- Research article
- Open Access
From RNA-seq to large-scale genotyping - genomics resources for rye (Secale cereale L.)
- Grit Haseneyer†1,
- Thomas Schmutzer†2,
- Michael Seidel3,
- Ruonan Zhou4,
- Martin Mascher2,
- Chris-Carolin Schön1,
- Stefan Taudien5,
- Uwe Scholz2,
- Nils Stein4,
- Klaus FX Mayer3 and
- Eva Bauer1Email author
© Haseneyer et al; licensee BioMed Central Ltd. 2011
- Received: 16 February 2011
- Accepted: 28 September 2011
- Published: 28 September 2011
The improvement of agricultural crops with regard to yield, resistance and environmental adaptation is a perpetual challenge for both breeding and research. Exploration of the genetic potential and implementation of genome-based breeding strategies for efficient rye (Secale cereale L.) cultivar improvement have been hampered by the lack of genome sequence information. To overcome this limitation we sequenced the transcriptomes of five winter rye inbred lines using Roche/454 GS FLX technology.
More than 2.5 million reads were assembled into 115,400 contigs representing a comprehensive rye expressed sequence tag (EST) resource. From sequence comparisons 5,234 single nucleotide polymorphisms (SNPs) were identified to develop the Rye5K high-throughput SNP genotyping array. Performance of the Rye5K SNP array was investigated by genotyping 59 rye inbred lines including the five lines used for sequencing, and five barley, three wheat, and two triticale accessions. A balanced distribution of allele frequencies ranging from 0.1 to 0.9 was observed. Residual heterozygosity of the rye inbred lines varied from 4.0 to 20.4% with higher average heterozygosity in the pollen compared to the seed parent pool.
The established sequence and molecular marker resources will improve and promote genetic and genomic research as well as genome-based breeding in rye.
- EST resource
- next generation sequencing
- Secale cereale L.
- Rye5K SNP array
- single nucleotide polymorphisms
The improvement of agricultural crops with regard to yield, resistance and environmental adaptation is a perpetual challenge for both breeding and research. With regard to prospected climate changes, improved tolerance against abiotic stresses like drought, low soil fertility, and extreme temperatures is required in crop improvement. The outcrossing species rye shows the highest freezing tolerance among small grain cereals  and exhibits excellent tolerance against many biotic and abiotic stresses. Understanding the functional genetic basis of stress tolerance in rye will facilitate the improvement of stress tolerance in wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). As a genetic research system, rye is intriguing due to its high genetic variability. In addition to being an economically important crop for Middle and Eastern Europe, rye provides valuable traits for other crops, as a parent of the amphiploid triticale, and as a donor of translocated chromosome segments in wheat . Rye benefits from being diploid and closely related to the more extensively characterized species wheat and barley. While reference sequences of grass genomes have become available for rice [3, 4], sorghum , Brachypodium  and maize , sequence information for rye is sparse which hampers the exploitation of its genetic potential.
The haploid genome size of rye is more than 8 Gbp  which is one of the largest among cereal crops. In addition, 92% of the genome is composed of repetitive sequences . Genetic and genomic resources are limited compared to other Triticeae. Currently, 1,073,668 wheat and 501,620 barley ESTs are publicly available whereas only 9,298 rye ESTs are deposited in public databases http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/dbEST/dbEST_summary.html (release 070111). Publicly available genomic resources for rye are restricted to one BAC library , a limited number of genetic markers http://wheat.pw.usda.gov/GG2/index.shtml, and genetic maps with low marker density [11–15].
Next-generation sequencing (NGS) technologies such as Illumina's Genome Analyzer and Roche's 454 sequencing platforms have opened the way to tackle sequencing of large genomes like those of barley and wheat which would be impossible to address by Sanger sequencing . NGS platforms produce hundreds of thousands of sequences in a massively parallel manner, are cost and labour effective and were proven to be reliable and accurate. Several studies have highlighted the success and usefulness of NGS for extending available genomics resources by transcriptome [e.g. [17, 18]] and whole-genome  sequencing. Furthermore, NGS has been used for gene expression profiling , analysis of genome organisation , DNA methylation studies , and molecular marker development , to name few.
Given the large genome size and the lack of sequence information and genomic resources in rye, identification and targeted isolation of genes underlying agronomic traits and understanding of gene function and trait variation is greatly hampered. The aim of the present study was to promote rye genome analysis through massive improvement of the public rye EST resource and development of the first high-throughput SNP genotyping array.
Plant material, RNA and sequencing
Five winter rye inbred lines Lo7, Lo152, Lo225, P87, and P105 were used for cDNA sequencing. Lo7, Lo152, and Lo225 were provided by KWS LOCHOW GMBH (Bergen, Germany) and represent lines from the seed parent and the pollen parent pool of the company's hybrid rye breeding program. P87 and P105 were developed at the Institute of Genetics and Cytology, Minsk, Belarus, and are parents of the mapping population P87 × P105 . Inbred lines Lo7, Lo152, and Lo225 were generated by six selfing generations, whereas P87 and P105 were selfed seven and eight times, respectively. In addition, 54 proprietary inbred lines from the breeding material of KWS LOCHOW GMBH, representing the two breeding pools were investigated. Lines from the pollen parent pool were generated by two to three selfing generations, whereas lines from the seed parent pool have undergone five selfing steps.
