Single nucleotide polymorphism discovery in elite north american potato germplasm
© Hamilton et al; licensee BioMed Central Ltd. 2011
Received: 1 February 2011
Accepted: 9 June 2011
Published: 9 June 2011
Current breeding approaches in potato rely almost entirely on phenotypic evaluations; molecular markers, with the exception of a few linked to disease resistance traits, are not widely used. Large-scale sequence datasets generated primarily through Sanger Expressed Sequence Tag projects are available from a limited number of potato cultivars and access to next generation sequencing technologies permits rapid generation of sequence data for additional cultivars. When coupled with the advent of high throughput genotyping methods, an opportunity now exists for potato breeders to incorporate considerably more genotypic data into their decision-making.
To identify a large number of Single Nucleotide Polymorphisms (SNPs) in elite potato germplasm, we sequenced normalized cDNA prepared from three commercial potato cultivars: 'Atlantic', 'Premier Russet' and 'Snowden'. For each cultivar, we generated 2 Gb of sequence which was assembled into a representative transcriptome of ~28-29 Mb for each cultivar. Using the Maq SNP filter that filters read depth, density, and quality, 575,340 SNPs were identified within these three cultivars. In parallel, 2,358 SNPs were identified within existing Sanger sequences for three additional cultivars, 'Bintje', 'Kennebec', and 'Shepody'. Using a stringent set of filters in conjunction with the potato reference genome, we identified 69,011 high confidence SNPs from these six cultivars for use in genotyping with the Infinium platform. Ninety-six of these SNPs were used with a BeadXpress assay to assess allelic diversity in a germplasm panel of 248 lines; 82 of the SNPs proved sufficiently informative for subsequent analyses. Within diverse North American germplasm, the chip processing market class was most distinct, clearly separated from all other market classes. The round white and russet market classes both include fresh market and processing cultivars. Nevertheless, the russet and round white market classes are more distant from each other than processing are from fresh market types within these two groups.
The genotype data generated in this study, albeit limited in number, has revealed distinct relationships among the market classes of potato. The SNPs identified in this study will enable high-throughput genotyping of germplasm and populations, which in turn will enable more efficient marker-assisted breeding efforts in potato.
The most widely cultivated potato species, Solanum tuberosum Group Tuberosum, is an autotetraploid (2n = 4x = 48) and the world's third most important food crop in overall production, after rice and wheat . Potato improvement is constrained by numerous challenges and bottlenecks [2–5] including a high level of heterozygosity, tetraploid genetics, restricted genetic base, biotic and abiotic constraints as well as the need to simultaneously select for market-based quality traits and agronomic performance. While genetic maps and markers have been described in potato [6–9], they have not yet had substantial impact on potato improvement. Mapping studies in potato (at the 2x and 4x levels) have been conducted since the late 1980's [10–15], but marker-assisted selection (MAS) is not widely practiced in varietal breeding. To date, only a few molecular markers for economically important traits have been developed in potato, and most of these are for resistance to pests and diseases, including late blight , Potato Virus Y [17–19], potato cyst nematode  and Verticillium wilt . Development of a genome-wide set of markers polymorphic in elite germplasm would allow more cultivars and breeding clones to be genotyped and substantially advance potato breeding.
With the emergence of genomics in the late 1990s, Expressed Sequence Tag (EST) projects were initiated for potato in which Sanger-based sequencing was used to catalog transcripts in an array of tissues and genotypes [22–26]. To date, 237,583 sequences derived by Sanger sequencing are available for potato in the National Center for Biotechnology Information (NCBI) dbEST (Release 011110;). While prior sequencing has provided a useful starting point for detecting polymorphic loci in potato, the polymorphisms that can be defined at present are restricted to the genotypes sequenced to date and the depth of sequencing performed. Three cultivars, 'Bintje' (1905), 'Kennebec' (released in 1948), and 'Shepody' (1980), have substantial Sanger sequence datasets, and for all three cultivars, relatively low-coverage Sanger sequencing was employed.
