RNA-seq analyses of multiple meristems of soybean: novel and alternative transcripts, evolutionary and functional implications
- Lei Wang†1,
- Chenlong Cao†1,
- Qibin Ma3, 4,
- Qiaoying Zeng3, 4,
- Haifeng Wang1, 2,
- Zhihao Cheng1,
- Genfeng Zhu1, 2,
- Ji Qi1, 2,
- Hong Ma1, 2, 5,
- Hai Nian3, 4Email author and
- Yingxiang Wang1Email author
© Wang et al.; licensee BioMed Central Ltd. 2014
Received: 5 April 2014
Accepted: 5 June 2014
Published: 17 June 2014
Soybean is one of the most important crops, providing large amounts of dietary proteins and edible oil, and is also an excellent model for studying evolution of duplicated genes. However, relative to the model plants Arabidopsis and rice, the present knowledge about soybean transcriptome is quite limited.
In this study, we employed RNA-seq to investigate transcriptomes of 11 soybean tissues, for genome-wide discovery of truly expressed genes, and novel and alternative transcripts, as well as analyses of conservation and divergence of duplicated genes and their functional implications. We detected a total of 54,132 high-confidence expressed genes, and identified 6,718 novel transcriptional regions with a mean length of 372 bp. We also provided strong evidence for alternative splicing (AS) events for ~15.9% of the genes with two or more exons. Among them, 1,834 genes exhibited stage-dependent AS, and 202 genes had tissue-biased exon-skipping events. We further defined the conservation and divergence in expression patterns between duplicated gene pairs from recent whole genome duplications (WGDs); differentially expressed genes, tissue preferentially expressed genes, transcription factors and specific gene family members were identified for shoot apical meristem and flower development.
Our results significantly improved soybean gene annotation, and also provide valuable resources for functional genomics and studies of the evolution of duplicated genes from WGDs in soybean.
KeywordsSoybean RNA-seq Transcriptome Novel transcriptional regions Alternative splicing Meristem Transcription factors
Legumes are one of the three largest families of flowering plants, have diverged from a common ancestor around 50 million years ago (mya), and are major players for biological nitrogen fixation with important contributions to agricultural systems . Soybean [Glycine max (L.) Merr.] is the most important crop among legumes, providing ~70% dietary proteins and ~30% edible oil . Soybean has 20 pairs of chromosomes with a predicted genome size of 1,115-Mb  and is a paleopolyploid with two lineage-specific whole genomic duplications (WGD). The most recent WGD in soybean history occurred at about 13 million years ago (mya) , more recent than those in the history of the model plants Arabidopsis and rice. The recently sequenced soybean genome with 950 megabase (Mb) (~85% of the estimated total) of assembled sequences has revealed the presence of many thousands of recent paralogs due to WGD , making it an excellent model for study the evolution of duplicate genes.
The genome sequences allowed the annotation of over 66,000 genes, including 46,430 that were designated as high-confidence genes, and ~20,000 that were predicted bioinformatically with lower confidence . Recent transcriptome data provided evidence that soybean has a total of 55,616 transcripts . The relatively recent WGD and tandem duplications (TD) have resulted in a genome with ~75% of the genes being members of multi-gene families [4, 6, 7]. In particular, among the 46,430 high-confidence genes, there are 15,632 groups of 2–6 close paralogs, including tandemly repetitive genes, while 15,166 other genes are single copy . A recent study updated the duplicated genes to 17,547 pairs/groups, 8910 of them are pairs driven from the latest WGD . Furthermore, soybean genome has 38,581 repetitive elements occupying 59% of the genome, which covers most types of the plant’s transposable elements . However, the gene annotation in the soybean genome is still incomplete, and can be further improved by using information from genome-wide information of gene expression, including detection of novel transcribed regions and alternative splicing events.
The recent development of high-throughput RNA sequencing (RNA-seq) technologies has greatly improved sensitivity of transcriptomics and allowed detection of transcripts without a priori gene models [10–12]. RNA-seq has been applied extensively and successfully to explore genome-wide expression patterns, to identify novel transcripts, to detect alternative splicing events and trans-splicing RNA, in organisms from yeast to human [13–16]. Transcriptomics have also been performed extensively in the model plants Arabidopsis and rice, at the level of specific tissues and even single cell types, and for identification of novel transcribed regions and splicing patterns [17–22]. It has also been applied increasingly in other plant species, such as Zea mays , wheat , Fragaria vesca , as well as soybean [5, 8, 26, 27]. However, the current knowledge about soybean transcriptome is still incomplete. For example, many predicted genes in the soybean genome are not yet supported by expression information; also, relatively little is known about the patterns of alternative splicing events in soybean. In this study, we conducted RNA-seq for 11 soybean tissues and obtained large datasets for discovery of novel transcriptional regions and splicing transcripts, tissue preferentially or differentially expressed genes, transcription factors, conservation and divergence in expression patterns between duplicated gene pairs from recent whole genome duplications, as well as for functional implications by comparative transcriptome analyses.
Results and discussion
RNA-seq reveals ~ 54,000 transcriptionally active genes in soybean
To analyze the soybean (G. max) transcriptome as we had previously done for Arabidopsis and zebrafish [21, 28, 29], we collected 11 tissues from soybean, including root tip, hypocotyl, cotyledon, callus, shoot apical meristem at 6, 17 and 38 day stage (referred to as SAM6D, SAM17D and SAM38D for convenience), as well as the axillary meristem (referred to as AM), inflorescences before and after the meiotic stage (referred to as IBM and IAM, similar to the Arabidopsis inflorescences at stages 1–9 and 9–12, respectively), and open flower (referred to as OF), and obtained from 111 to 326 million reads of ~50 bp for each sample, with ~30-50 times more data than previous RNA-seq studies in soybean [5, 30]. Among them, 52.3%-71.6% of the reads were mapped to the G. max reference genome , ~90% of the mapped reads matched annotated soybean genes (in Additional file 1: Figure S1a and in Additional file 2: Table S1). Furthermore, the genic distribution of reads showed that 75% of mapped reads corresponded to exons, while the remaining reads were distributed among introns (10%), intergenic regions (7%) and the splice junctions (8%) (in Additional file 1: Figure S1b and in Additional file 2: Table S2). Therefore, our RNA-seq provides high-quality data for further exploration of the soybean transcriptome.
