Accessibility / Report Error

Discovery and characterization of SSR markers in Eugenia uniflora L. (Myrtaceae) using low coverage genome sequencing

Abstract

Abstract: Eugenia uniflora L. (Myrtaceae) is a tree species widely distributed in South America suffering the effects of the exploitation of natural populations. In this study, we employed low coverage sequencing of the E. uniflora genome for mining of SSR markers. The de novo assembly generated 2,601 contigs with an average length of 1139 bp and spans 3.15 Mb. A total of 76 dimer, 33 trimer and two compound SSR loci were identified. Twelve selected SSR loci were employed to genotype 30 individuals from two natural populations. A total of 73 alleles were detected (mean A= 6.1) were observed, the mean effective number of alleles was Ae = 3.91, mean Ho was 0.23 and mean HE was 0.70). The mean Wright’s within population fixation index was FIS = 0.66 and significant deviation of HWE was observed in all loci, except one. The FST between populations equaled 0.27. The levels of genetic diversity and structure estimated with these 12 SSR markers are in accordance with data from genetics studies performed on other tree species of the Pampa biome, presenting moderate to high polymorphism and may be employed in studies of species conservation measures and breeding programs.

Key words
next generation sequencing; pitanga; population genetics; SSR; Surinam-cherry


INTRODUCTION

Eugenia uniflora L. (2n = 22) is a tree species of the Myrtaceae family, native to the Cerrado, Atlantic Forest and Pampa biomes in Brazil, with economic and ecological importance. It has been employed in the pharmaceutical and cosmetic industries with attested anti-inflammatory functions (AuricchioAURICCHIO MT and BACCHI EM. 2003. Folhas de Eugenia uniflora L (pitanga): Revisão. Rev Inst Adolfo Lutz 62: 55-61. and Bacchi 2003, CostaCOSTA DP, FILHO EGA, SILVA LMA, SANTOS SC, SILVA MRR, SERAPHIN JC and FERRI PH. 2010. Influence of fruits biotypes on the chemical composition and antifungal activity of the essential oils of Eugenia uniflora leaves. J Braz Chem Soc 21: 851-858. et al. 2010). Traditionally, the infusion of its leaves is used against gastrointestinal illnesses, while its fruits are consumed fresh and as juice and ice cream (LedermanLEDERMAN IE, BEZERRA JEF and CALADO G. 1992. A pitangueira em Pernambuco. IPA 19: 20. et al. 1992, FerreiraFERREIRA FR, FERREIRA SAN and CARVALHO JEU. 1987. Espécies frutíferas pouco exploradas, com potencial econômico e social para o Brasil. Rev Bras Fruticultura 9: 11-22. et al. 1987). This species is also used in the environmental recovery of degraded areas and is an important feed source to bees (SilvaSILVA ALG and PINHEIRO MCB. 2007. Biologia floral e da polinização de quatro espécies de Eugenia L. (Myrtaceae). Acta Bot Bras 21: 235-247. and Pinheiro 2007), while its wood is widely used by populations of rural areas for heating residences and manufacturing poles for fencing (CostellaCOSTELLA E, GARCIA LSC, CORNELEO NS, SCHÜNEMANN AL and STEFENON VM. 2013. Anthropogenic use of gallery forests in the Brazilian Pampa. Biological Sciences 35: 211-217. et al. 2013). Currently, there are few established orchards to economic use of this species (AlmeidaALMEIDA DJ, FARIA MV and SILVA PR. 2012. Effect of forest fragmentation on microsporogenesis and pollen viability in Eugenia uniflora, a tree native to the Atlantic Forest. Genet Mol Res 11: 4245-4255. et al. 2012).