Due to the high throughput and low costs, next generation sequencing methods provide a powerful means to generate large sequence datasets that can be used to characterize sequence diversity [28, 29]. In addition to discovery, next generation sequencing platforms can be used to rapidly generate polymorphisms and genotype data for genetic mapping [30–32]. To increase the number of single nucleotide polymorphisms (SNPs) available for basic and applied potato genetics, we conducted extensive transcriptome sequencing from three currently relevant potato cultivars, Atlantic , Premier Russet , and Snowden (released in 1990). Atlantic and Snowden are the two most widely grown public chipping cultivars in North America, while Premier Russet is a new, promising French fry clone. All three cultivars are used as parents in North American breeding programs. Using transcriptome data generated in this study, coupled with available Sanger potato ESTs, we computationally identified a large collection of SNPs for use in genotyping. We also created a germplasm panel of ~250 potato clones, which includes many representatives of each of the major market classes, Solanum species, genetic stocks, and represents a broad genetic base to assess the allelic distribution of a subset of SNPs and the population structure and relationships between market classes.
Results and Discussion
Sequencing and annotation of the potato transcriptome
Genotypes and sequence datasets used in this study,
S.tuberosum Group Tuberosum
S.tuberosum Group Tuberosum
S.tuberosum Group Tuberosum
French fry processing
S.tuberosum Group Tuberosum
French fry processing
S.tuberosum Group Tuberosum
S.tuberosum Group Tuberosum
S.tuberosum Group Phureja
Diploid Andean Fresh Market
Used in Genome Projecta
Potato sequence and assembly statistics.
Total No. sequences
Total No. Gb sequences
No. sequences passed quality filters
No. of Gb of sequences passed quality filters
Total No. contigs & singletons
Total No. Mb contigs & singletons
No. Mb contigs
No. Mb singletons
N50 contig size (bp)
Max contig size (bp)
Min contig size (bp)
Alignment of contigs to the A. thaliana proteome.
No. contigs with alignmenta
We compared our GA2-generated assemblies to EST collections generated previously using the Sanger platform [22–25]. The three Sanger EST datasets (Bintje, Kennebec, and Shepody) were more variable in number of reads: 15,866, 83,549, and 86,341, respectively, and consequently, the Sanger-derived assemblies were more variable in representation of the potato transcriptome: Bintje (4.3 Mb), Kennebec (19.9 Mb), Shepody (36.3 Mb)(Table 2). Due to smaller sampling of the transcriptome, Bintje was under-represented compared to Kennebec and Shepody as shown by the reduced number of total and non-redundant alignments to the Arabidopsis proteome compared to the GA2-generated transcriptomes (Table 3). When examined for overlap based on alignment to the Arabidopsis proteome (Figure 1B), these three datasets do overlap with each other, although the skew in total numbers of contigs between the three cultivars is reflected in overlap of non-redundant Arabidopsis alignments. The vast majority (>90%) of the Sanger-generated contigs were represented within the GA2 datasets (Figure 1C).
From these 69,011 SNPs, 96 were empirically tested using the Illumina BeadXpress genotyping platform, of which 82 were considered high quality (Additional File 3). Due to the partial nature of transcriptome sequence due to expression levels and sequencing depth, full coverage of each SNP for all three genotypes (Atlantic, Premier Russet, and Snowden) was not available. RNA-seq based genotypes were available for all three genotypes for 14 SNPs, two genotypes for 20 SNPs, and one genotype for 48 SNPs. Of the 82 high quality SNPs, 70 were congruent for all genotypes between the two platforms, 10 were inconsistent for one of the genotypes, and two of the SNPs failed for one of the alleles in the BeadXpress assay or are homozygous for the genotypes used in this study. Thus, our computational pipeline to predict SNPs solely from sequence data is robust. In total, from the 182,251 Sanger and GA2-generated contigs, 82,780 contigs have at least one high confidence SNP. The remaining 99,471 contigs lack a high confidence SNP that meets our filtering criteria.
