Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids
© Jiang et al.; licensee BioMed Central. 2015
Received: 30 July 2014
Accepted: 8 January 2015
Published: 27 January 2015
Brassica includes many successfully cultivated crop species of polyploid origin, either by ancestral genome triplication or by hybridization between two diploid progenitors, displaying complex repetitive sequences and transposons. The U’s triangle, which consists of three diploids and three amphidiploids, is optimal for the analysis of complicated genomes after polyploidization. Next-generation sequencing enables the transcriptome profiling of polyploids on a global scale.
We examined the gene expression patterns of three diploids (Brassica rapa, B. nigra, and B. oleracea) and three amphidiploids (B. napus, B. juncea, and B. carinata) via digital gene expression analysis. In total, the libraries generated between 5.7 and 6.1 million raw reads, and the clean tags of each library were mapped to 18547–21995 genes of B. rapa genome. The unambiguous tag-mapped genes in the libraries were compared. Moreover, the majority of differentially expressed genes (DEGs) were explored among diploids as well as between diploids and amphidiploids. Gene ontological analysis was performed to functionally categorize these DEGs into different classes. The Kyoto Encyclopedia of Genes and Genomes analysis was performed to assign these DEGs into approximately 120 pathways, among which the metabolic pathway, biosynthesis of secondary metabolites, and peroxisomal pathway were enriched. The non-additive genes in Brassica amphidiploids were analyzed, and the results indicated that orthologous genes in polyploids are frequently expressed in a non-additive pattern. Methyltransferase genes showed differential expression pattern in Brassica species.
Our results provided an understanding of the transcriptome complexity of natural Brassica species. The gene expression changes in diploids and allopolyploids may help elucidate the morphological and physiological differences among Brassica species.
Polyploidy is an important factor in the evolution of many plants and has attracted considerable scientific attention for a long period of time. Many important economical crops are of polyploid origin, including wheat, cotton, and rapeseed . Cruciferae includes the model species Arabidopsis thaliana and the economically important Brassica crops. These important crops include three diploid Brassica species, namely, B. rapa (AA, 2n = 20; Chinese cabbage, turnip, turnip rape), B. nigra (BB, 2n = 16; black mustard), and B. oleracea (CC, 2n = 18; cauliflower, broccoli, kale), and three allopolyploids spontaneously derived from pairwise hybridization of the diploids, which are B. napus (AACC, 2n = 38; oilseed rape, swede), B. juncea (AABB, 2n = 36; abyssinian or Ethiopian mustard), and B. carinata (BBCC, 2n = 34; Indian or brown mustard) . Lysak et al. (2005) confirmed the chromosome triplication history of Brassica that corresponds to that of Arabidopsis . Cheung et al. (2009) found that the divergence of Arabidopsis and Brassica lineage was approximately 17 Mya , and the replicated Brassica subgenomes (probably the divergence of A/C from B genome) was diverged by 14.3 Mya . In addition, the A and C genomes were estimated with 3.7 Mya divergence. Woodhouse et al. (2014) stated that the B. rapa lineage underwent a whole-genome triplication of 5–9 Mya . For the allopolyploids, B. napus probably arose from the natural hybridization of A and C genomes around 10,000 years ago. However, when the hybridization between A and B genomes and between B and C genomes happened is still unclear. The precise ancestors of B. napus, B. juncea, and B. carinata are not yet identified . The duplication of gene segments reported on Brassica is explained as frequent loss, remote genome duplication, or unbalanced homologous recombination . During the divergence of Brassica species, the sub-functionalization and/or neo-functionalization of these paralogs coupled with novel gene interactions contribute significantly to genome evolution . Moreover, genetic mapping and sequencing analysis substantiate the triplication hypothesis of diploid Brassica genomes [9-12]. The comparative mapping of Brassica by using genetic markers has successfully revealed homologous rearrangements, translocations, and fusions that are crucial to the diversification of the A, B, and C genomes from A. thaliana [13-15].