Based on flow cytometry analysis, E. uniflora has a predicted haploid genome of only 0.251 pg DNA and about 244.99 Mb (CostaCOSTA IR, DORNELAS MC and FORNI-MARTINS ER. 2008. Nuclear genome size variation in fleshy-fruited Neotropical Myrtaceae. Plant Syst Evol 276: 209-217. et al. 2008). With the advent of the next generation sequencing (NGS) platforms, drafting such small genomes becomes an attractive initiative towards generating genomic information of huge importance for biotechnological exploitation, conservation and breeding of non-model tree species. NGS platforms are quite useful for generating low coverage genome sequencing data. With a relatively reduced cost, this strategy enabled the discovery of novel repetitive elements in barley genome (WickerWICKER T, NARECHANIA A, SABOT F, STEIN J, VU GT, GRANER A, WARE D and STEIN N. 2008. Low-pass shotgun sequencing of the barley genome facilitates rapid identification of genes, conserved non-coding sequences and novel repeats. BMC Genomics 9: 518. et al. 2008), the identification of homolog genes among Dipteran species (RasmussenRASMUSSEN DA and NOOR MAF. 2009. What can you do with 0.1x genome coverage? A case study based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae). BMC Genomics 10: 382. and Noor 2009), characterization of the whole plastidial genome of a milkeweed species (StraubSTRAUB S, FISHBEIN M, LIVSHULTZ T, FOSTER Z, PARKS M, WEITEMIER K, CRONN RC and LISTON A. 2011. Building a model: developing genomic resources for common milkweed (Asclepias syriaca) with low coverage genome sequencing. BMC Genomics 12: 211. et al. 2011) and the discovery of genomic SSR molecular markers (StatonSTATON M et al. 2015. Preliminary genomic characterization of ten hardwood tree species from multiplexed low coverage whole genome sequencing. Plos One 10: e0145031. et al. 2015).

In this study we report the discovery of a large set of SSR loci based on low coverage genome sequencing data, and the characterization of 12 genomic SSR markers for E. uniflora. The characterized markers presented moderate to high polymorphism when employed for genotyping adult individuals from two Pampean populations of E. uniflora and will allow accessing genetic diversity of natural populations to better understand population dynamics, to plan reliable conservation measures and to advance breeding programs for this species.

MATERIAL AND METHODS

SAMPLING AND DNA EXTRACTION

Total genomic DNA was isolated from healthy leaves of one single adult plant of Eugenia uniflora L. (Myrtaceae) collected in a natural population within the Pampa biome in southern Brazil (30º20’05.00”S, 54º21’44.00”W). A voucher of the collected individual was deposited in the Herbarium Bruno Edgar Irgang (HBEI) of the Universidade Federal do Pampa, Campus São Gabriel (voucher HBEI1150). Total DNA was isolated with the DNeasy® Plant mini kit (Qiagen), following the manufacturers’ instructions. The quality and the amount of the isolated DNA were evaluated on a NanoVue™ Plus Spectrophotometer (GE Healthcare) and through electrophoresis on 1.0% agarose gel.

NGS SEQUENCING AND DE NOVO ASSEMBLY

Total genomic DNA was sheared in fragments of about 300 bp using Biorruptor® (Thermo Fisher Scientific) and the genomic libraries were built using the IonChef® (Thermo Fisher Scientific) system following the manufacturers’ specifications. DNA fragments were sequenced on Ion 314TM microchip using the Ion Torrent Personal Genome Machine (Thermo Fisher Scientific) and the Ion PGMTM 200 Sequencing Kit following the manufacturers’ specifications. After sequencing, the sequence reads were filtered within the PGM software, removing low quality and polyclonal sequences. All PGM filtered data were exported as a Fastq file that was used for the subsequent bioinformatics analysis.

The Fastq filtered sequences obtained from the PGM software were used for a de novo assembly of E. uniflora sequences using SPAdes 3.09 (BankevichBANKEVICH A et al. 2012. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. J Comput Biol 19: 455-477. et al. 2012), generating contigs with a minimal size of 1,000 bp.

