Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis
© Patil et al. 2015
Received: 29 December 2014
Accepted: 26 June 2015
Published: 11 July 2015
SWEET (MtN3_saliva) domain proteins, a recently identified group of efflux transporters, play an indispensable role in sugar efflux, phloem loading, plant-pathogen interaction and reproductive tissue development. The SWEET gene family is predominantly studied in Arabidopsis and members of the family are being investigated in rice. To date, no transcriptome or genomics analysis of soybean SWEET genes has been reported.
In the present investigation, we explored the evolutionary aspect of the SWEET gene family in diverse plant species including primitive single cell algae to angiosperms with a major emphasis on Glycine max. Evolutionary features showed expansion and duplication of the SWEET gene family in land plants. Homology searches with BLAST tools and Hidden Markov Model-directed sequence alignments identified 52 SWEET genes that were mapped to 15 chromosomes in the soybean genome as tandem duplication events. Soybean SWEET (GmSWEET) genes showed a wide range of expression profiles in different tissues and developmental stages. Analysis of public transcriptome data and expression profiling using quantitative real time PCR (qRT-PCR) showed that a majority of the GmSWEET genes were confined to reproductive tissue development. Several natural genetic variants (non-synonymous SNPs, premature stop codons and haplotype) were identified in the GmSWEET genes using whole genome re-sequencing data analysis of 106 soybean genotypes. A significant association was observed between SNP-haplogroup and seed sucrose content in three gene clusters on chromosome 6.
Present investigation utilized comparative genomics, transcriptome profiling and whole genome re-sequencing approaches and provided a systematic description of soybean SWEET genes and identified putative candidates with probable roles in the reproductive tissue development. Gene expression profiling at different developmental stages and genomic variation data will aid as an important resource for the soybean research community and can be extremely valuable for understanding sink unloading and enhancing carbohydrate delivery to developing seeds for improving yield.
Photosynthesis fixes carbon in the leaves to make sugars as the primary transportable form of energy. Sugar production, status, and transport to the various tissues modulate the growth, productivity, and yield of plants . In addition to their essential roles as substrates in carbon and energy metabolism, sugars also play an important role in signal transduction [1, 2]. In plants, sugars are accumulated in the form of simple sugars, carbohydrates, and starch. Stored sugars are then transported from leaves (source tissue) to the other plant parts (sink tissue) such as roots, modified leaves, and reproductive tissues (seeds). This transport from source to sink is modulated via phloem sap. Sucrose is synthesized in the cytosol and translocated to other non-photosynthetic tissues for direct metabolic use or for conversion to starch. Allocation of sucrose is facilitated by both short-distance transport systems and long-distance transport systems . Short distance transport is achieved at the intra-cellular and inter-cellular levels, where sucrose is transported via diffusion/protoplasmic streaming and plasmodesmata, respectively [4, 5]. It then moves from cell to cell via plasmodesmata until it reaches the phloem parenchyma cells, and in the phloem parenchyma cells, processes related to long-distance transport initiate [6, 7]. Among the sugars, only a few are allocated to the phloem long-distance transport system and sucrose is the main form of carbon found in the phloem tissue followed by polyols, raffinose, etc. [8, 9]. Of the many different sugars found in plants, it is mainly sucrose that is transported in the phloem, where it is the most abundant carbonaceous compound .
The amount of sucrose available for transport to the sink tissues is very crucial for plant development [8, 10]. Metabolite transport efficiency influences photosynthetic productivity by relieving product inhibition and contributes to plant vigor by controlling source/sink relationships and biomass partitioning. The sucrose transport is controlled or facilitated by SUT (sucrose transporter) [11–13] and SWEET (sucrose effluxer) proteins [14–16]. SUT has been widely studied in many plant species [4, 11–14, 17, 18]. SUT proteins are expressed at low levels and display saturable sucrose transport kinetics, suggesting that additional transport proteins are responsible for sucrose allocation across the membrane . The milestone efforts that identified the sucrose effluxer was led by Chen et. al. (2010) . They identified the role of the SWEET (Sugars Will Eventually be Exported Transporters) gene family as sucrose effluxers based on their role in transporting glucose molecules across a membrane. SWEET proteins contains a MtN3_slv transmembrane domain that is essential for the maintenance of animal blood glucose levels, plant nectar production, and plant seed and pollen development [19, 20]. The first member of the SWEET family, MtN3, was identified as a nodulin-specific EST in the leguminous plant Medicago truncatula , and MtN3_slv was identified as an embryonic salivary gland specific gene in drosophila . SWEET proteins function as uniporters, facilitate diffusion of sugars across cell membranes, and mediate sucrose efflux from putative phloem parenchyma into the phloem apoplasm [23–25]. In Arabidopsis, members of the SWEET gene family, AtSWEET11 and −12 were localized to the plasma membrane of the phloem parenchyma and are the main facilitators of sucrose flux. Mutations in AtSWEET11, −12 genes led to defective phloem loading without affecting the phenotype . Using optical sucrose sensors, SWEET proteins were identified as assisting movement of sucrose across cell membranes in preparation for long-distance transport. SWEET proteins are expressed in phloem parenchyma cells and are key to the export of sucrose from leaves .
SWEET transporters have diverse physiological roles and are essential for the maintenance of animal blood glucose levels, plant nectar production, and plant seed and pollen development [15, 23]. Arabidopsis AtSWEET8 is essential for pollen viability, and the rice homologous OsSWEET11 and OsSWEET14 are specifically exploited by bacterial pathogens for virulence by means of direct binding of a bacterial effector to the SWEET promoter [27, 28]. Bacterial and fungal symbionts/pathogens induce the expression of different SWEET genes by secreting the effector protein that binds and activates SWEET genes, indicating that the sugar efflux function of SWEET transporters is targeted and hijacked by pathogens and symbionts for nutritional gain [5, 6, 15, 28, 29].