Many linkage maps and karyotype analysis have identified extensive collinearity and genomic polymorphisms among Brassica genomes. Given the complexity of the gene copy number and syntenic conservation caused by polyploidization, Brassica genomes are difficult to study [16,17]. Identifying the genes related to specific traits based on the linkage maps is also challenging because of the complexity of the homologs and paralogs in polyploidy genomes [15,18]. Profiling arrays of A. thaliana are useful in the transcriptome analysis of Brassica . However, A. thaliana-based microarrays lack the resolution of Brassica specific genes and paralogous genes. Furthermore, Brassica microarrays were developed to confirm Brassica-specific expressed genes . Identifying different homologous copies of Brassica sequences is challenging for microarray expression analysis . Next-generation sequencing is an optimal method for genomic and transcriptomic studies and provides opportunities for polyploidy studies and enables the extensive genome profiling of crops with complex genomes, such as soybean, potato, tomato, cotton, maize, and common bean [21-26]. This technology also promotes sequencing analysis in Brassica; the genome sequence of B. rapa has already been released and annotated . The genome sequencing of B. oleracea, B. nigra, and B. napus is still in progress. However, the genome sequences of B. oleracea are available in the Basic Local Alignment Search Tool in Brassica database (www.brassica.info). The transcriptome profiling of B. napus has been analyzed via RNA sequencing [27-29]. This information is valuable for the investigation of Brassica genome evolution. Many technologies have been applied to quantify transcript abundance, including microarray, serial analysis of gene expression, digital gene expression (DGE), and RNA-seq. DGE and RNA-seq have been widely used to identify the molecular information of plant transcriptome and gene expression variation between comparable samples. DGE, as a well-known technique suitable to directly quantify transcript abundance counts, is optimized over RNA-seq because of its cost efficiency. RNA-seq is a flexible approach that can detect full-transcript sequence, alternative splicing, exon boundaries, and transcript abundance. However, each transcript in RNA-seq can be mapped multiple times, and the sequencing depth of RNA-seq is correlated with but is not equal to transcript abundance. Each read in DGE is expected with a sole hit on an RNA molecule. Therefore, DGE is better to represent rare transcripts or exclude transcripts with less interest than RNA-seq .
Many studies have analyzed the genomic and phenotypic changes in synthesized Brassica, particularly B. napus and hexaploid Brassica [31-33]. However, limited information is available for the natural species of Brassica. In the present research, we performed DGE analysis on three diploid Brassica species (B. rapa, B. nigra, and B. oleracea) and three allopolyploids (B. napus, B. juncea, and B. carinata) to determine the transcriptome changes after natural polyploidization. The expression profile of the genes in the six Brassica species was reported, and the multiple gene expression differences were observed. Differentially expressed genes (DEGs) are involved in a wide range of stress resistance and development processes. This study is the first transcriptomic research that identifies DEGs and the pathways involved in the natural polyploidization of the six Brassica species.
Statistics of categorization and abundance of DGE tags
Distinct Tag number
Tag Mapping to Gene
Tag Mapping to Gene
Distinct Tag number
Unambiguous Tag Mapping to Gene
Unambiguous Tag Mapping to Gene
Total% of clean tag
Unambiguous Tag Mapping to Gene
Distinct Tag number
Unambiguous Tag Mapping to Gene
Distinct Tag% of clean tag
% of ref genes
Unambiguous Tag-mapped Genes
Unambiguous Tag-mapped Genes
% of ref genes
Mapping to Genome
Mapping to Genome
Total% of clean tag
Mapping to Genome
Distinct Tag number
Mapping to Genome
Distinct Tag% of clean tag
Total% of clean tag
Distinct Tag number
Distinct Tag% of clean tag
The clean tags were then mapped onto the B. rapa genome with a maximum of one base-pair mismatch . Table 1 shows that the 1964909, 1990442, 1747843, 2253347, 1857572, and 1915305 tags in Br, Bg, Bo, Bn, Bj, and Bc were mapped to B. rapa genome, respectively. Statistical analysis of clean tag alignment was conducted, including the analysis of total clean tags and distinct clean tags (Additional files 2 and 3). More than 54% of the total clean tags and 42% of the distinct clean tags in each library were mapped onto the B. rapa genome. However, the tags mapped in the DGE library of Bg and Bc were lower than those in the other four libraries, which might be due to the divergence of the B genome to the A/C genome. Moreover, the tag mapping onto the B. rapa genome generated 19023 tag-mapped genes for Br, 16687 for Bg, 18547 for Bo, 19955 for Bn, 21995 for Bj, and 19436 for Bc. In total, approximately 61% of the genes in the B. rapa genome (25298 genes) could be mapped with unique tags (Additional file 4). Furthermore, we mapped all the clean tags of each DGE library to the genome of A. thaliana, and the summary and details of the mapping result are listed in Additional file 5. Only approximately 47% of A. thaliana genes (19557 genes) were successfully mapped, and the percent of unambiguous tag-mapped genes in A. thaliana is much lower than in B. rapa. The number of DGE tags in each library that well matched with Arabidopsis genome is also lower than that mapped to B. rapa. The difference in mapping rate is in accordance with the prediction that the A, B, and C genomes of Brassica diverged after the divergence of Arabidopsis and Brassica lineages . Thus, we chose the mapping information that used B. rapa as reference for further analysis. Saturation analysis was performed to check if the number of detected genes increased with sequencing amount. The result showed that the number of detected genes stopped increasing when the number of reads reached 2 million (Additional file 6). The distribution of the ratio of distinct tag copy numbers in each pair of libraries was analyzed. More than 90% of the distinct tags had ratios up to five folds (Additional file 7).
