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The fungal family Serendipitaceae encompasses root-associated lineages with endophytic, ericoid, orchid, and ectomycorrhizal lifestyles. Switchgrass is an important bioenergy crop for cellulosic ethanol production owing to high biomass production on marginal soils otherwise unfit for food crop cultivation. The aim of this study was to investigate the host plant responses to Serendipita spp. colonization by characterizing the switchgrass root transcriptome during different stages of symbiosis in vitro. For this, we included a native switchgrass strain, Serendipita bescii, and a related strain, S. vermifera, isolated from Australian orchids. Serendipita colonization progresses from thin hyphae that grow between root cells to, finally, the production of large, bulbous hyphae that fill root cells during the later stages of colonization. We report that switchgrass seems to perceive both fungi prior to physical contact, leading to the activation of chemical and structural defense responses and putative host disease resistance genes. Subsequently, the host defense system appears to be quenched and carbohydrate metabolism adjusted, potentially to accommodate the fungal symbiont. In addition, prior to contact, switchgrass exhibited significant increases in root hair density and root surface area. Furthermore, genes involved in phytohormone metabolism such as gibberellin, jasmonic acid, and salicylic acid were activated during different stages of colonization. Both fungal strains induced plant gene expression in a similar manner, indicating a conserved plant response to members of this fungal order. Understanding plant responsiveness to Serendipita spp. will inform our efforts to integrate them into forages and row crops for optimal plant–microbe functioning, thus facilitating low-input, sustainable agricultural practices. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

SUMMARYGLV/RGF peptide encoding genes can be identified in genomes of all plants that can form roots or root-like structures suggesting they were essential for transition of plants to land.In Medicago truncatula, five of fifteen GOLVEN(GLV)/ROOT MERISTEM GROWTH FACTOR (RGF) peptide coding genes were induced during nodule organogenesis and to a varying extent under nitrogen deficiency and auxin treatment. Expression of MtGLV9 and MtGLV10 at nodule initiation sites was dependent on the transcription factor NODULE INCEPTION.Overexpression of all five nodule-induced GLV genes in M. truncatula hairy roots as well as application of the corresponding synthetic peptides resulted in a 25-50% reduction in nodule number indicating GOLVENs are negative regulators of nodule organogenesis.The peptide GOLVEN10 shifted the position of the first formed lateral root (rhizotaxis) as well as the first formed nodule along the longitudinal primary root axis, a phenomenon we term ‘nodulotaxis’, thereby reducing the absolute length of the zone of lateral organ formation on roots.Application of synthetic GOLVEN10 peptide caused an increase in cell number but not cell length in each root cortical cell layer causing an increase in root length and a consequent spatiotemporal delay in formation of the first lateral organ.Plain Language SummaryNodule positioning is an understudied trait, yet it determines the length of the root that can support nodule formation and consequently the total number of functional nodules formed. We identify for the first time, genetic factors called GOLVEN peptides that alter nodule and lateral root positioning on the primary root along with several other traits including nodule organ initiation and root architecture.

Legumes have contributed to human health, sustainable food and feed production worldwide for centuries. The study of model legumes has played vital roles in deciphering key genes, pathways, and networks regulating biological mechanisms and agronomic traits. Along with emerging breeding technology such as genome editing, translation of the knowledge gained from model plants to crops is in high demand. The updated database (V3) was redesigned for translational genomics targeting the discovery of novel key genes in less-studied non-model legume crops by referring to the knowledge gained in model legumes. The database contains genomic data for all 22 included species, and transcriptomic data covering thousands of RNA-seq samples mostly from model species. The rich biological data and analytic tools for gene expression and pathway analyses can be used to decipher critical genes, pathways, and networks in model legumes. The integrated comparative genomic functions further facilitate the translation of this knowledge to legume crops. Therefore, the database will be a valuable resource to identify important genes regulating specific biological mechanisms or agronomic traits in the non-model yet economically significant legume crops. LegumeIP V3 is available free to the public at https://plantgrn.noble.org/LegumeIP. Access to the database does not require login, registration, or password.

Iron–sulfur (Fe–S) clusters are inorganic cofactors that are present in all kingdoms of life as part of a large number of proteins involved in several cellular processes, including DNA replication and metabolism. In this work, we demonstrate an additional role for two Fe–S cluster genes in biotic stress responses in plants. Eleven Fe–S cluster genes, including the NITROGEN FIXATION S-LIKE1 (NFS1) and its interactor FRATAXIN (FH), when silenced in Nicotiana benthamiana, compromised nonhost resistance to Pseudomonas syringae pv. tomato T1. NbNFS1 expression was induced by pathogens and salicylic acid. Arabidopsis (Arabidopsis thaliana) atnfs and atfh mutants, with reduced AtNFS1 or AtFH gene expression, respectively, showed increased susceptibility to both host and nonhost pathogen infection. Arabidopsis AtNFS1 and AtFH overexpressor lines displayed decreased susceptibility to infection by host pathogen P. syringae pv. tomato DC3000. The AtNFS1 overexpression line exhibited constitutive upregulation of several defense-related genes and enrichment of gene ontology terms related to immunity and salicylic acid responses. Our results demonstrate that NFS1 and its interactor FH are involved not only in nonhost resistance but also in basal resistance, suggesting a new role of the Fe–S cluster pathway in plant immunity.