The sink organs, especially developing seeds which are mainly heterotrophic, depend on nutrients from their parent plants [30, 31]. Early development of the embryo is controlled by the maternal tissue and then during maturation it is controlled by the filial tissues . Phloem unloading in most of the sink tissues follows symplasmic routes [30, 33]. In many dicot seeds, e.g. legumes  and Arabidopsis , the filial tissues are symplasmically isolated/interrupted by apoplast from the phloem in the maternal seed tissue. Transport of sucrose from phloem to the filial tissue is associated with the expression of sugar transporters, localized to the plasma membranes of filial cells. [5, 25, 33–36]. Ludewig et. al.  and Braun  have reviewed and discussed role of the SWEET family transporter as putative facilitator of phloem unloading or as the transporter mediating diffusion of sucrose in sink tissue. Similarly, it has been proposed that enhancing nutrient flow to the developing endosperm and embryo by overexpressing SWEET genes along with cell wall invertase and hexose symporter genes at seed maternal-filial interface can increase the seed yield [5, 38]. In M. domestica, the SWEET genes, including other sugar transporter genes, are involved in sugar accumulation in sink tissue and the concentration of sugars were positively correlated with the SWEET gene expression .
To date SWEET genes are well studied in Arabidposis [15, 26] and rice [20, 40] but no genome-wide exploration and characterization of the SWEET gene family has been performed in soybean. Studying the sucrose efflux system across different species and genera will lead us to understand the evolutionary aspects of the SWEET gene family. In this study, we first collected the SWEET gene family in a number of plant species, then focused on soybean where 52 putative SWEET genes were identified. The publicly available transcriptome datasets were explored and the expression pattern of 23 genes were analyzed using qRT-PCR in reproductive tissues. The wealth of whole genome re-sequencing resources in soybean provided an opportunity to explore natural variation in the soybean SWEET genes. The data presented here lays the foundation for further investigations into the biological and physiological processes of SWEET genes in soybean.
Identification of SWEET genes in soybean and other species
List of 52 soybean SWEET genes and their sequence details (aa- amino acid)
Glyma ID V1.0
Soybean SWEET genes are highly conserved and points to duplication events in higher plants
Comparative genomics of SWEET genes were performed using 25 plant genomes encompassing monocots, dicots and lower plants with subsequent focus on the soybean SWEET family. According to a database of conserved protein families (PFAM), MtN3-like clan (http://pfam.xfam.org/clan/MtN3-like) contains five subfamilies: MtN3_slv (PF03083), PQ-loop (PF04193), MPC (PF03650), ER Lumen Receptor (PF00810), and Lab-N (PF07578). The SWEET genes belongs to MtN3_slv subfamily and serve function in sugar transport whereas other proteins have different roles, for example PQ-loop subfamily involved in amino acid transport . SWEET gene (MtN3_slv) homologues from algae, moss and higher plants were collected from Genbank and Plaza 2.5 and 3.0 comparative genomics platforms . Genome-wide distribution of the SWEET gene family showed that the unicellular plants and blue green algae have fewer copies (1–4) of SWEET genes, followed by 6 and 15 genes in Physcomitrella patens (non-vascular) and Selaginella moellendorffii (vascular) from lower plant group, respectively (Fig. 1).
The phylogeny of the soybean SWEET genes was compared to the Arabidopsis and rice SWEET genes since they have been functionally characterized and their duplication events represented by a whole-genome duplication. The lineage-specific arrangement of SWEET genes proposes that the genes may be expanded and then diversified after the monocot and dicot division. Soybean contains the highest number (52) of SWEET homologues as compared to other plant species included in the present study. To gain further insight into the structural diversity of GmSWEET genes, we compared intron/exon organization in the coding sequences of paralog pairs and found that most of the paralogs shared similar gene organization, consistent with the phylogenetic analysis (Additional file 3).
Soybean SWEET gene family expansion
Identification of substitution rates for homologues GmSWEET genes
No. of syn sites
No. of non-syn sites
Syno. substitution rate (Ks)
Non-syn substitution rate (Ka)
Duplication Date (MYA)
As an interesting side finding, we found one SWEET protein from V. vinifera (Vv14G09070) that has duplication of 7-TM within the gene (Fig. 4, Additional file 4). This is a novel sub-type which we named extaSWEET. The extraSWEET gene could be another internal duplication of 7-TM, similar to the duplication of semiSWEET (3-TM) to evolved in SWEET gene (7-TM) . V.vinifera accumulates high levels of sugar compounds in their berries and this extraSWEET gene might have a role to mediate more sucrose transport. It has been reported that sucrose (VvSUC) and hexose (VvHT) transporter genes are preferentially expressed during berry development in V. vinifera . In addition to the VvSUC and VvHT, it would be interesting to see the expression sites and function of VvSWEET (Vv14G09070) for long distance sugar transport during flower and/or berry development in V. vinifera.
The protein architecture and TM domains in soybean were conserved showing 36 SWEET genes with 7-TMs (SWEET), and the rest had less than 6 TMs (partial/semiSWEET) (Additional file 5). In addition to this, conserved cis-elements in the proximal promoter region (2 Kb upstream) among 52 GmSWEET genes were identified using INCLUSive MotifSampler . Identification and comparing the cis-motif consensus pattern and discovery of expression modules within gene co-expression networks are crucial to understand the common regulatory networks. The top five significant cis-motif patterns were sampled from GmSWEET genes (Additional file 6). Motifs such as TBP binding sites, GT-2 (Grass TF 2), ATHB1 (A. thaliana Homeobox 1), HAHB4 (H. annuus Homeobox 4) and TaMYB80 (T. aestivum MYB80) were identified in SWEET gene promoters, indicating differential regulation and also they might have a putative role of sugar signaling  (Additional file 6). Interestingly, cis-motif elements of GT-2 and GT-3 were significantly enriched in soybean SWEET genes (Additional files 6 and 7). GT-2, −3 are plant transcriptional activators in higher plants and are involved in seed development and other diverse functions in rice, Arabidopsis and soybean [55, 56]. Further functional characterization of these cis-regulatory motifs and TFs (Transcription Factor) binding sites in GmSWEET genes will be helpful to understand the precise roles in development.
Soybean SWEET genes are highly expressed during reproduction and seed development
In the present study 21 paralogous gene pairs for GmSWEET were identified (Fig. 3). The relationships between paralogous GmSWEET pairs with their expression pattern during development was compared. Nine out of 21 pairs showed a similar expression pattern and rest showed divergence in expression patterns (Additional file 9). For example, paralog pair Glyma05g38340 (GmSWEET11) and Glyma08g01310 (GmSWEET21) were up-regulated in cotyledonary tissue while simultaneously being down-regulated in leaf tissue. Similar expression levels of paralog genes suggests that they have retained the promoter element. Expression patterns of the remaining 12 paralog pairs has diverged (Fig. 5a), to either non-functionalization, neo-functionalization or sub-functionalization. Therefore, it would be interesting to see the expression pattern of those genes in soybean under different conditions. In soybean, SWEET genes are also associated with the iron deficiency . Lauter et. al. (2014) observed the repression of two SWEET genes (Glyma05g38340 and Glyma08g01310) and other sucrose transporter genes in the leaves one hour after iron stress and concluded that SWEET genes might play a role in regulation of the SnRK1/TOR (SNORKEL) signaling pathway in response to iron deficiency .