Analysis of the duplicated genes caused by latest WGD
Gene duplication is one of the most important mechanisms for understanding the evolutionary novelties, while divergence of the duplicated gene expression is highly correlated with their functional divergence . Soybean has served as an attractive model plant to study this aspect due to the occurrence of two recent WGDs. Based on the annotated genes in the soybean genome, we identified 2,713 and 37,746 duplicate genes (2–6 copies) caused by TD and WGD, respectively. These genes were further divided into five types regarding copies of 2 (9728/WGD and 574/TD), 3 (283/WGD and 90/TD), 4 (145/WGD and 13/TD), 5 (22/WGD and 8/TD) and 6 or more (7/WGD and 1/TD) (Figure 1d). Our 11 samples detected 35,569 (94.23%) and 2,139 (78.84%) duplicated genes by WGD and TD, indicating that the vast majority of the existing duplicated genes by WGD are expressed. To further investigate the expression difference among tested tissues between duplicated genes, we focused on the 9,728 pairs of paralogs from WGD. Our results showed that 8,768 pairs had detectable expression for both copies, 701 pairs showed expression in one of the copies, while 259 pairs has no detectable expression in either copy (Figure 1e). Among the 8,768 two-copy expressed genes (unless otherwise noted, paralogs mentioned in following text refer to the pairs), t-test statistical analysis showed that 4,407 of them (50.26%) showed significant expression difference between the two paralogs (p < 0.05) (Figure 1f and in Additional file 2: Table S3), indicative of regulatory subfunctionalization and/or neofunctionalization, whereas the other 4,361 paralogs (49.74%) had no significant difference each other (p < 0.05) (Figure 1f, in Additional file 2: Table S3), suggesting functional conservation and possible redundancy between two paralogs. In addition, the lack of expression for one copy of the 701 pairs with single copy expression suggested that they are likely candidates for regulatory nonfunctionalization, although some of them are possibly additional examples of sub/neofunctionalization as they might be expressed in other tissues not sampled here or under different growth conditions. Similar trends were also found for 574 TD genes (Figure 1e).
Transcriptome analysis identifies ~6,718 high-confidence NTRs in soybean
RNA-Seq has been widely applied to identify NTRs in S. cerevisiae and S. pombe [13, 34], Arabidopsis , rice [19, 22], mouse  and human . Our transcriptome data showed that a large number of reads mapped to annotated intergenic regions, suggesting that they are potential NTRs. We therefore assembled the mapped reads to obtain 19,752 NTRs. By placing stringent requirements for the size >150 bp and read number >10, as well as being detected in at least two samples, we obtained a total of 6,718 high-confidence NTRs with a mean length of ~372 bp, 2,265 of which were reported previously .
Among 4,949 nTUs, 2,326 (47%) were supported by the annotated 1,532 soybean ESTs in National Center for Biotechnology Information (NCBI) (in Additional file 2: Table S6), but not currently annotated in the G. max genome. 698 of the other 2,623 (53%) nTUs were found to have homologs from other species (in Additional file 2: Table S7), suggesting that they might be conserved genes. Only 47 nTUs were located in the transposable element (TE) regions, indicating TE activity (in Additional file 2: Table S8). To identify potential non-coding RNAs from the 2,623 nTUs, we performed a BLASTN alignment using nTUs against Rfam, and found that 40 nTUs were annotated non-coding RNA as either tRNA, rRNA, snoRNA or miRNA (in Additional file 2: Table S9). For example, XLOC_015015 was annotated as miR159, suggesting that some of the novel nTUs are functional as non-coding RNAs. The nature of the remaining nTUs needs to be further investigated.
We then analyzed the spatial-temporal distribution of 4,949 nTUs in the 11 tissues (in Additional file 1: Figure S3), and found that 1,393 of them showed constitutive expression, while 3,556 were tissue preferentially expressed. Interestingly, the current soybean genome only annotates one CLAVATA1A (CLV1A) gene as the ortholog of the Arabidopsis CLV1 gene regulating meristem sizes , while the identified XLOC_047893 nTU is a paralog of CLV1A in soybean. Both genes showed specific expression in SAM17D and SAM38D, suggesting a redundant function of CLV1A and XLOC_047893 for regulating SAM in soybean.
Alternative spliced transcripts and their differential expression
AS is one of major contributors for generation of proteomic and functional complexity in higher organisms , but at present little is known about AS events in soybean. Among the previously annotated 66,210 soybean genes, 52,460 genes have at least two exons . We identified a total of 12,810 AS events covering 7,084 genes (including 504 paralogs) in the 11 samples (in Additional file 2: Table S10), indicating that ~15.9% of multiple-exon genes have AS patterns. This is significantly lower than 48% observed in either Arabidopsis or rice [19, 20, 22]. A possible reason is that soybean has experienced two recent genome duplications, which resulted in many retained duplicated genes that are also a major source of proteomic and functional complexity .