Germplasm population structure
Computational SNP analysis across six sequenced cultivars
While the BeadXpress assay allowed us to examine 82 SNPs across 248 germplasm clones, it is also possible to compare a much larger number of SNPs across the six potato cultivars for which there is available transcriptome sequence. Using a separate computational pipeline that mapped sequence reads directly to the DM reference genome , we identified 2,117,754 raw SNP calls, of which, 147,525 SNPs remained after filtering with the Bowtie/SAMTools pipeline. Collectively, these mapped to 101,487 unique genome positions and represented approximately 25% more SNPs than the Maq pipeline described above, which was based on transcript assemblies.
Total and cultivar-restricted SNPs in six potato cultivars.
Pairwise comparison of SNPs between potato accessions.
By combining RNA-Seq of three current cultivars (Atlantic, Premier Russet, Snowden) with data mining of existing ESTs from three older cultivars (Bintje, Kennebec, Shepody), we were able to identify an abundance of SNPs in elite potato germplasm. These SNPs will facilitate future marker analyses by potato geneticists and breeders alike. Breeders, in particular, will soon be able to incorporate large amounts of genotypic data into their decision making. This will lead to deeper understanding of breeding germplasm, as well as more efficient QTL mapping, association mapping and marker-assisted selection, collectively resulting in more predictable and directed breeding.
With stringent filtering of sequence data in combination with alignment to a reference potato genome, we were able to identify 69,011 high confidence SNPs for use with the Infinium genotyping platform. A subset of these SNPs was recently used to design a 8300 marker SNP array . The current study sought to validate 96 of these SNPs on 244 potato clones; 82 of these SNPs (85%) could be reliably scored. Genotyping with the validated 82 SNP markers allowed us to examine population structure and relationships between market classes. Even with this small number of SNPs, we were able to gain insight into the genetic structure of cultivated potato. Somewhat unexpectedly, we observed that chip processing germplasm is discernibly different from other market classes, even though intense selection for chip processing traits is a relatively recent phenomenon, only practiced for the past 50 years or so, and in a crop where meioses are relatively infrequent. We also found that chip and French fry processing germplasm appear more closely related to round white table and table russet germplasm, respectively, even though the traits required for processing are similar across these two market classes.
Materials and methods
Germplasm and datasets used in this study
The germplasm panel was compiled from elite potato germplasm from 16 breeding programs across the U.S. including six international programs. Germplasm panel member names, market classes and species composition are noted in Additional File 5. Clones in the germplasm panel were assigned to market classes as follows. The long shaped potatoes were classified as table russet or French fry processing based upon their utilization. Similarly, the round white potatoes were classified as table or chip processing. The yellow market class is composed of yellow-fleshed clones, but does not include chip processing clones. The pigmented market class combines red and purple-skinned clones, some of which also have red or purple flesh. The diploid breeding lines consist of clones used by breeders for breeding or mapping purposes. The genetic stocks consist of clones used for genetic studies only; these clones have little or no value for breeding. To define the clone's genome composition, contributing breeders were asked to note if a potato clone contains wild species in its background, either as a parent or as a grandparent. A core set of Solanum species and accessions (provided by D. S. Spooner, USDA/ARS) that have previously been used for introgression into tetraploid germplasm were included in the panel to provide a taxonomic perspective. These clones were designated as "species" in our analyses. Sequences used in this study are listed in Table 1. Sanger ESTs from Bintje, Kennebec, and Shepody were obtained from NCBI dbEST . Genomic sequences for Solanum tuberosum Group Phureja DM1-3 516R44 (DM) potato were obtained from the Potato Genome Sequencing Consortium (;v3 assembly).
RNA was isolated from young tuber meristems, leaves, flowers and callus of Atlantic, Premier Russet, and Snowden  and pooled in equimolar concentration. cDNA was synthesized and prepared for paired-end sequencing as described . Samples were sheared, 300-350 bp fragments selected, and were normalized using double-stranded nuclease that digests high copy double-stranded DNA during re-association after denaturation. Each normalized library was sequenced in two paired-end (forward and reverse) lanes of 61 bp on the Illumina Genome Analyzer (Illumina Inc., San Diego, CA). Sequences are available in the SequenceRead Archive at NCBI (Study number SRP006384).