Examination of SWEET gene expression in reproductive tissue by qRT-PCR
To confirm the expression patterns determined by the RNA-seq analysis, qRT-PCR was employed to analyze the expression patterns of 23 genes in three reproductive development tissues of soybean, Williams 82 (W82), specifically pedicel, pods, and developing seeds (Fig. 5c). The expression patterns (Fig. 5c) were largely consistent with those obtained by the RNAseq analysis (Fig. 5a), even though some smaller variations can be seen. GmSWEET 12 and 21 were highly expressed in all three developmental stages, but in the seeds they are so abundant that the total relative SWEET gene expression far exceeds that of the other tissues (Fig. 5c). The expression of GmSWEET genes 5, 10, 23, and 48 were also much higher in seeds than in the other tissues, and may be considered seed-specific. In pods, GmSWEET 12, 21, and 40 had comparatively higher expression, and in pedicels the expression of GmSWEET 12, 21, and 38 stands out.
Exploring natural variation in GmSWEET genes using soybean whole genome re-sequencing data
The elucidation of the soybean SWEET genes gave us an unprecedented opportunity to obtain a comprehensive overview of the allelic variation in soybean whole genome re-sequencing data. The wealth of whole genome resources of soybean provides a unique angle to study natural variation in germplasm and further allows functional characterization of the particular gene [61–63]. Complete genome sequences for 106 soybean genotype, sequenced at approximately 15X coverage, were obtained from the Soybean Genetics and Genomics Laboratory at The University of Missouri (Valliyodan et. al. Unpublished) and analyzed for synonymous and non-synonymous SNPs, premature stop codon and haplotype variation in selected GmSWEET genes. In Arabidopsis, AtSWEET11 and −12 double mutants accumulated sucrose in the leaves and had lower levels in the phloem, identifying them as the long sought main sucrose effluxers in the leaf sugar export pathway . It has been observed that when AtSWEET17 expression is reduced, either by induced or natural variation, fructose accumulates in the leaves, suggesting an enhanced storage capacity . Site directed mutagenesis of AtSWEET1 at four conserved positions (P23T, Y57A, G58D, and G180D) led to abolishment of glucose transport activity in a yeast complementation assay. Also, SNP in the coding or promoter region can also abolish protein localization and function . In the present study, wide natural variations were observed in non-synonymous SNPs and a total of 37 SNPs were observed in 21 (~40 %) GmSWEET genes (Table S5). GmSWEET41 (Glyma15g27530) showed a premature stop codon in the 1st exon in 15 sequenced lines.
To understand and visualize the genetic variation in whole genome re-sequencing data for the SWEET genes, a cluster of genes (GmSWEET15, 16, and 17) including their 2 kb promoter region was examined. The haplogroup gave three major distinct clusters based on the SNP variation in promoter and coding regions similar or dissimilar to the soybean reference genome, W82 (Additional file 10). As sugar derivatives are associated with SWEET genes [8, 43], we further examined the association between the haplogroup cluster and different sugar content (sucrose, raffinose, and stachyose) in soybean seeds and observed a correlation between three SNP-haplogroups and average sucrose content. The SNP-haplogroup similar to reference genotype W82 showed intermediate sucrose concentration of average 5.26 ± 0.14 %. The other two groups were distinct from W82 haplogroup showing an average sucrose concentration of 4.8 ± 0.4 % and 5.5 ± 0.28 %, (Additional file 10). Out of 10 wild soybean lines (G. soja), seven lines were identified in the first haplogroup which showed a relatively lower sucrose content. No significant association was found for raffinose and stachyose concentrations. It has been reported that the transport of Raffinose family oligosaccharides (RFOs) are not detectable when associated with apoplastic loading [23, 65] and several higher plants accumulate RFO during the seed maturation process , hence SWEET genes might have no role in efflux for RFOs. However, to fully understand their roles, detailed functional characterization of the individual gene is needed.
Discussion and conclusions
In-silico analysis and phylogenetic studies generate valuable information on the evolutionary and functional relationships between genes of different species, genomic complexity, and lineage-specific adaptations. Previous work on sugar transporter genes SWEET (MtN3_slv), along with the rapidly expanding availability of genomics sequence data has enabled us to examine the SWEET content of multiple plant genomes.
The SWEET gene family has been studied in Arabidopsis [15, 26], rice [20, 58, 59] and bacteria [43, 50]. However, this family has not previously been studied in soybean. Here, we explore these genes in soybean with an analysis of their phylogeny, gene structure, domain architecture, expression profiles and natural genetic variation. A total of 52 full-length SWEET genes were identified in the soybean genome, which is highest among the analyzed plants and implies a genome expansion. The exon/intron layouts and the TM motifs were quite conserved when compared to the paralogs. A phylogenetic tree was constructed (Fig. 2) to identify putative orthologous and paralogous SWEET genes and to study the pattern of the SWEET gene family expansion in the course of evolution.
The salt water living chlorophyta algae O. tauri, O. lucimarius and Micromonos sp. have only a single gene. On the other hand, the fresh water algae, V. carteri and C. reinhardtii, contain 2 and 3 SWEET genes, respectively. This leads us to suspect that during the transition phase to fresh water, a more involved mechanism for sugar transport was required by environmental conditions. The evolution to multi-cellularity led to further expansion of the SWEET gene family. Recent studies on the evolution of the SUT transporter family showed that divergence of different SUT types were likely associated with evolution of vascular cambium and phloem transport . Higher plants evolved phloem for long-distance, source-to-sink transport. Although different phloem loading strategies are recognized, lineages that evolved apoplasmic phloem loading required a mechanism for efflux from phloem parenchyma and subsequent energized uptake into the companion cell/sieve element complex, SWEETs provided the former function . P. patens is an early diverging land plant and many families of P. patens genes for metabolic enzymes (e.g. cytokinin , glutathione , pectin ) have large copy numbers. P. patens has only a primitive protophloem, and the increase in the SWEET genes here could be due to the recent genome duplication , without the new genes necessarily having acquired differentiated functionalities. S. moellendorffii does have a phloem, and the number of SWEET genes here approaches that of many angiosperms (Fig. 1).