DEGs in Brassica diploids
DEGs among allopolyploids and ancestral diploid progenitors
Functional annotation of DEGs
Pathway enrichment analysis was performed on the expressed transcripts of the six DGE libraries. This analysis was performed by mapping all annotated genes in the KEGG database to search for significantly enriched genes involved in the metabolic or signal transduction pathways (Additional file 9). Among the genes with KEGG annotation, DEGs were identified in Bn compared with Br. In total, 894 DEGs were assigned to 109 KEGG pathways, and 13 of these pathways were significantly enriched with Q values ≤ 0.05 (red border region). The enriched pathways include metabolic, biosynthesis of secondary metabolites, and peroxisome. A similiar pathway enrichment was discovered in pair comparison of other libraries (Bo vs. Bn, Br vs. Bj, Bg vs. Bj, Bg vs. Bc, and Bo vs. Bc). The 1562 DEGs identified in Bn vs. Bo were assigned to 122 KEGG pathways, 15 of which were significantly enriched. The 1171 DEGs identified in Bj vs. Br were assigned to 116 KEGG pathways, the 2373 DEGs identified in Bj vs. Bg were assigned to 121 pathways, the 1975 DEGs identified in Bc vs. Bg were assigned to 120 pathways, and the 1639 DEGs identified in Bc vs. Bo were assigned to 117 pathways. All these pathways are involved in the process of plant growth and stress reaction, which are important for the morphological and physiological differences among the Brassica species. The biosynthesis of unsaturated fatty acids, which was significantly enriched in Bo vs. Bn and Bg vs. Bc, explains the different fatty acid compositions in Brassica species [35,36]. The DEGs identified in the peroxisome pathway were related to the improved stress ability of Bn and Bj.
Clustering of DEGs
Analysis of methyltransferase genes differentially expressed in Brassica
Epigenetic variation has an important function in the evolution of plants. DNA methylation is included in this variation and has received much attention in previous years. Proteins such as methyltransferase are considered important for plant methylation [37,38]. Thus, the putative methyltransferase and methylase genes from all DEGs in each comparison were filtered to understand the mechanism of the changes in DNA methylation in Brassica (Additional file 12). Two methyltransferase genes (Bra003928 and Bra020452) were differentially expressed in Br, Bg, and Bo, and 30 genes exhibited differential expression in Br vs. Bo/Bg vs. Bo/Bg vs. Br. One methyltransferase gene (Bra008507) was differentially expressed in Bn, Br, and Bo, and 23 genes exhibited differential expression in Br vs. Bn/Bo vs. Bn/Br vs. Bo. Five methyltransferase genes (Bra003396, Bra004391, Bra010977, Bra022603, and Bra024271) were differentially expressed in Bj, Br, and Bg, and 36 genes exhibited differential expression in Br vs. Bj/Bg vs. Bj/Bg vs. Br. Three methyltransferase genes (Bra003928, Bra004391, and Bra012494) were differentially expressed in Bc, Bg, and Bo, and 33 genes exhibited differential expression in Bg vs. Bc/Bo vs. Bc/Bg vs. Bo. The results showed that Bra003928 was significantly down-regulated in Br compared with Bg/Bo, which was up-regulated in Bn compared with Br and down-regulated in Bn compared with Bo. The expression of Bra003928 in Bj was higher than in Br and lower than in Bg. The expression of this methyltransferase gene in Bc was significantly up-regulated than in Bg and Bo. Moreover, Bra020452 was obviously down-regulated in Bo compared with Br/Bg. Different expression values were also examined in Brassica amphidiploids compared with their ancestral diploid parents. The methyltransferase gene was up-regulated in Bn compared with Br and Bo, which was also up-regulated in Bc compared with Bg and Bo. However, the expression value of Bra020452 in Bj was similar to that of Br and Bg.
Non-additive genes expressed in Brassica amphidiploids
Number of non-additively expressed genes in Brassica amphidiploids
No. of non-additively expressed genes Amphidiploid versus MPV
No. of non-additively expressed genes Amphidiploid > MPV
No. of non-additively expressed genes Amphidiploid < MPV
Br > Bo
Br < Bo
Br > Bg
Br < Bg
Bg > Bo
Bg < Bo
Differences in gene expression among Brassica diploids
Global Brassica research programs aim to explore valuable information on novel traits and to create stable lines. Br, Bg, and Bo exhibit many valuable agronomic traits including resistance against diseases and stress. These Brassica diploids have been suggested to have a triplication history . Based on the DGE data of diploid Brassica species, multiple genes exhibited different expressional patterns in Br, Bg, and Bo. Moreover, 8932 genes were expressed in the leaf tissue of all three diploids. In addition, 2438, 2244, and 2029 genes were uniquely expressed in Br, Bg, and Bo, respectively. However, 5417 DEGs were differently expressed among Brassica diploids including genes that encode proteins with binding function, transmembrane transporter, glycosyltransferase (Bra013229 and Bra016237), acyltransferase (Bra018329, Bra018412, Bra033107, Bra037338, and Bra037725), and methyltransferase (Bra036774, Bra003928, Bra005371, Bra018386, and Bra021673). Different copies of homologs in A, B, and C Brassica genomes and a comparative mapping of Brassica have revealed extensive differences among the A, B, and C genomes [15,44]. The transcriptome changes in Brassica diploids are possibly due to the polyploid history during species formation. These changes cause different genome dosages and sub-functionalization/neo-functionalization of genes, as well as morphological/physiological differences in Br, Bg, and Bo. This result would facilitate the gene exploration related to species-specific characters, thereby accelerating the breeding of Brassica.