De novo assembly and annotation of transcripts
Illumina RNA-Seq reads from each cultivar (Atlantic, Premier Russet, and Snowden) were assembled separately using the Velvet assembler  in the paired-end mode with a hash length of 31 and a minimum contig length of 150 bp. The insert size and expected coverage parameters were 350 bp and 31.2X for Atlantic, 300 and 34.4X for Premier Russet, and 300 bp and 33X for Snowden, respectively. Sanger-generated ESTs for Bintje, Kennebec, and Shepody were passed twice through SeqClean and assembled into contigs using the TGICL clustering pipeline .
The contigs (Velvet or TGICL-generated) were searched against the A. thaliana proteome (TAIR9; ) and UniRef100  using BLASTX  with an E-value cutoff of 1e-5. To annotate the potato contigs, the first meaningful functional annotation was selected from the top 10 scoring BLAST matches to the UniRef100 database and transitively assigned to the potato contig. If no meaningful annotation was found in the top 10 UniRef100 matches yet there was a match meeting the cutoff criterion, the potato contig was annotated as a "conserved gene of unknown function". If no hits at all were found within the cutoff criterion, the potato contig was annotated as a "gene of unknown function". For representation of the Arabidopsis proteome, contigs were searched against A. thaliana proteome (TAIR9; ) with an E-value cutoff criterion of 1e-5 and the best alignment retained. For gene ontology associations, alignments to the A. thaliana proteome (TAIR9; ) were used to transitively assign gene ontology (GO; ) terms.
SNP discovery and allelic diversity in a potato germplasm panel
We computationally identified SNPs within our three GA2-generated transcriptomes (Atlantic, Premier Russet, Snowden) and designed a 96 SNP BeadXpress assay to 1) validate our computational predictions and 2) assess allelic diversity and population structure in a diverse set of potato germplasm.
Intra-varietal and inter-varietal SNPs were identified by aligning the RNA-Seq reads from each variety to the Velvet-generated contigs using the Maq easyrun.pl pipeline in the paired-end mode (Figure 2; ). We imposed multiple sets of filters for the SNPs to be included in the BeadXpress assay. First, raw SNP calls from the pipeline were filtered with the maq.pl SNP filter script using a minimum depth of 20 reads, a maximum depth of 225, a minimum consensus score of 30, a minimum adjacent consensus score of 20, and a required maximum mapping quality of 60. Additional constraints were a maximum of one other SNP in a 100 bp flanking window and that the SNP must be located 50 bp from areas identified as indels by the pipeline [maq.pl SNPfilter -d 20 -n 20 -Q 60 -q 30 -w 50 -N 2 -W100 -f cns.indelse -F cns.indelpe cns.snp]. Second, the SNPs were filtered to exclude SNPs near intron-exon junctions by aligning the Velvet contigs to the DM scaffolds  using GMAP . Only SNPs located within exons that aligned at >95% identity with no gaps were retained while SNPs within 50 bp of an exon-intron boundary were discarded. Third, only biallelic SNPs were retained. Fourth, remaining SNPs were scored by Illumina (San Diego, CA) for suitability for the Infinium BeadXpress platform and SNPs with a score <0.9 or a fail code were discarded. The final 96 SNPs selected for BeadXpress validation originated from Atlantic, Snowden, and Premier Russet (Additional File 3).