The expansion of a gene family in higher plants indicates the differentiation of physiological function of each isoform in terms of the expression site and the regulatory manner which subsequently helps the organism to adapt in different environmental conditions. The internal duplication of the 3-TM (semiSWEET) gene must have happened early to give rise to new genes with 7-TMs (SWEET) which allow a more sophisticated sucrose transport [43, 50, 71]. Here we also report a novel gene in V. vinifera which has further duplicated the TM regions. Collectively, phylogenetic and domain studies imply that biological, physiological or environmental conditions forces particular gene families to evolve and expand. As evolution of the higher plants have progressed, some species have acquired further SWEET genes (Fig. 1). This suggests that sugar transport evolution has followed as new plant structures and adaptations to new ecological niches have arisen.
The SWEET genes play a diverse functional role during plant development which is evident from their expression patterns in other plant species [25, 40, 43, 50, 58] and soybean (this report). In rice and Arabidopsis, the expression of the SWEET genes were relatively higher in flower, pollen, embryo sac and seeds suggesting their roles in reproductive tissue development [15, 19, 20]. In rice two members of the SWEET gene family were highly expressed in panicles and anthers and were associated with fertility and seed size [20, 58, 59]. In Arabidopsis, AtSWEET8 was expressed in the embryo sac suggesting that it might regulate female gametocyte development . Developing seeds are the strongest sink tissues in many plants and they need a higher carbon source for development which implies that nutrient transporters including the SWEET genes might be key component for their development. In Arabidopsis, AtSWEET11 and −12 showed a higher expression in leaves and had important roles in leaf sucrose export . The comparison of AtSWEET11 and −12 expression pattern with soybean orthologs GmSWEET6, and −15 showed a relatively higher expression in leaves, suggesting that these genes also might have similar role in leaf sucrose export.
Yuan and Wang  and Chen  have reviewed the functional role of SWEET genes in different tissues, pathogen infestation, and environmental responses. Interestingly, GmSWEET13, 14 and 15 fall under the fungal disease resistance QTL on chromosome 6 in soybean . It has been proven that fungal and bacterial symbionts induce SWEET gene expression for nutritional gain during pathogen infestation [15, 25, 40, 74, 75]. The statement that most of the reported SWEET genes are associated with reproductive development tissue is corroborated in this study using soybean transcriptome datasets. The transciptome and qRT-PCR data showed that multiple SWEET genes are expressed at higher levels in tissues involved in reproductive development. Relatively higher expression of GmSWEET5, −10, −23 and −48 in the seed tissue, suggest that collectively these genes might assist the movement of sucrose in the developing soybean seeds. Unloading of nutrient in the developing seeds occurs from the seed coat [32, 76]. In the developing legume seeds (P. vulgaris and P. sativum), a suite of sucrose transporters are expressed at a higher levels in seed coat tissue to facilitate the movement of sucrose . Sugar availability, starch content, and cytokinin levels are involved in the regulation of abscission of soybean flowers, the delay of which hampers seed development and leads to yield loss in soybean [77–79]. Soybean flower abortion is primarily caused by deficiency in or competition for photo-assimilates and nutrients among growing organs.
Beside the expression level, the genetic variation (natural or induced) also enforces the functionality of SWEET genes and causes a variation in phenotype [43, 64]. Mutation in the SWEET gene or abolishing the activation of the SWEET promoter leads to resistance to bacterial pathogens in rice [5, 59, 80]. Identification of several non-synonymous SNPs and large effect SNPs in GmSWEETs are expected to affect the integrity of encoded proteins. Additionally, exploring SNP-haplotype diversity using whole-genome sequencing data mining provides a powerful resource for investigating diversity in a particular gene family [81–83]. The data presented here, using a cluster of genes on chromosome 6 (GmSWEET13, −14 and −15), showed the association between the SNP-haplogroups and sucrose content in seeds. The allelic variation data presented in this study provides a valuable resource for association studies between the SNPs and important agronomic traits, although intensive studies with each candidate gene are required to examine this inference. Overall, the SWEET gene family signifies its role as a key component in reproductive tissue development, nutrient unloading and pathogen resistance. Manipulating SWEET expression in specific tissues (phloem sap, pedicel, and developing seeds) could enhance sugar delivery to developing seeds to increase yield.
Sequence and database search for SWEET gene family
SWEET (MtN3_slv) gene families were identified from 25 completely sequenced genomes representing the plant lineage (green plants) including members from unicellular green algae to multicellular plants (Fig. 1, Additional file 1). The protein BLAST search was performed using AtSWEET11 as a query sequence in Plaza  (http://bioinformatics.psb.ugent.be/plaza/news/index) and Phytozome  (http://www.phytozome.org) databases and the sequences were retrieved from the corresponding plant genome annotation resources and analyzed. The multiple sequence alignment was performed using MUSCLE program  and partial and redundant sequences were excluded. All proteins were examined for presence of MtN3_slv related TM domains (IPR018179) using Interpro database  (http://www.ebi.ac.uk/). Glycine max SWEET genes were designated as GmSWEET1 to GmSWEET52.
To understand the phylogenetic relationship, 173 SWEET genes from 13 species representing major clades were analyzed. Protein sequences were analyzed by the neighbor-joining (NJ) method  with genetic distance calculated by MEGA5.1  (www.megasoftware.net/). The numbers at the nodes represent bootstrap percentage value based on 1,000 replications.
Identification of conserved domains and cis-motif pattern
The Conserved Domain Architecture Retrieval Tool (CDART)  (http://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi) was searched using Arabidopsis AtSWEET11 as a query protein sequence (Additional file 4). Several MtN3_slv TM domains were preceded which were grouped into three major architecture based on 3-TMs and associated proteins (Fig. 4). Identification of the exon/intron organization of SWEET genes was performed by aligning cDNAs with their corresponding genomic DNA sequences and were also obtained by using the Plaza comparative database. Cis regulatory elements were identified by searching 2 kb upstream of the 5’ translation start base for all of the soybean SWEET genes using INCLUSive MotifSampler . 2 kb upstream sequences were annotated by similarity search (p value <0.05, motif score >5) with known plant transcription binding sites and motifs available in the Athamap database  (www.athamap.de, Additional files 6 and 7).