DISCOVERY AND CHARACTERIZATION OF SSR MARKERS

The software SSRLocator (MaiaMAIA LCD, PALMIERI DA, SOUZA VQD, KOPP MM, CARVALHO FIFD and OLIVEIRA ACD. 2008. SSR Locator: Tool for Simple Sequence Repeat discovery integrated with primer design and PCR simulation. Int J Plant Genomics 1-9. et al. 2008) was used to find di- and tri- nucleotide repeat motifs in the obtained contigs. The default parameters of SSRLocator were employed to identify SSR loci with a minimum of 6 repetitions. Primers for the identified SSR loci were designed using the Primer3 software (UntergasserUNTERGASSER A, CUTCUTACHE I, KORESSAAR T, YE J, FAIRCLOTH BC, REMM M and ROZEN SG. 2012. Primer3-new capabilities and interfaces. Nucleic Acids Res 40: 1-12. et al. 2012), searching for alleles with size ranging from 90 to 280 bp. All contigs containing SSR loci were deposited in the GenBank (ID numbers are listed in Table I and Supplementary Material – Table SI SUPPLEMENTARY MATERIAL Table SI - List of SSR loci discovered for Eugenia uniflora using low coverage sequencing strategy and selected after in silico amplification. Sequencing was performed using an Ion PGM® platform. Table includes primers sequence (forward and reverse), repeat motif, and GenBank accession number (GenBank ID). ).

TABLE I
Characterization of 12 SSR markers for Eugenia uniflora including primers sequence (forward and reverse), repeat motif, annealing temperature (Ta), length of the sequenced fragment, and GenBank accession number (GenBank ID).TATATTTGGACTCTGACCTGGAGATAGAGCATGAGACAGAAATGACACCATGAGTAAGATACTGCTTCTCTCCATACACTCTTGGTGATTTCTATTTGT

Potentially amplifiable SSR loci identified were tested for amplification in silico using the software SPCR (CaoCAO Y, WANG L, XU K, KOU C, ZHANG Y, WEI G, HE J, WANG Y and ZHAO L. 2005. Information theory-based algorithm for in silico prediction of PCR products with whoke genomic sequences as templates. BMC Bioinformatics 6: 190. et al. 2005). In silico amplification was performed using the contigs obtained from the present E. uniflora sequencing as template DNA (Figure 1). Using this strategy we are able to identify primer pairs that will amplify a single loci within the E. uniflora genome within the expected size range and discard primer pairs generating multi-loci amplifications and unfeasible band patterns.

Figure 1
Virtual electrophoresis gel from in silico amplification of three SSR loci discovered using low coverage sequencing of the Eugenia uniflora genome and selected for genotyping of 30 individuals of natural populations of the species. MW represents the molecular weight ladder. The arrow indicates the amplified SSR allele. For these three loci, a feasible amplification within the expected size is observed, leading to their selection.

Twelve loci with dimer and trimer motifs that revealed virtual amplification of a single locus with alleles within the expected size range (Table I) were tested in 30 individuals collected from two natural populations of E. uniflora located into the Pampa biome, Rio Grande do Sul State, Brazil. Populations SG (n = 12) and AL (n = 18) are about 200 km distant from each other and represent two characteristic forest formations that naturally occur in the Brazilian Pampa (RoeschROESCH LFW, VIEIRA FCB, PEREIRA VA, SCHUNEMANN AL, TEIXEIRA IF, SENNA AJT and STEFENON VM. 2009. The Brazilian Pampa: a fragile biome. Diversity 1: 182-198. et al. 2009). Population SG represents a “capão” formation (island of trees within the grassland, Roesch et al. 2009), while population AL characterizes a gallery forest. DNA was isolated from healthy leaves from each sampled plant using the DNeasy® Plant mini kit (Qiagen), following the manufacturers’ instructions.

SSR markers were amplified through PCR in a final volume of 25 μL reaction mix, containing about 30 ng of DNA, 0.25 μM of buffer, 0.5 μM of MgCl2, 1U of Taq DNA-Polymerase (Invitrogen®), 0.05 μM of each dNTP, 0.125 μM of each primer and 0.2 μg/μL of BSA. Amplifications were carried out with 95°C for 5 min, annealing temperature ranging from 48ºC to 51.4ºC (see Table I) for 1 min and extension at 72°C for 1 min, for a total of 30 cycles, with a final extension step of 72°C for 20 min. Alleles of each individual were resolved through electrophoresis on 6% polyacrylamide gels. Gels were stained with GelRed® and allele sizing was performed by comparison to a 100 bp ladder.