Genotyping potato germplasm
DNA was extracted from 248 potato lines using the Qiagen Qiaxtractor DX system (Qiagen Inc., Valencia, CA). Samples were loaded at 50 ng/μl on an Illumina BeadXpress Analyzer (Illumina inc., San Diego, CA) and data were analyzed using the Illumina GenomeStudio software. Cluster positions for three marker classes (AA, AB, and BB) were manually determined for each marker within the Illumina GenomeStudio software. Due to the difficulty of calling allelic dosage in the tetraploid clones, all heterozygous classes in tetraploids (AAAB, AABB and ABBB) were scored as AB. Of the 96 SNP markers, 14 were of low quality based on the tightness of clusters and/or signal intensity and were removed from downstream analysis (Additional File 3). Genotypic data for the remaining 82 high quality SNPs is provided (Additional File 7). Population structure was determined using the STRUCTURE software . Three iterations were run per K (number of populations) for K equals two through 10 using an admixture model with a burn-in time and replication number of 50,000. The population number with the maximum likelihood of the observed genotypes given the number of populations was used to determine population structure. PowerMarker version 3.25  was used to calculate the allele frequency based genetic distance between the market classes using the Rogers distance method  for the 244 genotypes with defined market classes. An unweighted pair group method with arithmetic mean (UPGMA) tree was constructed based on the Rogers distances; FigTree version 1.3.1 was used to produce the UPGMA tree image .
Cross-comparative analyses of SNPs across six cultivars
Access to large transcriptome sets for six potato cultivars provides an opportunity to examine allelic diversity across a wide range of loci, albeit from a limited set of germplasm. To compare SNPs across the Atlantic, Bintje, Kennebec, Premier Russet, Shepody, and Snowden transcriptomes, we used a computational approach modified from that described above. Instead of aligning transcripts with each other, reads were directly mapped to the genome.
Illumina transcript datasets (Atlantic, Premier Russet, Snowden)
RNA-Seq reads from Atlantic, Premier Russet, and Snowden were mapped directly onto the DM reference genome sequence  with Bowtie (version 0.12.3; ). Only alignments of reads that mapped uniquely to the 15 genome were retained. The resulting SAM alignment file was processed using the SAMTools (version 0.1.7; ) package and initial SNP calls made (samtools pileup -vcf). The SNPs were then filtered with the samtools.pl varFilter script (samtools.pl varFilter -d 20 -D 240 -W 100 -N 2 -w 50) retaining SNPs with a minimum read depth of 20, a maximum read depth of 240, a minimum distance of 50 bp from putative insertions/deletions (indels), and only one other SNP within a 100 bp window around the SNP. Further filtering of the SNPs was done with a custom Perl script that removed SNPs with a consensus score <20, a SNP quality score <20, and a minimum mapping score of 60. As a final constraint, SNP calls that had greater than 10% of 3' end of aligned reads were excluded to avoid calling alignment errors as SNPs. The genomic positions of the SNPs and associated metadata were stored in a PostgreSQL relational database using the Chado schema .
Sanger transcript datasets (Bintje, Kennebec, Shepody)
For Sanger-generated sequences, SNPs were called using a custom Perl SNP-calling script that required an overall read depth of 10, of which, 4 reads had to support the SNP call. The SNP calls were then filtered removing SNPs with 50 bp of an intron and SNPs with more than one additional SNP in a 100 bp window surrounding the SNP.
We thank David Francis, Lukas Mueller, and Alex Stone for contributions to the SolCAP project. Funding for this project was provided by grants to D.D, D.F., A.V D., W. D, L.M., A.S. and C. R. B. by the U.S. Department of Agriculture National Institute of Food and Agriculture (2008-55300-04757 and 2009-85606-05673). We acknowledge the provision of the DM genome sequence by the Potato Genome Sequencing Consortium.