Classification of AS in soybean
Type of events
A5SS or A3SS
IR1 + IR2
A5SS or A3SS
IR1 or IR2
A5SS + A3SS +ES
A5SS + A3SS +ES1 + ES2
Comparison of tissue transcriptomes indicative of conservation and divergence
Identification of tissue-preferentially expressed genes
As shown in Figure 4, AM was highly similar to both SAM38D and IBM, pairwise comparison would probably miss many genes active in meristems. To identify PEGs in these meristems (but not specifically in one of them), we grouped similar meristems together and detected 821 genes (20 paralogs). GO annotation indicated that the most enriched categories were associated with flower development and regulation, floral transition from vegetative to reproductive phase, or meristematic phase transition and transcription regulation (Figure 5, in Additional file 2: Tables S23 and S24), which is in good agreement with previous reports in soybean [50, 51]. For instance, the PEGs included several homologs of SHORT VEGETATIVE PHASE (SVP) that specify the reproductive organ identity and control flowering time in Arabidopsis and rice [52, 53] and genes involvement in WUSCHEL (WUS) regulatory network essential for SAM maintenance . We also found homologs (Gm14g15820 and Gm7g30920) of genes for auxin synthesis and response, such as YUCCA4, in accordance with the fact that the Arabidopsis YUCCA4 expression is restricted to the SAM and flower meristems or young floral primordia , as well as 20 genes related to auxin-responsive genes regulating SAM development . These good agreements between our GO enrichment results and known functions in meristem suggested the reliability of the collected samples for SAM and conservation of molecular mechanisms for controlling SAM between Arabidopsis and soybean.
Accordingly, AM, IBM and IAM together had 1,325 PEGs (60 paralogs) (Figure 5, in Additional file 2: Table S25), which were mainly involved in reproductive processes, such as floral organ determination and development, stamen development, tapetal layer development, pollen development (Figure 5). For instance, in addition to the identification of several flower organ identity genes from ABC model (in Additional file 2: Table S26), we also found genes specifically for meiosis, such as MS5 (Gm08g47070 and Gm18g38060) and MMD1 (Gm14g39310 and Gm02g41020) . Unlike the expression of Arabidopsis MS5 and MMD1 genes restricted in meiocytes, the soybean homologs showed high expression in AM, suggesting a possible unknown function in soybean. Interestingly, the Arabidopsis DREB1B is one of the critical regulators for cold responses, and is also widely expressed , whereas the soybean homologs (Gm11g19340 and Gm12g09130) showed special expression in AM, IBM and IAM, but not in other vegetative tissues, suggesting it might have gained novel functions in reproductive development in soybean. In addition, one homolog of DREB1A (Gm17g14110) was also identified, consistent with a recent novel discovery that the Arabidopsis DREB1A gene is important for flower development especially under unfavorable conditions .
Finally, open flower had 1,288 PEGs (78 paralogs) enriched for reproductive cellular process, cell wall modification, pollen tube growth, pollination and signal transduction (Figure 5, in Additional file 2: Tables S27 and S28). Particularly, at least 50 genes (most in two copies) encoded signal transduction proteins for interaction between the pollen and ovary, such as SNAP receptor 124, leucine-rich repeat protein kinase, ROP BINDING PROTEIN KINASES 1, calcium-dependent protein kinase 24 [60, 61].
Dynamic reprograming of soybean SAM transcriptome
Distinct expression of transcription factors in SAM
The most extensively characterized function of SPLs is promotion of the transition from vegetative and reproductive growth, and particularly for SPL3-5 in clade VI of Arabidopsis . Remarkably, this clade contains 15 SPLs from soybean, 14 of which showed high expression in SAM (Figure 8) and were nearly undetectable in other tissues, suggesting the conservation of molecular mechanism in regulation of the transition from vegetative and reproductive growth between Arabidopsis and soybean. The last two clades of VII and VIII include AtSPL13 and AtSPL9/15, respectively (in Additional file 1: Figure S5). AtSPL13 has been implicated in leaf development, while the AtSPL9 and AtSPL15 play a partially redundant role in phase transition [72, 73]. The seven and four SPL genes in soybean in clade VII and VIII had very similar gene expression patterns in SAM and floral tissues, consistent with the functions of the Arabidopsis homologs. Together, 7 paralogs pairs were included in SPL family (Figure 8). Comparison of expression patterns suggests that the paralogs in a pair might have undergone sub-functionalization, further supporting the idea that sub-functionalization might be predominant event for duplicated gene after WGD in soybean.
Different from G1, G2 mainly contained MADS, AS, BTB/POZ, WRKY, C2C2 (Zn) YABBY (Figure 7b). It has been reported that MADS-box gene family is not only key repressors or activators for flowering transition, but also as master regulators of reproductive organ identities . Our data detected 101 MADS-box genes during flower development (Figure 7b), such as Gm01g08150, Gm04g42420, Gm08g12730 and Gm08g27670, which are homologs of AP1, PI, AG and SEP2, respectively, consistent with their known function in floral organ identity. Therefore, the functions of the MADS-box gene family for regulation of flower development are likely conserved between soybean and Arabidopsis. In contrast, BZIP, C3H-type1 (Zn), C2H2 (Zn) Dof, AUX-IAA-ARF, LIM and CCAAT gene families were preferentially expressed in OF (Figure 7b). Many studies showed that auxin-dependent transcriptional regulation requires the auxin/indole-3-acetic acid (Aux/IAA) and auxin response factor (ARF) families of TFs  and formation of Aux/IAA-ARFs heterodimers repress auxin signaling . In addition to the known role of auxin in Arabidopsis pollen development, pollination and fertilization also seem to need increased auxin levels . Indeed, we detected 33 differentially expressed members in OF, suggesting Aux/IAA-ARF regulatory pathway for later reproductive development is also conserved. However, the function of other enriched TFs in OF is still largely unknown.
The paleopolyploidy and rapid divergence of the soybean genome makes it an ideal genome for evolutionary analyses. However, the present soybean genome annotation and gene expression message are incomplete. This study presents the overall transcriptional landscape and provides extensive evidence that transcriptional regulation in soybean is vastly more complex than previously expected. The data significantly improves annotation of the soybean genes predicted in genome, as well as provides essential sources for studying the expression level between duplicated genes by latest WGD and functional genome in soybean.