Total number of alleles (A), effective number of alleles (Ae), observed heterozygosity (HO), expected heterozygosity (HE), Wright’s within population fixation index [FIS = (HEHO)/HE], and deviation from Hardy-Weinberg equilibrium (HWE) were estimated for each locus in each population and overall. Differentiation between populations was estimated using the AMOVA approach (FST). All estimations were performed using the software GenAlEx 6.4 (PeakallPEAKALL R and SMOUSE PE. 2006. GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6: 288-295. and Smouse 2006, 2012PEAKALL R and SMOUSE PE. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28: 2537-2539.).

RESULTS

SEQUENCING OUTPUT

The obtained reads from the low coverage genome sequencing yielded around 7.0 Gb of sequences that were used in the de novo assembly. After assembling and exclusion of redundant regions, a total of 2,601 contigs were generated with an average length of 1139 bp (N50 length of 1168 bp) and a cumulative length of 3.15 Mb.

DISCOVERY OF SSR LOCI

Using the selected parameters, a total of 76 di-nucleotide repeats, 33 tri-nucleotide repeats and two compound SSR loci (i.e. di- and tri-nucleotide repeats as SSR motif) were identified within the contigs of the present genome draft. After the in silico test for amplification, 74 out of the 111 loci were considered viable, presenting amplification of a single locus within the expected range and considered as putative informative SSR markers. Repeat motifs, forward and reverse primers, annealing temperature and GenBank ID number of the loci are listed in Table I (12 characterized SSR markers) and Table SI (62 not characterized SSR loci).

CHARACTERIZATION OF SSR MARKERS

All twelve tested SSR markers were polymorphic in population AL and overall. However, amplification of loci P2, P8 and P13 failed in population SG (Table II). Overall, a total of 73 alleles, ranging from 3 to 12 (mean A = 6.1) alleles per locus were observed, while the mean effective number of alleles was Ae = 3.91, ranging from 2.27 to 8.49 (Table II). Estimations of HO ranged from 0.00 to 0.57 (mean HO = 0.23), while He measures ranged from 0.57 to 0.91 (mean HE = 0.70). The Wright’s within population fixation index (FIS) ranged from 0.34 to 1.00 (mean FIS = 0.66). A significant deviation of HWE (p < 0.05) was observed in all loci, except for Pit13 (Table II).

TABLE II
Genetic parameters estimated for Eugenia uniflora based on 12 SSR markers characterized in this study, overall populations and at population level. Estimations include the number of samples (N), number of allele per locus (A), effective allele number (Ae), observed (HO) and expected (HE) heterozigosities, Wright’s within population fixation index (FIS), and statistical significance of the deviation from Hardy-Weinberg equilibrium (HWE).

At population level, the number of alleles ranged from three to 11 in population AL and from two to seven in population SG. The effective number of alleles ranged from 2.00 to 8.76 (mean Ae = 3.43) in population AL and from 1.22 to 5.54 (mean Ae = 2.67) in population SG. Estimations of observed heterozygosity ranged from HO = 0.00 to HO = 0.50 (mean HO = 0.22) in population AL and from HO = 0.00 and HE = 0.67 (mean HO = 0.27) in population SG. The expected heterozigosity ranged from HE = 0.51 to HE = 0.91 (mean HE = 0.67) in population AL and from HE = 0.19 to HE = 0.86 (mean HE = 0.60) in population SG. The estimations of Wright’s within population fixation index in population AL ranged from FIS = 0.33 to FIS = 1.00 (mean FIS = 0.66), while in population SG, it ranged from FIS = 0.19 to FIS = 1.00 (mean FIS = 0.60). Eleven out of the 12 loci presented significant deviation of HWE in population AL. In population SG, four out of the eight tested loci presented significant deviation of HWE. All estimations overall and for each population are summarized in Table II. The AMOVA approach revealed statistically significant (p < 0.001) differentiation between populations, FST = 0.27 (Table III).