- FAOSTAT. [http://faostat.fao.org/site/339/default.aspx]
- Douches DS, Jastrzebski K: Potato Breeding. Genetic Improvement of Vegetable Crops. Edited by: G Kalloo RB. 1992, Pergamon PressGoogle Scholar
- Ross H: Potato breeding-Problems and perspectives. 1986, Berlin: Verlag Paul PareyGoogle Scholar
- Jansky S: Breeding for disease resistance in potato. Plant Breeding Reviews. 2000, 19: 69-155.Google Scholar
- Bradshaw JE, Hackett CA, Pande B, Waugh R, Bryan GJ: QTL mapping of yield, agronomic and quality traits in tetraploid potato (Solanum tuberosum subsp. tuberosum). Theor Appl Genet. 2008, 116 (2): 193-211. 10.1007/s00122-007-0659-1.PubMedView ArticleGoogle Scholar
- Milbourne D, Meyer RC, Collins AJ, Ramsay LD, Gebhardt C, Waugh R: Isolation, characterisation and mapping of simple sequence repeat loci in potato. Mol Gen Genet. 1998, 259 (3): 233-245. 10.1007/s004380050809.PubMedView ArticleGoogle Scholar
- Gebhardt C, Ritter E, Debener T, Schachtschabel U, Walkemeier B, Uhrig H, Salamini F: RFLP Analysis and Linkage Mapping in Solanum tuberosum. Theoretical and Applied Genetics. 1989, 78 (1): 65-75. 10.1007/BF00299755.PubMedView ArticleGoogle Scholar
- van Os H, Andrzejewski S, Bakker E, Barrena I, Bryan GJ, Caromel B, Ghareeb B, Isidore E, de Jong W, van Koert P, Lefebvre V, Milbourne D, Ritter E, van der Voort JN, Rousselle-Bourgeois F, van Vliet J, Waugh R, Visser RG, Bakker J, van Eck HJ: Construction of a 10,000-marker ultradense genetic recombination map of potato: providing a framework for accelerated gene isolation and a genomewide physical map. Genetics. 2006, 173 (2): 1075-1087. 10.1534/genetics.106.055871.PubMed CentralPubMedView ArticleGoogle Scholar
- Tanksley SD, Ganal MW, Prince JP, de Vicente MC, Bonierbale MW, Broun P, Fulton TM, Giovannoni JJ, Grandillo S, Martin GB, Messeguer R, Miller JC, Miller L, Paterson AH, Pineda O, Roder MS, Wing RA, Wu W, Young ND: High density molecular linkage maps of the tomato and potato genomes. Genetics. 1992, 132 (4): 1141-1160.PubMed CentralPubMedGoogle Scholar
- Bradshaw JE, Mackay GR: Breeding strategies for clonally propagated potatoes. Potato Genetics. Edited by: Mackay JEBaGR. 1994, Wallingford: CAB InternationalGoogle Scholar
- Li XQ, De Jong H, De Jong DM, De Jong WS: Inheritance and genetic mapping of tuber eye depth in cultivated diploid potatoes. Theor Appl Genet. 2005, 110 (6): 1068-1073. 10.1007/s00122-005-1927-6.PubMedView ArticleGoogle Scholar
- Li L, Paulo MJ, Strahwald J, Lubeck J, Hofferbert HR, Tacke E, Junghans H, Wunder J, Draffehn A, van Eeuwijk F, Gebhardt C: Natural DNA variation at candidate loci is associated with potato chip color, tuber starch content, yield and starch yield. Theor Appl Genet. 2008, 116 (8): 1167-1181. 10.1007/s00122-008-0746-y.PubMed CentralPubMedView ArticleGoogle Scholar
- Gebhardt C, Bellin D, Henselewski H, Lehmann W, Schwarzfischer J, Valkonen JPT: Marker-assisted combination of major genes for pathogen resistance in potato. Theoretical and Applied Genetics. 2006, 112 (8): 1458-1464. 10.1007/s00122-006-0248-8.PubMedView ArticleGoogle Scholar
- Schafer-Pregl R, Ritter E, Concilio L, Hesselbach J, Lovatti L, Walkemeier B, Thelen H, Salamini F, Gebhardt C: Analysis of quantitative trait loci (QTLs) and quantitative trait alleles (QTAs) for potato tuber yield and starch content. Theoretical and Applied Genetics. 1998, 97: 834-846. 10.1007/s001220050963.View ArticleGoogle Scholar
- Chen X, Salamini F, Gebhardt C: A potato molecular-function map for carbohydrate metabolism and transport. Theoretical and Applied Genetics. 2001, 102 (2-3): 284-295. 10.1007/s001220051645.View ArticleGoogle Scholar
