- Methodology article
- Open Access
Rapid gene-based SNP and haplotype marker development in non-model eukaryotes using 3'UTR sequencing
© Koepke et al; licensee BioMed Central Ltd. 2012
- Received: 5 October 2011
- Accepted: 12 January 2012
- Published: 12 January 2012
Sweet cherry (Prunus avium L.), a non-model crop with narrow genetic diversity, is an important member of sub-family Amygdoloideae within Rosaceae. Compared to other important members like peach and apple, sweet cherry lacks in genetic and genomic information, impeding understanding of important biological processes and development of efficient breeding approaches. Availability of single nucleotide polymorphism (SNP)-based molecular markers can greatly benefit breeding efforts in such non-model species. RNA-seq approaches employing second generation sequencing platforms offer a unique avenue to rapidly identify gene-based SNPs. Additionally, haplotype markers can be rapidly generated from transcript-based SNPs since they have been found to be extremely utile in identification of genetic variants related to health, disease and response to environment as highlighted by the human HapMap project.
RNA-seq was performed on two sweet cherry cultivars, Bing and Rainier using a 3' untranslated region (UTR) sequencing method yielding 43,396 assembled contigs. In order to test our approach of rapid identification of SNPs without any reference genome information, over 25% (10,100) of the contigs were screened for the SNPs. A total of 207 contigs from this set were identified to contain high quality SNPs. A set of 223 primer pairs were designed to amplify SNP containing regions from these contigs and high resolution melting (HRM) analysis was performed with eight important parental sweet cherry cultivars. Six of the parent cultivars were distantly related to Bing and Rainier, the cultivars used for initial SNP discovery. Further, HRM analysis was also performed on 13 seedlings derived from a cross between two of the parents. Our analysis resulted in the identification of 84 (38.7%) primer sets that demonstrated variation among the tested germplasm. Reassembly of the raw 3'UTR sequences using upgraded transcriptome assembly software yielded 34,620 contigs containing 2243 putative SNPs in 887 contigs after stringent filtering. Contigs with multiple SNPs were visually parsed to identify 685 putative haplotypes at 335 loci in 301 contigs.
This approach, which leverages the advantages of RNA-seq approaches, enabled rapid generation of gene-linked SNP and haplotype markers. The general approach presented in this study can be easily applied to other non-model eukaryotes irrespective of the ploidy level to identify gene-linked polymorphisms that are expected to facilitate efficient Gene Assisted Breeding (GAB), genotyping and population genetics studies. The identified SNP haplotypes reveal some of the allelic differences in the two sweet cherry cultivars analyzed. The identification of these SNP and haplotype markers is expected to significantly improve the genomic resources for sweet cherry and facilitate efficient GAB in this non-model crop.
- Single Nucleotide Polymorphism
- High Resolution Melting
- Sweet Cherry
- Transcriptome Assembly
- Single Nucleotide Polymorphism Locus
Sweet cherry (Prunus avium L.), a non-model crop, is an important non-climacteric member of sub family Amygdoloideae where other members like peach and plum demonstrate climacteric fruit ripening. Sweet cherry is a diploid (2n = 16) and is estimated to be slightly larger than peach, 225-300 MB [1, 2]. Sweet cherry underwent a recent breeding-related genetic bottleneck that reduced the diversity present in the germplasm . Genetic variability can be utilized to screen for resistance to diseases and improve the efficiency of selecting desirable genotypes through breeding especially in sweet cherry where natural diversity is lacking. Types of variation at the nucleotide level are: microsatellites or simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), insertions and deletions (indels) and genomic rearrangements . Identification of genetic diversity in species which lack significant genomic resources has typically been a time-consuming and laborious process.