TABLE III
Summary of the analysis of molecular variance (AMOVA) for all populations, based on 12 microsatellite markers.

DISCUSSION

The use of low coverage whole genome sequencing has proved to be useful to generate SSR markers for different hardwood species, although the proportion of identified polymorphic loci with feasible interpretation of the alleles is relatively low (KhodwekarKHODWEKAR S, STATON M, COGGESHALL MV, CARLSON JE and GAILING O. 2015. Nuclear microsatellite markers for population genetic studies in sugar maple (Acer saccharum Marsh.). Ann For Res 58: 193-204. et al. 2015). In this study, 74 out of 111 SSR loci were selected based on their in silico single locus amplification with feasible banding pattern. Using low coverage whole genome sequencing approach for the development of SSR markers, Khodwekar et al. (2015) obtained only seven polymorphic markers out of 96 SSR loci identified in Acer saccharum and OwusuOWUSU SA, STATON M, JENNINGS TN, SCHLARBAUM S, COGGESHALL MV, ROMERO-SEVERSON J, CARLSON JE and GAILING O. 2013. Development of genomic microsatellites in Gleditsia triacanthos (Fabaceae) using Illumina Sequencing. Applic Plant Sc 1: 1300050. et al. (2013) obtained 14 polymorphic SSR markers out of 144 primer pairs tested in Gleditsia triacanthos. All 12 SSR markers validated in this study presented polymorphism in the genotyped individuals.

In comparison to the values of expected and observed heterozygosities (HE and HO, respectively) summarized by NybomNYBOM H. 2004. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Molecular Ecology 13: 1143-1155. (2004) for plant species according to their life traits, the SSR markers we developed for E. uniflora presented overall estimations of HE (mean HE = 0.70, ranging from 0.57 to 0.91) within the range determined for long-lived perennial species (mean HE = 0.68), widespread (mean HE = 0.62), with mixed breeding system (mean HE = 0.60), species of the early successional status (mean HE = 0.46), and ingested seed dispersal (mean HE = 0.73). On the other hand, estimations of HO (mean HO = 0.23, ranging from 0.00 to 0.57) were lower than the summarized data for long-lived perennial species (mean HO = 0.63), widespread (mean HO = 0.57), species of the early successional status (mean HO = 0.39), and ingested seed dispersal species (mean HO = 0.72). Ferreira-RamosFERREIRA-RAMOS R, GUERRIERI A, KLAUS A, ROSSI A, GUIDUGLI MC, MESTRINER MA, MARTINEZ CA and ALZATE-MARIN AL. 2008. Genetic diversity assessment for Eugenia uniflora L., E. pyriformis Cambess., E. brasiliensis Lam. and E. francavilleana O. Berg neotropical tree species (Myrtaceae) with heterologous SSR markers. Genet Resour Crop Evol 61: 267-272. et al. (2008) characterized seven SSR markers for E. uniflora and reported estimations of HE ranging from 0.71 to 0.94 (mean HE = 0.82), HO ranging from 0.00 to 0.80 (mean HO = 0.42), A ranging from five to 22 (mean A = 10.8), and FIS ranging from -0.008 to 1.00 (mean FIS = 0.478) genotyping 84 individuals from three populations naturally occurring in the Atlantic Forest.

