these Chloroplast SSRs have been deposited in a data base http://www.mcr.org.in/chloromitossrdb/. Also, in rice around 4.5% of the chloroplast genome has been covered by SSRs .
RNA preparation and cDNA library synthesis
Total RNA was extracted from leaves of seedling collected from natural populations of P.alba from different provinces of Argentina: population 1 from Campo Durán (province of Salta), population 2 from Isla Cuba (province of Formosa) and population 3 from Chañar Bajada (province of Santiago del Estero)
The RNA extraction method used in this research is the one described by Chang el al, (1993) . Briefly, one gram of fresh tissue was ground to a fine powder under liquid nitrogen. Then, after two extractions with chloroform, the RNA was precipitated with LiCl2, extracted again with chloroform and finally precipitated with ethanol. The resultant RNA was resuspended in 50 μl of DEPC treated water. The RNA was quantified using a Nanodrop 1,000 spectrophotometer and its quality was measured with a 2,100 Bioanalyzer (Agilent Technologies Inc.). Then, it was subjected to purification using the Poly (A) Purist kit (Ambion) and its quality was once more assessed with the 2,100 Bioanalyzer. cDNA was synthesized using cDNA Kit (Roche) and used to construct a shotgun library for pyrosequencing technology (Roche). The Prosopis cDNA library was subjected to a 1/3 of plate production run on the 454-GS-FLX sequencing instrument. This run was conducted at INDEAR (Rosario Biotechnology Institute, Rosario, Argentina).
Transcript assembly and analysis
The sequences were subjected to filtering for adaptors, primer sequences and low-quality sequences After removing the low quality sequences, the curated raw 454 read sequences were assembled into contigs, isotigs and isogroups using Newbler Assembler software 2.5p1 (Roche, IN, USA). The reads identified like singletons (i.e., reads not assembled into isotigs) after assembly were subjected to CD-HIT-454 clustering algorithm at 95% identity cutoff, which eliminates redundant sequences .
BLASTX (e-value cut off ≤ 10e-10) searches were performed against a Viridiplantae protein database first. Then, the sequences with no hits were used to perform a successive BLASTX against the NCBI nr protein database in order to make an assessment of the putative identities of the sequences. Unigenes (>200 bp) were deposited at the National Centre for Biotechnology Information (NCBI) Transcriptome Shotgun Assembly (TSA) Database under BioProject: 218545 TSA- SUB336788.
Annotation and mapping routines were run with BLAST2GO , which assigns Gene Ontology (, http://www.geneontology.org) annotation, KEGG maps (Kyoto Encyclopedia of Genes and Genomes, KASS) and an enzyme classification number (EC number) using a combination of similarity searches and statistical analysis . In addition to BLAST2GO, the full suite of InterProScan was ran with default parameters. InterProScan combines different protein signature recognition methods native to the InterPro member databases into one resource that searches for the corresponding InterPro and GO annotations.
Chloroplast assembly analysis was carried out using AMOScmp . To search for chloroplast sequences, BLASTN and TBLASTX (BLASTN e-50 TBLASTX e-10) were performed. The analysis was based on similarity with and without translation to 109 chloroplast genomes (ftp://ftp.ncbi.nlm.nih.gov/genomes/Chloroplasts/plastids/).
Circos software tool  was used to visualize P. alba sequences with M. truncatula and G. max´s genomes, through circular concentric ideograms layout to facilitate the display data as scatter, line and histogram plots for each different sample. Promer analysis was performed and filtered by using a window size of 100kb through their genome sequences. Homologous sequences for the three species were determined when reciprocal TBLASTX best hits were found for the three “genes” tested.
In order to perform matching, alignment of DNA sequences and searching for putative SNPs, the SSAHAsnp Program (Sequence Search and Alignment by Hashing Algorithm) was used . The criterion designed to reduce the probability of false positive identification was that the minority allele (the second most common nucleic allele) should be found in at least 4 sequences and that at least the 10% of reads had an SNP from total coverage, which should be at least 8x.
