FungiDB (fungidb.org) is a free online resource for data mining and functional genomics analysis for fungal and oomycete species. FungiDB is part of the Eukaryotic Pathogen Genomics Database Resource (EuPathDB, eupathdb.org) platform that integrates genomic, transcriptomic, proteomic, and phenotypic datasets, and other types of data for pathogenic and nonpathogenic, free-living and parasitic organisms. FungiDB is one of the largest EuPathDB databases containing nearly 100 genomes obtained from GenBank, Aspergillus Genome Database (AspGD), The Broad Institute, Joint Genome Institute (JGI), Ensembl, and other sources. FungiDB offers a user-friendly web interface with embedded bioinformatics tools that support custom in silico experiments that leverage FungiDB-integrated data. In addition, a Galaxy-based workspace enables users to generate custom pipelines for large-scale data analysis (e.g., RNA-Seq, variant calling, etc.). This review provides an introduction to the FungiDB resources and focuses on available features, tools, and queries and how they can be used to mine data across a diverse range of integrated FungiDB datasets and records.

Environmental conditions profoundly affect plant disease development; however, the underlying molecular bases are not well understood. Here we show that elevated temperature significantly increases the susceptibility of Arabidopsis to Pseudomonas syringae pv. tomato (Pst) DC3000 independently of the phyB/PIF thermosensing pathway. Instead, elevated temperature promotes translocation of bacterial effector proteins into plant cells and causes a loss of ICS1-mediated salicylic acid (SA) biosynthesis. Global transcriptome analysis reveals a major temperature-sensitive node of SA signalling, impacting ~60% of benzothiadiazole (BTH)-regulated genes, including ICS1 and the canonical SA marker gene, PR1. Remarkably, BTH can effectively protect Arabidopsis against Pst DC3000 infection at elevated temperature despite the lack of ICS1 and PR1 expression. Our results highlight the broad impact of a major climate condition on the enigmatic molecular interplay between temperature, SA defence and function of a central bacterial virulence system in the context of a widely studied susceptible plant–pathogen interaction.

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions.

Summary Nannochloropsis oceanica is an oleaginous microalga rich in ω3 long‐chain polyunsaturated fatty acids (LC‐PUFAs) content, in the form of eicosapentaenoic acid (EPA). We identified the enzymes involved in LC‐PUFA biosynthesis in N. oceanica CCMP1779 and generated multigene expression vectors aiming at increasing LC‐PUFA content in vivo. We isolated the cDNAs encoding four fatty acid desaturases (FAD) and determined their function by heterologous expression in S. cerevisiae. To increase the expression of multiple fatty acid desaturases in N. oceanica CCMP1779, we developed a genetic engineering toolkit that includes an endogenous bidirectional promoter and optimized peptide bond skipping 2A peptides. The toolkit also includes multiple epitopes for tagged fusion protein production and two antibiotic resistance genes. We applied this toolkit, towards building a gene stacking system for N. oceanica that consists of two vector series, pNOC‐OX and pNOC‐stacked. These tools for genetic engineering were employed to test the effects of the overproduction of one, two or three desaturase‐encoding cDNAs in N. oceanica CCMP1779 and prove the feasibility of gene stacking in this genetically tractable oleaginous microalga. All FAD overexpressing lines had considerable increases in the proportion of LC‐PUFAs, with the overexpression of Δ12 and Δ5 FAD encoding sequences leading to an increase in the final ω3 product, EPA.