Soybean SWEET gene chromosomal location and gene duplication
The location of soybean SWEET genes was determined based on their physical positions on chromosomes corresponding to their locus numbers in the SoyKB browser . The duplication of SWEET genes on segmentally duplicated regions was determined using Plaza 2.5 whole genome mapping tool (http://bioinformatics.psb.ugent.be/plaza/versions/plaza_v2_5/genome_mapping/genome_mapping), and were visualized using genome search and synteny view tool (CViT) (http://comparative-legumes.org/) . The comparative duplicate block representing homologous chromosome segments were anchored on 15 out of 20 soybean chromosomes and indicated by tandem/block duplication (Fig. 3).
Calculation of Ka/Ks values
Non-synonymous (Ka) to synonymous (Ks) substitution rates were used to estimate the selection mode for all orthologous gene pairs of soybean SWEET family . Subsequently, the PAL2NAL program (http://www.bork.embl.de/pal2nal/) was used to convert a multiple sequence alignment of proteins and the corresponding DNA (or mRNA) sequences into a codon alignment . PAL2NAL automatically calculates Ks and Ka by the CODEML program in PAML. The divergence time (T) was calculated by T = Ks/(2 × 6.1 × 10−9)×10−6 MYA, where 6.1 × 10−9 is divergence rate in millions of years translated from Ks value .
RNA-seq datasets and qRT-PCR analysis
Genome-wide public RNA-seq datasets (Reads/Kb/Million (RPKM) normalized data) for soybean developmental stages were downloaded from soybean RNA-seq Atlas  and Gene Expression Omnibus (GEO) database (accession number GSE29163) from Goldberg et. al. (Unpublished). Sources of the samples for first dataset are as follows: YL (young leaves), FL (flower), PD.1 cm (one cm pod), PS.10d (pod shell 10 Days After Flowering (DAF)), PS.14d (pod shell 14DAF), S.10d (seed 10DAF), S.14d (seed 14 DAF), S.21d (seed 21DAF), S.25d (seed 25DAF), S.28d (seed 28DAF), S.35d (seed 35DAF), S.42d (seed 42DAF), R1 (root), and Nod (nodule). Sources of the samples for second dataset are as follows: GSS (Globular stage whole seed), HRT (Heart stage), COT (Cotyledonary stage), EM (Early maturation), DWS (Dry whole seed), LF (Leaf), R2 (Root), STM (Stem), FB (Floral bud), and SDL (Seedling). Average linkage method provided in Cluster 3.0 was used to cluster gene and tissue types and visualized using TreeView software .
Total RNA was extracted from soybean pedicel, pod, and seed tissues using a Qiagen RNeasy mini kit (Qiagen, CA, USA). First strand cDNA from 1 μg of total RNA was synthesized by using Superscript III reverse transcriptase (Invitrogen) with oligo(dT) primer. Primers for quantitative reverse transcription PCR (qPCR) were designed using Primer3 (http://frodo.wi.mit.edu) (Additional file 11). Quantitative RT-PCR was performed using cDNA product in a 10 μl reaction volume using Maxima SYBR Green/ROX qPCR master mix (Thermo, USA) on ABI7900HT detection system (Life Technologies, NY, USA). Three biological replicates and two technical replicates were used for analysis. The PCR conditions were: 50 °C for 2 min., 95 °C for 10 min., then 40 cycles of 95 °C for 15 sec., and 60 °C for 1 min. To normalize the gene expression, Actin (Glyma18g52780) was used as an internal control.
Analysis of sequence variants, non-synonymous SNP and haplotype variation
One hundred and six soybean lines with carbohydrate phenotypes (sucrose, stachyose, and raffinose) and whole genome re-sequencing (sequencing depth approximately 15X) data were obtained for soybean SWEET genes from Soybean Genetics and Genomics Laboratory at the University of Missouri (Valliyodan et. al. Unpublished). The processed data was aligned to the Williams 82 Gmax v9.0 from Phytozome as the reference genome . SNPs were identified using an in-house built pipeline using with SOAP3  and were analyzed for possible synonymous/non-synonymous SNP variation annotations using SnpEFF  and v9.0 gene models from Phytozome (Additional file 12). SNP haplotypes were examined by generating map and genotype data files using TASSEL 5.0 program  and then clustering pictorial output for a specific genic region was visualized using FLAPJACK software .
Availability of supporting data
All supporting data of this article are included as additional files.
We acknowledge Missouri Soybean Merchandising Council for the funding support (Grant # 288 and # 368). The authors also would like to thank Theresa A. Musket for reviewing and editing the manuscript.