In order to identify SSRs for all possible combinations of dinucleotide, trinucleotide, tetranucleotide and pentanucleotide repeats, we performed a run using the SSR webserver (GDR) (http://www.rosaceae.org/bio/content?title=&url=/cgi-bin/gdr/gdr_ssr). This webserver uses the GETORF algorithm (EMBOSS Package) and selects the longest ORF as the putative coding region. This webserver also uses Primer 3 (v.0.4.0)  to design primer pairs. The criteria used for the SSR selection based on the minimum number of repeats was as follows: five for dinucleotide, four for trinucleotide, three for tetranucleotide and three for penta and hexanucleotide motives.
The presence of expressed repetitive DNA was revealed using the BLASTN (e-value cut off ≤10e-10) searches against all Viridiplantae Repbase.
For validation of SSR primers, total DNA was extracted from young leaves of six Prosopis alba seedlings from three native populations described previously (two for each one). For DNA extraction, the Dneasy Plant mini kit (Quiagen) was used following the manufacturer´s instructions. Regular primers were synthesized (AlphaDNA, Montreal, CA, USA) and used for PCR (polymerase chain reaction) amplification. PCR reactions consisted of 20 ng of total DNA, 0.25 μM of each primer, 3 mM of MgCl2, 0.2 mM of each dNTP, 1X of PCR buffer and 1 U of Platinum Taq polymerase (Invitrogen). All PCRs were performed with the following conditions: a denaturation step of 2 min at 94°C, a regular touchdown PCR ranging from 60°C to 50°C with 28 cycles at the touchdown temperature of 50°C (45 s at 92°C, 45 s at 50°C and 45 s at 72°C). The final extension step was of 10 min at 72°C and then the temperature conditions were adjusted for each particular microsatellite. Samples were mixed with denaturing loading buffer, incubated for 5 min at 95°C, and separated on a 6% polyacrylamide gel. Amplification products were stained using the DNA silver staining procedure of Promega (USA) following the manufacturer’s instructions. Details of primers sequences, SSR location and amplicon sizes are described in Table 4.
The P.alba transcriptome database obtained and characterized here represents a major contribution for Prosopis sp. genomics and genetics. It will be useful for discovering genes of interest and genetic markers, which could allow to investigate functional diversity in natural populations. These tools will also lead to conduct comparative genomics studies with other Prosopis species taking advantage of their remarkable ecophysiological differences. This work highlights the utility of transcriptome high performance sequencing as a fast and cost effective way for obtaining rapid information on the coding of genetic variation in Prosopis genus. This study allowed us to: (i) obtain 1,103,231 transcript raw reads and 54,814 unigene sequences from P.alba, (ii) identify putative function in 37,563 unigenes for the genus, (iii) identify 700 putative stress-response genes, (iv) discover 4,593 genomic SSRs with designed primers, validate 87 and detect 11 polymorphic SSRs, several of them related to response to stress, (v) identify probably 52 effectively polymorphic after in silico analysis, and (vi) identify 6,158 higher confidence nuclear SNPs, some of them related to the production of cellulose, together with the lignin biosynthetic pathway and with stress, among others.
CVA and MFP thank PROMEF for their fellowships. This research was supported by the INTA-PE 242421, PPR 242001 and PPR 245001. Special thanks to Julia Sabio for the critical English edition of the manuscript.