To capture a comprehensive part of the rye transcriptome 20 samples of total RNA per inbred line were obtained from a set of plant tissues harvested at five developmental stages and after three stress treatments, respectively (Additional file 1). Three plants per inbred line were pooled to obtain each of the 20 RNA samples. For all non-stress treatments tissue samples from leaves, stems and/or roots were harvested at three- to four-leaf stage, tillering, stem extension, heading and harvest ripe stage. Coleoptiles, florets, early and mature spikes were harvested. To enrich stress induced genes in the cDNA sample, cold stress, dehydration shock, and nutrient-starvation stress treatments were applied in the three- to four-leaf stage. Cold stress was induced by placing plants in a freezer at -15°C. Root, stem and leaf tissues were harvested after 1, 3, and 6 hours of stress treatment and pooled. Dehydration shock experiments were conducted by removing well-watered plants from soil and leaving them on Whatman® 3 MM paper (Whatman GmbH, Dassel, Germany) at room temperature . Root, stem, and leaf tissues were harvested after 3, 6, and 12 hours of stress and pooled. Three plants per inbred line were densely planted leading to nutrient-starvation stress. Root and leaf tissues were harvested and pooled. All tissue samples were frozen in liquid nitrogen and stored at -80°C until use. Total RNA was isolated according to manufacturer's instructions using the NucleoSpin RNA Plant kit (#740949, Macherey-Nagel, Düren, Germany) and quantified with the SPECTRONIC GENESYS™ 10 BIO spectrometer (Thermo ELECTRON CORPORATION, Madison, USA).
Five micrograms of the 20 RNA samples of each inbred line were pooled and 100 μg total RNA per inbred line was sent for cDNA synthesis to vertis Biotechnology AG (Freising, Germany). Poly(A)+ RNA was prepared from total RNA. First-strand cDNA synthesis was primed with random hexanucleotide primers. Then 454 sequencing adapters A (5'-GCCTCCCTCGCGCCATCAG-3') and B (5'-CTGAGCGGGCTGGCAAGGC-3') were ligated to the 5' and 3' cDNA ends. Finally, cDNAs were amplified in 20 (Lo152) and 21 (Lo7, Lo225, P87, P105) PCR cycles using a proof reading enzyme. Normalization was carried out by one cycle of denaturation and reassociation of the cDNA. Reassociated ds-cDNA was separated from the ss-cDNA on hydroxylapatite columns to obtain the normalized cDNA samples. After hydroxylapatite chromatography, the ss-cDNA samples were amplified in 8 PCR cycles. The cDNA fraction in the size range of 600 to 800 bp was eluted from preparative agarose gels. As a control, aliquots of the fractionated cDNAs were analyzed on 1.5% agarose gels. Approximately 150 to 250 μg of the normalized, adapter-ligated, and size selected cDNA samples were used for GS FLX 454 sequencing. All 454 sequence raw data were submitted to the EBI sequence read archive (SRA) and are available under the study accession number ERP000274.
De novo sequence assembly
After 454 sequencing, raw sequence reads were passed through quality filtering where cDNA synthesis primer and sequencing adapter sequences were removed. After pre-processing, cleaned and trimmed reads were subjected to inbred line-specific assemblies. Therefore, we adapted the strategy of Kumar and Blaxter  for assembling transcriptome data using multiple assembly programs and combining the outcomes to create longer contigs that are less likely to be in-silico artefacts brought forth by a single algorithm. The strategy has been modified to be applicable for various lines (Figure 1). We used three independent assemblers to achieve most credible consensus contig sequences. Initially, all reads from each of the five lines were assembled separately into first-order contigs with the programs CLC assembly cell v3.20 http://www.clcbio.com, Mira v3.21  and Newbler v2.5 . While MIRA and Newbler follow the overlap-consensus-layout paradigm (OLC), CLC attempts to find paths in De Bruijn graphs. To obtain line-specific assemblies, all first-order contigs constructed by the three assemblers were merged using the OLC assembler CAP3 . We considered only line-specific contigs whose constituents included first-order contigs from all three assemblers. For EST resource generation (Sce_Assembly03), we employed CAP3 a second time to co-assemble the high confidence line-specific contigs and denoted those supported by constituents from more than one line as multi-line contigs, while contigs with evidence from only one line were deemed single-line contigs. The assembly process of Sce_Assembly03 has been accomplished with a screening for potential DNA and foreign RNA contamination. We applied a BlastN against chloroplast genome sequences of barley (GenBank: NC_008590) and wheat (GenBank: NC_002762), mitochondrial genome sequences of rice (GenBank: AP011077), sorghum (GenBank: DQ984518), and wheat (GenBank: GU985444), and plastids genome sequences of Brachypodium (GenBank:EU325680), rice (GenBank: GU592207), sorghum (GenBank: NC_008602), and wheat (GenBank: AB042240). Further purity was gained by excluding hits against CDS sequences of Acyrthosiphon pisum (GenBank: ACFK00000000), Buchnera aphidicola (GenBank: AE013218), Fusarium graminearum (GenBank: AACM00000000), and the draft sequence of Puccinia triticina available at the Broad Institute. We discarded contigs from the Sce_Assembly03 sequence set that showed E-values larger than E-20 and the proposed best hits representing at least 10% of the full contig size. The established EST resource Sce_Assembly03 is available from the GABI primary database , http://www.gabipd.org.
Sequences between the five rye inbred lines potentially differ to a degree that prevents the de novo assembly of two lines. Blast  comparisons which do not require strict sequence identity were carried out to analyze for overlaps between the different assemblies. Line-specific assemblies generated by CAP3 were used together with the Sce_Assembly03 in an "all versus all" BlastN analysis. Each line-specific assembly as well as the multi-line and single-line contigs of the Sce_Assembly03 were used as both, subject and query sequences. The best query hit to a subject sequence was counted to identify homologs in the respective assemblies. Hits were considered significant when they exceeded a conservative cut-off value of > = 70% identity and 30 bp coverage.
Comparisons of the Sce_Assembly03 against the four currently available protein databases of maize [ZmB73_v5b.60, http://www.maizesequence.org], rice [RAP2, ], sorghum , and Brachypodium , two EST databases of barley and wheat (Barley assembly 35 and Wheat assembly WK, http://harvest.ucr.edu), and two full length cDNA (flcDNA) library databases of barley  and wheat  were performed using BlastX and tBlastX, respectively. Hits were only considered significant when they exceeded a conservative cut-off value of > 70% identity and 30 bp coverage. To prevent hits found based on low-complexity sequences or repeats the Sce_Assembly03 was masked using RepeatMasker  and the internal MIPS repeat database .