- Rolland F, Moore B, Sheen J. Sugar sensing and signaling in plants. Plant Cell. 2002;14(Supplement):S185–205.PubMed CentralPubMedGoogle Scholar
- Wind J, Smeekens S, Hanson J. Sucrose: metabolite and signaling molecule. Phytochem. 2010;71(14):1610–4.View ArticleGoogle Scholar
- Ayre BG. Membrane-transport systems for sucrose in relation to whole-plant carbon partitioning. Mol Plant. 2011;4:ssr014.View ArticleGoogle Scholar
- Sauer N. Molecular physiology of higher plant sucrose transporters. FEBS Lett. 2007;581(12):2309–17.PubMedView ArticleGoogle Scholar
- Ruan Y-L. Sucrose metabolism: gateway to diverse carbon use and sugar signaling. Annu Rev Plant Biol. 2014;65:33–67.PubMedView ArticleGoogle Scholar
- Baker RF, Leach KA, Braun DM. SWEET as sugar: new sucrose effluxers in plants. Mol Plant. 2012;5(4):766–8.PubMedView ArticleGoogle Scholar
- Turgeon R, Wolf S. Phloem transport: cellular pathways and molecular trafficking. Annu Rev Plant Biol. 2009;60:207–21.PubMedView ArticleGoogle Scholar
- Lemoine R, La Camera S, Atanassova R, Dedaldechamp F, Allario T, Pourtau N, et al. Source-to-sink transport of sugar and regulation by environmental factors. Front Plant Sci. 2013;4:272.Google Scholar
- Braun DM, Slewinski TL. Genetic control of carbon partitioning in grasses: roles of sucrose transporters and tie-dyed loci in phloem loading. Plant Physiol. 2009;149(1):71–81.PubMed CentralPubMedView ArticleGoogle Scholar
- Rennie EA, Turgeon R. A comprehensive picture of phloem loading strategies. Proc Natl Acad Sci U S A. 2009;106(33):14162–7.PubMed CentralPubMedView ArticleGoogle Scholar
- Lohaus G, Burba M, Heldt H. Comparison of the contents of sucrose and amino acids in the leaves, phloem sap and taproots of high and low sugar-producing hybrids of sugar beet (Beta vulgaris L.). J Exp Bot. 1994;45(8):1097–101.View ArticleGoogle Scholar
- Slewinski TL, Meeley R, Braun DM. Sucrose transporter1 functions in phloem loading in maize leaves. J Exp Bot. 2009;60(3):881–92.PubMed CentralPubMedView ArticleGoogle Scholar
- Srivastava AC, Ganesan S, Ismail IO, Ayre BG. Functional characterization of the Arabidopsis AtSUC2 sucrose/H+ symporter by tissue-specific complementation reveals an essential role in phloem loading but not in long-distance transport. Plant Physiol. 2008;148(1):200–11.PubMed CentralPubMedView ArticleGoogle Scholar
- Aoki N, Hirose T, Scofield GN, Whitfeld PR, Furbank RT. The sucrose transporter gene family in rice. Plant Cell Physiol. 2003;44(3):223–32.PubMedView ArticleGoogle Scholar
- Chen L-Q, Hou B-H, Lalonde S, Takanaga H, Hartung ML, Qu X-Q, et al. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature. 2010;468(7323):527–32.Google Scholar
- Sonnewald U. SWEETS–the missing sugar efflux carriers. Front Plant Sci. 2011;2:1–2.View ArticleGoogle Scholar
- Kühn C. A comparison of the sucrose transporter systems of different plant species. Plant Biol. 2003;5(3):215–32.View ArticleGoogle Scholar
- Contim LAS, Waclawovsky AJ, Delú‐Filho N, Pirovani CP, Clarindo WR, Loureiro ME, et al. The soybean sucrose binding protein gene family: genomic organization, gene copy number and tissue‐specific expression of the SBP2 promoter. J Exp Bot. 2003;54(393):2643–53.Google Scholar
- Guan Y-F, Huang X-Y, Zhu J, Gao J-F, Zhang H-X, Yang Z-N. RUPTURED POLLEN GRAIN1, a member of the MtN3/saliva gene family, is crucial for exine pattern formation and cell integrity of microspores in Arabidopsis. Plant Physiol. 2008;147(2):852–63.PubMed CentralPubMedView ArticleGoogle Scholar
- Yuan M, Wang S. Rice MtN3/saliva family genes and their homologues in cellular organisms. Mol Plant. 2013;6:sst035.View ArticleGoogle Scholar
- Gamas P, de Carvalho NF, Lescure N, Cullimore JV. Use of a subtractive hybridization approach to identify new Medicago truncatula genes induced during root nodule development. MPMI. 1996;9(4):233–42.PubMedView ArticleGoogle Scholar
- Artero RD, Terol-Alcayde J, Paricio N, Ring J, Bargues M, Torres A, et al. Saliva, a new Drosophila gene expressed in the embryonic salivary glands with homologues in plants and vertebrates. Mech Dev. 1998;75(1):159–62.Google Scholar
- Slewinski TL. Diverse functional roles of monosaccharide transporters and their homologs in vascular plants: a physiological perspective. Mol Plant. 2011;4(4):641–62.PubMedView ArticleGoogle Scholar
- Braun DM. SWEET! The pathway is complete. Science. 2012;335(6065):173–4.PubMedView ArticleGoogle Scholar
- Chen LQ. SWEET sugar transporters for phloem transport and pathogen nutrition. New Phytol. 2014;201(4):1150–5.PubMedView ArticleGoogle Scholar
- Chen L-Q, Qu X-Q, Hou B-H, Sosso D, Osorio S, Fernie AR, et al. Sucrose efflux mediated by SWEET proteins as a key step for phloem transport. Science. 2012;335(6065):207–11.Google Scholar
- Streubel J, Pesce C, Hutin M, Koebnik R, Boch J, Szurek B. Five phylogenetically close rice SWEET genes confer TAL effector‐mediated susceptibility to Xanthomonas oryzae pv. oryzae. New Phytol. 2013;200(3):808–19.PubMedView ArticleGoogle Scholar
- Denancé N, Szurek B, Noël LD. Emerging functions of nodulin-like proteins in non-nodulating plant species. Plant Cell Physiol. 2014;55(3):469–74.PubMedView ArticleGoogle Scholar
- Doidy J, Grace E, Kühn C, Simon-Plas F, Casieri L, Wipf D. Sugar transporters in plants and in their interactions with fungi. Trends Plant Sci. 2012;17(7):413–22.PubMedView ArticleGoogle Scholar
- Patrick JW. PHLOEM UNLOADING: sieve element unloading and post-sieve element transport. Annu Rev Plant Physiol Plant Mol Biol. 1997;48(1):191–222.PubMedView ArticleGoogle Scholar
- Baud S, Dubreucq B, Miquel M, Rochat C, Lepiniec L. Storage reserve accumulation in Arabidopsis: metabolic and developmental control of seed filling. Am Soc Plant Biologists. 2008;6:e0113.Google Scholar
- Weber H, Borisjuk L, Wobus U. Molecular physiology of legume seed development. Annu Rev Plant Biol. 2005;56:253–79.PubMedView ArticleGoogle Scholar
- Zhang W-H, Zhou Y, Dibley KE, Tyerman SD, Furbank RT, Patrick JW. Review: Nutrient loading of developing seeds. Funct Plant Biol. 2007;34(4):314–31.View ArticleGoogle Scholar