Just few investigations about population genetics of tree species growing in the Brazilian Pampa have been reported. These studies reported low levels of genetic diversity and high levels of inbreeding in Pampean populations of Schinus molle (LemosLEMOS RPM, D’OLIVEIRA CB and STEFENON VM. 2015. Genetic structure and internal gene flow in populations of Schinus molle (Anacardiaceae) in the Brazilian Pampa. Tree Genet Genomes 11: 75. et al. 2015) and Luehea divaricata (NagelNAGEL JC, CECONI DE, POLETTO I and STEFENON VM. 2015. Historical gene flow within and among populations of Luehea divaricata in the Brazilian Pampa. Genetica 143: 317-1329. et al. 2015). In addition, StefenonSTEFENON VM, NAGEL JC, CECONI DE and POLETTO I. 2016. Evidences of genetic bottleneck and fitness decline in Luehea divaricata populations from southern Brazil. Silva Fennica 50: 1566. et al. (2016) showed that this fact may have led to reduction of population fitness in these species. Thus, the comparatively lower estimations of genetic parameters obtained with the 12 SSR markers validated in this study likely are characteristics of the isolated small forest formations found in the Brazilian Pampa and reflects a trend for different tree species.

The SSR markers validated in this study are important tools that can be employed for identifying genetic control of key biotechnological and horticultural traits, for characterizing the genetic diversity and structure of the natural remnants, and will enable the wide application of marker-assisted and genomic selection that may promote the establishment of commercial orchards with improved cultivars of the species. Based on the results of this study, it is reasonable to speculate that we may obtain a large number of informative molecular markers among the 62 SSR loci we discovered for E. uniflora through low coverage whole genome sequencing and did not characterize in this study.

ACKNOWLEGMENTS

We would like to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support and scholarships, and PROPESQ/UNIPAMPA (Edital AGP/2016) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Process 442995/2014-8) for funding the research. We specially thank to Prof. Dr. Luiz Fernando Würdig Roesch, Prof. Dr. Filipe de Carvalho Victoria and Prof. Dr. Victor Pyrlo by technical support for sequencing and data analysis.