Genome-wide distribution of the Sce_Assembly03 contig sequences was investigated by chromosome-wise BlastX analysis comparing multi-line and single-line contigs with Brachypodium protein sequences. Sce_Assembly03 sequences were mapped onto the Brachypodium genome by using a sliding window approach with a window size of 0.5 Mb and a shift of 0.1 Mb along the Brachypodium chromosomes. The number of BlastX hits and the percent bp coverage of the respective Brachypodium genes were determined. These density values were corrected for the number of Ns per window, if the N content exceeded 60% the value was set to zero. Density values were extrapolated to genes  or hits (rye) per Mb to facilitate comparisons. To visualize the mapping results heatmaps were created from the density values using the Python matplotlib module in combination with the jet colormap .
Functional gene annotation
The 115,400 sequences of the Sce_Assembly03 were functionally annotated performing a Blast search with Blast2GO default parameters against the non-redundant (nr) protein sequence database  after masking repetitive sequences and excluding the singletons. Gene ontology (GO) terms were assigned using B2G4PIPE http://www.blast2go.org and a locally installed Blast2GO database. The annotation file was extended by its respective GO category - biological process, cellular component, and molecular function - using a custom built Python script that is available upon request.
SSR mining and SNP discovery
Simple sequence repeat (SSR) motifs within 338,536 contigs of the line-specific assemblies were identified by MISA  under standard settings. Out of the five inbred lines, Lo225 was selected as reference dataset as it provided the highest number of SSR containing contigs. The MISA output of the four remaining lines was cross-matched with the Lo225 dataset to detect redundant SSRs. A non-redundant SSR dataset was generated by combining "unique" SSR motifs detected in Lo7, Lo152, Lo225, P87, and P105. Mononucleotide repeat motifs were discarded since monomer runs are known to be the most frequent sequencing errors in Roche/454 data. For experimental validation of in silico detected SSRs, primers flanking the SSR motifs were designed using Primer3 . Amplification of the fragments was performed in Lo7, Lo225, P87, and P105 as they are the parents of two mapping populations. Thus, polymorphisms detected between Lo7 and Lo225 and/or P87 and P105 enable the genetic mapping of discovered SSRs. PCR was conducted in a total volume of 20 μl, including 20 ng of genomic DNA, 1× HotStar Taq PCR buffer (Qiagen, Hilden, Germany), 250 nM of each primer, 200 μM dNTPs, and 0.5 U HotStar Taq DNA polymerase (Qiagen, Hilden, Germany). Using a touch-down PCR profile, an initial denaturation step of 15 min at 95°C was followed by 45 cycles of denaturation at 94°C for 1 min, annealing for 1 min, and extension at 72°C for 1 min. Annealing temperature was decreased by 1°C per cycle from 65°C to 55°C and was kept constant for 35 subsequent cycles. A final extension step was performed at 72°C for 10 min. Successful amplification was checked on 1.5% agarose gels.
For the discovery of SNPs in assembled sequences, a second assembly strategy was pursued. Reads assembled in line-specific contigs were selected from all reads and subjected to an overall assembly, merging the extracted reads of all five genotypes (Sce_Assembly02, Figure 1). With this strategy information about nucleotide coverage is maintained which is important for reliable SNP discovery. The Sce_Assembly02 is described in Additional file 2 and is available from the GABI primary database http://www.gabipd.org. The workflow from in silico SNP discovery in the Sce_Assembly02 to selection of high confidence SNP candidates was a three-step procedure: First, the tool GigaBayes V0.4.1  was applied with parameter settings given in Additional file 3. Second, characteristics for discovered SNPs were extracted by in-house implementations to compute defined selection criteria for candidate SNPs. Candidate SNPs were filtered by these selection criteria to meet the following requirements: SNPs should be bi-allelic and polymorphic between parents of the two mapping populations Lo7 × Lo225 and/or P87 × P105. For successful probe design they should have a distance to homopolymeres > 5 bp, to the next Indel > 60 bp, and to the contig end > 60 bp. Third, filtered SNPs were manually inspected in the assembled sequences using EagleView  to ensure high quality of the SNP genotyping array. We considered putative sequencing errors, SNP position in individual reads, and haplotype information. Oligo-probes for 5,234 SNP were designed and the Rye5K array was produced by Illumina Inc. (San Diego, USA) as Infinium iSelect HD Custom BeadChip. To demonstrate genome-wide coverage of the SNPs represented on the genotyping array SNP containing contig sequences were in silico mapped against the Brachypodium genome by BlastN analysis.
SNP array performance was assessed by analyzing 59 rye inbred lines including the five inbred lines used for sequencing as well as accessions from barley (Barke, Morex, OWB Dom, OWB Rec, Steptoe), wheat (Chinese Spring, Dream, Mulgara), and triticale (Modus, breeding line SaKa3006). A total of 300 ng genomic DNA per plant was used for genotyping on the Illumina iScan platform and the Infinium HD assay following manufacturer's protocol. The fluorescence images of an array matrix carrying Cy3- and Cy5-labeled beads were generated with the two-channel scanner. Raw hybridization intensity data processing, clustering and genotype calling (AA, AB, BB) were performed using the genotyping module in the GenomeStudio software V2009.1 (Illumina, San Diego, USA). Genotype data were cleaned through exclusion of all SNP assays with more than 5% missing data. Frequencies of the A and B allele for a given SNP were calculated directly by dividing the number of occurrences of one allele (AA + 1/2 AB or BB + 1/2 AB) by twice the number of assayed lines per SNP. Residual heterozygosity of 59 inbred lines was calculated by the relation of heterozygous SNPs (AB) to the number of assayed SNPs per inbred line. Significant deviation of the observed value from the expected value was tested with an exact binomial test using R . Genotyping data of the 10 non-rye accessions were analyzed to investigate the applicability of the Rye5K SNP array to other small grain cereals.
Establishment and description of the rye EST resource
Descriptive statistics of five independent Roche/454 GS FLX sequencing runs.