We report an advanced web server, the plant-specific small noncoding RNA interference tool pssRNAit, which can be used to design a pool of small interfering RNAs (siRNAs) for highly effective, specific, and nontoxic gene silencing in plants. In developing this tool, we integrated the transcript dataset of plants, several rules governing gene silencing, and a series of computational models of the biological mechanism of the RNA interference (RNAi) pathway. The designed pool of siRNAs can be used to construct a long double-strand RNA and expressed through virus-induced gene silencing (VIGS) or synthetic transacting siRNA vectors for gene silencing. We demonstrated the performance of pssRNAit by designing and expressing the VIGS constructs to silence Phytoene desaturase (PDS) or a ribosomal protein-encoding gene, RPL10 (QM), in Nicotiana benthamiana. We analyzed the expression levels of predicted intended-target and off-target genes using reverse transcription quantitative PCR. We further conducted an RNA-sequencing-based transcriptome analysis to assess genome-wide off-target gene silencing triggered by the fragments that were designed by pssRNAit, targeting different homologous regions of the PDS gene. Our analyses confirmed the high accuracy of siRNA constructs designed using pssRNAit. The pssRNAit server, freely available at https://plantgrn.noble.org/pssRNAit/, supports the design of highly effective and specific RNAi, VIGS, or synthetic transacting siRNA constructs for high-throughput functional genomics and trait improvement in >160 plant species.

SUMMARY Brachypodium distachyon is an annual C3 grass used as a monocot model system in functional genomics research. Insertional mutagenesis is a powerful tool for both forward and reverse genetics studies. In this study, we explored the possibility of using the tobacco retrotransposon Tnt1 to create a transposon‐based insertion mutant population in B. distachyon. We developed transgenic B. distachyon plants expressing Tnt1 (R0) and in the subsequent regenerants (R1) we observed that Tnt1 actively transposed during somatic embryogenesis, generating an average of 6.37 insertions per line in a population of 19 independent R1 regenerant plants analyzed. In seed‐derived progeny of R1 plants, Tnt1 segregated in a Mendelian ratio of 3:1 and no new Tnt1 transposition was observed. A total of 126 flanking sequence tags (FSTs) were recovered from the analyzed R0 and R1 lines. Analysis of the FSTs showed a uniform pattern of insertion in all the chromosomes (1–5) without any preference for a particular chromosome region. Considering the average length of a gene transcript to be 3.37 kb, we estimated that 29 613 lines are required to achieve a 90% possibility of tagging a given gene in the B. distachyon genome using the Tnt1‐based mutagenesis approach. Our results show the possibility of using Tnt1 to achieve near‐saturation mutagenesis in B. distachyon, which will aid in functional genomics studies of other C3 grasses., Significance Statement Retrotransposable elements transpose from one position in the DNA to another during embryogenesis, causing mutations at the genomic landing site. Here we show that Tnt1, a retrotransposable element from tobacco, can transpose in a monocot plant, Brachypodium distachyon. Based on the analyses of Tnt1 insertions in B. distachyon, we estimated that 29 613 lines would be needed to achieve a 90% probability of tagging every gene in the B. distachyon genome.

A growing number of small secreted peptides (SSPs) in plants are recognized as important regulatory molecules with roles in processes such as growth, development, reproduction, stress tolerance, and pathogen defense. Recent discoveries further implicate SSPs in regulating root nodule development, which is of particular significance for legumes. SSP-coding genes are frequently overlooked, because genome annotation pipelines generally ignore small open reading frames, which are those most likely to encode SSPs. Also, SSP-coding small open reading frames are often expressed at low levels or only under specific conditions, and thus are underrepresented in non-tissue-targeted or non-condition-optimized RNA-sequencing projects. We previously identified 4,439 SSP-encoding genes in the model legume Medicago truncatula To support systematic characterization and annotation of these putative SSP-encoding genes, we developed the M. truncatula Small Secreted Peptide Database (MtSSPdb; https://mtsspdb.noble.org/). MtSSPdb currently hosts (1) a compendium of M. truncatula SSP candidates with putative function and family annotations; (2) a large-scale M. truncatula RNA-sequencing-based gene expression atlas integrated with various analytical tools, including differential expression, coexpression, and pathway enrichment analyses; (3) an online plant SSP prediction tool capable of analyzing protein sequences at the genome scale using the same protocol as for the identification of SSP genes; and (4) information about a library of synthetic peptides and root and nodule phenotyping data from synthetic peptide screens in planta. These datasets and analytical tools make MtSSPdb a unique and valuable resource for the plant research community. MtSSPdb also has the potential to become the most complete database of SSPs in plants.

Phased small interfering RNAs (phasiRNAs) are a class of non-coding RNAs that perform essential functions in plants. Unlike microRNA biogenesis from a hairpin structure, the production of phasiRNAs usually requires a phase initiator and an RNA-dependent RNA polymerase (RDR) to form double-strand RNAs. By using full-length rice cDNA (KL-cDNA) to identify phasiRNA loci, we found that a putative non-coding sequence with a long hairpin structure generates the phasiRNAs, which we name Long Hairpin-structure containing non-coding RNA (LHR). The biogenesis of LHR-derived phasiRNAs was dependent on rice DCL4, but not on RDR2/6, DCL1, or DCL3. Since all of the LHR-phasiRNAs (-5p from the forward strand and -3p from the reverse strand of the dsRNAs) are mapped to the forward strand of LHR, LHR-phasiRNAs should be derived from its hairpin structure, similar to a microRNA precursor. A degradome-based validation suggested that several thylakoid-related genes were targeted by LHR-phasiRNAs. In addition, the production of LHR-phasiRNAs was completely abolished in the lhr mutant, which also exhibited decreased plant height, leaf size, and grain weight, probably through the regulation of photosynthesis. Based on our results, we propose a microRNA biogenesis-like pathway for producing phased siRNAs that expands our understanding of the current model of phased siRNA biogenesis in plants.