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BackgroundThe cyclic peptide toxins of Amanita mushrooms, such as α-amanitin and phalloidin, are encoded by the “MSDIN” gene family and ribosomally biosynthesized. Based on partial genome sequence and PCR analysis, some members of the MSDIN family were previously identified in Amanita bisporigera, and several other members are known from other species of Amanita. However, the complete complement in any one species, and hence the genetic capacity for these fungi to make cyclic peptides, remains unknown.ResultsDraft genome sequences of two cyclic peptide-producing mushrooms, the “Death Cap” A. phalloides and the “Destroying Angel” A. bisporigera, were obtained. Each species has ~30 MSDIN genes, most of which are predicted to encode unknown cyclic peptides. Some MSDIN genes were duplicated in one or the other species, but only three were common to both species. A gene encoding cycloamanide B, a previously described nontoxic cyclic heptapeptide, was also present in A. phalloides, but genes for antamanide and cycloamanides A, C, and D were not. In A. bisporigera, RNA expression was observed for 20 of the MSDIN family members. Based on their predicted sequences, novel cyclic peptides were searched for by LC/MS/MS in extracts of A. phalloides. The presence of two cyclic peptides, named cycloamanides E and F with structures cyclo(SFFFPVP) and cyclo(IVGILGLP), was thereby demonstrated. Of the MSDIN genes reported earlier from another specimen of A. bisporigera, 9 of 14 were not found in the current genome assembly. Differences between previous and current results for the complement of MSDIN genes and cyclic peptides in the two fungi probably represents natural variation among geographically dispersed isolates of A. phalloides and among the members of the poorly defined A. bisporigera species complex. Both A. phalloides and A. bisporigera contain two prolyl oligopeptidase genes, one of which (POPB) is probably dedicated to cyclic peptide biosynthesis as it is in Galerina marginata.ConclusionThe MSDIN gene family has expanded and diverged rapidly in Amanita section Phalloideae. Together, A. bisporigera and A. phalloides are predicted to have the capacity to make more than 50 cyclic hexa-, hepta-, octa-, nona- and decapeptides.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3378-7) contains supplementary material, which is available to authorized users.

A contiguous assembly of the inbred ‘EL10’ sugar beet (Beta vulgaris ssp. vulgaris) genome was constructed using PacBio long read sequencing, BioNano optical mapping, Hi-C scaffolding, and Illumina short read error correction. The EL10.1 assembly was 540 Mb, of which 96.7% was contained in nine chromosome-sized pseudomolecules with lengths from 52 to 65 Mb, and 31 contigs with a median size of 282 kb that remained unassembled. Gene annotation incorporating RNAseq data and curated sequences via the MAKER annotation pipeline generated 24,255 gene models. Results indicated that the EL10.1 genome assembly is a contiguous genome assembly highly congruent with the published sugar beet reference genome. Gross duplicate gene analyses of EL10.1 revealed little large-scale intra-genome duplication. Reduced gene copy number for well-annotated gene families relative to other core eudicots was observed, especially for transcription factors. Variation in genome size in B. vulgaris was investigated by flow cytometry among 50 individuals producing estimates from 633 to 875 Mb/1C. Read depth mapping with short-read whole genome sequences from other sugar beet germplasm suggested that relatively few regions of the sugar beet genome appeared associated with high-copy number variation.

BackgroundAccurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. When gene prediction programs are trained on a subset of grass genes with random GC content, they are effectively being trained on two classes of genes at once, and this can be expected to result in poor results when genes are predicted in new genome sequences.ResultsWe find that gene prediction programs trained on grass genes with random GC content do not completely predict all grass genes with extreme GC content. We show that gene prediction programs that are trained with grass genes with high or low GC content can make both better and unique gene predictions compared to gene prediction programs that are trained on genes with random GC content. By separately training gene prediction programs with genes from multiple GC ranges and using the programs within the MAKER genome annotation pipeline, we were able to improve the annotation of the Oryza sativa genome compared to using the standard MAKER annotation protocol. Gene structure was improved in over 13% of genes, and 651 novel genes were predicted by the GC-specific MAKER protocol.ConclusionsWe present a new GC-specific MAKER annotation protocol to predict new and improved gene models and assess the biological significance of this method in Oryza sativa. We expect that this protocol will also be beneficial for gene prediction in any organism with bimodal or other unusual gene GC content.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1942-z) contains supplementary material, which is available to authorized users.