Gene expression differences among allopolyploids and ancestral diploid progenitors
The expression differences in allotetraploids and diploids were analyzed by comparing the normalized value of genes expressed in each Brassica species. The results indicated that a larger number of gene expressional differences were identified between allotetraploids and diploids than among diploids. Although 11810 genes were conserved in Bn, Br, and Bo, 3102 DEGs were up-regulated in Bn compared with Br or Bo, and 1121 DEGs were down-regulated in Bn after natural polyploidization. Similarly, DEGs were also analyzed in Bj and Bc after polyploidization, and gene expressional changes were considered with parental preference. Zhao et al. (2013) also found that the gene expression in Brassica hexaploid is more similar to Br than to Bc . In accordance with previous results, a large number of DEGs in natural Bn and Br/Bo suggests that the gene expression differences in resynthesized Bn might be effectively inherited after polyploidization [32,45,46]. These results indicated that long-term and immediate responses to polyploidization are complicated. Genome-biased expression and silencing of genes are also observed in natural and synthetic cotton . Zhao et al. (2013) suggested that this observation might be due to the interactions of cytoplasm and nuclear genome during polyploidization . Hitherto, Bj and Bc have been used for the creation of synthesized Brassica allopolyploids (AABBCC, AABC, BBAC, and CCAB) . However, polyploidization of Bj and Bc have not been thoroughly studied. Given that the B genome possesses valuable agronomic traits including black-leg resistance , the study of B-genome evolution during the polyploidization of Bj and Bc is meaningful to the exploration of B-genome desirable traits. In the present research, many gene expressional differences in Bj and Bc after polyploidization were explored. The results showed that 5590 genes were differentially expressed in Bj, Br, and Bg, including genes that encode acyltransferase and metyltransferase. Moreover, the DEGs in Bj and Bc after polyploidization were more than that in Bn, which is partially due to the lack of a reference genome in this research. The B genome is more diversified than the A and C genomes ; hence, some B genome-specific information were neglected during the analysis of DGE data. Most of the commonly expressed genes in the diploids were non-additively expressed in allotetraploids, which is similar to the non-additive pattern in synthesized Bn and Arabidopsis [32,49]. The repression and activation of these genes in allotetraploids are responsible for the sub-functionalization of duplicated genes , which indicates a more complicated gene expression in allopolyploids rather than a simple combination of two genomes [46,48]. All of these non-additively expressed genes are important in studying the genome polyploidization of Brassica. The expressional changes in allotetraploids are necessary for the adjustment to the environment during natural polyploidization .
Putative methyltransferase genes in Brassica allotetraploids
One of the epigenetic variations is DNA methylation, which is important to genome activity. Plant polyploidization is normally accompanied with various phenotypic changes that are partially induced by new methylation changes during the interaction of different genomes . Extensive DNA methylation differences have been reported in synthesized Bn [45,51]. In the present research, 23, 36, and 33 methyltransferase genes were differentially expressed after the polyploidization of Bn, Bj, and Bc, respectively. The methyltransferase gene Bra020452 was up-regulated in Bn compared with Br and Bo, whereas the expression value of this gene in the early generations of resynthesized Bn was lower than that of natural Bn . This finding implies the complexity of gene activation and silencing mechanism during Brassica polyploidization. Whether these methylation changes in Brassica are responsible for the different expressions of DEGs in allotetraploids needs to be verified. Further research of these genes is important to comprehend the transcriptome changes during Brasssica polyploidization.