The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called. Additionally, allelic heterozygosity can be very high in some lines, causing genetic and sometimes phenotypic segregation in their descendants. Genetic and phenotypic segregation degrades the original accession’s specificity and makes it difficult to distinguish one accession from another. Therefore, it is vitally important to determine and validate HapMap accessions before one conducts a GWAS analysis. However, to the best of our knowledge, there are no prior methodologies or tools that can readily distinguish or validate multiple accessions in a HapMap population. We devised a bioinformatics approach to distinguish multiple HapMap accessions using only a minimum number of genetic markers. First, we assign each candidate marker with a distinguishing score (DS), which measures its capability in distinguishing accessions. The DS score prioritizes those markers with higher percentages of homozygous genotypes (allele combinations), as they can be stably passed on to offspring. Next, we apply the “set-partitioning” concept to select optimal markers by recursively partitioning accession sets. Subsequently, we build a hierarchical decision tree in which a specific path represents the selected markers and the homogenous genotypes that can be used to distinguish one accession from others in the HapMap population. Based on these algorithms, we developed a web tool named MAD-HiDTree (Multiple Accession Distinguishment-Hierarchical Decision Tree), designed to analyze a user-input genotype matrix and construct a hierarchical decision tree. Using genetic marker data extracted from the Medicago truncatula HapMap population, we successfully constructed hierarchical decision trees by which the original 262 M. truncatula accessions could be efficiently distinguished. PCR experiments verified our proposed method, confirming that MAD-HiDTree can be used for the identification of a specific accession. MAD-HiDTree was developed in C/C++ in Linux. Both the source code and test data are publicly available at https://bioinfo.noble.org/MAD-HiDTree/.

Betula L. (birch) is a pioneer hardwood tree species with ecological, economic, and evolutionary importance in the Northern Hemisphere. We sequenced the Betula platyphylla genome and assembled the sequences into 14 chromosomes. The Betula genome lacks evidence of recent whole-genome duplication and has the same paleoploidy level as Vitis vinifera and Prunus mume. Phylogenetic analysis of lignin pathway genes coupled with tissue-specific expression patterns provided clues for understanding the formation of higher ratios of syringyl to guaiacyl lignin observed in Betula species. Our transcriptome analysis of leaf tissues under a time-series cold stress experiment revealed the presence of the MEKK1–MKK2–MPK4 cascade and six additional mitogen-activated protein kinases that can be linked to a gene regulatory network involving many transcription factors and cold tolerance genes. Our genomic and transcriptome analyses provide insight into the structures, features, and evolution of the B. platyphylla genome. The chromosome-level genome and gene resources of B. platyphylla obtained in this study will facilitate the identification of important and essential genes governing important traits of trees and genetic improvement of B. platyphylla.

Genome-wide association study data analyses often face two significant challenges: (i) high dimensionality of single-nucleotide polymorphism (SNP) genotypes and (ii) imputation of missing values. SNPs are not independent due to physical linkage and natural selection. The correlation of nearby SNPs is known as linkage disequilibrium (LD), which can be used for LD conceptual SNP bin mapping, missing genotype inferencing and SNP dimension reduction. We used a stochastic process to describe the SNP signals and proposed two types of autocorrelations to measure nearby SNPs’ information redundancy. Based on the calculated autocorrelation coefficients, we constructed LD bins. We adopted a k-nearest neighbors algorithm (kNN) to impute the missing genotypes. We proposed several novel methods to find the optimal synthetic marker to represent the SNP bin. We also proposed methods to evaluate the information loss or information conservation between using the original genome-wide markers and using dimension-reduced synthetic markers. Our performance assessments on the real-life SNP data from a rice recombinant inbred line (RIL) population and a rice HapMap project show that the new methods produce satisfactory results. We implemented these functional modules in C/C++ and streamlined them into a web-based pipeline named PIP-SNP (https://bioinfo.noble.org/PIP_SNP/) for processing SNP data.

CLE53 negatively regulates root colonization with arbuscular mycorrhizal fungi in a SUNN- and RDN1-dependent manner., Plants and arbuscular mycorrhizal fungi (AMF) engage in mutually beneficial symbioses based on a reciprocal exchange of nutrients. The beneficial character of the symbiosis is maintained through a mechanism called autoregulation of mycorrhization (AOM). AOM includes root-to-shoot-to-root signaling; however, the molecular details of AOM are poorly understood. AOM shares many features of autoregulation of nodulation (AON) where several genes are known, including the receptor-like kinase SUPER NUMERIC NODULES (SUNN), root-to-shoot mobile CLAVATA3/ENDOSPERM SURROUNDING REGION (ESR)-RELATED (CLE) peptides, and the hydroxyproline O-arabinosyltransferase ROOT DETERMINED NODULATION1 (RDN1) required for post-translational peptide modification. In this work, CLE53 was identified to negatively regulate AMF symbiosis in a SUNN- and RDN1-dependent manner. CLE53 expression was repressed at low phosphorus, while it was induced by AMF colonization and high phosphorus. CLE53 overexpression reduced AMF colonization in a SUNN- and RDN1 dependent manner, while cle53, rdn1, and sunn mutants were more colonized than the wild type. RNA-sequencing identified 700 genes with SUNN-dependent regulation in AMF-colonized plants, providing a resource for future identification of additional AOM genes. Disruption of AOM genes in crops potentially constitutes a novel route for improving AMF-derived phosphorus uptake in agricultural systems with high phosphorus levels.