- Burkart A: A monograph of the genus Prosopis (Leguminosae subfam. Mimosoideae). [Part 1.]. Journal of the Arnold Arboretum. 1976, 57 (Suppl 3): 219-249.Google Scholar
- Palacios R, Carmaran C, Iglesias L, Picca P, Torregrosa S, Gonzales S: Taxonomía numérica (descriptores). 1988, Prosopis en Argentina, UNC-UBA-FAO Press; Buenos Aires- Córdoba, 91-96.Google Scholar
- Cabrera A: Regiones Fitogeográficas Argentinas. In Enciclopedia Argentina de Agricultura y Jardinería. Volume II, Fascicle 1. 1976, Buenos Aires: ACME PressGoogle Scholar
- Mottura M: Development of microsatellites in Prosopis spp. and their application to study the reproduction system. PhD Thesis. Institute of Forest Genetics and Forest Tree Breeding, Faculty of Forest Sciences and Forest Ecology. 2006, Germany: Georg-August University of GöttingenGoogle Scholar
- Saidman BO: Isoenzymatic studies of alcohol dehydrogenase and glutamate oxalacetate transaminase in four South American species of Prosopis and their natural hybrids. Silvae Genetica. 1986, 35: 3-10.Google Scholar
- Saidman BO, Vilardi JC: Analysis of the genetic similarities among seven species of Prosopis (Leguminosae: Mimosoideae). Theoretical and Applied Genetics (TAG). 1987, 75: 109-116.Google Scholar
- Saidman BO, Bessega CF, Ferreira LI, Julio N, Vilardi JC: The use of genetic markers to assess population structure and relationships among species of the genus Prosopis (Leguminosae). Boletín de la Sociedad Argentina de Botánica. 2000, 34 (Suppl 3): 315-324.Google Scholar
- Verga A: Genetic study of Prosopis chilensis y Prosopis flexuosa (Mimosaceae) in the dry Chaco of Argentina. PhD Thesis. 1995, Universität Göttingen, AlemaniaGoogle Scholar
- Demaio P, Karlin UO, Medina M: Árboles Nativos del Centro de Argentina. 2002, Buenos Aires, Argentina: L.O.L.A. PressGoogle Scholar
- Karlin U: Recursos forrajeros naturales del Chaco Seco: Manejo de Leñosas. 1983, Córdoba, Argentina: ll Reunión de Intercambio Tecnológico en Zonas Áridas y Semiáridas, 78-96.Google Scholar
- Bregaglio M, Karlin U, Oirini R: Efecto del desmonte selectivo sobre la regeneración de la masa forestal y la producción de pasturas, en el chaco árido de la provincia de Córdoba, Argentina. Multequina. 2001, 10: 17-24.Google Scholar
- Karlin UO, Coirini R, Catalán L, Zapata R: Especies arbóreas y arbustivas para zonas áridas y semiáridas de América Latina. Serie Zonas Áridas y Semiáridas N°12 OEA. 1997, 41-51.Google Scholar
- Verga A, Navall M, Joseau J, Royo O, Degano W: Caracterización morfológica de los algarrobos (Prosopis sp.) en las regiones fitogeográficas Chaqueña y Espinal norte de Argentina. Quebracho. 2009, 17: 31-40.Google Scholar
- López Lauenstein D, Luna C, Verga A: Respuesta al estrés hídrico en dos grupos morfológicos de Prosopis alba. En resúmenes de la XXVIII Reunión Argentina de Fisiología Vegetal. 2010, La Plata, ArgentinaGoogle Scholar
- SAyDS: Informe Regional Parque Chaqueño. 2007, Argentina: Primer Inventario Nacional De Bosques Nativos, Proyecto Bosques Nativos y Áreas Protegidas BIRF 4085-ARGoogle Scholar
- Verga A: Algarrobos como especies para forestación. Una estrategia de mejoramiento. SAGPyA Forestal. 2000, 17: 2-9.Google Scholar
- George S, Venkataraman G, Parida A: Identification of stress-induced genes from the drought-tolerant plant Prosopis juliflora (Swartz) DC. through analysis of expressed sequence tags. Genome. 2007, 50 (Suppl 5): 470-478.PubMedGoogle Scholar