Raw sequence data
Number of sequences
Average read length [bp]
After quality filtering
Number of sequences
Average read length [bp]
25% quantile [bp]
75% quantile [bp]
Description of the Sce_Assembly03.
Number of reads
Number of reads/contig
Number of contigs
< 500 bp
> 5000 bp
Longest sequence [bp]
BlastN comparisons of the five line-specific assemblies generated with CAP3 and the Sce_Assembly03.
Functional gene annotation
After masking repetitive sequences of the Sce_Assembly03 111,150 sequences (32,725 multi-line and 78,425 single-line contigs) remained for Blast2GO analysis. Out of these sequences 49,294 revealed a hit against the nr database and subsequently 35,356 (71.7%) unique rye contig sequences (16,970 multi-line and 18,386 single-line contigs) were assigned to one or more GO annotations. In total 35,186, 38,280 and 51,950 GO terms were obtained for biological processes, cellular components and molecular functions, respectively (Additional file 5). Across the three GO categories, 4,997 unique GO terms were identified. More than 350 sequences in the Sce_Assembly03 were related to biotic and abiotic stress response (data not shown).
Marker discovery, SNP array design and high-throughput genotyping
SSR marker development
Within the 338,536 contigs of the line-specific assemblies a fraction of 12,317 (3.6%) contigs contained SSR motifs. Primer sequences could be designed for 5,230 of these contigs. Restriction to di-, tri-, tetra-, penta- or hexa-nucleotide motifs reduced the number of SSR candidates to 3,799. Cross-match analysis filtered a final SSR dataset comprising 1,385 unique, non-redundant SSRs (Additional file 6). A random subset of 155 SSRs was chosen for experimental validation by PCR amplification of the four parental genotypes Lo7, Lo225, P87, and P105. 146 primer pairs (94%) immediately amplified fragments of expected size without further optimization of PCR conditions. Twelve primer combinations produced fragments larger than expected indicating the presence of introns. These were excluded from further analyses. Finally, 61 (46%) out of 134 PCR products with expected fragment size revealed naked-eye polymorphisms on agarose gels between either P87 and P105 (29) or Lo7 and Lo225 (37).
SNP discovery requires sufficient coverage with high quality sequence reads in order to allow for distinguishing true SNPs from sequencing errors. Therefore, the assembly Sce_Assembly02 was performed that excluded singletons from the line-specific assemblies when merging sequences of the five inbred lines. Overall 277,033 putative polymorphisms in 138,339 contigs cumulating 55 Mb consensus sequences were identified in a first data mining step using GigaBayes. The number of SNP candidates was reduced to 17,917 by filtering those SNPs that fulfilled the selection criteria and quality requirements such as bi-allelic and polymorphic between parents of the two mapping populations Lo7 × Lo225 and/or P87 × P105, distance to homopolymeres > 5 bp, distance to the next Indel > 60 bp, and distance to the contig end > 60 bp. Subsequent manual inspection in the Sce_Assembly02 reduced the dataset to 5,211 SNP candidates from 3,961 contigs. This dataset together with additional 23 SNPs discovered in non-public rye sequences was used for the design and production of the Rye5K SNP genotyping array. Out of the 3,961 unique contigs, 2,835 contigs (71.6%) were in silico mapped to the Brachypodium genome. The contigs were evenly distributed with 826, 641, 688, 416, and 262 hits on chromosomes Bd1 to 5, respectively (Additional file 4). Blast2GO analysis of 3,961 contig sequences represented on the Rye5K array assigned 2,096 sequences with associated GO identifiers (Additional file 7).
Application of the Rye5K SNP array
Heterozygosity of five sequenced rye inbred lines after genotyping with the Rye5K array.
Expected heterozygosity [%]
Observed heterozygosity [%]
Genotyping data were used to calculate the observed residual heterozygosity of the rye inbred lines. The observed percentage of heterozygous loci for each line varied between 4.1 and 4.8% in the five rye inbred lines used for 454 sequencing and between 4.0 to 20.4% in the 54 inbred lines from the two heterotic breeding pools. On average, a higher level of residual heterozygosity was observed for the pollen parent pool (11.5%) than for the seed parent pool (5.5%).
Applicability of the Rye5K SNP array to other small grain cereals was investigated. Out of the 4,557 SNP assays that generated a signal in rye, 63.0% (2,871), 75.8% (3,452), and 84.1% (3,831) could be scored in barley, wheat, and triticale, respectively. However, 86.7, 91.6, and 76.5% of the scored SNPs did not show a polymorphism between the investigated barley, wheat, and triticale accessions.
Dual-purpose transcriptome sequencing
In this study we report the establishment of rye genomic resources comprising 115,400 EST sequences, 1,385 SSRs, more than 5,000 SNPs, and the Rye5K SNP array for large-scale genotyping. NGS was used to generate transcriptome sequences of the five rye inbred lines Lo7, Lo152, Lo225, P87, and P105. The number of reads per sequencing run of the present study was in line or even surpassed results obtained in other studies [17, 23, 44]. Due to the massive number of 2.5 Mio read sequences obtained by 454 sequencing the de novo assembly of such datasets remains a computational and bioinformatic challenge. Two purpose-oriented assembly strategies were followed in order to first provide a comprehensive EST resource and second enable discovery of polymorphisms between inbred lines. A second assembly on top of the five line-specific assemblies reduced the possibility of creating chimeric artefacts in the Sce_Assembly03. In addition, sequence redundancy introduced by variations between lines is removed. This was achieved by bringing together related sequences while accepting line specific nucleotide differences. In contrast this fact was essential for SNP detection, where only reads that were pre-assembled in line-specific contigs were subjected to the Sce_Assembly02. Thus, information about allele coverage at the SNP position was retained which increased the reliability of SNP candidates. A challenge in our study was the detection of SNPs without a reference sequence. Many SNP detection tools such as GMAP  or MAQ  are only applicable to de novo assemblies that are aligned to a reference sequence. This was a strong challenge in our approach and much effort was invested in the detection of high confidence SNPs. Manual inspection of SNP candidates in more than 10,000 contigs indicated that many sequencing errors occurred in the beginning of read sequences which, as a consequence, lead to false positives. Exclusion of SNP candidates detected in such regions of read sequences might reduce the false positive rate and improve automated tools that detect polymorphisms in de novo assembled sequence data without a reference sequence.