The interactions among genes and between genes and environment contribute significantly to the phenotypic variation of complex traits and may be possible explanations for missing heritability. However, to our knowledge no existing tool can address the two kinds of interactions. Here we propose a novel linear mixed model that considers not only the additive effects of biological markers but also the interaction effects of marker pairs. Interaction effect is demonstrated as a 2D association. Based on this linear mixed model, we developed a pipeline, namely PATOWAS. PATOWAS can be used to study transcriptome-wide and metabolome-wide associations in addition to genome-wide associations. Our case analysis with real rice recombinant inbred lines (RILs) at three omics levels demonstrates that 2D association mapping and integrative omics are able to provide a systems biology view into the analyzed traits, leading toward an answer about how genes, transcripts, proteins, and metabolites work together to produce an observable phenotype.

Plant regulatory small RNAs (sRNAs), which include most microRNAs (miRNAs) and a subset of small interfering RNAs (siRNAs), such as the phased siRNAs (phasiRNAs), play important roles in regulating gene expression. Although generated from genetically distinct biogenesis pathways, these regulatory sRNAs share the same mechanisms for post-translational gene silencing and translational inhibition. psRNATarget was developed to identify plant sRNA targets by (i) analyzing complementary matching between the sRNA sequence and target mRNA sequence using a predefined scoring schema and (ii) by evaluating target site accessibility. This update enhances its analytical performance by developing a new scoring schema that is capable of discovering miRNA–mRNA interactions at higher ‘recall rates’ without significantly increasing total prediction output. The scoring procedure is customizable for the users to search both canonical and non-canonical targets. This update also enables transmitting and analyzing ‘big’ data empowered by (a) the implementation of multi-threading chunked file uploading, which can be paused and resumed, using HTML5 APIs and (b) the allocation of significantly more computing nodes to its back-end Linux cluster. The updated psRNATarget server has clear, compelling and user-friendly interfaces that enhance user experiences and present data clearly and concisely. The psRNATarget is freely available at http://plantgrn.noble.org/psRNATarget/.

Motivation Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information Supplementary data are available at Bioinformatics online.

Puccinia novopanici is an important biotrophic fungal pathogen that causes rust disease in switchgrass. Lack of genomic resources for P. novopanici has hampered the progress toward developing effective disease resistance against this pathogen. Therefore, we have sequenced the whole genome of P. novopanici and generated a framework to understand pathogenicity mechanisms and identify effectors, repeat element invasion, genome evolution, and comparative genomics among Puccinia spp. in the future. Long- and short-read sequences were generated from P. novopanici genomic DNA by PacBio and Illumina technologies, respectively, and assembled a 99.9-Mb genome. Transcripts of P. novopanici were predicted from assembled genome using MAKER and were further validated by RNAseq data. The genome sequence information of P. novopanici will be a valuable resource for researchers working on monocot rusts and plant disease resistance in general.

SummaryWe present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.Availability and implementationGWASpro is freely available at https://bioinfo.noble.org/GWASPRO.Supplementary informationSupplementary data are available at Bioinformatics online.

Growing evidence indicates that small, secreted peptides (SSPs) play critical roles in legume growth and development, yet the annotation of SSP-coding genes is far from complete. Systematic reannotation of the Medicago truncatula genome identified 1,970 homologs of established SSP gene families and an additional 2,455 genes that are potentially novel SSPs, previously unreported in the literature. The expression patterns of known and putative SSP genes based on 144 RNA sequencing data sets covering various stages of macronutrient deficiencies and symbiotic interactions with rhizobia and mycorrhiza were investigated. Focusing on those known or suspected to act via receptor-mediated signaling, 240 nutrient-responsive and 365 nodulation-responsive Signaling-SSPs were identified, greatly expanding the number of SSP gene families potentially involved in acclimation to nutrient deficiencies and nodulation. Synthetic peptide applications were shown to alter root growth and nodulation phenotypes, revealing additional regulators of legume nutrient acquisition. Our results constitute a powerful resource enabling further investigations of specific SSP functions via peptide treatment and reverse genetics.

Genome-wide association study (GWAS) is a powerful approach that has revolutionized the field of quantitative genetics. Two-dimensional GWAS that accounts for epistatic genetic effects needs to consider the effects of marker pairs, thus quadratic genetic variants, compared to one-dimensional GWAS that accounts for individual genetic variants. Calculating genome-wide kinship matrices in GWAS that account for relationships among individuals represented by ultra-high dimensional genetic variants is computationally challenging. Fortunately, kinship matrix calculation involves pure matrix operations and the algorithms can be parallelized, particular on graphics processing unit (GPU)-empowered high-performance computing (HPC) architectures. We have devised a new method and two pipelines: KMC1D and KMC2D for kinship matrix calculation with high-dimensional genetic variants, respectively, facilitating 1D and 2D GWAS analyses. We first divide the ultra-high-dimensional markers and marker pairs into successive blocks. We then calculate the kinship matrix for each block and merge together the block-wise kinship matrices to form the genome-wide kinship matrix. All the matrix operations have been parallelized using GPU kernels on our NVIDIA GPU-accelerated server platform. The performance analyses show that the calculation speed of KMC1D and KMC2D can be accelerated by 100–400 times over the conventional CPU-based computing.

Hundreds to thousands of small secreted peptides (SSPs) are encoded in plant genomes but have been overlooked, and most remain unannotated and unstudied. Despite their low profile, they have been found to confer dramatic effects on growth and development of plants. With the growing appreciation of their significance, the development of appropriate methods to identify and functionally assess the myriad SSPs encoded in plant genomes has become critical. Here, we provide protocols for the computational and physiological analysis of SSPs in plant genomes. We first describe our methodology successfully used for genome-wide identification and annotation of SSP-coding genes in the model legume Medicago truncatula, which can be readily adapted for other plant species. We then provide protocols for the functional analysis of SSPs using various synthetic peptide screens. Considerations for the design and handling of peptides are included. © 2019 by John Wiley & Sons, Inc.