- Mottura MC, Finkeldey R, Verga AR, Gailing O: Development and characterization of microsatellite markers for Prosopis chilensis and Prosopis flexuosa and cross-species amplification. Molecular Ecology Notes. 2005, 5 (Suppl 3): 487-489.View ArticleGoogle Scholar
- Bessega CF, Pometti CL, Miller JT, Watts R, Saidman BO, Vilardi JC: New Microsatellite Loci for Prosopis alba and P. chilensis (Fabaceae). Applications in Plant Sciences. 2013, 1 (Suppl 5): 1200324-View ArticleGoogle Scholar
- Torales SL, Rivarola M, Pomponio MF, Fernández P, Acuña CV, Marchelli P, Gonzalez S, Azpilicueta MM, Hopp HE, Gallo L, Paniego NB, Poltri SNM: Transcriptome survey of Patagonian southern beech Nothofagus nervosa (= N. Alpina): assembly, annotation and molecular marker discovery. BMC genomics. 2012, 13 (Suppl 1): 291-PubMed CentralView ArticlePubMedGoogle Scholar
- Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, Marden JH: Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Molecular ecology. 2008, 17 (Suppl 7): 1636-1647.View ArticlePubMedGoogle Scholar
- Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics. 2009, 10 (Suppl 1): 219-PubMed CentralView ArticlePubMedGoogle 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: 180-10.1186/1471-2164-11-180.PubMed CentralView ArticlePubMedGoogle Scholar
- Rismani-Yazdi H, Haznedaroglu BZ, Bibby K, Peccia J: Transcriptome sequencing and annotation of the microalgae Dunaliella tertiolecta: pathway description and gene discovery for production of next-generation biofuels. BMC genomics. 2011, 12 (Suppl 1): 148-PubMed CentralView ArticlePubMedGoogle Scholar
- Pazos-Navarro M, Dabauza M, Correal E, Hanson K, Teakle N, Real D, Nelson MN: Next generation DNA sequencing technology delivers valuable genetic markers for the genomic orphan legume species, Bituminaria bituminosa. BMC genetics. 2011, 12 (Suppl 1): 104-PubMed CentralView ArticlePubMedGoogle Scholar
- Gish W, States DJ: Identification of protein coding regions by database similarity search. Nature genetics. 1993, 3 (Suppl 3): 266-272.View ArticlePubMedGoogle Scholar
- Logacheva MD, Kasianov AS, Vinogradov DV, Samigullin TH, Gelfand MS, Makeev VJ, Penin A: De novo sequencing and characterization of floral transcriptome in two species of buckwheat (Fagopyrum). BMC genomics. 2011, 12 (Suppl 1): 30-PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (Suppl 18): 3674-3676.View ArticlePubMedGoogle Scholar
- Zdobnov EM, Apweiler R: InterProScan - an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001, 17 (Suppl 9): 847-848.View ArticlePubMedGoogle Scholar
- Blanca J, Cañizares J, Roig C, Ziarsolo P, Nuez F, Picó B: Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC genomics. 2011, 12 (Suppl 1): 104-PubMed CentralView ArticlePubMedGoogle Scholar
- Aharoni A, Galili G: Metabolic engineering of the plant primary-secondary metabolism interface. Current opinion in biotechnology. 2011, 22 (Suppl 2): 239-244.View ArticlePubMedGoogle Scholar
- Garzón-Martínez GA, Zhu ZI, Landsman D, Barrero LS, Mariño-Ramírez L: The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction. BMC genomics. 2012, 13: 151-10.1186/1471-2164-13-151.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Y, Luo H-M, Sun C, Song J-Y, Sun Y-Z, Wu Q, Wang N, Yao H, Steinmetz A, Chen S-L: EST analysis reveals putative genes involved in glycyrrhizin biosynthesis. BMC Genomics. 2010, 11 (Suppl 1): 268-PubMed CentralView ArticlePubMedGoogle Scholar
- Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M: KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007, 35 (Web Server issue): 182-185.View ArticleGoogle Scholar