- Colton LM, Groza HI, Wielgus SM, Jiang JM: Marker-assisted selection for the broad-spectrum potato late blight resistance conferred by gene RB derived from a wild potato species. Crop Science. 2006, 46 (2): 589-594. 10.2135/cropsci2005.0112.View ArticleGoogle Scholar
- Heldak J, Bezo M, Stefunova V, Gallikova A: Selection of DNA markers for detection of extreme resistance to Potato virus Y in tetraploid potato (Solanum tuberosum L.) F1 progenies. Czech Journal of Genetics and Plant Breeding. 2007, 43: 125-134.Google Scholar
- Ottoman RJ, Hane D, Brown C, Yilma S, Mosley A: Usefulness of molecular markers to screen for PVY resistance (Ryadg gene) in potato. American Journal of Potato Research. 2007, 84: 108-Google Scholar
- Kasai K, Morikawa Y, Sorri VA, Valkonen JPT, Gebhardt C, Watanabe KN: Development of SCAR markers to the PVY resistance gene Ry(adg) based on a common feature of plant disease resistance genes. Genome. 2000, 43 (1): 1-8.PubMedView ArticleGoogle Scholar
- Gebhardt C, Mugniery D, Ritter E, Salamini F, Bonnel E: Identification of RFLP Markers Closely Linked to the H1 Gene Conferring Resistance to Globodera rostochiensis in Potato. Theoretical and Applied Genetics. 1993, 85 (5): 541-544.PubMedView ArticleGoogle Scholar
- Bae J, Halterman D, Jansky S: Development of a molecular marker associated with Verticillium wilt resistance in diploid interspecific potato hybrids. Molecular Breeding. 2008, 22 (1): 61-69. 10.1007/s11032-008-9156-8.View ArticleGoogle Scholar
- Ronning CM, Stegalkina SS, Ascenzi RA, Bougri O, Hart AL, Utterbach TR, Vanaken SE, Riedmuller SB, White JA, Cho J, Pertea GM, Lee Y, Karamycheva S, Sultana R, Tsai J, Quackenbush J, Griffiths HM, Restrepo S, Smart CD, Fry WE, Van Der Hoeven R, Tanksley S, Zhang P, Jin H, Yamamoto ML, Baker BJ, Buell CR: Comparative analyses of potato expressed sequence tag libraries. Plant Physiol. 2003, 131 (2): 419-429. 10.1104/pp.013581.PubMed CentralPubMedView ArticleGoogle Scholar
- Rensink W, Hart A, Liu J, Ouyang S, Zismann V, Buell CR: Analyzing the potato abiotic stress transcriptome using expressed sequence tags. Genome. 2005, 48 (4): 598-605. 10.1139/g05-034.PubMedView ArticleGoogle Scholar
- Fei Z, Tang X, Alba RM, White JA, Ronning CM, Martin GB, Tanksley SD, Giovannoni JJ: Comprehensive EST analysis of tomato and comparative genomics of fruit ripening. Plant J. 2004, 40 (1): 47-59. 10.1111/j.1365-313X.2004.02188.x.PubMedView ArticleGoogle Scholar
- Flinn B, Rothwell C, Griffiths R, Lague M, DeKoeyer D, Sardana R, Audy P, Goyer C, Li XQ, Wang-Pruski G, Regan S: Potato expressed sequence tag generation and analysis using standard and unique cDNA libraries. Plant Mol Biol. 2005, 59 (3): 407-433. 10.1007/s11103-005-0185-y.PubMedView ArticleGoogle Scholar
- Crookshanks M, Emmersen J, Welinder KG, Nielsen KL: The potato tuber transcriptome: analysis of 6077 expressed sequence tags. FEBS Lett. 2001, 506 (2): 123-126. 10.1016/S0014-5793(01)02888-5.PubMedView ArticleGoogle Scholar
- dbEST. [http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html]
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437 (7057): 376-380.PubMed CentralPubMedGoogle Scholar
- Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Keira Cheetham R, Cox AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS, Kirk SM, Li H, Liu X, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML, Pratt MR, et al: Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008, 456 (7218): 53-59. 10.1038/nature07517.PubMed CentralPubMedView ArticleGoogle Scholar
- Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Weng Q, Huang T, Dong G, Sang T, Han B: High-throughput genotyping by whole-genome resequencing. Genome Res. 2009, 19 (6): 1068-1076. 10.1101/gr.089516.108.PubMed CentralPubMedView ArticleGoogle Scholar