Plant material and growth condition
Soybean (Glycine max) plant materials used here were from the HX3 cultivar. Three-day after germination and older seedlings were generated on a quartz sand culture under a 14 h/10 h light/dark regime at 28°C (in light)/25°C (in dark) with 70% relative humidity and used to obtain root tips of 0.2-0.3 cm in length. Similarly prepared four-day seedlings were used to collect cotyledons and hypocotyls. SAMs (shoot apical meristems) at 6, 17 and 38 days after germination were collected from soil grown plants, using tweezers and a dissecting needle. Axillary meristems were collected under the second or third internode of shoot apex of soil grown plants after 38-day germination. Each meristem RNA-seq sample included materials from ~1000 plants. For inflorescences pre- or post-meiotic stage, we defined an appropriate size of inflorescence by analyzing tetrad and chromosome spread, and then dissected the inflorescences from 45-day soil-grown plants under microscopy, and separated open flowers from unopened buds. Callus induction was carried out using the cotyledonary-node method as described previously  with minor modification . All samples were taken at room temperature 25°C and quickly placed in liquid nitrogen.
RNA isolation, RNA-seq library preparation and sequencing, real-time RT-PCR
RNA isolation, RNA-seq library preparation and sequencing were performed using the protocols described previously [21, 28, 29]. RT-PCR was carried out according to a previous procedure [21, 29]. Primers used in this study were listed in Additional file 2: Table S30. Fold change for gene expression was calculated by normalizing Ct values at each developmental stage against endogenous control (Gmβ-actin: Gm15g05570) using the 2-ΔΔCt method .
Mapping of reads and calculation of gene expression level
Reads obtained by SOLiD sequencing were aligned against soybean genome assembly version 9 (Glyma1.1; http://www.phytozome.net/), using the Lifescope software package. Lifescope used a seed-and-extend approach to map reads against the reference. The normalized gene expression level was calculated as Reads Per Kilo-base of mRNA length per Millions of mapped reads (RPKM) by the GFOLD V1.0.7 software . A comparison between the expression levels of genes and intergenic regions was used to find a threshold for detectable expression above background. The value of 0.25 RPKM was the threshold classifying annotated genes into two large clusters, and was defined as the threshold between “expressed” and “unexpressed”. Next, DEGs (differentially expressed genes) were defined using GFOLD diff program (GFOLD >1 or GFOLD < -1; log2 (fold change) >2 or log2 (fold change) < -2). The preferentially expressed gene for specific tissue was defined by meeting at least GFOLD >1 and RPKM > 4 in the tissue in question compared to all the other tissues.
Identification of putative paralogs and differential expression analysis
We used the MCScanx software  to identify potential paralogous clusters. WGD genes and TD genes were detected with default parameters. The differential expression of paralogs was analyzed based on the Log2-normalized RPKM values across 11 samples and t-test to assess statistical significance.
A correlation matrix was prepared using the R software and Pearson’s correlation coefficient as the statistical metric to compare the values of the whole transcriptome (54,132 genes) in 11 samples. Log2-normalized RPKM values from RNA-seq dataset were used to create the correlation matrix, and then R scripts were used to analyze the correlation among samples. Correlation coefficient values were converted into distance to define the height scale of the dendrogram. The heat map of the correlation was implemented by the pheatmap (Pretty Heatmaps) function in the pheatmap package (R version, 2.15, pheatmap version, 0.6.1; R Core Team, Vienna, Austria).
Discovery of NTRs and RT-PCR validation
We used the Cufflinks software  to assembly transcripts using high quality mapped reads (no mismatch) from Lifescope, and obtained intergenic transcripts based on Class Code “u” comparing the annotated soybean genome (http://www.plantgdb.org/GmGDB/), using the following criteria: (1) larger than 150 bp in size, (2) reads number > 10 and (3) supported by detection in at least two tissue samples. Based on these criteria, we obtained ~6,718 high confidence NTRs. RNA-seq reads were visualized on the soybean genome using the inGap software . 10 randomly selected NTRs were verified by performing RT-PCR using specific primers designed for this study (in Additional file 2: Table S37). Additionally, the BLAST was used to identify nTUs agaist the Rfam [84, 85].
Alternative splicing analysis
We used the ASTALAVISTA software  to quantify the type of AS events based on the assembled transcripts by the Cufflinks software. MISO  and a MISO pipeline were used, respectively, to evaluate the expressed transcripts and their differential expression across the 11 samples. First, we need to generate two file libraries:annotation file of alternative splicing events and indexed alignment file. For the AS events file, we use MISO to measure differential expression by Bayesian inference. For the alignment file, the high quality-filtered reads for the different samples were aligned against soybean genome with Lifescope using the soybean genome feature file to improve the detection of splicing junctions. A combination of different cut-offs and filters were tested yielding the MISO output, culminating in the use of a Bayes factor of 0.7 as cut-off value to detect differential AS events. RNA-seq reads were visualized on the soybean genome using the sashimi plot tool with RPKM.
We used the SOM (Self-Organizing Maps) method  for both clustering and visualization of the patterns of DEGs during SAM and flower development. The SOM Toolbox for MATLAB developed by the Laboratory of Information and Computer Science at the Helsinki University of Technology was used (http://www.cis.hut.fi/projects/somtoolbox/). One SOM was fitted to mean normalized log2-transformed (RPKM values) gene expression estimates from the data of a specific developmental stage/tissue. Regions in the SOM corresponding to characteristic and coherent expression patterns were afterward identified by k-means clustering of the SOM units (k = 8 for the developmental data set). The top half of more coherent SOM units was identified by means of silhouette coefficients resulting in the revealing clusters. Finally, we visualized prototypical gene expression patterns for each SOM region. Genes are plotted with a best-matching SOM unit within one of these regions.