SSR markers have been used extensively for population genetics and genome mapping studies in several members of Rosaceae [5, 6]. SSR identification techniques are typically costly and time consuming [7–9]. Most published SSRs are located in the intergenic regions . A recent study in Populus attempted to identify SSRs in exons or expressed gene fragments. The abundance of microsatellites within the coding region was three-fold lower than intergenic regions and, when present, microsatellites do not show useful allelic variability. Further, the authors concluded that candidate gene approach for development of microsatellites may not be the best strategy . While SSRs remain difficult to develop, SNP identification and validation has rapidly improved in past years mostly due to reduction of sequencing costs. Previously, direct sequencing of a gene of interest related to supernodulation was used to identify SNPs . Similar studies in non-model species lacking such resources require sequence information from related species. SNPs have also been used for anchoring a linkage map and bovine genome . Ganal et al.  reviewed recent SNP identification methods including DNA arrays, amplicon sequencing, mining existing EST resources, and using sequence data generated with second generation sequencing technologies. Compared to other methods, re-sequencing applications were determined to produce a higher percentage of validated SNPs, while non-reference based next-generation sequencing, or de novo, approaches required the least amount of a priori genetic/genomic information. A major caveat of using second generation sequencing de novo is the ability to acquire sufficient depth to accurately identify SNPs. Therefore, a reduced representation sequencing approach was recommended. Many reduced representation methods integrating high throughput sequencing are discussed by Davey et al.  and the authors further elaborated on the utility of SNP-based molecular markers.
Continued improvements in second generation DNA sequencing technologies have increased the ability to obtain significant sequencing depth in a rapid and cost efficient manner, compared to Sanger sequencing approaches . Bundock et al.,  performed amplicon sequencing on genes of interest with 454 technology to produce a large number of reliable SNPs from two parents of a QTL mapping population of sugar cane finding high success rates for SNP verification (93%). Recently, next generation technologies have been widely utilized for sequencing transcriptomes of various species [16–18]. Eveland et al.  reported a quantitative transcriptomics approach based on selective sequencing of the 3'UTR of mRNA from Zea mays. Their work demonstrated a clear ability to resolve the expression of nearly identical genes (99% nucleotide identity) based on variation in the 3'UTR (97% nucleotide identity). Through comparison with sequences in multiple maize databases, 93.8% of the SNPs identified by Eveland et al. were confirmed . Use of a 3'UTR directed approach exploits the higher number of variations found in the 3'UTR region compared to the coding region of a gene. Higher sequence variation, combined with physical linkage to a specific gene, increases the potential impact of 3'UTR polymorphisms in connecting genetics and functional genomics studies especially in non-model eukaryotes. This is in contrast to current approaches where intergenic polymorphisms are used for scoring a segregating phenotype without the associated gene-related information. The method presented here utilized the positive aspects of 3'UTR sequencing, as a reduced representation approach, to facilitate rapid gene-linked SNP identification.
In addition to identifying polymorphisms, current research in human genomics has demonstrated the utility of developing haplotype information as a way to more fully understand genotype to phenotype relationships, especially in context of health, disease and response to environmental cues [20–22]. Generally, haplotypes are comprised of allelic variants on each of the two chromosomes at the same locus, though the definition and utilization varies in application from linking multiple polymorphisms across several loci down to multiple polymorphisms in a single gene . Additionally, haplotype determination has been aided by DNA strand specific or genomic phase-based information generated using second generation sequencing technologies since each sequencing read is from only one homologous chromosome and not a consensus of the two . Similarly, next generation RNA-seq and 3'UTR sequencing has the ability to reveal haplotypes within a gene  and thus enable identification of allele specific sequence and its expression simultaneously. Here we present our approach that utilizes 3'UTR sequencing to rapidly develop SNP and haplotype markers in sweet cherry, a species without a published genome sequence and a non-model crop. Through de novo assembly of 454 generated-3'UTR sequencing reads and strict filtering, we initially identified a putative set of contigs containing SNPs. Primer sets designed to amplify the regions of these contigs with putative SNPs were developed and used for High Resolution Melting (HRM) analysis among eight currently utilized parental cultivars of sweet cherry and 13 hybrid seedlings derived from a cross between two of the parental cultivars, respectively. We determined that 68 out of 223 (30.5%) and 65 out of 217 (30.0%) of the tested primer pairs are able to detect genetic variability. From these polymorphic sites, 685 haplotypes were identified from 301 contigs containing multiple SNPs.
RNA Extraction and cDNA preparation
Tissue samples from developing floral buds of two commercially important cherry cultivars, Bing and Rainier, were excised from the trees and flash frozen in liquid nitrogen. The frozen tissues were pulverized uniformly in a SPEX SamplePrep 6870 FreezerMill (SPEX SamplePrep, Metuchen, NJ) for five cycles each with cooling for two minutes and grinding at 15 counts per second for four minutes. Total RNA from each sample was extracted using the RNeasy Plant DNA Extraction Kit (Qiagen, Germany). First strand cDNA was then synthesized using the Ambion aRNA synthesis kit with a biotinylated poly-T B-adaptor [see Additional File 1 for adaptor sequences] for 3'UTR profiling as described by Eveland et al. (2008). Second strand cDNA was created, cleaved with MspI, and ligated to modified A-adaptors containing indexing tags [see Additional File 1 for adaptor sequences] as per the Eveland protocol.
