RNA-Seq analysis of soft rush (Juncus effusus): transcriptome sequencing, de novo assembly, annotation, and polymorphism identification.

BACKGROUND: Juncus effusus L. (family: Juncaceae; order: Poales) is a helophytic rush growing in temperate damp or wet terrestrial habitats and is of almost cosmopolitan distribution. The species has been studied intensively with respect to its interaction with co-occurring plants as well as microbes being involved in major biogeochemical cycles. J. effusus has biotechnological value as component of Constructed Wetlands where the plant has been employed in phytoremediation of contaminated water. Its genome has not been sequenced. RESULTS: In this study we carried out functional annotation and polymorphism analysis of de novo assembled RNA-Seq data from 18 genotypes using 249 million paired-end Illumina HiSeq reads and 2.8 million 454 Titanium reads. The assembly comprised 158,591 contigs with a mean contig length of 780 bp. The assembly was annotated using the dammit! annotation pipeline, which queries the databases OrthoDB, Pfam-A, Rfam, and runs BUSCO (Benchmarking Single-Copy Ortholog genes). In total, 111,567 contigs (70.3%) were annotated with functional descriptions, assigned gene ontology terms, and conserved protein domains, which resulted in 30,932 non-redundant gene sequences. Results of BUSCO and KEGG pathway analyses were similar for J. effusus as for the well-studied members of the Poales, Oryza sativa and Sorghum bicolor. A total of 566,433 polymorphisms were identified in transcribed regions with an average frequency of 1 polymorphism in every 171 bases. CONCLUSIONS: The transcriptome assembly was of high quality and genome coverage was sufficient for global analyses. This annotated knowledge resource can be utilized for future gene expression analysis, genomic feature comparisons, genotyping, primer design, and functional genomics in J. effusus.

Network Analysis Reveals a Role for Salicylic Acid Pathway Components in Shade Avoidance.

Plants have sophisticated mechanisms for sensing neighbor shade. To maximize their ability to compete for light, plants respond to shade through enhanced elongation and physiological changes. The shade avoidance response affects many different organs and growth stages, yet the signaling pathways underlying this response have mostly been studied in seedlings. We assayed transcriptome changes in response to shade across a 2-d time course in the wild type and 12 Arabidopsis (Arabidopsis thaliana) mutants. The resulting temporal map of transcriptional responses to shade defines early and late responses in adult plants, enabling us to determine connections between key signaling genes and downstream responses. We found a pervasive and unexpectedly strong connection between shade avoidance and genes related to salicylic acid, suggesting salicylic acid signaling to be an important shade avoidance growth regulator. We tested this connection and found that several mutants disrupting salicylic acid levels or signaling were defective in shade avoidance. The effect of these mutations on shade avoidance was specific to petiole elongation; neither hypocotyl nor flowering time responses were altered, thereby defining important stage-specific differences in the downstream shade avoidance signaling pathway. Shade treatment did not change salicylic acid levels, indicating that the mediation of shade avoidance by salicylic acid is not dependent on the modulation of salicylic acid levels. These results demonstrate that salicylic acid pathway genes also are key components of petiole shade avoidance.

Improved RNA-seq Workflows Using CyVerse Cyberinfrastructure.

RNA-seq is a vital method for understanding gene structure and expression patterns. Typical RNA-seq analysis protocols use sequencing reads of length 50 to 150 nucleotides for alignment to the reference genome and assembly of transcripts. The resultant transcripts are quantified and used for differential expression and visualization. Existing tools and protocols for RNA-seq are vast and diverse; given their differences in performance, it is critical to select an analysis protocol that is scalable, accurate, and easy to use. Tuxedo, a popular alignment-based protocol for RNA-seq analysis, has been updated with HISAT2, StringTie, StringTie-merge, and Ballgown, and the updated protocol outperforms its predecessor. Similarly, new pseudo-alignment-based protocols like Kallisto and Sleuth reduce runtime and improve performance. However, these tools are challenging for researchers lacking command-line experience. Here, we describe two new RNA-seq analysis protocols, in which all tools are deployed on CyVerse Cyberinfrastructure with user-friendly graphical user interfaces, and validate their performance using plant RNA-seq data. © 2018 by John Wiley & Sons, Inc.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Bringing your tools to CyVerse Discovery Environment using Docker.