The genus Brassica includes a group of crops with important economic and nutritional values, and these crops are most closely related to Arabidopsis. Brassica and Arabidopsis have been confirmed to originate from a putative hexaploid ancestor. Triplication occurred after the divergence of Brassica and Arabidopsis to form a genomic complexity of Brassica . Three allopolyploids, which arose from the natural hybridization of A, B, and C genomes, introduced extensive genome reshuffling and gene loss, as well as neo- and sub-functionalization of duplicate genes . Therefore, the Brassica species are taken as an important model for the evolution of polyploids. Unfortunately, acknowledging the ancestors of Brassica polyploids is difficult, and these tetraploids are suspected to have multiple origins . Resynthesizing Brassica allopolyploids have provided an alternative way to study polyploidization, but the research in this area mainly focused on B. napus . An overview of the transcriptome differences among natural Brassica species would be interesting to gain initial knowledge on species diversification and polyploidization. This study demonstrated the DGE approach in characterizing the transcriptome of Brassica diploids and allotetraploids. However, the sampling from each genotype of Brassica may not capture the true range of phenotypes that exists within this genus. The DEGs during the evolution of Brassica diploids from a common hexaploid ancestor with Arabidopsis were revealed. Moreover, the DEGs in the natural polyploidization of Brassica allotetraploids from the hybridization of diploids were determined. Future work should concentrate on the function analysis of these DEGs, particularly stress resistance and methylase genes. Analysis should be performed to uncover the genetic and epigenetic mechanisms that would result in the phenotypic and physiologic differences among Brassica species. Elucidation of these differences is important to the discovery and utilization of genes for Brassica breeding and to shed light on Brassica evolution.
Diploid species B. rapa cv. Aikangqing (AA, 2n = 20), B. nigra cv. Marathi (BB, 2n = 16), and B. oleracea cv. Zhonghua Jielan (CC, 2n = 18) were used in the experiment. Amphidiploids B. napus cv. Yangyou 6 (AACC, 2n = 38), B. juncea cv. Luzhousileng (AABB, 2n = 36), and B. carinata cv. Dodolla (BBCC, 2n = 34) were also used as experimental materials. Plant materials were prepared and collected according to the procedures described by Kong et al. (2011) and Jiang et al. (2013) [32,53]. All plants were cultivated in climate chambers at 25°C, 16 h light/8 h dark photoperiod, and 70% relative humidity. The first true leaves from the three plants of each genotype were pooled at the same physiologic stage (28-day-old plants) and frozen at 80°C prior to use.
RNA preparation, illumina RNA-sequencing, and data processing
Total RNA was extracted from the leaves by using an RNAiso Plus (Takara) according to the manufacturer’s protocol. RNA concentrations were measured using a Qubit fluorometer, and the integrity was confirmed using a 2100 Bioanalyzer (Agilent Technologies). DGE libraries were prepared using an Illumina Gene Expression Sample Prep Kit, and NlaIII and MmeI were used for tag preparation. Single-chain molecules were fixed onto a Solexa sequencing chip (flowcell) and sequenced by an Illumina HiSeq™ 2000 System. Millions of raw 35 bp sequences were generated. Image analysis, base calling, generation of raw tags, and counting of tags were performed using the Illumina pipeline . Empty tags (no tag sequence between the adaptors), adaptors, low-quality tags (tags containing one or more unknown nucleotides “N”), and tags with a copy number of 1 were removed from the raw sequences to obtain clean tags (21 bp) that contain CATG.
Mapping of reads to the reference sequence
To identify the gene expression patterns in each genotype of Brassica, all clean tags were annotated by mapping onto the B. rapa genome  by using the SOAP2 software, and a maximum of one nucleotide mismatch is allowed . All tags mapped to reference sequences were filtered, and the remaining tags were designated as ambiguous tags. Mapping events on sense and antisense sequences were included in the data processing. For gene expression analysis, the number of expressed tags was calculated and then normalized to the number of transcripts per million tags (TPM) [34,55]. The DEGs were screened and used for mapping and annotation [56,57]. Gene annotation was conducted using Blast2GO . When the gene ontology (GO) database was searched, the GO categorization of all DEGs was displayed as three hierarchies for cellular component, molecular function, and biological process. Web gene ontology annotation plot was used to classify the genes mapped by each DGE library . Clustering analysis of differential gene expression pattern was also conducted using a hierarchical clustering explorer [60,61]. In the present study, statistical comparison of the gene expression was performed according to the script written by Audic and Claverie . False discovery rate (FDR) ≤ 0.001 and log2 ratio ≥1 were used as threshold to judge significance of gene expression difference . Pathway enrichment analysis of differential gene expression was conducted to understand further the gene function through blasting the KEGG database. A P-value of 0.05 was selected as the threshold for considering a gene set as significantly enriched.
Availability of supporting data
The sequence datasets that support the results of this article have been deposited in the Gene Expression Omnibus (GEO) at NCBI and are accessible under the accession GSE43246 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43246).
This study was supported by the National Key Basic Research Program of China (2015CB150201), the NSFC projects (31330057,31401414), the Jiangsu Province Science Foundation (BK20140478, 14KJB210008), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Innovation Team of Yangzhou University, China. We sincerely appreciated Prof. Dr. Rod Snowdon and Dr. Christian Obermeier for their helpful suggestions and discussions on the manuscript.