Sequencing and assembly
The 3'UTR libraries were sequenced as per the 454 FLX protocol (Roche, USA) on a single LR-70 sequencing plate. After sequencing, the 454 produced reads were processed using a custom script [see Additional File 2] to remove the multiplexing barcode and rename each read with its appropriate sample name at the end of the header. All of the modified reads were then assembled using SeqMan from the Lasergene 7 suite (DNASTAR, Madison, WI).
For method development, a total of 10,100 contigs were examined for the presence of putative SNPs using Lasergene 7's SeqMan (DNASTAR, Madison, WI). The high confidence SNPs have at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. Primer pairs flanking potential SNP loci were designed using the PRIMER3 program  to amplify 50-100 base pair amplicons. This yielded 223 primers from regions of 207 contigs for HRM analyses.
Population Variation Screen
Eight sweet cherry cultivars: Bing, Chelan, Emperor Francis, New York 54, Regina, Selah, Stella and Cowiche used as parental material in the Washington State University (WSU) Sweet Cherry Breeding Program (Prosser, WA) were used to test the polymorphisms of the identified SNP loci across Bing and Rainier cultivars. For segregant analysis, 13 seedlings from an F1 mapping population of Selah × Cowiche were used. Leaves of these accessions were collected from the WSU Irrigated Agriculture Research & Extension Center in Prosser, WA and DNA was extracted from dried leaves using a CTAB extraction protocol . The reaction mixture for HRM analysis consisted of 0.6 μL of each primer (10 μM), 12.0 μL SYBR® Green, 5 ng of genomic DNA and autoclaved nanopure water to a total volume of 20 μL. The Cultivar panel comprised of 223 primer sets tested on all eight parental cultivars and the Seedling panel included 217 primers sets tested on one reaction of each parent, Cowiche and Selah, and one of each hybrid seedling. Analyses were performed on the LightCycler® 480 System (Roche Branford, CT) using the following PCR cycling and HRM conditions. Initial melting for 10 minutes at 95°C was followed by 45 cycles of 95°C for 10 seconds, 57°C for 15 seconds, and 72°C for 15 seconds, then heated to 95°C for 1 minute and cooled to 40°C. High Resolution Melting analysis was then automatically initiated whereby the amplicons were heated from 60°C to 90°C with 25 acquisitions per degree. As the temperature slowly increased, the dye fluorescence was recorded, plotted and later analyzed using the LightCycler® 480 Gene Scanning Software. Since the Tm can vary based on the HRM reaction conditions, curve shapes were visually examined and the number of distinct curve profiles was identified for each primer set.
Secondary Assembly and SNP reporting
After the HRM analysis, a second assembly using SeqMan NGen v3.0 (DNASTAR, Madison, WI) was performed due to its improved algorithm and the results were used for SNP reporting on the entire data set. This assembly was completed using the default parameters for NGen 3.0's de novo transcriptome assembly of: 85% match, match size 21, genome length 225 MB. The whole SNP report was initially filtered to retain the HRM confirmed SNPs using a minimum total depth of 10 reads at the polymorphic base and at least 20% variance from the consensus. Further filtering into high confidence SNPs was performed by screening for at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. This minimum depth per allele for each SNP equals or exceeds the published depths using either 454 data [28, 29] or Illumina data [30, 31]. Additionally, SNPs resulting from the first or last five bases of reads were rejected. The transition and transversions ratio (R value) was determined by summing all of the transitions (C/T and A/G) and transversions (A/C, A/T, C/G, and G/T).
Haplotypes were identified visually by analyzing the combined transcriptome assembly generated using NGen 3.0 in SeqMan (DNASTAR). Similar to the SNP screening, at least three reads of an allele spanning two SNP loci were required to link SNPs into a haplotype. When two or more haplotypes were present at one locus, they were differentiated and recorded as separate haplotypes for their use as haplotype markers.