GO enrichment analysis
Gene lists were analyzed for gene ontology (GO) enrichment using the online tools AgriGO (http://bioinfo.cau.edu.cn/agriGO/analysis.php) with Fisher’s exact test and false discovery rate (FDR) correction . Transcription factor (TF) family annotations were downloaded from the soybean genome annotation, containing 5,671 TFs in 63 families for Glycine max . The heat map of the expressed TFs was generated by a heatmap.2 function in the gplots package (R version, 2.15, R Core Team, Vienna, Austria). In addition, all gene functional descriptions were from modified MapMan annotations .
Availability of supporting data
The data sets supporting the results of this article are available in the NCBI GenBank repository [http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA241144] and in the NCBI SRA repository [http://www.ncbi.nlm.nih.gov/sra/?term=SRP040057].
Shoot apical meristem
Polymerase chain reaction
Quantitative reverse transcription polymerase chain reaction
Novel transcribed regions.
The authors gratefully acknowledge financial support from the National Key Project for Research on Transgenic Biology in China (2014ZX0800921B-001), the Zhuoxue Plan of Fudan University, and the Shanghai Committee of Science and Technology Fund for 2013 Qimingxing Project (13QA1400200) (to Y.W.), and the China Agricultural Research System (to H.N.).
- Shoemaker RC, Schlueter J, Doyle JJ: Paleopolyploidy and gene duplication in soybean and other legumes. Curr Opin Plant Biol. 2006, 9 (2): 104-109.View ArticlePubMedGoogle Scholar
- Lam HM, Xu X, Liu X, Chen W, Yang G, Wong FL, Li MW, He W, Qin N, Wang B, Li J, Jian M, Wang J, Shao G, Wang J, Sun SS, Zhang G: Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet. 2010, 42 (12): 1053-1059.View ArticlePubMedGoogle Scholar
- Findley SD, Cannon S, Varala K, Du J, Ma J, Hudson ME, Birchler JA, Stacey G: A fluorescence in situ hybridization system for karyotyping soybean. Genetics. 2010, 185 (3): 727-744.PubMed CentralView ArticlePubMedGoogle Scholar
- Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL, Song Q, Thelen JJ, Cheng J, Xu D, Hellsten U, May GD, Yu Y, Sakurai T, Umezawa T, Bhattacharyya MK, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S, Goodstein D, Barry K, Futrell-Griggs M, Abernathy B, Du J, Tian Z, Zhu L, et al: Genome sequence of the palaeopolyploid soybean. Nature. 2010, 463 (7278): 178-183.View ArticlePubMedGoogle Scholar
- Libault M, Farmer A, Joshi T, Takahashi K, Langley RJ, Franklin LD, He J, Xu D, May G, Stacey G: An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. Plant J. 2010, 63 (1): 86-99.PubMedGoogle Scholar
- Schlueter JA, Lin JY, Schlueter SD, Vasylenko-Sanders IF, Deshpande S, Yi J, O'Bleness M, Roe BA, Nelson RT, Scheffler BE, Jackson SA, Shoemaker RC: Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing. BMC Genomics. 2007, 8: 330-PubMed CentralView ArticlePubMedGoogle Scholar
- Gill N, Findley S, Walling JG, Hans C, Ma J, Doyle J, Stacey G, Jackson SA: Molecular and chromosomal evidence for allopolyploidy in soybean. Plant Physiol. 2009, 151 (3): 1167-1174.PubMed CentralView ArticlePubMedGoogle Scholar
- Roulin A, Auer PL, Libault M, Schlueter J, Farmer A, May G, Stacey G, Doerge RW, Jackson SA: The fate of duplicated genes in a polyploid plant genome. Plant J. 2012, 73 (1): 143-153.View ArticlePubMedGoogle Scholar
- Du J, Tian Z, Hans CS, Laten HM, Cannon SB, Jackson SA, Shoemaker RC, Ma J: Evolutionary conservation, diversity and specificity of LTR-retrotransposons in flowering plants: insights from genome-wide analysis and multi-specific comparison. Plant J. 2010, 63 (4): 584-598.View ArticlePubMedGoogle Scholar
- Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008, 321 (5891): 956-960.View ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63.PubMed CentralView ArticlePubMedGoogle Scholar
- Martin JA, Wang Z: Next-generation transcriptome assembly. Nat Rev Genet. 2011, 12 (10): 671-682.View ArticlePubMedGoogle Scholar
- Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M: The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008, 320 (5881): 1344-1349.PubMed CentralView ArticlePubMedGoogle Scholar
- Aanes H, Winata CL, Lin CH, Chen JP, Srinivasan KG, Lee SG, Lim AY, Hajan HS, Collas P, Bourque G, Gong Z, Korzh V, Alestrom P, Mathavan S: Zebrafish mRNA sequencing deciphers novelties in transcriptome dynamics during maternal to zygotic transition. Genome Res. 2011, 21 (8): 1328-1338.PubMed CentralView ArticlePubMedGoogle Scholar
- Graveley BR, Brooks AN, Carlson JW, Duff MO, Landolin JM, Yang L, Artieri CG, van Baren MJ, Boley N, Booth BW, Brown JB, Cherbas L, Davis CA, Dobin A, Li R, Lin W, Malone JH, Mattiuzzo NR, Miller D, Sturgill D, Tuch BB, Zaleski C, Zhang D, Blanchette M, Dudoit S, Eads B, Green RE, Hammonds A, Jiang L, Kapranov P, et al: The developmental transcriptome of Drosophila melanogaster. Nature. 2011, 471 (7339): 473-479.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB: Alternative isoform regulation in human tissue transcriptomes. Nature. 2008, 456 (7221): 470-476.PubMed CentralView ArticlePubMedGoogle Scholar
- Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, Wong WK, Mockler TC: Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res. 2010, 20 (1): 45-58.PubMed CentralView ArticlePubMedGoogle Scholar
- Jiao Y, Tausta SL, Gandotra N, Sun N, Liu T, Clay NK, Ceserani T, Chen M, Ma L, Holford M, Zhang HY, Zhao H, Deng XW, Nelson T: A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nat Genet. 2009, 41 (2): 258-263.View ArticlePubMedGoogle Scholar
- Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Li W, Huang X, Han B: Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 2010, 20 (9): 1238-1249.PubMed CentralView ArticlePubMedGoogle Scholar
- Marquez Y, Brown JWS, Simpson C, Barta A, Kalyna M: Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res. 2012, 22 (6): 1184-1195.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang H, Lu P, Wang Y, Ma H: The transcriptome landscape of Arabidopsis male meiocytes from high-throughput sequencing: the complexity and evolution of the meiotic process. Plant J. 2011, 65 (4): 503-516.View ArticlePubMedGoogle Scholar
- Zhang GJ, Guo GW, Hu XD, Zhang Y, Li QY, Li RQ, Zhuang RH, Lu ZK, He ZQ, Fang XD, Chen L, Tian W, Tao Y, Kristiansen K, Zhang XQ, Li SG, Yang HM, Wang J, Wang J: Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome. Genome Res. 2010, 20 (5): 646-654.PubMed CentralView ArticlePubMedGoogle Scholar
- Li P, Ponnala L, Gandotra N, Wang L, Si Y, Tausta SL, Kebrom TH, Provart N, Patel R, Myers CR, Reidel EJ, Turgeon R, Liu P, Sun Q, Nelson T, Brutnell TP: The developmental dynamics of the maize leaf transcriptome. Nat Genet. 2010, 42 (12): 1060-1067.View ArticlePubMedGoogle Scholar
- Krasileva KV, Buffalo V, Bailey P, Pearce S, Ayling S, Tabbita F, Soria M, Wang S, Akhunov E, Uauy C, Dubcovsky J: Separating homeologs by phasing in the tetraploid wheat transcriptome. Genome Biol. 2013, 14 (6): R66-PubMed CentralView ArticlePubMedGoogle Scholar
- Kang C, Darwish O, Geretz A, Shahan R, Alkharouf N, Liu Z: Genome-scale transcriptomic insights into early-stage fruit development in woodland strawberry Fragaria vesca. Plant Cell. 2013, 25 (6): 1960-1978.PubMed CentralView ArticlePubMedGoogle Scholar
- Liew LC, Singh MB, Bhalla PL: An RNA-seq transcriptome analysis of histone modifiers and RNA silencing genes in soybean during floral initiation process. PLoS One. 2013, 8 (10): e77502-PubMed CentralView ArticlePubMedGoogle Scholar
- Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, Graham MA, Cannon SB, May GD, Vance CP, Shoemaker RC: RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biol. 2010, 10: 160-PubMed CentralView ArticlePubMedGoogle Scholar
- Yang H, Zhou Y, Gu J, Xie S, Xu Y, Zhu G, Wang L, Huang J, Ma H, Yao J: Deep mRNA sequencing analysis to capture the transcriptome landscape of zebrafish embryos and larvae. PLoS One. 2013, 8 (5): e64058-PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Y, Xiao R, Wang H, Cheng Z, Li W, Zhu G, Ma H: The Arabidopsis RAD51 paralogs RAD51B, RAD51D and XRCC2 play partially redundant roles in somatic DNA repair and gene regulation. New Phytol. 2014, 201 (1): 292-304.View ArticlePubMedGoogle Scholar
- Libault M, Farmer A, Brechenmacher L, Drnevich J, Langley RJ, Bilgin DD, Radwan O, Neece DJ, Clough SJ, May GD, Stacey G: Complete transcriptome of the soybean root hair cell, a single-cell model, and its alteration in response to Bradyrhizobium japonicum infection. Plant Physiol. 2010, 152 (2): 541-552.PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5 (7): 621-628.View ArticlePubMedGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008, 18 (9): 1509-1517.PubMed CentralView ArticlePubMedGoogle Scholar
- Taylor JS, Raes J: Duplication and divergence: the evolution of new genes and old ideas. Annu Rev Genet. 2004, 38: 615-643.View ArticlePubMedGoogle Scholar
- Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J, Bahler J: Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008, 453 (7199): 1239-1243.View ArticlePubMedGoogle Scholar
- Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR: Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008, 133 (3): 523-536.PubMed CentralView ArticlePubMedGoogle Scholar
- Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, McKernan KJ, Grimmond SM: Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008, 5 (7): 613-619.View ArticlePubMedGoogle Scholar
- Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, McDonald H, Varhol R, Jones S, Marra M: Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques. 2008, 45 (1): 81-94.View ArticlePubMedGoogle Scholar
- Durbak AR, Tax FE: CLAVATA signaling pathway receptors of Arabidopsis regulate cell proliferation in fruit organ formation as well as in meristems. Genetics. 2011, 189 (1): 177-194.PubMed CentralView ArticlePubMedGoogle Scholar
- Su Z, Wang J, Yu J, Huang X, Gu X: Evolution of alternative splicing after gene duplication. Genome Res. 2006, 16 (2): 182-189.PubMed CentralView ArticlePubMedGoogle Scholar