- Lalonde S, Tegeder M, Throne‐Holst M, Frommer W, Patrick J. Phloem loading and unloading of sugars and amino acids. Plant Cell Environ. 2003;26(1):37–56.View ArticleGoogle Scholar
- Marschner H, Marschner P. Marschner’s mineral nutrition of higher plants. London: Academic press; 2012.Google Scholar
- Zhou Y, Qu H, Dibley KE, Offler CE, Patrick JW. A suite of sucrose transporters expressed in coats of developing legume seeds includes novel pH‐independent facilitators. Plant J. 2007;49(4):750–64.PubMedView ArticleGoogle Scholar
- Ludewig F, Flügge U-I. Role of metabolite transporters in source-sink carbon allocation. Front Plant Sci. 2013;4:231.PubMed CentralPubMedView ArticleGoogle Scholar
- Weschke W, Panitz R, Gubatz S, Wang Q, Radchuk R, Weber H, et al. The role of invertases and hexose transporters in controlling sugar ratios in maternal and filial tissues of barley caryopses during early development. Plant J. 2003;33(2):395–411.Google Scholar
- Wei X, Liu F, Chen C, Ma F, Li M. The Malus domestica sugar transporter gene family: identifications based on genome and expression profiling related to the accumulation of fruit sugars. Front Plant Sci. 2014;5:569.PubMed CentralPubMedView ArticleGoogle Scholar
- Yang B, Sugio A, White FF. Os8N3 is a host disease-susceptibility gene for bacterial blight of rice. Proc Natl Acad Sci U S A. 2006;103(27):10503–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, et al. The Pfam protein families database. Nucleic Acids Res. 2011;40:gkr1065.Google Scholar
- Van Bel M, Proost S, Wischnitzki E, Movahedi S, Scheerlinck C, Van De Peer Y, et al. Dissecting plant genomes with the PLAZA comparative genomics platform. Plant Physiol. 2011;111:189514.Google Scholar
- Xuan YH, Hu YB, Chen L-Q, Sosso D, Ducat DC, Hou B-H, et al. Functional role of oligomerization for bacterial and plant SWEET sugar transporter family. Proc Natl Acad Sci U S A. 2013;110(39):E3685–94.Google Scholar
- Severin AJ, Cannon SB, Graham MM, Grant D, Shoemaker RC. Changes in twelve homoeologous genomic regions in soybean following three rounds of polyploidy. Plant Cell. 2011;23(9):3129–36.PubMed CentralPubMedView ArticleGoogle Scholar
- Soltis DE, Visger CJ, Soltis PS. The polyploidy revolution then… and now: Stebbins revisited. Am J Bot. 2014;101(7):1057–78.PubMedView ArticleGoogle Scholar
- Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010;463(7278):178–83.Google Scholar
- Roulin A, Auer PL, Libault M, Schlueter J, Farmer A, May G, et al. The fate of duplicated genes in a polyploid plant genome. Plant J. 2013;73(1):143–53.Google Scholar
- Li W-H, Gojobori T, Nei M. Pseudogenes as a paradigm of neutral evolution. Nature. 1981;292(5820):237–9.PubMedView ArticleGoogle Scholar
- Lynch M, Conery JS. The evolutionary fate and consequences of duplicate genes. Science. 2000;290(5494):1151–5.PubMedView ArticleGoogle Scholar
- Xu Y, Tao Y, Cheung LS, Fan C, Chen L-Q, Xu S, et al. Structures of bacterial homologues of SWEET transporters in two distinct conformations. Nature. 2014;515:448–52.Google Scholar
- Geer LY, Domrachev M, Lipman DJ, Bryant SH. CDART: protein homology by domain architecture. Genome Res. 2002;12(10):1619–23.PubMed CentralPubMedView ArticleGoogle Scholar
- Afoufa-Bastien D, Medici A, Jeauffre J, Coutos-Thevenot P, Lemoine R, Atanassova R, et al. The Vitis vinifera sugar transporter gene family: phylogenetic overview and macroarray expression profiling. BMC Plant Biol. 2010;10(1):245.Google Scholar
- Thijs G, Moreau Y, De Smet F, Mathys J, Lescot M, Rombauts S, et al. INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling. Bioinformatics. 2002;18(2):331–2.Google Scholar
- Klepek YS, Volke M, Konrad KR, Wippel K, Hoth S, Hedrich R, et al. Arabidopsis thaliana POLYOL/MONOSACCHARIDE TRANSPORTERS 1 and 2: fructose and xylitol/H+ symporters in pollen and young xylem cells. J Exp Bot. 2009;61:erp322.Google Scholar
- Smalle J, Kurepa J, Haegman M, Gielen J, Van Montagu M, Van Der Straeten D. The trihelix DNA-binding motif in higher plants is not restricted to the transcription factors GT-1 and GT-2. Proc Natl Acad Sci U S A. 1998;95(6):3318–22.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhou D-X. Regulatory mechanism of plant gene transcription by GT-elements and GT-factors. Trends Plant Sci. 1999;4(6):210–4.PubMedView ArticleGoogle Scholar
- Severin AJ, Woody JL, Bolon Y-T, Joseph B, Diers BW, Farmer AD, et al. RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biol. 2010;10(1):160.Google Scholar
- Antony G, Zhou J, Huang S, Li T, Liu B, White F, et al. Rice xa13 recessive resistance to bacterial blight is defeated by induction of the disease susceptibility gene Os-11 N3. Plant Cell. 2010;22(11):3864–76.Google Scholar
- Chu Z, Yuan M, Yao J, Ge X, Yuan B, Xu C, et al. Promoter mutations of an essential gene for pollen development result in disease resistance in rice. Genes Dev. 2006;20(10):1250–5.Google Scholar
- Lauter ANM, Peiffer GA, Yin T, Whitham SA, Cook D, Shoemaker RC, et al. Identification of candidate genes involved in early iron deficiency chlorosis signaling in soybean (Glycine max) roots and leaves. BMC Genomics. 2014;15(1):702.Google Scholar
- Lam H-M, Xu X, Liu X, Chen W, Yang G, Wong F-L, et al. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet. 2010;42(12):1053–9.Google Scholar
- Qi X, Li M-W, Xie M, Liu X, Ni M, Shao G, et al. Identification of a novel salt tolerance gene in wild soybean by whole-genome sequencing. Nat Comm. 2014;5:4340.Google Scholar
- Patil G. Identification of sequence variants in candidate genes for Oil content using whole genome Re-sequencing of soybean germplasm. In: Plant and animal genome XXII conference. San Diego, CA Plant and Animal Genome; 2014.Google Scholar
- Chardon F, Bedu M, Calenge F, Klemens PA, Spinner L, Clement G, et al. Leaf fructose content is controlled by the vacuolar transporter SWEET17 in Arabidopsis. Curr Biol. 2013;23(8):697–702.Google Scholar