Sequencing and Assembly of 3'UTRs
Summary of 3'UTR sequencing results
Number of bases
Number of reads
Avg. read length
Initial SNP identification
To test our experimental approach, analysis of a subset of the assembled contigs was performed to identify SNPs within the dataset. The 100 contigs with the highest number of reads and contigs 1-10,000 as produced by SeqMan 7 (Lasergene Suite 7.0, 2009) were analyzed yielding 600 contigs containing at least one high confidence SNP. These high confidence SNPs have at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. Since false polymorphism of indels can be high , indels were not included in this analysis to avoid identification of false polymorphisms as previously described . The total number of SNPs in this dataset was not calculated as only the described subset was examined. A total of 223 primer sets were designed from 207 contigs with PRIMER3  to amplify the small regions around the identified SNPs [see Additional File 3 for primer sequences and associated contigs].
Population Variation Screening
Experimental assessment of SNPs
Seedling panel (Selah × Cowiche)
Number with variation
Number with variation
SNP primer sets
Secondary Assembly and SNP reporting
DNA assembly programs continued to improve since the initial assembly which was used to design primers and analysis of population variability. Subsequently, the trimmed reads were re-assembled using NGen v3.0 (DNASTAR, 2011). This assembly produced 34,620 contigs [see Additional File 4] with an average length of 149 bp (Table 3). Since the aim was to obtain high depth of coverage of around 100 bp upstream of the poly-A tail, the longer contigs were unexpected. Analysis of this issue confirmed that the poly-T sequence containing primer used for first strand cDNA synthesis annealed to some poly-A regions in coding regions of the transcripts as well. While not all the sequencing reads were from the direct vicinity of the poly-A tail, the contigs remain gene-linked due to their cDNA origin. This could reduce the total number of identified SNPs since genic regions have a greater selection against mutations when compared to the 3'UTR as previously described .
Transcriptome assembly results
Number of contigs
Avg. contig length
Median contig length
Total contig bases
Number of putative SNPs
The 2243 putative SNPs identified in the assembled gene space (expressed sequences) of 5.19 Mb yields a SNP frequency of 1 in 2,315 bp (0.43 SNPs per kb of gene space). The sweet cherry gene space of 5.19 MB generated in this study represents approximately 2.3 - 1.7% of the estimated genome size of 225 - 300 MB. Previous studies utilizing whole genome sequence have reported a frequency of 1 SNP in 114 bp (8.8 SNPs per kb) and 1 SNP in 208 bp (4.8 SNPs per kb) in almond Prunus armenica (genome size = ~200 MB) and apple Malus × domestica (genome size = 740 MB) respectively [39, 41]. The recent genetic bottleneck and Bing being a parent of Rainier reduces the number of potential alleles present in the dataset to 3 whereas the almond and apple studies examined 25 and 5 cultivars respectively.
Summary of transitions and transversions across Bing and Rainier
Access to Sequence and SNP data
Due to the nature of the contigs and SNPs, many of them did not fit the requirements for typical submission to NCBI. All contigs and high quality SNPs are available as additional files to this manuscript. All of the raw sff files were uploaded to NCBI's Sequence Read Archive (SRA046001.1). Contigs greater than or equal to 200 bp in length were added to GenBank's Transcriptome Sequence Assemblies (TSA) database (GenBank JP376615-JP382830) as Prunus avium assemblies and SNPs corresponding to these sequences larger than 200 bp were uploaded to dbSNP (NCBI ss# 469992783-469995036 except 469992784, 469992792, 469992801, 469992809, 469992818, 469992823, 469992825-7, 469992834-5, 469992842, 469992851, 469992853-4, 469992859, and 469992866-7).
A method for developing gene-linked SNP and haplotype markers through high-throughput 3'UTR sequencing for species lacking genome sequences was demonstrated. Through this process, 2243 putative SNPs were identified and 34,620 contig sequences were obtained and added to NCBI database for use by the plant research community. To our knowledge, the 685 haplotypes developed in this study are the largest set of reported SNP-based haplotypes in sweet cherry and demonstrates that haplotypes can be identified using 3'UTR sequencing. These haplotypes can be utilized for the development of CAPS markers to resolve allelic differences in 301 sites on the sweet cherry genome. These genomic resources represent a large advance in sweet cherry genomics. Potential applications of these SNPs may involve high-throughput amplicon sequencing with these primer sets using next generation sequencing technologies to obtain digital or sequence-based information in genetics studies. This is in contrast to the SNP-arrays that produce an analog signal in genotyping experiments and represent mostly intergenic polymorphisms derived from a few individuals limiting its potential applicability beyond the included polymorphisms. This methodology is expected to be of great utility in polyploid species where allele-specific haplotypes can be highly informative.