Genome sequencing has progressed rapidly in model plants. Given the increased sequencing throughput and the decreasing costs, NGS technologies pave the way for sequencing even large genomes [47–49]. Although of major importance for research and breeding, sequence resources for rye were sparse imposing serious limitations for trait mapping, association studies, and functional genomics in rye. Rye is of interest especially for Middle and Eastern European economic markets due to its high tolerance to abiotic stresses. As a first step towards deciphering the rye genome we aimed to sequence a large portion of the rye transcriptome. To achieve this we first sampled RNA from plants under various stress conditions, different plant tissues and developmental stages. Rye-specific sequences e.g. related to stress tolerance were generated in the present study which are indispensable for functional genomic studies in rye. Second, we reduced the complexity of the transcriptome by cDNA normalization prior to sequencing. cDNA normalization lead to a significant increase in transcriptome sequencing efficiency by equalizing the representation of high, medium and rarely expressed transcripts in the cDNA population [50–52]. Since many transcripts are temporally and/or spatially expressed during plant development, RNA pooled from different tissues at different developmental stages ensured the coverage of temporal- and spatial-specific transcripts.
Linking rye to grass genome sequence resources
To assess, how much of the rye transcriptome is represented by the established EST resource, we compared the Sce_Assembly03 sequences to currently available grass genome, flcDNA, and EST sequences. Generally, the number of sequences with significant BlastX hit in public databases was higher for multi-line contigs than for single-line contigs. This finding is in line with results of Schafleitner et al.  who compared EST sequences of sweet potato (Ipomea batatas) with sequences contained in the UniRef100 protein database.
The overall gene content across the grass subfamilies Ehrhartoideae (rice), Panicoideae (maize, sorghum), and Pooideae  is in a similar range. A total of 25,532 protein coding gene loci were found for Brachypodium  which is in line with rice [RAP2, 28,236 protein coding gene loci, ], maize [ZmB73_v5b.60, 39,656 protein coding loci, ], and sorghum [v1.4, 27,640 protein coding gene loci, ]. Due to a close evolutionary relationship with these model genomes a pronounced overlap with rye transcripts was expected. The comparison of the Sce_Assembly03 against flcDNA, EST, and genomic sequences revealed a higher homology to barley, Brachypodium, and wheat than to maize, rice, and sorghum which was expected, as rye is phylogenetically more closely related to other members of the Pooideae than to maize, rice, and sorghum [54, 55]. The GO annotation analysis reveals that a broad spectrum of genes was sampled in our normalized cDNA pool from multiple tissues and developmental stages. The large number of reads generated by 454 sequencing entails a substantial gain at the level of gene discovery which provides a valuable resource for forward and reverse genetics approaches in rye as well as for comparative gene analyses. A significant fraction of multi-line contigs (31%) gave no hits with the public grass sequence resources. In part this finding can be attributed to species specific and tribe specific genes and gene families. The Pooideae contain 265 subfamily-specific gene families leading to subfamily-specific Blast hits . Given our stringent BlastX/tBlastX cut-off value of > 70% sequence identity, non-conserved and non-coding sequences such as 3'- or 5'- untranslated regions and non-coding RNAs are assumed to contribute to the fraction that lacks homology with other grass species. Around 2% of all rye 454 reads revealed hits to the MIPS Repeat Element database , suggesting that transcriptional activity of retrotransposons contributed to the sampled RNA pool. Transcriptome sequencing in two rice subspecies detected alternative splicing patterns in about half of the rice genes and more than 15,000 novel transcriptional active regions of which more than half had no homolog in public protein data . This might suggest that the rye EST resource contains rare, tissue-specific and/or stress-related transcripts that are not represented in sequence resources of the closely related species wheat and barley despite their extensive EST resources. It is anticipated that rye transcriptome sequence analysis will greatly benefit from a reference genome sequence for a member of the Triticeae family. Whole genome sequencing is in progress for barley [49, 57] and wheat  and exploratory BAC end sequencing of rye 1RS-specific BAC libraries  has been reported. In silico mapping of rye ESTs to the model genome of Brachypodium revealed an even distribution of rye transcripts when anchored to their Brachypodium homologs. The large extent of synteny between grass genomes will facilitate the construction of a virtual gene map of rye representing the ancestral gene scaffold. Genetic mapping of the SNPs represented on the Rye5K array and of SSRs developed from our rye ESTs is underway and will lead to fine-scale comparative maps between rye and other grasses. A fully annotated genome sequence for rye is still out of reach due to the complexity and highly repetitive nature of the rye genome. However, with the tools established in our study, rye catches up with other grass genome resources and a far more detailed glimpse into the rye genome and its evolution will be possible.