- Shen Y, Zhou Z, Wang Z, Li W, Fang C, Wu M, Ma Y, Liu T, Kong L-A, Peng DL: Global dissection of alternative splicing in paleopolyploid soybean. Plant Cell. 2014, 26 (3): 996-1008.PubMed CentralView ArticlePubMedGoogle Scholar
- Katz Y, Wang ET, Airoldi EM, Burge CB: Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010, 7 (12): 1009-1015.PubMed CentralView ArticlePubMedGoogle Scholar
- Sugimoto K, Jiao Y, Meyerowitz EM: Arabidopsis regeneration from multiple tissues occurs via a root development pathway. Dev Cell. 2010, 18 (3): 463-471.View ArticlePubMedGoogle Scholar
- He C, Chen X, Huang H, Xu L: Reprogramming of H3K27me3 is critical for acquisition of pluripotency from cultured Arabidopsis tissues. PLoS Genet. 2012, 8 (8): e1002911-PubMed CentralView ArticlePubMedGoogle Scholar
- Rogers ED, Jackson T, Moussaieff A, Aharoni A, Benfey PN: Cell type-specific transcriptional profiling: implications for metabolite profiling. Plant J. 2012, 70 (1): 5-17.PubMed CentralView ArticlePubMedGoogle Scholar
- Laskowski M, Grieneisen VA, Hofhuis H, Hove CA, Hogeweg P, Maree AF, Scheres B: Root system architecture from coupling cell shape to auxin transport. PLoS Biol. 2008, 6 (12): e307-PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao Y: Auxin biosynthesis and its role in plant development. Annu Rev Plant Biol. 2010, 61: 49-64.PubMed CentralView ArticlePubMedGoogle Scholar
- Mravec J, Skupa P, Bailly A, Hoyerova K, Krecek P, Bielach A, Petrasek J, Zhang J, Gaykova V, Stierhof YD, Dobrev PI, Schwarzerova K, Rolcik J, Seifertova D, Luschnig C, Benkova E, Zazimalova E, Geisler M, Friml J: Subcellular homeostasis of phytohormone auxin is mediated by the ER-localized PIN5 transporter. Nature. 2009, 459 (7250): 1136-1140.View ArticlePubMedGoogle Scholar
- Ding Z, Wang B, Moreno I, Duplakova N, Simon S, Carraro N, Reemmer J, Pencik A, Chen X, Tejos R, Skupa P, Pollmann S, Mravec J, Petrasek J, Zazimalova E, Honys D, Rolcik J, Murphy A, Orellana A, Geisler M, Friml J: ER-localized auxin transporter PIN8 regulates auxin homeostasis and male gametophyte development in Arabidopsis. Nat Commun. 2012, 3: 941-View ArticlePubMedGoogle Scholar
- Motchoulski A, Liscum E: Arabidopsis NPH3: A NPH1 photoreceptor-interacting protein essential for phototropism. Science. 1999, 286 (5441): 961-964.View ArticlePubMedGoogle Scholar
- Haerizadeh F, Wong CE, Singh MB, Bhalla PL: Genome-wide analysis of gene expression in soybean shoot apical meristem. Plant Mol Biol. 2009, 69 (6): 711-727.View ArticlePubMedGoogle Scholar
- Jung C-H, Wong CE, Singh MB, Bhalla PL: Comparative genomic analysis of soybean flowering genes. PLoS One. 2012, 7 (6): e38250-PubMed CentralView ArticlePubMedGoogle Scholar
- Dreni L, Pilatone A, Yun D, Erreni S, Pajoro A, Caporali E, Zhang D, Kater MM: Functional analysis of all AGAMOUS subfamily members in rice reveals their roles in reproductive organ identity determination and meristem determinacy. Plant Cell. 2011, 23 (8): 2850-2863.PubMed CentralView ArticlePubMedGoogle Scholar
- Dorca-Fornell C, Gregis V, Grandi V, Coupland G, Colombo L, Kater MM: The Arabidopsis SOC1-like genes AGL42, AGL71 and AGL72 promote flowering in the shoot apical and axillary meristems. Plant J. 2011, 67 (6): 1006-1017.View ArticlePubMedGoogle Scholar
- Shani E, Yanai O, Ori N: The role of hormones in shoot apical meristem function. Curr Opin Plant Biol. 2006, 9 (5): 484-489.View ArticlePubMedGoogle Scholar
- Cheng Y, Dai X, Zhao Y: Auxin biosynthesis by the YUCCA flavin monooxygenases controls the formation of floral organs and vascular tissues in Arabidopsis. Genes Dev. 2006, 20 (13): 1790-1799.PubMed CentralView ArticlePubMedGoogle Scholar
- Vanneste S, Friml J: Auxin: a trigger for change in plant development. Cell. 2009, 136 (6): 1005-1016.View ArticlePubMedGoogle Scholar
- Yang X, Makaroff CA, Ma H: The Arabidopsis MALE MEIOCYTE DEATH1 gene encodes a PHD-finger protein that is required for male meiosis. Plant Cell. 2003, 15 (6): 1281-1295.PubMed CentralView ArticlePubMedGoogle Scholar
- Novillo F, Alonso JM, Ecker JR, Salinas J: CBF2/DREB1C is a negative regulator of CBF1/DREB1B and CBF3/DREB1A expression and plays a central role in stress tolerance in Arabidopsis. Proc Natl Acad Sci U S A. 2004, 101 (11): 3985-3990.PubMed CentralView ArticlePubMedGoogle Scholar
- Su Z, Ma X, Guo H, Sukiran NL, Guo B, Assmann SM, Ma H: Flower development under drought stress: morphological and transcriptomic analyses reveal acute responses and long-term acclimation in Arabidopsis. Plant Cell. 2013, 25 (10): 3785-3807.PubMed CentralView ArticlePubMedGoogle Scholar
- Blackmore S, Wortley AH, Skvarla JJ, Rowley JR: Pollen wall development in flowering plants. New Phytol. 2007, 174 (3): 483-498.View ArticlePubMedGoogle Scholar
- Yang WC, Shi DQ, Chen YH: Female gametophyte development in flowering plants. Annu Rev Plant Biol. 2010, 61: 89-108.View ArticlePubMedGoogle Scholar
- Spencer WC, Zeller G, Watson JD, Henz SR, Watkins KL, McWhirter RD, Petersen S, Sreedharan VT, Widmer C, Jo J, Reinke V, Petrella L, Strome S, Von Stetina SE, Katz M, Shaham S, Ratsch G, Miller DM: A spatial and temporal map of C. elegans gene expression. Genome Res. 2011, 21 (2): 325-341.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang Z, Wang X, Gu S, Hu Z, Xu H, Xu C: Comparative study of SBP-box gene family in Arabidopsis and rice. Gene. 2008, 407 (1): 1-11.View ArticlePubMedGoogle Scholar