Docker has become a very popular container-based virtualization platform for software distribution that has revolutionized the way in which scientific software and software dependencies (software stacks) can be packaged, distributed, and deployed. Docker makes the complex and time-consuming installation procedures needed for scientific software a one-time process. Because it enables platform-independent installation, versioning of software environments, and easy redeployment and reproducibility, Docker is an ideal candidate for the deployment of identical software stacks on different compute environments such as XSEDE and Amazon AWS. CyVerse's Discovery Environment also uses Docker for integrating its powerful, community-recommended software tools into CyVerse's production environment for public use. This paper will help users bring their tools into CyVerse Discovery Environment (DE) which will not only allows users to integrate their tools with relative ease compared to the earlier method of tool deployment in DE but will also help users to share their apps with collaborators and release them for public use.

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/.

Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato.

Although applied over extremely short timescales, artificial selection has dramatically altered the form, physiology, and life history of cultivated plants. We have used RNAseq to define both gene sequence and expression divergence between cultivated tomato and five related wild species. Based on sequence differences, we detect footprints of positive selection in over 50 genes. We also document thousands of shifts in gene-expression level, many of which resulted from changes in selection pressure. These rapidly evolving genes are commonly associated with environmental response and stress tolerance. The importance of environmental inputs during evolution of gene expression is further highlighted by large-scale alteration of the light response coexpression network between wild and cultivated accessions. Human manipulation of the genome has heavily impacted the tomato transcriptome through directed admixture and by indirectly favoring nonsynonymous over synonymous substitutions. Taken together, our results shed light on the pervasive effects artificial and natural selection have had on the transcriptomes of tomato and its wild relatives.

The RAD51 and DMC1 homoeologous genes of bread wheat: cloning, molecular characterization and expression analysis.

BACKGROUND: Meiotic recombination in eukaryotes requires two homologues of the E. coli RecA proteins: Rad51 and Dmc1. Both proteins play important roles in the binding of single stranded DNA, homology search, strand invasion and strand exchange. Meiotic recombination has been well studied in Arabidopsis, rice, maize and the orthologues of RAD51 and DMC1 have been characterized. However genetic analysis of the RAD51 and DMC1 genes in bread wheat has been hampered due to the absence of complete sequence information and because of the existence of multiple copies of each gene in the hexaploid wheat genome. FINDINGS: In this study we have identified that TaRAD51 and TaDMC1 homoeologues are located on group 7 and group 5 chromosomes of hexaploid wheat, respectively. Comparative sequence analysis of cDNA derived from the TaRAD51 and TaDMC1 homoeologues revealed limited sequence divergence at both the nucleotide and the amino acid level. Indeed, comparisons between the predicted amino acid sequences of TaRAD51 and TaDMC1 and those of other eukaryotes reveal a high degree of evolutionary conservation. Despite the high degree of sequence conservation at the nucleotide level, genome-specific primers for cDNAs of TaRAD51 and TaDMC1 were developed to evaluate expression patterns of individual homoeologues during meiosis. QRT-PCR analysis showed that expression of the TaRAD51 and TaDMC1 cDNA homoeologues was largely restricted to meiotic tissue, with elevated levels observed during the stages of prophase I when meiotic recombination occurs. All three homoeologues of both strand-exchange proteins (TaRAD51 and TaDMC1) are expressed in wheat. CONCLUSIONS: Bread wheat contains three expressed copies of each of the TaRAD51 and TaDMC1 homoeologues. While differences were detected between the three cDNA homoeologues of TaRAD51 as well as the three homoeologues of TaDMC1, it is unlikely that the predicted amino acid substitutions would have an effect on the protein structure, based on our three-dimensional structure prediction analyses. There are differences in the levels of expression of the three homoeologues of TaRAD51 and TaDMC1 as determined by QRT-PCR and if these differences are reflected at the protein level, bread wheat may be more dependent upon a particular homoeologue to achieve full fertility than all three equally.