Molecular toolbox for rye
Sequence information of the five rye inbred lines was used to detect sequence variation that was transferred into more than 1,300 SSRs and about 5,000 SNPs. Molecular markers have been developed for a range of crop species and play an essential role in modern plant breeding. They have been used to monitor DNA sequence diversity within and among species, to identify genes responsible for desired traits, to disclose sources of genetic variation that allow for the production of new varieties by introducing favorable traits from landraces and related grass species, and to manage backcrossing programs . Together with amplified fragment length polymorphisms (AFLPs), SSRs are currently the most popular marker system in cereals. They have been developed for major crop plants including cereals and when applied in breeding programs this marker system is predicted to lead to accelerated progress . Currently, the availability of public rye SSRs is very limited. Our resource significantly increases this marker resource that might facilitate the assessment of genetic variability and the estimation of genetic distances between populations. Besides SSRs the marker system receiving the greatest attention nowadays are SNPs . SNPs have shown huge potential in highly efficient fingerprinting, genetic map construction, marker assisted selection as well as population and evolutionary genetics. The Rye5K SNP array provides a powerful new resource for large-scale genotyping in molecular and genome-centric research in rye. Recently whole-genome genotyping arrays became available for crops and livestock and are used for genome-wide association studies and to investigate genetic variation [e.g. ]. In a pilot experiment, we analyzed 59 rye inbred lines including the five lines used for sequencing with the Rye5K SNP array to estimate the degree of residual heterozygosity. Theoretical expectation after two, three or six cycles of selfing is about 12.5%, 6.3%, and 1.6%, respectively. Genotyping of these 59 lines using the Rye5K array showed that the degree of heterozygosity significantly (p-value < 0.05) exceeds this theoretical expectation. This might be in part explained by the allogamous behaviour of rye resulting in remaining heterozygosity . Despite forced selfing during inbred line production some degree of cross-pollination cannot be excluded as the seed was produced as single-ear progenies in a commercial breeding program. The lower levels of residual heterozygosity observed for the seed parent pool is in agreement with the higher advanced selfing generations in rye seed parent lines (P. Wilde, personal communication). A detailed analysis of sequences that remained heterozygous indicated sequences belonging to large gene families, such as transferases and hydroxylases. Detection of SNPs in paralogs or members of gene families may mimic a substantial part of the detected heterozygosity, thus leading to an overestimation of the true remaining heterozygosity in the rye inbred lines.
In conclusion, the Sce_Assembly03 provides a new and comprehensive EST resource that integrates rye in the comparative analysis between small grain cereals. The Rye5K SNP array allows the analysis of large sets of individuals to obtain genotyping data for association studies, estimating linkage disequilibrium, and population genetic approaches. Our genomic resources comprise 115,400 EST sequences, 1,385 SSRs, more than 5,000 SNPs, and the Rye5K SNP array for large-scale genotyping that will improve and promote genetic and genomic research as well as genome-based breeding in rye.
We thank Fritz Thümmler (vertis AG, Freising, Germany) for synthesizing and normalizing the cDNA samples, KWS LOCHOW GMBH for providing seed and DNA samples, and Christof Pietsch for his initial work on the SNP discovery pipeline. This work was supported by a grant [0315063A to E.B., 0315063B to N.S., 0315063C to K.M.] in the framework of the initiative 'GABI-Future' of the German Ministry of Education and Research (BMBF).
- Limin AE, Fowler DB: Cold hardiness of forage grasses grown on the canadian prairies. Can J Plant Sci. 1987, 67 (4): 1111-1115. 10.4141/cjps87-150.View ArticleGoogle Scholar
- Ko JM, Seo BB, Suh DY, Do GS, Park DS, Kwack YH: Production of a new wheat line possessing the 1BL.1RS wheat-rye translocation derived from Korean rye cultivar Paldanghomil. Theor Appl Genet. 2002, 104 (2-3): 171-176. 10.1007/s00122-001-0783-2.PubMedView ArticleGoogle Scholar
- Goff SA, Ricke D, Lan TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H, et al: A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science. 2002, 296 (5565): 92-100. 10.1126/science.1068275.PubMedView ArticleGoogle Scholar
- Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, Deng Y, Dai L, Zhou Y, Zhang X, et al: A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science. 2002, 296 (5565): 79-92. 10.1126/science.1068037.PubMedView ArticleGoogle Scholar
- Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G, Hellsten U, Mitros T, Poliakov A, et al: The Sorghum bicolor genome and the diversification of grasses. Nature. 2009, 457 (7229): 551-556. 10.1038/nature07723.PubMedView ArticleGoogle Scholar
- The International Brachypodium Initiative: Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature. 2010, 463 (7282): 763-768. 10.1038/nature08747.View ArticleGoogle Scholar
- Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, et al: The B73 maize genome: complexity, diversity, and dynamics. Science. 2009, 326 (5956): 1112-1115. 10.1126/science.1178534.PubMedView ArticleGoogle Scholar
- Doležel J, Greilhuber J, Lucretti S, Meister A, Lysák MA, Nardi L, Obermayer R: Plant genome size estimation by flow cytometry: Inter-laboratory comparison. Ann Bot. 1998, 82 (suppl 1): 17-26.View ArticleGoogle Scholar