As sequencing costs plummet, the general approach reported here could be broadly implemented in identifying gene-linked polymorphisms amongst parental individuals which can then be rapidly utilized in segregation studies of a desirable set of phenotypes in the derived progeny. Polymorphisms that co-segregate with the phenotype are expected to represent the gene or set of genes that regulate the said phenotype. Establishment of these correlations is expected to open avenues for directly linking genetic and functional genomics approaches with phenomics, an emerging discipline focused on understanding genotype-phenotype relationships.
This project was supported by WSU startup funds to AD and NO and Washington Tree Fruit Research Commission funds to AD and MW. TK and SS acknowledge support received from NIH Protein Biotechnology Training Program and ARCS fellowship. VK and AH were supported by US Department of Agriculture National Research Initiative (USDA-NRI) grant 2008 -35300-04676 to AD. Technical assistance of Dr. Kahraman Gurcan for preliminary SNP analysis, primer design and performing of HRM experiments is gratefully acknowledged. We are thankful to David Rockefeller and Devaswa Bhagawati for their assistance in SNP analysis and primer design. Authors are thankful to Dr. Ted Kisha, USDA-ARS and Dr. Katherine Evans at Tree Fruit Research and Extension Center, WSU for useful discussions and critical reading of the manuscript. We are thankful to anonymous reviewers for their excellent suggestions.
- Arumuganathan K, Earle E: Nuclear DNA content of some important plant species. Plant Molecular Biology Reporter. 1991, 9 (3): 208-218. 10.1007/BF02672069.View ArticleGoogle Scholar
- Peach Genome v1.0, International Peach Genome Initiative. [http://www.phytozome.net/peach.php#A]
- Mariette S, Tavaud M, Arunyawat U, Capdeville G, Millan M, Salin F: Population structure and genetic bottleneck in sweet cherry estimated with SSRs and the gametophytic self-incompatibility locus. BMC Genetics. 2010, 11:Google Scholar
- Li S, Yin T, Wang M, Tuskan G: Characterization of microsatellites in the coding regions of the Populus genome. Molecular Breeding. 2011, 27 (1): 59-66. 10.1007/s11032-010-9413-5.View ArticleGoogle Scholar
- Celton JM, Tustin D, Chagne D, Gardiner S: Construction of a dense genetic linkage map for apple rootstocks using SSRs developed from Malus ESTs and Pyrus genomic sequences. Tree Genetics & Genomes. 2009, 5 (1): 93-107. 10.1007/s11295-008-0171-z.View ArticleGoogle Scholar
- Aranzana M, Carbo J, Arus P: Microsatellite variability in peach Prunus persica (L.) Batsch: cultivar identification, marker mutation, pedigree inferences and population structure. TAG Theoretical and Applied Genetics. 2003, 106 (8): 1341-1352.PubMedGoogle Scholar
- Rafalski JA, Tingey SV: Genetic Diagnostics in plant-breeding - RAPDs, Microsatellites and Machines. Trends in Genetics. 1993, 9 (8): 275-280. 10.1016/0168-9525(93)90013-8.View ArticlePubMedGoogle Scholar
- Zeid M, Mitchell S, Link W, Carter M, Nawar A, Fulton T, Kresovich S: Simple sequence repeats (SSRs) in faba bean: new loci from Orobanche-resistant cultivar 'Giza 402'. Plant Breeding. 2009, 128 (2): 149-155. 10.1111/j.1439-0523.2008.01584.x.View ArticleGoogle Scholar
- Zane L, Bargelloni L, Patarnello T: Strategies for microsatellite isolation: a review. Molecular Ecology. 2002, 11 (1): 1-16. 10.1046/j.0962-1083.2001.01418.x.View ArticlePubMedGoogle Scholar
- Kim MY, Van K, Lestari P, Moon JK, Lee SH: SNP identification and SNAP marker development for a GmNARK gene controlling supernodulation in soybean. Theoretical and Applied Genetics. 2005, 110 (6): 1003-1010. 10.1007/s00122-004-1887-2.View ArticlePubMedGoogle Scholar