- Salinas M, Xing S, Höhmann S, Berndtgen R, Huijser P: Genomic organization, phylogenetic comparison and differential expression of the SBP-box family of transcription factors in tomato. Planta. 2012, 235 (6): 1171-1184.View ArticlePubMedGoogle Scholar
- Preston JC, Hileman LC: Functional evolution in the plant SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) gene family. Front Plant Sci. 2013, 4: 80-PubMed CentralPubMedGoogle Scholar
- Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ: Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol. 2008, 18 (10): 758-762.PubMed CentralView ArticlePubMedGoogle Scholar
- Yamasaki H, Hayashi M, Fukazawa M, Kobayashi Y, Shikanai T: SQUAMOSA promoter binding protein–like7 is a central regulator for copper homeostasis in Arabidopsis. Plant Cell. 2009, 21 (1): 347-361.PubMed CentralView ArticlePubMedGoogle Scholar
- Unte US, Sorensen A-M, Pesaresi P, Gandikota M, Leister D, Saedler H, Huijser P: SPL8, an SBP-box gene that affects pollen sac development in Arabidopsis. Plant Cell. 2003, 15 (4): 1009-1019.PubMed CentralView ArticlePubMedGoogle Scholar
- Cho SH, Coruh C, Axtell MJ: miR156 and miR390 regulate tasiRNA accumulation and developmental timing in Physcomitrella patens. Plant Cell. 2012, 24 (12): 4837-4849.PubMed CentralView ArticlePubMedGoogle Scholar
- Stone JM, Liang X, Nekl ER, Stiers JJ: Arabidopsis AtSPL14, a plant-specific SBP-domain transcription factor, participates in plant development and sensitivity to fumonisin B1. Plant J. 2005, 41 (5): 744-754.View ArticlePubMedGoogle Scholar
- Bäurle I, Dean C: The timing of developmental transitions in plants. Cell. 2006, 125 (4): 655-664.View ArticlePubMedGoogle Scholar
- Martin RC, Asahina M, Liu P-P, Kristof JR, Coppersmith JL, Pluskota WE, Bassel GW, Goloviznina NA, Nguyen TT, Martínez-Andújar C: The regulation of post-germinative transition from the cotyledon-to vegetative-leaf stages by microRNA-targeted SQUAMOSA PROMOTERNNNBINDING PROTEIN LIKE13 in Arabidopsis. Seed Sci Res. 2010, 20 (02): 89-96.View ArticleGoogle Scholar
- Schwarz S, Grande AV, Bujdoso N, Saedler H, Huijser P: The microRNA regulated SBP-box genes SPL9 and SPL15 control shoot maturation in Arabidopsis. Plant Mol Biol. 2008, 67 (1-2): 183-195.PubMed CentralView ArticlePubMedGoogle Scholar
- Alvarez-Buylla ER, Benítez M, Corvera-Poiré A, Cador ÁC, De Folter S, Gamboa de Buen A, Garay-Arroyo A, García-Ponce B, Jaimes-Miranda F, V R, Pérez-Ruiz RV, Piñeyro-Nelson A, Sánchez-Corralesa YE: Flower development. Arabidopsis Book. 2010, 8: e0127-PubMed CentralView ArticlePubMedGoogle Scholar
- Reed JW: Roles and activities of Aux/IAA proteins in Arabidopsis. Trends Plant Sci. 2001, 6 (9): 420-425.View ArticlePubMedGoogle Scholar
- Sundberg E, Ostergaard L: Distinct and dynamic auxin activities during reproductive development. Cold Spring Harb Perspect Biol. 2009, 1 (6): a001628-PubMed CentralView ArticlePubMedGoogle Scholar
- Paz MM, Shou H, Guo Z, Zhang Z, Banerjee AK, Wang K: Assessment of conditions affecting Agrobacterium-mediated soybean transformation using the cotyledonary node explant. Euphytica. 2004, 136 (2): 167-179.View ArticleGoogle Scholar
- Wang Y, Suo H, Zheng Y, Liu K, Zhuang C, Kahle KT, Ma H, Yan X: The soybean root-specific protein kinase GmWNK1 regulates stress-responsive ABA signaling on the root system architecture. Plant J. 2010, 64 (2): 230-242.View ArticlePubMedGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001, 25 (4): 402-408.View ArticlePubMedGoogle Scholar
- Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y: GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics. 2012, 28 (21): 2782-2788.View ArticlePubMedGoogle Scholar
- Wang Y, Tang H, DeBarry JD, Tan X, Li J, Wang X, Lee T-h, Jin H, Marler B, Guo H: MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012, 40 (7): e49-e49.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012, 7 (3): 562-578.PubMed CentralView ArticlePubMedGoogle Scholar
- Qi J, Zhao F: inGAP-sv: a novel scheme to identify and visualize structural variation from paired end mapping data. Nucleic Acids Res. 2011, 39: W567-W575.PubMed CentralView ArticlePubMedGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.View ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A: Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005, 33: D121-D124.PubMed CentralView ArticlePubMedGoogle Scholar
- Kohonen T: Self-organized formation of topologically correct feature maps. Biol Cybern. 1982, 43 (1): 59-69.View ArticleGoogle Scholar
- Du Z, Zhou X, Ling Y, Zhang Z, Su Z: agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010, 38: W64-W70.PubMed CentralView ArticlePubMedGoogle Scholar
- Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M: Mapman: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37 (6): 914-939.View ArticlePubMedGoogle Scholar
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