- Flavell RB, Bennett MD, Smith JB, Smith DB: Genome size and the proportion of repeated nucleotide sequence DNA in plants. Biochemical Genetics. 1974, 12 (4): 257-269. 10.1007/BF00485947.PubMedView ArticleGoogle Scholar
- Shi B, Collins N-C, Langridge P, Gustafson J: Construction of a rye cv. Blanco BAC library, and progress towards cloning the rye Alt3 aluminium tolerance gene. Vortr Pflanzenzuchtg. 2007, 71: 205-209.Google Scholar
- Hackauf B, Rudd S, van der Voort JR, Miedaner T, Wehling P: Comparative mapping of DNA sequences in rye (Secale cereale L.) in relation to the rice genome. Theor Appl Genet. 2009, 118 (2): 371-384. 10.1007/s00122-008-0906-0.PubMedView ArticleGoogle Scholar
- Khlestkina EK, Than MH, Pestsova EG, Roder MS, Malyshev SV, Korzun V, Borner A: Mapping of 99 new microsatellite-derived loci in rye (Secale cereale L.) including 39 expressed sequence tags. Theor Appl Genet. 2004, 109 (4): 725-732. 10.1007/s00122-004-1659-z.PubMedView ArticleGoogle Scholar
- Korzun V, Malyshev S, Voylokov AV, Börner A: A genetic map of rye (Secale cereale L.) combining RFLP, isozyme, protein, microsatellite and gene loci. Theor Appl Genet. 2001, 102 (5): 709-717. 10.1007/s001220051701.View ArticleGoogle Scholar
- Ma XF, Wanous MK, Houchins K, Milla MAR, Goicoechea PG, Wang Z, Xie M, Gustafson JP: Molecular linkage mapping in rye (Secale cereale L.). Theor Appl Genet. 2001, 102 (4): 517-523. 10.1007/s001220051676.View ArticleGoogle Scholar
- Senft P, Wricke G: An extended genetic map of rye (Secale cereale L.). Plant Breeding. 1996, 115 (6): 508-510. 10.1111/j.1439-0523.1996.tb00966.x.View ArticleGoogle Scholar
- Metzker ML: Sequencing technologies - the next generation. Nat Rev Genet. 2010, 11 (1): 31-46. 10.1038/nrg2626.PubMedView ArticleGoogle Scholar
- Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics. 2009, 10: 219-10.1186/1471-2164-10-219.PubMedPubMed CentralView ArticleGoogle Scholar
- Sun C, Li Y, Wu Q, Luo H, Sun Y, Song J, Lui EM, Chen S: De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis. BMC Genomics. 2010, 11: 262-10.1186/1471-2164-11-262.PubMedPubMed CentralView ArticleGoogle Scholar
- Hillier LW, Marth GT, Quinlan AR, Dooling D, Fewell G, Barnett D, Fox P, Glasscock JI, Hickenbotham M, Huang W, et al: Whole-genome sequencing and variant discovery in C. elegans. Nat Methods. 2008, 5 (2): 183-188. 10.1038/nmeth.1179.PubMedView ArticleGoogle Scholar
- Torres TT, Metta M, Ottenwalder B, Schlotterer C: Gene expression profiling by massively parallel sequencing. Genome Res. 2008, 18 (1): 172-177.PubMedPubMed CentralView ArticleGoogle Scholar
- Wicker T, Taudien S, Houben A, Keller B, Graner A, Platzer M, Stein N: A whole-genome snapshot of 454 sequences exposes the composition of the barley genome and provides evidence for parallel evolution of genome size in wheat and barley. Plant J. 2009.Google Scholar
- Lister R, Ecker JR: Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 2009, 19 (6): 959-966. 10.1101/gr.083451.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Novaes E, Drost D, Farmerie W, Pappas G, Grattapaglia D, Sederoff R, Kirst M: High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics. 2008, 9 (1): 312-10.1186/1471-2164-9-312.PubMedPubMed CentralView ArticleGoogle Scholar
- Korzun V, Malyshev S, Kartel N, Westermann T, Weber WE, Börner A: A genetic linkage map of rye (Secale cereale L.). Theor Appl Genet. 1998, 96 (2): 203-208. 10.1007/s001220050728.View ArticleGoogle Scholar
- Oztur ZN, Talame V, Deyholos M, Michalowski CB, Galbraith DW, Gozukirmizi N, Tuberosa R, Bohnert HJ: Monitoring large-scale changes in transcript abundance in drought- and salt-stressed barley. Plant Mol Biol. 2002, 48 (5-6): 551-573.PubMedView ArticleGoogle Scholar
- Kumar S, Blaxter ML: Comparing de novo assemblers for 454 transcriptome data. BMC Genomics. 2010, 11: 571-10.1186/1471-2164-11-571.PubMedPubMed CentralView ArticleGoogle Scholar
- Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WE, Wetter T, Suhai S: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004, 14 (6): 1147-1159. 10.1101/gr.1917404.PubMedPubMed CentralView ArticleGoogle Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437 (7057): 376-380.PubMedPubMed CentralGoogle Scholar
- Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Res. 1999, 9 (9): 868-877. 10.1101/gr.9.9.868.PubMedPubMed CentralView ArticleGoogle Scholar
- Riano-Pachón DM, Nagel A, Neigenfind J, Wagner R, Basekow R, Weber E, Mueller-Roeber B, Diehl S, Kersten B: GabiPD: the GABI primary database - a plant integrative 'omics' database. Nucleic Acids Res. 2009, D954-959. 37 Database.Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
- Rice Annotation Project: The Rice Annotation Project Database (RAP-DB): 2008 update. Nucleic Acids Res. 2008, 36 (suppl_1): D1028-1033.PubMed CentralGoogle Scholar
- Matsumoto T, Tanaka T, Sakai H, Amano N, Kanamori H, Kurita K, Kikuta A, Kamiya K, Yamamoto M, Ikawa H, et al: Comprehensive sequence analysis of 24,783 barley full-length cDNAs derived from 12 clone libraries. Plant Physiology. 2011, 156 (1): 20-28. 10.1104/pp.110.171579.PubMedPubMed CentralView ArticleGoogle Scholar
- Mochida K, Yoshida T, Sakurai T, Ogihara Y, Shinozaki K: TriFLDB: a database of clustered full-length coding sequences from Triticeae with applications to comparative grass genomics. Plant Physiology. 2009, 150 (3): 1135-1146. 10.1104/pp.109.138214.PubMedPubMed CentralView ArticleGoogle Scholar
- Jurka J: Repeats in genomic DNA: mining and meaning. Curr Opin Struct Biol. 1998, 8 (3): 333-337. 10.1016/S0959-440X(98)80067-5.PubMedView ArticleGoogle Scholar
- Spannagl M, Noubibou O, Haase D, Yang L, Gundlach H, Hindemitt T, Klee K, Haberer G, Schoof H, Mayer KF: MIPSPlantsDB--plant database resource for integrative and comparative plant genome research. Nucleic Acids Res. 2007, D834-840. 35 Database.Google Scholar