- Zimmermann MH, Ziegler H. List of sugars and sugar alcohols in sieve-tube exudates. New Ser: Encycl Plant Physiol; 1975.Google Scholar
- Ayre BG, Keller F, Turgeon R. Symplastic continuity between companion cells and the translocation stream: long-distance transport is controlled by retention and retrieval mechanisms in the phloem. Plant Physiol. 2003;131(4):1518–28.PubMed CentralPubMedView ArticleGoogle Scholar
- Patil G, Nicander B. Identification of two additional members of the tRNA isopentenyltransferase family in Physcomitrella patens. Plant Mol Biol. 2013;82(4–5):417–26.PubMedView ArticleGoogle Scholar
- Liu Y-J, Han X-M, Ren L-L, Yang H-L, Zeng Q-Y. Functional divergence of the glutathione S-transferase supergene family in Physcomitrella patens reveals complex patterns of large gene family evolution in land plants. Plant Physiol. 2013;161(2):773–86.PubMed CentralPubMedView ArticleGoogle Scholar
- McCarthy TW, Der JP, Honaas LA, Anderson CT. Phylogenetic analysis of pectin-related gene families in Physcomitrella patens and nine other plant species yields evolutionary insights into cell walls. BMC Plant Biol. 2014;14(1):79.PubMed CentralPubMedView ArticleGoogle Scholar
- Rensing SA, Lang D, Zimmer AD, Terry A, Salamov A, Shapiro H, et al. The Physcomitrella genome reveals evolutionary insights into the conquest of land by plants. Science. 2008;319(5859):64–9.Google Scholar
- Keller R, Ziegler C, Schneider D. When two turn into one: evolution of membrane transporters from half modules. Biol Chem. 2014;395(12):1379–88.PubMedView ArticleGoogle Scholar
- Talbot NJ. Cell biology: Raiding the sweet shop. Nature. 2010;468(7323):510–1.PubMedView ArticleGoogle Scholar
- Wang J-L, Liu C-Y, Wang J, Qi Z-M, Li H, Hu G-H, et al. An integrated QTL Map of fungal disease resistance in soybean (glycine max L. Merr): a method of meta-analysis for mining R genes. Agric Sci China. 2010;9(2):223–32.Google Scholar
- Yuan M, Chu Z, Li X, Xu C, Wang S. The bacterial pathogen Xanthomonas oryzae overcomes rice defenses by regulating host copper redistribution. Plant Cell. 2010;22(9):3164–76.PubMed CentralPubMedView ArticleGoogle Scholar
- van Ooij C. Pathogenesis: The SWEET life of pathogens. Nat Rev Microb. 2011;9(1):4–5.View ArticleGoogle Scholar
- Patrick J, Offler C. Post-sieve element transport of sucrose in developing seeds. Funct Plant Biol. 1995;22(4):681–702.Google Scholar
- Antos M, Wiebold W. Abscission, total soluble sugars, and starch profiles within a soybean canopy. Agron J. 1984;76(5):715–9.View ArticleGoogle Scholar
- Nagel L, Brewster R, Riedell W, Reese R. Cytokinin regulation of flower and pod set in soybeans (Glycine max (L.) Merr.). Ann Bot. 2001;88(1):27–31.View ArticleGoogle Scholar
- Dybing CD, Reese ZN. Nitrogen and carbohydrate nutrient concentrations and flower Set in soybean glycine max (L.) merr.). J Biol Sci. 2008;8(1):24–33.View ArticleGoogle Scholar
- Li C, Wei J, Lin Y, Chen H. Gene silencing using the recessive rice bacterial blight resistance gene xa13 as a new paradigm in plant breeding. Plant Cell Rep. 2012;31(5):851–62.PubMedView ArticleGoogle Scholar
- Fernandez L, Le Cunff L, Tello J, Lacombe T, Boursiquot JM, Fournier Level A, et al. This P: Haplotype diversity of VvTFL1A gene and association with cluster traits in grapevine (V. vinifera). BMC Plant Biol. 2014;14(1):209.Google Scholar
- Langewisch T, Zhang H, Vincent R, Joshi T, Xu D, Bilyeu K. Major soybean maturity gene haplotypes revealed by SNPViz analysis of 72 sequenced soybean genomes. PLoS One. 2014;9(4):e94150.PubMed CentralPubMedView ArticleGoogle Scholar
- Prince SJ, Song L, Qiu D, Maldonado Dos Santos JV, Chai C, Joshi T, et al. Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean. BMC Genomics. 2015;16(1):132.Google Scholar
- Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40(Database issue):D1178–86.Google Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.PubMed CentralPubMedView ArticleGoogle Scholar
- Apweiler R, Attwood TK, Bairoch A, Bateman A, Birney E, Biswas M, et al. The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Res. 2001;29(1):37–40.Google Scholar
- Tamura K, Nei M, Kumar S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc Natl Acad Sci U S A. 2004;101(30):11030–5.PubMed CentralPubMedView ArticleGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28(10):2731–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Thijs G, Lescot M, Marchal K, Rombauts S, De Moor B, Rouze P, et al. A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics. 2001;17(12):1113–22.Google Scholar
- Bülow L, Brill Y, Hehl R. AthaMap-assisted transcription factor target gene identification in Arabidopsis thaliana. Database. 2010;2010:034.View ArticleGoogle Scholar
- Joshi T, Fitzpatrick MR, Chen S, Liu Y, Zhang H, Endacott RZ, et al. Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding. Nucleic Acids Res. 2014;42(Database issue):D1245–52.Google Scholar
- Cannon EK, Cannon SB. Chromosome visualization tool: a whole genome viewer. Int J Plant Geno. 2011;2011:373875.Google Scholar
- Suyama M, Torrents D, Bork P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006;34 suppl 2:W609–12.PubMed CentralPubMedView ArticleGoogle Scholar
- Page RD. Visualizing phylogenetic trees using TreeView. In: Curr protoc bioinformatics. 2002. p. 6.2. 1–6.2. 15. vol. Chapter 6.Google Scholar
- Liu C-M, Wong T, Wu E, Luo R, Yiu S-M, Li Y, et al. SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics. 2012;28(6):878–9.Google Scholar
- Van Dongen JT, Ammerlaan AM, Wouterlood M, Van Aelst AC, Borstlap AC. Structure of the developing pea seed coat and the post-phloem transport pathway of nutrients. Ann Bot. 2003;91(6):729–37.PubMedView ArticleGoogle Scholar
- Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.PubMedView ArticleGoogle Scholar
- Milne I, Shaw P, Stephen G, Bayer M, Cardle L, Thomas WT, et al. Flapjack—graphical genotype visualization. Bioinformatics. 2010;26(24):3133–4.Google Scholar
- Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567–80.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.