- Nilsen H, Hayes B, Berg PR, Roseth A, Sundsaasen KK, Nilsen K, Lien S: Construction of a dense SNP map for bovine chromosome 6 to assist the assembly of the bovine genome sequence. Anim Genet. 2008, 39 (2): 97-104. 10.1111/j.1365-2052.2007.01686.x.View ArticlePubMedGoogle Scholar
- Ganal MW, Altmann T, Roder MS: SNP identification in crop plants. Current Opinion in Plant Biology. 2009, 12 (2): 211-217. 10.1016/j.pbi.2008.12.009.View ArticlePubMedGoogle Scholar
- Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML: Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics. 2011, 12 (7): 499-510. 10.1038/nrg3012.View ArticlePubMedGoogle Scholar
- Shendure J, Ji HL: Next-generation DNA sequencing. Nature Biotechnology. 2008, 26 (10): 1135-1145. 10.1038/nbt1486.View ArticlePubMedGoogle Scholar
- Bundock PC, Eliott FG, Ablett G, Benson AD, Casu RE, Aitken KS, Henry RJ: Targeted single nucleotide polymorphism (SNP) discovery in a highly polyploid plant species using 454 sequencing. Plant Biotechnology Journal. 2009, 7 (4): 347-354. 10.1111/j.1467-7652.2009.00401.x.View ArticlePubMedGoogle Scholar
- Folta KM, Clancy MA, Chamala S, Brunings AM, Dhingra A, Gomide L, Kulathinal RJ, Peres N, Davis TM, Barbazuk WB: A transcript accounting from diverse tissues of a cultivated strawberry. Plant Genome. 2010, 3 (2): 90-105. 10.3835/plantgenome2010.02.0003.View ArticleGoogle Scholar
- Isom SC, Spollen WG, Blake SM, Bauer BK, Springer GK, Prather RS: Transcriptional profiling of day 12 porcine embryonic disc and Trophectoderm samples using ultra-deep sequencing technologies. Molecular Reproduction and Development. 2010, 77 (9): 812-819. 10.1002/mrd.21226.View ArticlePubMedGoogle Scholar
- Cantacessi C, Mitreva M, Jex AR, Young ND, Campbell BE, Hall RS, Doyle MA, Ralph SA, Rabelo EM, Ranganathan S, Sternberg PW, Loukas A, Gasser RB: Massively parallel sequencing and analysis of the Necator americanus transcriptome. PLOS Neglected Tropical Diseases. 2010, 4 (5): 11-View ArticleGoogle Scholar
- Eveland AL, McCarty DR, Koch KE: Transcript profiling by 3'-untranslated region sequencing resolves expression of gene families. Plant Physiology. 2008, 146 (1): 32-44.PubMed CentralView ArticlePubMedGoogle Scholar
- Tewhey R, Bansal V, Torkamani A, Topol EJ, Schork NJ: The importance of phase information for human genomics. Nature Reviews Genetics. 2011, 12: 215-223. 10.1038/nrg2950.PubMed CentralView ArticlePubMedGoogle Scholar
- Suk E-KK, McEwen GK, Duitama J, Nowick K, Schulz S, Palczewski S, Schreiber S, Holloway DT, McLaughlin S, Peckham H, Lee C, Huebsch T, Hoehe MR: A comprehensively molecular haplotype-resolved genome of a European individual. Genome Research. 2011Google Scholar
- Deloukas P, Bentley D: The HapMap project and its application to genetic studies of drug response. Pharmacogenomics J. 2003, 4 (2): 88-90.View ArticleGoogle Scholar
- Johnson GCL, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RCJ, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SCL, Clayton DG, Todd JA: Haplotype tagging for the identification of common disease genes. Nature Genetics. 2001, 29 (2): 233-237. 10.1038/ng1001-233.View ArticlePubMedGoogle Scholar
- He D, Choi A, Pipatsrisawat K, Darwiche A, Eskin E: Optimal algorithms for haplotype assembly from whole-genome sequence data. Bioinformatics. 2010, 26 (12): i183-i190. 10.1093/bioinformatics/btq215.PubMed CentralView ArticlePubMedGoogle Scholar
- Snyder M, Wang Z, Gerstein M: RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009, 10 (1): 57-63. 10.1038/nrg2484.PubMed CentralView ArticlePubMedGoogle Scholar
- Primer3 on the www for general users and for biologist programmers. Edited by: Rozen S, Skaletsky HJ. 2000, Totowa, NJ: Humana PressGoogle Scholar
