TOA: A software package for automated functional annotation in non-model plant species

Comparative Transcriptome Analyses of Different Rheum officinale Tissues Reveal Differentially Expressed Genes Associated with Anthraquinone, Catechin, and Gallic Acid Biosynthesis

Rheum officinale Baill. is an important traditional Chinese medicinal herb, its dried roots and rhizomes being widely utilized to cure diverse diseases. However, previous studies mainly focused on the active compounds and their pharmacological effects, and the molecular mechanism underlying the biosynthesis of these ingredients in R. officinale is still elusive. Here, we performed comparative transcriptome analyses to elucidate the differentially expressed genes (DEGs) in the root, stem, and leaf of R. officinale. A total of 236,031 unigenes with N50 of 769 bp was generated, 136,329 (57.76%) of which were annotated. A total of 5884 DEGs was identified after the comparative analyses of different tissues; 175 and 126 key enzyme genes with tissue-specific expression were found in the anthraquinone, catechin/gallic acid biosynthetic pathway, respectively, and some of these key enzyme genes were verified by qRT-PCR. The phylogeny of the PKS III family in Polygonaceae indicated that probably only PL_741 PKSIII1, PL_11549 PKSIII5, and PL_101745 PKSIII6 encoded PKSIII in the polyketide pathway. These results will shed light on the molecular basis of the tissue-specific accumulation and regulation of secondary metabolites in R. officinale, and lay a foundation for the future genetic diversity, molecular assisted breeding, and germplasm resource improvement of this essential medicinal plant.

A simple guide to de novo transcriptome assembly and annotation

A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.

Transcriptomic Complexity in Strawberry Fruit Development and Maturation Revealed by Nanopore Sequencing

The use of alternative transcription start or termination sites (aTSS or aTTS) as well as alternative splicing (AS) produce diverse transcript isoforms, playing indispensable roles in the plant development and environmental adaptations. Despite the advances in the finding of the genome-wide alternatively spliced genes in strawberry, it remains unexplored how AS responds to the developmental cues and what relevance do these outcomes have to the gene function. In this study, we have systematically investigated the transcriptome complexity using long-read Oxford Nanopore Technologies along the four successive developmental stages. The full-length cDNA sequencing results unraveled thousands of previously unexplored transcript isoforms raised from aTSS, aTTS, and AS. The relative contributions of these three processes to the complexity of strawberry fruit transcripts were compared. The aTSS and aTTS were more abundant than the AS. Differentially expressed transcripts unraveled the key transitional role of the white fruit stage. Isoform switches of transcripts from 757 genes were observed. They were associated with protein-coding potential change and domain gain or loss as the main consequences. Those genes with switched isoforms take part in the key processes of maturation in the late stages. A case study using yeast two hybrid analysis supported the functional divergence of the two isoforms of the B-box protein 22. Our results provided a new comprehensive overview of the dynamic transcriptomic landscape during strawberry fruit development and maturation.

NGScloud2: optimized bioinformatic analysis using Amazon Web Services

Background

NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment.

Methods

NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon’s large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures.

Results

The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.