- Zrenner R, Stitt M, Sonnewald U, Boldt R: Pyrimidine and purine biosynthesis and degradation in plants. Annual review of plant biology. 2006, 57: 805-836. 10.1146/annurev.arplant.57.032905.105421.View ArticlePubMedGoogle Scholar
- Stasolla C, Katahira R, Thorpe TA, Ashihara H: Purine and pyrimidine nucleotide metabolism in higher plants. Journal of plant physiology. 2003, 160 (Suppl 11): 1271-1295.View ArticlePubMedGoogle Scholar
- Boldt R, Zrenner R: Purine and pyrimidine biosynthesis in higher plants. Physiol Plant. 2003, 117 (Suppl 3): 297-304.View ArticlePubMedGoogle Scholar
- Goyer A: Thiamine in plants: aspects of its metabolism and functions. Phytochemistry. 2010, 71 (Suppl 14–15): 1615-1624.View ArticlePubMedGoogle Scholar
- Delcher AL, Phillippy A, Carlton J, Salzberg SL: Fast algorithms for large-scale genome alignment and comparison. Nucleic acids research. 2002, 30 (Suppl 11): 2478-2483.PubMed CentralView ArticlePubMedGoogle Scholar
- Young N, Cannon S, Sato S, Kim D, Cook D, Town C, Roe B, Tabata S: Sequencing the Genespaces of Medicago truncatula and Lotus japonicus. Plant Physiol. 2005, 137: 1174-1181. 10.1104/pp.104.057034.PubMed CentralView ArticlePubMedGoogle Scholar
- Choi H-K, Mun J-H, Kim D-J, Zhu H, Baek J-M, Mudge J, Roe B, Ellis N, Doyle J, Kiss GB, Young ND, Cook DR: Estimating genome conservation between crop and model legume species. Proc Natl Acad Sci U S A. 2004, 101 (Suppl 43): 15289-15294.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhu H, Cannon S, Young N, Cook D: Phylogeny and genomic organization of the TIR and non-TIR NBS-LRR resistance gene family in Medicago truncatula. Mol Plant Microbe Interact. 2002, 15 (Suppl 6): 529-539.View ArticlePubMedGoogle Scholar
- Kulikova O, Geurts R, Lamine M, Kim D-J, Cook DR, Leunissen J, De Jong H, Roe BA, Bisseling T: Satellite repeats in the functional centromere and pericentromeric heterochromatin of Medicago truncatula. Chromosoma. 2004, 113 (Suppl 6): 276-283.View ArticlePubMedGoogle Scholar
- Ashrafi H, Hill T, Stoffel K, Kozik A, Yao J, Chin-Wo SR, Van Deynze A: De novo assembly of the pepper transcriptome (Capsicum annuum): a benchmark for in silico discovery of SNPs, SSRs and candidate genes. BMC genomics. 2012, 13 (Suppl 1): 571-PubMed CentralView ArticlePubMedGoogle Scholar
- Ueno S, Le Provost G, 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 (Suppl 1): 650-PubMed CentralView ArticlePubMedGoogle Scholar
- Novaes E, Drost DR, Farmerie WG, Pappas GJ, Grattapaglia D, Sederoff RR, Kirst M: High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC genomics. 2008, 9 (Suppl 1): 312-PubMed CentralView ArticlePubMedGoogle 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, Cabané 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 (Suppl 1): 570-PubMed CentralView ArticlePubMedGoogle Scholar
- Chagné D, Chaumeil P, Ramboer A, Collada C, Guevara A, Cervera MT, Vendramin GG, Garcia V, Frigerio J-M, Echt C, Richardson T, Plomion C: Cross-species transferability and mapping of genomic and cDNA SSRs in pines. TAG. 2004, 109 (Suppl 6): 1204-1214.View ArticlePubMedGoogle Scholar
- Metzgar D, Bytof J, Wills C: Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome research. 2000, 10 (Suppl 1): 72-80.PubMed CentralPubMedGoogle Scholar
- Humphreys JM, Chapple C: Rewriting the lignin roadmap. Current opinion in plant biology. 2002, 5: 224-229. 10.1016/S1369-5266(02)00257-1.View ArticlePubMedGoogle Scholar