REFERENCES

  • ALMEIDA DJ, FARIA MV and SILVA PR. 2012. Effect of forest fragmentation on microsporogenesis and pollen viability in Eugenia uniflora, a tree native to the Atlantic Forest. Genet Mol Res 11: 4245-4255.
  • AURICCHIO MT and BACCHI EM. 2003. Folhas de Eugenia uniflora L (pitanga): Revisão. Rev Inst Adolfo Lutz 62: 55-61.
  • BANKEVICH A et al. 2012. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. J Comput Biol 19: 455-477.
  • CAO Y, WANG L, XU K, KOU C, ZHANG Y, WEI G, HE J, WANG Y and ZHAO L. 2005. Information theory-based algorithm for in silico prediction of PCR products with whoke genomic sequences as templates. BMC Bioinformatics 6: 190.
  • COSTA IR, DORNELAS MC and FORNI-MARTINS ER. 2008. Nuclear genome size variation in fleshy-fruited Neotropical Myrtaceae. Plant Syst Evol 276: 209-217.
  • COSTA DP, FILHO EGA, SILVA LMA, SANTOS SC, SILVA MRR, SERAPHIN JC and FERRI PH. 2010. Influence of fruits biotypes on the chemical composition and antifungal activity of the essential oils of Eugenia uniflora leaves. J Braz Chem Soc 21: 851-858.
  • COSTELLA E, GARCIA LSC, CORNELEO NS, SCHÜNEMANN AL and STEFENON VM. 2013. Anthropogenic use of gallery forests in the Brazilian Pampa. Biological Sciences 35: 211-217.
  • FERREIRA FR, FERREIRA SAN and CARVALHO JEU. 1987. Espécies frutíferas pouco exploradas, com potencial econômico e social para o Brasil. Rev Bras Fruticultura 9: 11-22.
  • FERREIRA-RAMOS R, GUERRIERI A, KLAUS A, ROSSI A, GUIDUGLI MC, MESTRINER MA, MARTINEZ CA and ALZATE-MARIN AL. 2008. Genetic diversity assessment for Eugenia uniflora L., E. pyriformis Cambess., E. brasiliensis Lam. and E. francavilleana O. Berg neotropical tree species (Myrtaceae) with heterologous SSR markers. Genet Resour Crop Evol 61: 267-272.
  • KHODWEKAR S, STATON M, COGGESHALL MV, CARLSON JE and GAILING O. 2015. Nuclear microsatellite markers for population genetic studies in sugar maple (Acer saccharum Marsh.). Ann For Res 58: 193-204.
  • LEDERMAN IE, BEZERRA JEF and CALADO G. 1992. A pitangueira em Pernambuco. IPA 19: 20.
  • LEMOS RPM, D’OLIVEIRA CB and STEFENON VM. 2015. Genetic structure and internal gene flow in populations of Schinus molle (Anacardiaceae) in the Brazilian Pampa. Tree Genet Genomes 11: 75.
  • MAIA LCD, PALMIERI DA, SOUZA VQD, KOPP MM, CARVALHO FIFD and OLIVEIRA ACD. 2008. SSR Locator: Tool for Simple Sequence Repeat discovery integrated with primer design and PCR simulation. Int J Plant Genomics 1-9.
  • NAGEL JC, CECONI DE, POLETTO I and STEFENON VM. 2015. Historical gene flow within and among populations of Luehea divaricata in the Brazilian Pampa. Genetica 143: 317-1329.
  • NYBOM H. 2004. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Molecular Ecology 13: 1143-1155.
  • OWUSU SA, STATON M, JENNINGS TN, SCHLARBAUM S, COGGESHALL MV, ROMERO-SEVERSON J, CARLSON JE and GAILING O. 2013. Development of genomic microsatellites in Gleditsia triacanthos (Fabaceae) using Illumina Sequencing. Applic Plant Sc 1: 1300050.
  • PEAKALL R and SMOUSE PE. 2006. GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6: 288-295.
  • PEAKALL R and SMOUSE PE. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28: 2537-2539.
  • RASMUSSEN DA and NOOR MAF. 2009. What can you do with 0.1x genome coverage? A case study based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae). BMC Genomics 10: 382.
  • ROESCH LFW, VIEIRA FCB, PEREIRA VA, SCHUNEMANN AL, TEIXEIRA IF, SENNA AJT and STEFENON VM. 2009. The Brazilian Pampa: a fragile biome. Diversity 1: 182-198.
  • SILVA ALG and PINHEIRO MCB. 2007. Biologia floral e da polinização de quatro espécies de Eugenia L. (Myrtaceae). Acta Bot Bras 21: 235-247.
  • STATON M et al. 2015. Preliminary genomic characterization of ten hardwood tree species from multiplexed low coverage whole genome sequencing. Plos One 10: e0145031.
  • STEFENON VM, NAGEL JC, CECONI DE and POLETTO I. 2016. Evidences of genetic bottleneck and fitness decline in Luehea divaricata populations from southern Brazil. Silva Fennica 50: 1566.
  • STRAUB S, FISHBEIN M, LIVSHULTZ T, FOSTER Z, PARKS M, WEITEMIER K, CRONN RC and LISTON A. 2011. Building a model: developing genomic resources for common milkweed (Asclepias syriaca) with low coverage genome sequencing. BMC Genomics 12: 211.
  • UNTERGASSER A, CUTCUTACHE I, KORESSAAR T, YE J, FAIRCLOTH BC, REMM M and ROZEN SG. 2012. Primer3-new capabilities and interfaces. Nucleic Acids Res 40: 1-12.
  • WICKER T, NARECHANIA A, SABOT F, STEIN J, VU GT, GRANER A, WARE D and STEIN N. 2008. Low-pass shotgun sequencing of the barley genome facilitates rapid identification of genes, conserved non-coding sequences and novel repeats. BMC Genomics 9: 518.

Publication Dates

  • Publication in this collection
    08 Apr 2019
  • Date of issue
    2019

History

  • Received
    27 Apr 2018
  • Accepted
    3 June 2018
Academia Brasileira de Ciências Rua Anfilófio de Carvalho, 29, 3º andar, 20030-060 Rio de Janeiro RJ Brasil, Tel: +55 21 3907-8100 - Rio de Janeiro - RJ - Brazil
E-mail: aabc@abc.org.br