- Leitch AR, Leitch IJ. Genomic plasticity and the diversity of polyploidy plants. Science. 2008;320:481–3.PubMedView ArticleGoogle Scholar
- U N. Genomic analysis in Brassica with specific reference to the experimental formation of B. napus and peculiar mode of fertilization. Jpn J Bot. 1935;7:389–452.Google Scholar
- Lysak MA, Koch MA, Pecinka A, Schubert I. Chromosome triplication found across the tribe Brassiceae. Genome Res. 2005;15:516–25.PubMed CentralPubMedView ArticleGoogle Scholar
- Koch MA, Haubold B, Mitchell-Olds T. Comparative evolutionary analysis of chalcone synthase and alcohol dehydragenase loci in Arabidopsis, Arabis, and related genera (Brassicaceae). Mol Biol Evol. 2000;17:1483–98.PubMedView ArticleGoogle Scholar
- Woodhouse MR, Cheng F, Pires JC, Lisch D, Freeling M, Wang XW. Origin, inheritance, and gene regulatory consequences of genome dominance in polyploids. Proc Natl Acad Sci U S A. 2014;111(1):5283–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Cheung F, Trick M, Drou N, Lim YP, Park JY, Kwon SJ, et al. Comparative analysis between homologous genome segments of Brassica napus and its progenitor species reveals extensive sequence-level divergence. Plant Cell. 2009;21:1912–28.PubMed CentralPubMedView ArticleGoogle Scholar
- Osborn TC. The contribution of polyploidy to variation in Brassica species. Physiol Plant. 2004;121:531–6.View ArticleGoogle Scholar
- He X, Zhang J. Rapid subfunctionalization accompanied by prolonged and substantial neofunctionalization in duplicate gene evolution. Genetics. 2005;169:1157–64.PubMed CentralPubMedView ArticleGoogle Scholar
- Rana D, van den Boogaart T, O’Neill CM, Hynes L, Bent E, Macpherson L, et al. Conservation of the microstructure of genome segments in Brassica napus and its diploid relatives. Plant J. 2004;40:725–33.PubMedView ArticleGoogle Scholar
- Town CD, Cheung F, Maiti R, Crabtree J, Haas BJ, Wortman JR, et al. Comparative genomics of Brassica oleracea and Arabidopsis thaliana reveals gene loss, fragmentation and dispersal following polyploidy. Plant Cell. 2006;18:1348–59.PubMed CentralPubMedView ArticleGoogle Scholar
- Yang TJ, Kim JS, Kwon SJ, Lim KB, Choi BS, Kim JA, et al. Sequence-level analysis of the diploidization process in the triplicated FLOWERING LOCUS C region of Brassica rapa. Plant Cell. 2006;18:1339–47.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang XW, Wang HZ, Wang J, Sun RF, Wu J, Liu SY, et al. The genome of mesopolyploid crop species Brassica rapa. Nat Genet. 2011;43:1035–9.PubMedView ArticleGoogle Scholar
- Parkin IAP, Gulden SM, Sharpe AG, Lukens L, Trick M, Osborn TC, et al. Segmental structure of the Brassica napus genome based on comparative analysis with Arabidopsis thaliana. Genetics. 2005;171:765–81.PubMed CentralPubMedView ArticleGoogle Scholar
- Ramchiary N, Padmaja KL, Sharma S, Gupta V, Sodhi YS, Mukhopadhyay A, et al. Mapping of yield influencing QTL in Brassica juncea: implications for breeding of a major oilseed crop of dryland areas. Theor Appl Genet. 2007;115:807–17.PubMedView ArticleGoogle Scholar
- Panjabi P, Jagannath A, Bisht NC, Padmaja KL, Sharma S, Gupta V, et al. Comparative mapping of Brassica juncea and Arabidopsis thaliana using Intron Polymorphism (IP) markers: homoeologous relationships, diversification and evolution of the A, B and C Brassica genomes. BMC Genomics. 2008;9:113–31.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang J, Lydiate DJ, Parkin IAP, Falentin C, Delourme R, Carion PWC, et al. Integration of linkage maps for the amphidiploid Brassica napus and comparative mapping with Arabidopsis and Brassica rapa. BMC Genomics. 2011;12:101–20.PubMed CentralPubMedView ArticleGoogle Scholar
- Szadkowski E, Eber F, Huteau V, Lode M, Huneau C, Belcram H, et al. The first meiosis of resynthesized Brassica napus, a genome blender. New Phytol. 2010;186:102–12.PubMedView ArticleGoogle Scholar
- Gaeta RT, Pires JC. Homoeologous recombination in allopolyploids: the polyploidy ratchet. New Phytol. 2010;186:18–28.PubMedView ArticleGoogle Scholar
- Trick M, Cheung F, Drou N, Fraser F, Lobenhofer EK, Hurban P, et al. A newly-developed community microarray resource for transcriptome profiling in Brassica species enables the confirmation of Brassica-specific expressed sequences. BMC Plant Biol. 2009;9:50–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Edwards D, Batley J, Snowdon RJ. Accessing complex crop genomics with next-generation sequencing. Theor Appl Genet. 2013;126:1–11.PubMedView ArticleGoogle Scholar