Summary Improving nitrogen (N) remobilization from aboveground to underground organs during yearly shoot senescence is an important goal for sustainable production of switchgrass (Panicum virgatum) as a biofuel crop. Little is known about the genetic control of senescence and N use efficiency in perennial grasses such as switchgrass, which limits our ability to improve the process. Switchgrass aboveground organs (leaves, stems and inflorescences) and underground organs (crowns and roots) were harvested every month over a 3-yr period. Transcriptome analysis was performed to identify genes differentially expressed in various organs during development. Total N content in aboveground organs increased from spring until the end of summer, then decreased concomitant with senescence, while N content in underground organs exhibited an increase roughly matching the decrease in shoot N during fall. Hundreds of senescence-associated genes were identified in leaves and stems. Functional grouping indicated that regulation of transcription and protein degradation play important roles in shoot senescence. Coexpression networks predict important roles for five switchgrass NAC (NAM, ATAF1,2, CUC2) transcription factors (TFs) and other TF family members in orchestrating metabolism of carbohydrates, N and lipids, protein modification/degradation, and transport processes during senescence. This study establishes a molecular basis for understanding and enhancing N remobilization and conservation in switchgrass.

The LegumeIP 2.0 database hosts large-scale genomics and transcriptomics data and provides integrative bioinformatics tools for the study of gene function and evolution in legumes. Our recent updates in LegumeIP 2.0 include gene and protein sequences, gene models and annotations, syntenic regions, protein families and phylogenetic trees for six legume species: Medicago truncatula, Glycine max (soybean), Lotus japonicus, Phaseolus vulgaris (common bean), Cicer arietinum (chickpea) and Cajanus cajan (pigeon pea) and two outgroup reference species: Arabidopsis thaliana and Poplar trichocarpa. Moreover, the LegumeIP 2.0 features the following new data resources and bioinformatics tools: (i) an integrative gene expression atlas for four model legumes that include 550 array hybridizations from M. truncatula, 962 gene expression profiles of G. max, 276 array hybridizations from L. japonicas and 56 RNA-Seq-based gene expression profiles for C. arietinum. These datasets were manually curated and hierarchically organized based on Experimental Ontology and Plant Ontology so that users can browse, search, and retrieve data for their selected experiments. (ii) New functions/analytical tools to query, mine and visualize large-scale gene sequences, annotations and transcriptome profiles. Users may select a subset of expression experiments and visualize and compare expression profiles for multiple genes. The LegumeIP 2.0 database is freely available to the public at http://plantgrn.noble.org/LegumeIP/.

This study provides a timely comparative genomic and transcriptomic analysis of the terpene synthase (TPS) gene family in Medicago truncatula (bears glandular and non‐glandular trichomes) and Arabidopsis thaliana (bears non‐glandular trichomes). The authors’ efforts aimed to gain insight into TPS function, phylogenetic relationships and the role of trichomes in terpene biosynthesis and function. In silico analysis identified 33 and 23 putative full‐length TPS genes in Arabidopsis and Medicago, respectively. All AtTPS and MtTPS fall into the five established angiosperm TPS subfamilies, with lineage‐specific expansion of Subfamily A in Arabidopsis and Subfamily G in Medicago. Large amounts of tandem duplication have occurred in both species, but only one syntenic duplication seems to have occurred in Arabidopsis, with no such duplication apparent in Medicago. Expression analysis indicates that there is much more trichome‐localised TPS expression in Medicago than in Arabidopsis. However, TPS genes were expressed in non‐glandular trichomes in both species. One trichome‐specific gene has been identified in each Medicago and Arabidopsis along with flower‐, seed‐, stem‐ and root‐specific genes. Of these, MtTPS11 is a promising candidate for trichome‐specific genetic engineering, a technology that may be possible for both plants according to the findings of this manuscript. These results suggest that non‐glandular trichomes may play a role in plant chemical defense and/or ecological communication instead of only in physical defence. Finally, the general lack of correlation between expression patterns and phylogenetic relationships in both species suggests that phylogenetic analysis alone is insufficient to predict gene function even for phylogenetically close paralogs.

In this paper, we present a novel liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and clusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms. On the basis of the deduced neutral molecular mass and retention time, we have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different electrospray ionization (ESI) modes, different instruments, even different experimental conditions. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. We compared the performances of MET-COFEA with the existing publicly available tools at LC/MS peak analysis level and demonstrated its excellent performance in this arena. MET-COFEA is freely available at http://bioinfo.noble.org/manuscript-support/met-cofea/.