- Hunter JD: Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007, 9 (3): 90-95.View ArticleGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676. 10.1093/bioinformatics/bti610.Google Scholar
- Thiel T, Michalek W, Varshney RK, Graner A: Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet. 2003, 106 (3): 411-422.PubMedGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
- Marth GT, Korf I, Yandell MD, Yeh RT, Gu Z, Zakeri H, Stitziel NO, Hillier L, Kwok PY, Gish WR: A general approach to single-nucleotide polymorphism discovery. Nat Genet. 1999, 23 (4): 452-456. 10.1038/70570.PubMedView ArticleGoogle Scholar
- Huang W, Marth G: EagleView: a genome assembly viewer for next-generation sequencing technologies. Genome Res. 2008, 18 (9): 1538-1543. 10.1101/gr.076067.108.PubMedPubMed CentralView ArticleGoogle Scholar
- R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2004, ISBN 3-900051-07-0, [http://www.r-project.org]Google Scholar
- Zagrobelny M, Scheibye-Alsing K, Jensen NB, Moller BL, Gorodkin J, Bak S: 454 pyrosequencing based transcriptome analysis of Zygaena filipendulae with focus on genes involved in biosynthesis of cyanogenic glucosides. BMC Genomics. 2009, 10: 574-10.1186/1471-2164-10-574.PubMedPubMed CentralView 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
- Li H, Ruan J, Durbin R: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008, 18 (11): 1851-1858. 10.1101/gr.078212.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang S, Li R, Zhang Z, Li L, Gu X, Fan W, Lucas WJ, Wang X, Xie B, Ni P, et al: The genome of the cucumber, Cucumis sativus L. Nat Genet. 2009, 41 (12): 1275-1281. 10.1038/ng.475.PubMedView ArticleGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, Huang Q, Cai Q, Li B, Bai Y, et al: The sequence and de novo assembly of the giant panda genome. Nature. 2010, 463 (7279): 311-317. 10.1038/nature08696.PubMedPubMed CentralView ArticleGoogle Scholar
- Mayer KF, Taudien S, Martis M, Simkova H, Suchankova P, Gundlach H, Wicker T, Petzold A, Felder M, Steuernagel B, et al: Gene content and virtual gene order of barley chromosome 1H. Plant Physiol. 2009, 151 (2): 496-505. 10.1104/pp.109.142612.PubMedPubMed CentralView ArticleGoogle Scholar
- Coche T, Dewez M: Reducing bias in cDNA sequence representation by molecular selection. Nucleic Acids Res. 1994, 22 (21): 4545-4546. 10.1093/nar/22.21.4545.PubMedPubMed CentralView ArticleGoogle Scholar
- Emrich SJ, Barbazuk WB, Li L, Schnable PS: Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 2007, 17 (1): 69-73.PubMedPubMed CentralView ArticleGoogle Scholar
- Patanjali SR, Parimoo S, Weissman SM: Construction of a uniform-abundance (normalized) cDNA library. Proc Natl Acad Sci USA. 1991, 88 (5): 1943-1947. 10.1073/pnas.88.5.1943.PubMedPubMed CentralView ArticleGoogle Scholar
- Schafleitner R, Tincopa LR, Palomino O, Rossel G, Robles RF, Alagon R, Rivera C, Quispe C, Rojas L, Pacheco JA, et al: A sweet potato gene index established by de novo assembly of pyrosequencing and Sanger sequences and mining for gene-based microsatellite markers. BMC Genomics. 2010, 11: 604-10.1186/1471-2164-11-604.PubMedPubMed CentralView ArticleGoogle Scholar
- Bolot S, Abrouk M, Masood-Quraishi U, Stein N, Messing J, Feuillet C, Salse J: The 'inner circle' of the cereal genomes. Curr Opin Plant Biol. 2009, 12 (2): 119-125. 10.1016/j.pbi.2008.10.011.PubMedView ArticleGoogle Scholar
- Gaut BS: Evolutionary dynamics of grass genomes. New Phytologist. 2002, 154 (1): 15-28. 10.1046/j.1469-8137.2002.00352.x.View ArticleGoogle Scholar
- Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Huang X, et al: Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 2010, 20 (9): 1238-1249. 10.1101/gr.106120.110.PubMedPubMed CentralView ArticleGoogle Scholar
- Steuernagel B, Taudien S, Gundlach H, Seidel M, Ariyadasa R, Schulte D, Petzold A, Felder M, Graner A, Scholz U, et al: De novo 454 sequencing of barcoded BAC pools for comprehensive gene survey and genome analysis in the complex genome of barley. BMC Genomics. 2009, 10: 547-10.1186/1471-2164-10-547.PubMedPubMed CentralView ArticleGoogle Scholar
- Paux E, Sourdille P, Salse J, Saintenac C, Choulet F, Leroy P, Korol A, Michalak M, Kianian S, Spielmeyer W, et al: A physical map of the 1-gigabase bread wheat chromosome 3B. Science. 2008, 322 (5898): 101-104. 10.1126/science.1161847.PubMedView ArticleGoogle Scholar
- Bartos J, Paux E, Kofler R, Havrankova M, Kopecky D, Suchankova P, Safar J, Simkova H, Town C, Lelley T, et al: A first survey of the rye (Secale cereale) genome composition through BAC end sequencing of the short arm of chromosome 1R. BMC Plant Biol. 2008, 8 (1): 95-10.1186/1471-2229-8-95.PubMedPubMed CentralView ArticleGoogle Scholar
- Korzun V: Molecular markers and their application in cereals breeding. InProceedings of the workshop “Marker assisted selection: A fast track to increasegenetic gain in plant and animal breeding?": 17-18 October 2003; University ofTurin, Italy Edited by: Lanteri S 2003, 18-22, Electronic forum onbiotechnology in food and agriculture.Google Scholar
- Gupta PK, Varshney RK, Sharma PC, Ramesh B: Molecular markers and their applications in wheat breeding. Plant Breeding. 1999, 118 (5): 369-390. 10.1046/j.1439-0523.1999.00401.x.View ArticleGoogle Scholar
- Schulman AH: Molecular markers to assess genetic diversity. Euphytica. 2007, 158 (3): 313-321. 10.1007/s10681-006-9282-5.View ArticleGoogle Scholar
- Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O'Connell J, Moore SS, Smith TPL, Sonstegard TS, et al: Development and Characterization of a High Density SNP Genotyping Assay for Cattle. PLoS ONE. 2009, 4 (4): e5350-10.1371/journal.pone.0005350.PubMedPubMed CentralView ArticleGoogle Scholar
- Adams MW, Shank DB: The relationship of heterozygosity to homeostasis in maize hybrids. Genetics. 1959, 44 (5): 777-786.PubMedPubMed CentralGoogle 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.