- Wei W, Qi X, Wang L, Zhang Y, Hua W, Li D, Lv H, Zhang X: Characterization of the sesame (Sesamum indicum L.) global transcriptome using Illumina paired-end sequencing and development of EST-SSR markers. BMC genomics. 2011, 12 (Suppl 1): 451-PubMed CentralView ArticlePubMedGoogle Scholar
- Acuña CV, Fernandez P, Villalba PV, García MN, Hopp HE, Marcucci Poltri SN: Discovery, validation, and in silico functional characterization of EST-SSR markers in Eucalyptus globulus. Tree Genetics & Genomes. 2012, 8 (Suppl 2): 289-301.View ArticleGoogle Scholar
- Zhao Y, Williams R, Prakash CS, He G: Identification and characterization of gene-based SSR markers in date palm (Phoenix dactylifera L.). BMC Plant Biol. 2012, 12 (Suppl 1): 237-PubMed CentralView ArticlePubMedGoogle Scholar
- Tangphatsornruang S, Sangsrakru D, Chanprasert J, Uthaipaisanwong P, Yoocha T, Jomchai N, Tragoonrung S: The chloroplast genome sequence of mungbean (Vigna radiata) determined by high-throughput pyrosequencing: structural organization and phylogenetic relationships. DNA research. 2010, 17 (Suppl 1): 11-22.PubMed CentralView ArticlePubMedGoogle Scholar
- Powell W, Morgante M, Mcdevitt R, Vendramin G, Rafaslki J: Polymorphic Simple Sequence Repeat Regions in Chloroplast Genomes: Applications to the Population Genetics of Pines. Proc Natl Acad Sci. 1995, 92 (Suppl 17): 7759-7763.PubMed CentralView ArticlePubMedGoogle Scholar
- Cato SA, Richardson TE: Inter- and intraspecific polymorphism at chloroplast SSR loci and the inheritance of plastids in Pinus radiata D. Don. Theoretical and Applied Genetics. 1996, 93 (Suppl 4): 587-592.View ArticlePubMedGoogle Scholar
- Provan J, Corbett G, Waugh R, McNicol JW, Morgante M, Powell W: DNA fingerprints of rice (Oryza sativa) obtained from hypervariable chloroplast simple sequence repeats. Proceedings Biological sciences. 1996, 263 (Suppl 1375): 1275-1281.View ArticleGoogle Scholar
- Sablok G, Mudunuri SB, Patnana S, Popova M, Fares MA, La Porta N: ChloroMitoSSRDB: Open Source Repository of Perfect and Imperfect Repeats in Organelle Genomes for Evolutionary Genomics. DNA research. 2013, 20 (Suppl 2): 127-133.PubMed CentralView ArticlePubMedGoogle Scholar
- Rajendrakumar P, Biswal AK, Balachandran SM, Srinivasarao K, Sundaram RM: Simple sequence repeats in organellar genomes of rice: frequency and distribution in genic and intergenic regions. Bioinformatics. 2007, 23 (Suppl 1): 1-4.View ArticlePubMedGoogle Scholar
- Chang S, Puryear J, Cairney J: A simple and efficient method for isolating RNA from pine trees. Plant Molecular Biology Reporter. 1993, 11 (Suppl 2): 113-116.View 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 (Suppl 13): 1658-1659.View ArticlePubMedGoogle 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. Nature genetics. 2000, 25 (Suppl 1): 25-29.PubMed CentralView ArticlePubMedGoogle Scholar
- Pop M, Phillippy A, Delcher AL, Salzberg SL: Comparative genome assembly. Briefings in bioinformatics. 2004, 5 (Suppl 3): 237-248.View ArticlePubMedGoogle Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA: Circos: an information aesthetic for comparative genomics. Genome research. 2009, 19 (Suppl 9): 1639-1645.PubMed CentralView ArticlePubMedGoogle Scholar
- Ning Z, Caccamo M, Mullikin JC: SSAHAsnp: A polymorphism D detection tool on a whole genome scale. IEEE Computational Systems Bioinformatics Conference. 2005, 251-254.Google Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods in molecular biology. 2000, 132 (Suppl 3): 365-386.PubMedGoogle Scholar
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