Transcription factors (TFs) are proteins that regulate the expression of target genes by binding to specific cis-elements in promoter regions. Transcriptional regulators (TRs) also regulate the expression of target genes; however, they operate indirectly via interaction with the basal transcription apparatus (e.g. TFs), or by altering the accessibility of DNA to TFs via chromatin remodeling. Another type of regulatory proteins, protein kinases (PKs), function in signal transduction pathways and alter the activity of target proteins by phosphorylating them. These three important classes of regulatory proteins have been associated with numerous aspects of plant growth and development (Gapper et al., 2014; Xu and Zhang, 2015), and response to biotic and abiotic stimuli (Mickelbart et al., 2015; Zhang et al., 2013). Effective and accurate identification and classification of these genes is important for understanding their evolution, biological functions, and regulatory networks. Currently, more than 100 plant genomes have been sequenced and regulatory proteins have been systematically identified from several of these plant genomes. Databases presenting these regulatory proteins, especially TFs, have been developed, such as PlnTFDB (Pérez-Rodríguez et al., 2010) and PlantTFDB (Jin et al., 2013). However, annotations of TF/TR families and the associated classification rules have been inconsistent among different studies. For example, the PlantTFDB does not include TRs that are presented in PlnTFDB. As another example, the ?forbidden? domain (a domain that the specific TF families should not contain) of the C2H2 family is annotated as an RNase_T domain in PlantTFDB, but as a PHD domain in PlnTFDB. Presently, while the collection of genome sequences is rapidly expanding, cataloged and annotated TFs/TRs vary across different databases due to inconsistent identification and characterization criteria with serious consequences for genome scale and targeted analyses. Furthermore, in contrast to many studies focusing on specific families of plant regulators, computational tools for identification and classification of these regulatory proteins on a genome scale are very limited. Fil: Zheng, Yi. Boyce Thompson Institute; Estados Unidos Fil: Jiao, Chen. Boyce Thompson Institute; Estados Unidos Fil: Sun, Honghe. Boyce Thompson Institute; Estados Unidos Fil: Rosli, Hernan Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Fisiología Vegetal. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Fisiología Vegetal; Argentina. Boyce Thompson Institute; Estados Unidos Fil: Pombo, Marina Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Fisiología Vegetal. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Fisiología Vegetal; Argentina. Boyce Thompson Institute; Estados Unidos Fil: Zhang, Peifen. Carnegie Institution for Science; Estados Unidos Fil: Banf, Michael. Carnegie Institution for Science; Estados Unidos Fil: Dai, Xinbin. Samuel Roberts Noble Foundation; Estados Unidos Fil: Martin, Gregory B.. Boyce Thompson Institute; Estados Unidos. Cornell University; Estados Unidos Fil: Giovannoni, James J.. Boyce Thompson Institute; Estados Unidos. Center for Agriculture and Health; Estados Unidos Fil: Zhao, Patrick X.. Samuel Roberts Noble Foundation; Estados Unidos Fil: Rhee, Seung Y.. Carnegie Institution for Science; Estados Unidos Fil: Fei, Zhangjun. Boyce Thompson Institute; Estados Unidos. Center for Agriculture and Health; Estados Unidos

Aluminum (Al) toxicity in acid soils is one of the factors limiting crop production. Alfalfa (Medicago sativa L.) is the most important for - age legume worldwide and is susceptible to Al toxicity. our objective was to identify genomic regions associated with Al tolerance in tetra - ploid alfalfa. A segregating population obtained from a cross between Altet-4 (Al-tolerant geno - type) and NECS-141 (Al-sensitive genotype) was evaluated for Al tolerance using a callus bioassay and a whole plant assay in media and genotyped using molecular markers. p heno- typing results for both callus and root growth assays exhibited a normal distribution and pat - terns of transgressive segregation. The linkage maps covered 761 cM for Altet-4 and 721 cM for NECS-141. Markers associated with Al tol - erance from both assays were detected using single-factor analysis. Quantitative trait loci (QTL) for Al tolerance were identified on linkage groups (LGs) 1, 4, and 7 using interval mapping, and these QTL explained 20.8, 15.2, and 21.7% of the variation, respectively. A second popula - tion was used to evaluate the identified Al tol- erance QTL in a different genetic background. Although mechanisms of Al tolerance in tetra - ploid alfalfa are quantitative, the identification of relevant genomic regions and candidate genes involved in abiotic stress responses can facili - tate the selection of desirable alleles to acceler - ate the development of alfalfa cultivars that can be productive in acid and Al-toxic soils.

Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

The Medicago truncatula gene encoding an evolutionarily conserved antifungal defensin MtDef4.2 was cloned and characterized. In silico expression analysis indicated that MtDef4.2 is expressed in many tissues during the normal growth and development of M. truncatula. MtDef4.2 exhibits potent broad-spectrum antifungal activity against various Fusarium spp. Transgenic Arabidopsis thaliana lines in which MtDef4.2 was targeted to three different subcellular compartments were generated. These lines were tested for resistance to the obligate biotrophic oomycete Hyaloperonospora arabidopsidis Noco2 and the hemibiotrophic fungal pathogen Fusarium graminearum PH-1. MtDef4.2 directed to the extracellular space, but not to the vacuole or retained in the endoplasmic reticulum, conferred robust resistance to H. arabidopsidis. Siliques of transgenic Arabidopsis lines expressing either extracellularly or intracellularly targeted MtDef4.2 displayed low levels of resistance to F. graminearum, but accumulated substantially reduced levels of the mycotoxin deoxynivalenol. The data presented here suggest that extracellularly targeted MtDef4.2 is sufficient to provide strong resistance to the biotrophic oomycete, consistent with the extracellular lifestyle of this pathogen. However, the co-expression of extracellular and intracellular MtDef4.2 is probably required to achieve strong resistance to the hemibiotrophic pathogen F. graminearum which grows extracellularly and intracellularly.

Metabolomics Ion-based Data Extraction Algorithm (MET-IDEA) is a computer program for processing large-scale metabolomics data. MET-IDEA utilizes network Common Data Form (netCDF) data files available from a diversity of chromatographically coupled mass spectrometry (MS) systems, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized data. This article reports on recent improvements to MET-IDEA which include new visualization of peak integrations, display of mass spectra associated with integrated peaks, and optional manual peak integration. The computational performance of MET-IDEA has also been improved to avoid memory overflow during the processing of large data sets and the software made compatible with 64 bit CPUs and operating systems. These new functions improve the performance of MET-IDEA, and they allow users to visualize peak integrations and curate the results through manual integration if desired. The improved version of MET-IDEA better facilitates the quantitative analysis of complex MS-based metabolomics data. MET-IDEA is freely available for academic and non commercial use at (http://bioinfo.noble.org/gateway/index.php?option=com_wrapper&Itemid=57). Commercial use is available via licensing agreement.

Chantier qualité GA; International audience; Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation1. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species2. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ~94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa’s genomic toolbox.