- Schmutz J, Cannon SB, Schlueter J, Ma JX, Mitros T, Nelson W, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010;463:178–83.PubMedView ArticleGoogle Scholar
- The Potato Genome Sequencing Consortium. Genome sequence and analysis of the tuber crop potato. Nature. 2011;475:189–95.View ArticleGoogle Scholar
- The Tomato Genome Sequencing Consortium. The tomato genome sequence provides insights into fleshy fruit evolution. Nature. 2012;485:635–41.View ArticleGoogle Scholar
- Wang KB, Wang ZW, Li FG, Ye WW, Wang JY, Song GL, et al. The draft genome of a diploid cotton Gossypium raimondii. Nat Genet. 2012;44:1098–103.PubMedView ArticleGoogle Scholar
- Shan X, Li Y, Jiang Y, Jiang Z, Hao W, Yuan Y. Transcriptome profile analysis of maize seedling in response to high-salinity, drought and cold stresses by deep sequencing. Plant Mol Biol Rep. 2013;31:1485–91.View ArticleGoogle Scholar
- Mueller BSF, Sakamoto T, Silverira RDD, Zambussi-Carvalho PF, Pereira M, Pappas Jr GJ, et al. Differentially expressed genes during flowering and grain filling common bean (Phaseolus vulgaris) grown under drought stress conditions. Plant Mol Biol Rep. 2014;32:438–51.View ArticleGoogle Scholar
- Trick M, Long Y, Meng JL, Bancroft I. Single nucleotide polymorphysn (SNP) discovery in the polyploidy Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J. 2009;7:334–46.PubMedView ArticleGoogle Scholar
- Bancroft I, Morgan C, Fraser F, Higgins J, Wells R, Clissold L, et al. Dissecting the genome of the polyploidy crop oilseed rapa by transcriptome sequencing. Nat Biotechnol. 2011;29:762–6.PubMedView ArticleGoogle Scholar
- Higgins J, Magusin A, Trick M, Fraser F, Bancroft I. Use of mRNA-seq to discriminate contributions to the transcriptome from the constituent genome of the polyploidy crop species Brassica napus. BMC Genomics. 2012;13:247–60.PubMed CentralPubMedView ArticleGoogle Scholar
- Renaud G, LaFave MC, Liang J, Wolfsberg TG, Burgess SM. trieFinder: an efficient program for annotating digital gene expression (DGE) tags. BMC Bioinformatics. 2014;15:329–35.PubMed CentralPubMedView ArticleGoogle Scholar
- Gaeta RT, Yoo SY, Pires JC, Doerge RW, Chen ZJ, Osborn TC. Analysis of gene expression in resynthesized Brassica napus allopolyploids using Arabidopsis 70mer oligo microarrays. Plos One. 2009;4(3):e4760.PubMed CentralPubMedView ArticleGoogle Scholar
- Jiang JJ, Shao YL, Du K, Ran LP, Fang XP, Wang YP. Use of digital gene expression to discriminate gene expression differences in early generations of resynthesized Brassica napus and its diploid progenitors. BMC Genomics. 2013;14:72–82.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhao Q, Zou J, Meng JL, Mei SY, Wang JB. Tracing the transcriptome changes in synthetic trigenomic allohexaploids of Brassica using an RNA-seq approach. Plos One. 2013;8(7):e68883.PubMed CentralPubMedView ArticleGoogle Scholar
- Hoen PAC, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RHAM, Menezes RX, et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucl Acids Res. 2008;36:141–51.View ArticleGoogle Scholar
- Batista C, Barro L, Carvalho AM, Ferreira I. Nutritional and nutraceutical potential of rape (Brassica napus L. var. napus) and ‘tronchuda’ cabbage (Brassica oleracea L. var. costata) inflorescences. Food Chem Toxicol. 2011;49:1208–14.PubMedView ArticleGoogle Scholar
- Vuorinen AL, Kalpio M, Linderborg KM, Kortesniemi M, Lehto K, Niemi J, et al. Coordinate changes in gene expression and triacylglycerol composition in the developing seeds of oilseed rape (Brassica napus) and turnip rape (Brassica rapa). Food Chem. 2014;145:664–73.PubMedView ArticleGoogle Scholar
- Bird A. Perceptions of epigenetics. Nature. 2007;447:396–8.PubMedView ArticleGoogle Scholar
- Chinnusamy V, Zhu JK. Epigenetic regulation of stress responses in plants. Curr Opin Plant Biol. 2009;12(2):133–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Loven J, Orlando DA, Sigova AA, Lin CY, Rahi PB, Burge CB, et al. Revisiting global gene expression analysis. Cell. 2012;151(3):476–82.PubMed CentralPubMedView ArticleGoogle Scholar