- Doyle JJ, Doyle JL: Isolation of plant DNA from fresh tissue. Focus. 1990, 12 (13-15):Google Scholar
- Emrich SJ, Li' L, Wen TJ, Yandeau-Nelson MD, Fu Y, Guo L, Chou HH, Aluru S, Ashlock DA, Schnable PS: Nearly identical paralogs: Implications for maize (Zea mays L.) genome evolution. Genetics. 2007, 175 (1): 429-439.PubMed CentralView ArticlePubMedGoogle Scholar
- Kulheim C, Yeoh SH, Maintz J, Foley WJ, Moran GF: Comparative SNP diversity among four Eucalyptus species for genes from secondary metabolite biosynthetic pathways. Bmc Genomics. 2009, 10:Google Scholar
- Hyten DL, Cannon SB, Song QJ, Weeks N, Fickus EW, Shoemaker RC, Specht JE, Farmer AD, May GD, Cregan PB: High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. Bmc Genomics. 2010, 11:Google Scholar
- Varala K, Swaminathan K, Li Y, Hudson ME: Rapid Genotyping of Soybean Cultivars Using High Throughput Sequencing. Plos One. 2011, 6 (9):Google Scholar
- Whiting , Matthew D, Lang , Gregory , Ophardt , David : Rootstock and training system affect sweet cherry growth, yield, and fruit quality. 2005, Alexandria, VA, ETATS-UNIS: American Society for Horticultural Science, 40:Google Scholar
- Xiong M, Zhao Z, Arnold J, Yu F: Next-Generation Sequencing. Journal of Biomedicine and Biotechnology. 2010, 2010:Google Scholar
- Chaisan T, Van K, Kim M, Kim K, Choi BS, Lee SH: In silico single nucleotide polymorphism discovery and application to marker-assisted selection in soybean. Molecular Breeding. 1-13.Google Scholar
- Li J, Wang X, Dong R, Yang Y, Zhou J, Yu C, Cheng Y, Yan C, Chen J: Evaluation of High-Resolution Melting for Gene Mapping in Rice. Plant Molecular Biology Reporter. 2011, 29 (4): 979-985. 10.1007/s11105-011-0289-2.View ArticleGoogle Scholar
- Mader , Eduard , Lohwasser , Ulrike , Rner , Andreas , Novak , Johannes : Population structures of genebank accessions of Salvia officinalis L. (Lamiaceae) revealed by high resolution melting analysis. 2010, Kidlington, ROYAUME-UNI: Elsevier, 38:Google Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen ZT, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer MLI, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J : Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437 (7057): 376-380.PubMed CentralPubMedGoogle Scholar
- Barbazuk WB, Emrich SJ, Chen HD, Li L, Schnable PS: SNP discovery via 454 transcriptome sequencing. Plant Journal. 2007, 51 (5): 910-918. 10.1111/j.1365-313X.2007.03193.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu SB, Wirthensohn M, Hunt P, Gibson J, Sedgley M: High resolution melting analysis of almond SNPs derived from ESTs. TAG Theoretical and Applied Genetics. 2008, 118 (1): 1-14. 10.1007/s00122-008-0870-8.View ArticlePubMedGoogle Scholar
- A guide to HRM Analysis. [http://www.appliedbiosystems.com/etc/medialib/appliedbio-media-library/documents/application-and-technology/real-time-pcr/hrm.Par.73223.File.pdf]
- Velasco R, Zharkikh A, Affourtit J, Dhingra A, Cestaro A, Kalyanaraman A, Fontana P, Bhatnagar SK, Troggio M, Pruss D, Salvi S, Pindo M, Baldi P, Castelletti S, Cavaiuolo M, Coppola G, Costa F, Cova V, Dal Ri A, Goremykin V, Komjanc M, Longhi S, Magnago P, Malacarne G, Malnoy M, Micheletti D, Moretto M, Perazzolli M, Si-Ammour A, Vezzulli S: The genome of the domesticated apple (Malus × domestica Borkh.). Nature Genetics. 2010, 42 (10): 833-+. 10.1038/ng.654.View ArticlePubMedGoogle Scholar
- Fang JG, Twito T, Zhang Z, Chao CCT: Genetic relationships among fruiting-mei (Prunus mume Sieb. et Zucc.) cultivars evaluated with AFLP and SNP markers. Genome. 2006, 49 (10): 1256-1264. 10.1139/g06-097.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.