Legumes play a vital role in maintaining the nitrogen cycle of the biosphere. They conduct symbiotic nitrogen fixation through endosymbiotic relationships with bacteria in root nodules. However, this and other characteristics of legumes, including mycorrhization, compound leaf development and profuse secondary metabolism, are absent in the typical model plant Arabidopsis thaliana. We present LegumeIP (http://plantgrn.noble.org/LegumeIP/), an integrative database for comparative genomics and transcriptomics of model legumes, for studying gene function and genome evolution in legumes. LegumeIP compiles gene and gene family information, syntenic and phylogenetic context and tissue-specific transcriptomic profiles. The database holds the genomic sequences of three model legumes, Medicago truncatula, Glycine max and Lotus japonicus plus two reference plant species, A. thaliana and Populus trichocarpa, with annotations based on UniProt, InterProScan, Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes databases. LegumeIP also contains large-scale microarray and RNA-Seq-based gene expression data. Our new database is capable of systematic synteny analysis across M. truncatula, G. max, L. japonicas and A. thaliana, as well as construction and phylogenetic analysis of gene families across the five hosted species. Finally, LegumeIP provides comprehensive search and visualization tools that enable flexible queries based on gene annotation, gene family, synteny and relative gene expression.

A legume specific protein database (LegProt) has been created containing sequences from seven legume species, i.e., Glycine max, Lotus japonicus, Medicago sativa, Medicago truncatula, Lupinusalbus, Phaseolus vulgaris, and Pisum sativum. The database consists of amino acid sequences translated from predicted gene models and 6-frame translations of tentative consensus (TC) sequences assembled from expressed sequence tags (ESTs) and singleton ESTs. This database was queried using mass spectral data for protein identification and identification success rates were compared to the NCBI nr database. Specifically, Mascot MS/MS ion searches of tandem nano-LC Q-TOFMS/MS mass spectral data showed that relative to the NCBI nr protein database, the LegProt database yielded a 54% increase in the average protein score (i.e., from NCBI nr 480 to LegProt 739) and a 50% increase in the average number of matched peptides (i.e., from NCBI nr 8 to LegProt 12). The overall identification success rate also increased from 88% (NCBI nr) to 93% (LegProt). Mascot peptide mass fingerprinting (PMF) searches of the LegProt database using MALDI-TOFMS data yielded a significant increase in the identification success rate from 19% (NCBI nr) to 34% (LegProt) while the average scores and average number of matched peptides showed insignificant changes. The results demonstrate that the LegProt database significantly increases legume protein identification success rates and the confidence levels compared to the commonly used NCBI nr. These improvements are primarily due to the presence of a large number of legume specific TC sequences in the LegProt database that were not found in NCBI nr. The LegProt database is freely available for download (http://bioinfo.noble.org/manuscript-support/legumedb) and will serve as a valuable resource for legume proteomics.

Plant endogenous non-coding short small RNAs (20-24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to 'open' secondary structure around small RNA's target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/.

Plant microRNAs (miRNA) target recognition mechanism was once thought to be simple and straightforward, i.e. through perfect reverse complementary matching; therefore, very few target prediction tools and algorithms were developed for plants as compared to those for animals. However, the discovery of transcription suppression and the more recent observation of widespread translational regulation by miRNAs highlight the enormous diversity and complexity of gene regulation in plant systems. This, in turn, necessitates the need for advanced computational tools/algorithms for comprehensive miRNA target analysis to help understand miRNA regulatory mechanisms. Yet, advanced/comprehensive plant miRNA target analysis tools are still lacking despite the desirability and importance of such tools, especially the ability of predicting translational inhibition and integrating transcriptome data. This review focuses on recent progress in plant miRNA target recognition mechanism, principles of target prediction based on these understandings, comparison of current prediction tools and algorithms for plant miRNA target analysis and the outlook for future directions in the development of plant miRNA target tools and algorithms.

A complete map of the Arabidopsis (Arabidopsis thaliana) proteome is clearly a major goal for the plant research community in terms of determining the function and regulation of each encoded protein. Developing genome-wide prediction tools such as for localizing gene products at the subcellular level will substantially advance Arabidopsis gene annotation. To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (for Arabidopsis subcellular localization predictor) that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews correlation coefficient values of 90.9% and 0.89, respectively. Benchmarking AtSubP on two independent data sets, one from Swiss-Prot and another containing green fluorescent protein- and mass spectrometry-determined proteins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some widely used “general” tools such as TargetP, LOCtree, PA-SUB, MultiLoc, WoLF PSORT, Plant-PLoc, and our newly created All-Plant method. Cross-comparison of AtSubP on six nontrained eukaryotic organisms (rice [Oryza sativa], soybean [Glycine max], human [Homo sapiens], yeast [Saccharomyces cerevisiae], fruit fly [Drosophila melanogaster], and worm [Caenorhabditis elegans]) revealed inferior predictions. AtSubP significantly outperformed all the prediction tools being currently used for Arabidopsis proteome annotation and, therefore, may serve as a better complement for the plant research community. A supplemental Web site that hosts all the training/testing data sets and whole proteome predictions is available at http://bioinfo3.noble.org/AtSubP/.