- Coate JE, Doyle JJ. Quantifying whole transcriptome size, a prerequisite for understanding transcriptome evolution across species: an example from a plant allopolyploid. Genome Biol Evol. 2010;2:534–46.PubMed CentralPubMedView ArticleGoogle Scholar
- Coate JE, Doyle JJ. Variation in transcriptome size: are we getting the message? Chromosoma. 2014; DOI 10.1007/s00412-014-0496-3.Google Scholar
- Ilut DC, Coate JE, Luciano AK, Owens TG, May GD, Farmer A, et al. A comprehensive transcriptome study of an allotetraploid and its diploid progenitors illustrates the unique advantages and challenges of RNA-seq in plant species. Am J Bot. 2012;99(2):383–96.PubMedView ArticleGoogle Scholar
- Jackson S, Chen ZJ. Genomic and expression plasticity of polyploidy. Curr Opin Plant Biol. 2010;13:153–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Schranz ME, Lysak MA, Mitchell-Olds T. The ABC’s of comparative genomics in the Brassicaceae: building blocks of crucifer genomes. Trends Plant Sci. 2006;11:535–42.PubMedView ArticleGoogle Scholar
- Xu YH, Zhong L, Wu XM, Fang XP, Wang JB. Rapid alterations of gene expression and cytosine methylation in newly synthesized Brassica napus allopolyploids. Planta. 2009;229:471–83.PubMedView ArticleGoogle Scholar
- Szadkowski E, Eber F, Huteau V, Lode M, Coriton O, Jenczewski E, et al. Polyploid formation pathways have an impact on genetic rearrangement in resynthesized Brassica napus. New Phytol. 2011;191:884–94.PubMedView ArticleGoogle Scholar
- Adams KLR, Gronn R, Percifield R, Wendel JF. Genes duplicated by polyploidy show unequal contributions to the transcriptome and organ-specific reciprocal silencing. Proc Natl Acad Sci U S A. 2003;100:4649–54.PubMed CentralPubMedView ArticleGoogle Scholar
- Mason AS, Huteau V, Eber F, Coriton O, Yan GJ, Nelson MN, et al. Genome structure affects the rate of autosyndesis and allosyndesis in AABC, BBAC, CCAB Brassica interspecific hybrids. Chromosome Res. 2010;18:655–66.PubMedView ArticleGoogle Scholar
- Wang JL, Tian L, Lee HS, Wei NE, Jiang HM, Watson B, et al. Genome wide non-additive gene regulation in Arabidopsis allotetraploids. Genetics. 2006;172:507–17.PubMed CentralPubMedView ArticleGoogle Scholar
- Gaeta RT, Pires JC, Iniguez-Luy F, Leon E, Osborn TC. Genomic changes in resynthesized Brassica napus and their effect on gene expression and phenotype. Plant Cell. 2007;19:3403–17.PubMed CentralPubMedView ArticleGoogle Scholar
- Lukens LN, Pires JC, Leon E, Vogelzang R, Oslach L, Osborn T. Patterns of sequence loss and cytosine methylation within a population of newly synthesized Brassica napus allopolyploids. Plant Physiol. 2006;140:336–48.PubMed CentralPubMedView ArticleGoogle Scholar
- Flannery ML, Mitchell FJG, Coyne S, Kavanagh TA, Burke JI, Salamin N, et al. Plastid genome characterization in Brassica and Brassicaceae using a new set of nine SSR. Theor Appl Genet. 2006;113:1221–31.PubMedView ArticleGoogle Scholar
- Kong F, Mao SJ, Jiang JJ, Wang J, Fang XP, Wang YP. Proteomic changes in newly synthesized Brassica napus allotetraploids and their early generations. Plant Mol Biol Rep. 2011;29:927–35.View ArticleGoogle Scholar
- Li RQ, Yu C, Li YR, Lam TW, Yiu SM, Kristiansen K, et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009;25:1966–7.PubMedView ArticleGoogle Scholar
- Morrissy AS, Morin RD, Delaney A, Zeng T, McDonald H, Jones S, et al. Next-generation tag sequencing for cancer gene expression profiling. Genome Res. 2009;19:1825–35.PubMed CentralPubMedView ArticleGoogle Scholar
- Audic S, Claverie JM. The significance of digital gene expression profiles. Genome Res. 1997;7:986–95.PubMedGoogle Scholar
- Benjamini BY, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2011;29:1165–88.Google 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;21:3674–6.PubMedView ArticleGoogle Scholar
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, et al. WEGO: a web tool for plotting GO annotations. Nucl Acids Res. 2006;34:293–7.View ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression. Proc Natl Acad Sci U S A. 1998;95:14863–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Saldanha AJ. Java tree view-extensible visualization of microarray data. Bioinformatics. 2004;20:3246–8.PubMedView ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.