- Myles S, Chia JM, Hurwitz B, Simon C, Zhong GY, Buckler E, Ware D: Rapid genomic characterization of the genus Vitis. PLoS One. 2010, 5 (1): e8219-10.1371/journal.pone.0008219.PubMed CentralPubMedView ArticleGoogle Scholar
- Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE: A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011Google Scholar
- Webb RE, Wilson DR, Shumaker JR, Graves B, Henninger MR, Watts J, Frank JA, Murphy HJ: Atlantic - New Potato Variety with High Solids, Good Processing Quality, and Resistance to Pests. Am Potato J. 1978, 55 (3): 141-145. 10.1007/BF02852087.View ArticleGoogle Scholar
- Novy RG, Whitworth JL, Stark JC, Love SL, Corsini DL, Pavek JJ, Vales MI, James SR, Hane DC, Shock CC, Charlton BA, Brown CR, Knowles NR, Pavek MJ, Brandt TL, Olsen N: Premier Russet: A dual-purpose, potato cultivar with significant resistance to low temperature sweetening during long-term storage. American Journal of Potato Research. 2008, 85 (3): 198-209. 10.1007/s12230-008-9013-7.View ArticleGoogle Scholar
- Potato Genome Sequencing Consortium. [http://www.potatogenome.net]
- SolCAP. [http://solcap.msu.edu]
- Chang S, Puryear J, Cairney J: A simple and efficient method for isolating RNA from pine trees. Plant Molecular Biology Reporter. 1993, 11: 113-116. 10.1007/BF02670468.View ArticleGoogle Scholar
- Illumina: mRNA sequencing sample preparation guide. Illumina. 2009, 24-Google Scholar
- Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008, 18 (5): 821-829. 10.1101/gr.074492.107.PubMed CentralPubMedView ArticleGoogle Scholar
- Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S, Lee Y, White J, Cheung F, Parvizi B, Tsai J, Quackenbush J: TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics. 2003, 19 (5): 651-652. 10.1093/bioinformatics/btg034.PubMedView ArticleGoogle Scholar
- Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, Tissier C, Zhang P, Huala E: The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 2008, D1009-1014. 36 Database
- Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH: UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics. 2007, 23 (10): 1282-1288. 10.1093/bioinformatics/btm098.PubMedView ArticleGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25 (17): 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralPubMedView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene Ontology: tool for the unification of biology. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMed CentralPubMedView ArticleGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079. 10.1093/bioinformatics/btp352.PubMed CentralPubMedView ArticleGoogle Scholar
- Wu TD, Watanabe CK: GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005, 21 (9): 1859-1875. 10.1093/bioinformatics/bti310.PubMedView ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155 (2): 945-959.PubMed CentralPubMedGoogle Scholar
- Liu K, Muse SV: PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005, 21 (9): 2128-2129. 10.1093/bioinformatics/bti282.PubMedView ArticleGoogle Scholar
- Rogers JS: Measures of genetic similarity and genetic distance. Studies in genetics VII. 1972, University of Texas Publication 7213. Univ. of Texas, Austin, 145-153.Google Scholar
- Figtree. [http://tree.bio.ed.ac.uk/software/figtree/]
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10 (3): R25-10.1186/gb-2009-10-3-r25.PubMed CentralPubMedView ArticleGoogle Scholar
- Mungall CJ, Emmert DB: A Chado case study: an ontology-based modular schema for representing genome-associated biological information. Bioinformatics. 2007, 23 (13): i337-346. 10.1093/bioinformatics/btm189.PubMedView ArticleGoogle 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.