Plant secretory trichomes have a unique capacity for chemical synthesis and secretion and have been described as biofactories for the production of natural products. However, until recently, most trichome-specific metabolic pathways and genes involved in various trichome developmental stages have remained unknown. Furthermore, only a very limited amount of plant trichome genomics information is available in scattered databases. We present an integrated “omics” database, TrichOME, to facilitate the study of plant trichomes. The database hosts a large volume of functional omics data, including expressed sequence tag/unigene sequences, microarray hybridizations from both trichome and control tissues, mass spectrometry-based trichome metabolite profiles, and trichome-related genes curated from published literature. The expressed sequence tag/unigene sequences have been annotated based upon sequence similarity with popular databases (e.g. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Transporter Classification Database). The unigenes, metabolites, curated genes, and probe sets have been mapped against each other to enable comparative analysis. The database also integrates bioinformatics tools with a focus on the mining of trichome-specific genes in unigenes and microarray-based gene expression profiles. TrichOME is a valuable and unique resource for plant trichome research, since the genes and metabolites expressed in trichomes are often underrepresented in regular non-tissue-targeted cDNA libraries. TrichOME is freely available at http://www.planttrichome.org/.

Hop (Humulus lupulus L. Cannabaceae) is an economically important crop for the brewing industry, where it is used to impart flavor and aroma to beer, and has also drawn attention in recent years due to its potential pharmaceutical applications. Essential oils (mono- and sesquiterpenes), bitter acids (prenylated polyketides), and prenylflavonoids are the primary phytochemical components that account for these traits, and all accumulate at high concentrations in glandular trichomes of hop cones. To understand the molecular basis for terpene accumulation in hop trichomes, a trichome cDNA library was constructed and 9,816 cleansed expressed sequence tag (EST) sequences were obtained from random sequencing of 16,152 cDNA clones. The ESTs were assembled into 3,619 unigenes (1,101 contigs and 2,518 singletons). Putative functions were assigned to the unigenes based on their homology to annotated sequences in the GenBank database. Two mono- and two sesquiterpene synthases identified from the EST collection were expressed in Escherichia coli. Hop MONOTERPENE SYNTHASE2 formed the linear monterpene myrcene from geranyl pyrophosphate, whereas hop SESQUITERPENE SYNTHASE1 (HlSTS1) formed both caryophyllene and humulene from farnesyl pyrophosphate. Together, these enzymes account for the production of the major terpene constituents of the hop trichomes. HlSTS2 formed the minor sesquiterpene constituent germacrene A, which was converted to β-elemene on chromatography at elevated temperature. We discuss potential functions for other genes expressed at high levels in developing hop trichomes.

International audience; Legumes played central roles in the development of agriculture and civilization, and today account for approximately one-third of the world's primary crop production. Unfortunately, most cultivated legumes are poor model systems for genomic research. Therefore, Medicago truncatula, which has a relatively small diploid genome, has been adopted as a model species for legume genomics. To enhance its value as a model, we have generated a gene expression atlas that provides a global view of gene expression in all major organ systems of this species, with special emphasis on nodule and seed development. The atlas reveals massive differences in gene expression between organs that are accompanied by changes in the expression of key regulatory genes, such as transcription factor genes, which presumably orchestrate genetic reprogramming during development and differentiation. Interestingly, many legume-specific genes are preferentially expressed in nitrogen-fixing nodules, indicating that evolution endowed them with special roles in this unique and important organ. Comparative transcriptome analysis of Medicago versus Arabidopsis revealed significant divergence in developmental expression profiles of orthologous genes, which indicates that phylogenetic analysis alone is insufficient to predict the function of orthologs in different species. The data presented here represent an unparalleled resource for legume functional genomics, which will accelerate discoveries in legume biology.

In plants, short RNAs including approximately 21-nt microRNA (miRNA) and 21-nt trans-acting siRNA (ta-siRNA) compose a 'miRNA --> ta-siRNA --> target gene' cascade pathway that regulates gene expression at the posttranscriptional level. In this cascade, biogenesis of ta-siRNA clusters requires 21-nt intervals (i.e. phasing) and miRNA (phase-initiator) cleavage sites on its TAS transcript. Here, we report a novel web server, pssRNAMiner, which is developed to identify both the clusters of phased small RNAs as well as the potential phase-initiator. To detect phased small RNA clusters, the pssRNAMiner maps input small RNAs against user-specified transcript/genomic sequences, and then identifies phased small RNA clusters by evaluating P-values of hypergeometric distribution. To identify potential phase-initiators, pssRNAMiner aligns input phase-initiators with transcripts of TAS candidates using the Smith-Waterman algorithm. Potential cleavage sites on TAS candidates are further identified from complementary regions by weighting the alignment expectation and its distance to detected phased small RNA clusters. The pssRNAMiner web server is freely available at http://bioinfo3.noble.org/pssRNAMiner/.

Medicago truncatula is a fast-emerging model for the study of legume functional biology. We used the tobacco retrotransposon Tnt1 to tag the Medicago genome and generated over 7600 independent lines representing an estimated 190,000 insertion events. Tnt1 inserted on average at 25 different locations per genome during tissue culture, and insertions were stable during subsequent generations in soil. Analysis of 2461 Tnt1 flanking sequence tags (FSTs) revealed that Tnt1 appears to prefer gene-rich regions. The proportion of Tnt1 insertion in coding sequences was 34.1%, compared to the expected 15.9% if random insertions were to occur. However, Tnt1 showed neither unique target site specificity nor strong insertion hot spots, although some genes were more frequently tagged than others. Forward-genetic screening of 3237 R(1) lines resulted in identification of visible mutant phenotypes in approximately 30% of the regenerated lines. Tagging efficiency appears to be high, as all of the 20 mutants examined so far were found to be tagged. Taking the properties of Tnt1 into account and assuming 1.7 kb for the average M. truncatula gene size, we estimate that approximately 14,000-16,000 lines would be sufficient for 90% gene tagging coverage in M. truncatula. This is in contrast to more than 500,000 lines required to achieve the same saturation level using T-DNA tagging. Our data demonstrate that Tnt1 is an efficient insertional mutagen in M. truncatula, and could be a primary choice for other plant species with large genomes.