BackgroundThe prevailing paradigm of host-parasite evolution is that arms races lead to increasing specialisation via genetic adaptation. Insect herbivores are no exception and the majority have evolved to colonise a small number of closely related host species. Remarkably, the green peach aphid, Myzus persicae, colonises plant species across 40 families and single M. persicae clonal lineages can colonise distantly related plants. This remarkable ability makes M. persicae a highly destructive pest of many important crop species.ResultsTo investigate the exceptional phenotypic plasticity of M. persicae, we sequenced the M. persicae genome and assessed how one clonal lineage responds to host plant species of different families. We show that genetically identical individuals are able to colonise distantly related host species through the differential regulation of genes belonging to aphid-expanded gene families. Multigene clusters collectively upregulate in single aphids within two days upon host switch. Furthermore, we demonstrate the functional significance of this rapid transcriptional change using RNA interference (RNAi)-mediated knock-down of genes belonging to the cathepsin B gene family. Knock-down of cathepsin B genes reduced aphid fitness, but only on the host that induced upregulation of these genes.ConclusionsPrevious research has focused on the role of genetic adaptation of parasites to their hosts. Here we show that the generalist aphid pest M. persicae is able to colonise diverse host plant species in the absence of genetic specialisation. This is achieved through rapid transcriptional plasticity of genes that have duplicated during aphid evolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1145-3) contains supplementary material, which is available to authorized users.

Summary: RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods. However, many bioinformatics tools do not scale accordingly, are cumbersome, or require extensive support from bioinformatics experts. Therefore, researchers need user-friendly, robust tools, capable of not only processing large sRNA datasets in a reasonable time frame but also presenting the results in an intuitive fashion and visualizing sRNA genomic features. Herein, we present the UEA sRNA workbench, a suite of tools that is a successor to the web-based UEA sRNA Toolkit, but in downloadable format and with several enhanced and additional features.Availability: The program and help pages are available at http://srna-workbench.cmp.uea.ac.uk.Contact: vincent.moulton@cmp.uea.ac.uk

Motivation De novo assembly of whole genome shotgun (WGS) next-generation sequencing (NGS) data benefits from high-quality input with high coverage. However, in practice, determining the quality and quantity of useful reads quickly and in a reference-free manner is not trivial. Gaining a better understanding of the WGS data, and how that data is utilized by assemblers, provides useful insights that can inform the assembly process and result in better assemblies.ResultsWe present the K-mer Analysis Toolkit (KAT): a multi-purpose software toolkit for reference-free quality control (QC) of WGS reads and de novo genome assemblies, primarily via their k-mer frequencies and GC composition. KAT enables users to assess levels of errors, bias and contamination at various stages of the assembly process. In this paper we highlight KAT’s ability to provide valuable insights into assembly composition and quality of genome assemblies through pairwise comparison of k-mers present in both input reads and the assemblies.Availability and ImplementationKAT is available under the GPLv3 license at: https://github.com/TGAC/KAT.Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: De novo assembly of whole genome shotgun (WGS) next-generation sequencing (NGS) data benefits from high-quality input with high coverage. However, in practice, determining the quality and quantity of useful reads quickly and in a reference-free manner is not trivial. Gaining a better understanding of the WGS data, and how that data is utilized by assemblers, provides useful insights that can inform the assembly process and result in better assemblies. Results: We present the K-mer Analysis Toolkit (KAT): a multi-purpose software toolkit for reference-free quality control (QC) of WGS reads and de novo genome assemblies, primarily via their k-mer frequencies and GC composition. KAT enables users to assess levels of errors, bias and contamination at various stages of the assembly process. In this paper we highlight KAT's ability to provide valuable insights into assembly composition and quality of genome assemblies through pairwise comparison of k-mers present in both input reads and the assemblies.

Summary Microalgae play a major role as primary producers in aquatic ecosystems. Cell signalling regulates their interactions with the environment and other organisms, yet this process in phytoplankton is poorly defined. Using the marine planktonic diatom Pseudo‐nitzschia multistriata, we investigated the cell response to cues released during sexual reproduction, an event that demands strong regulatory mechanisms and impacts on population dynamics.We sequenced the genome of P. multistriata and performed phylogenomic and transcriptomic analyses, which allowed the definition of gene gains and losses, horizontal gene transfers, conservation and evolutionary rate of sex‐related genes. We also identified a small number of conserved noncoding elements.Sexual reproduction impacted on cell cycle progression and induced an asymmetric response of the opposite mating types. G protein‐coupled receptors and cyclic guanosine monophosphate (cGMP) are implicated in the response to sexual cues, which overall entails a modulation of cell cycle, meiosis‐related and nutrient transporter genes, suggesting a fine control of nutrient uptake even under nutrient‐replete conditions.The controllable life cycle and the genome sequence of P. multistriata allow the reconstruction of changes occurring in diatoms in a key phase of their life cycle, providing hints on the evolution and putative function of their genes and empowering studies on sexual reproduction.

BackgroundThe performance of RNA sequencing (RNA-seq) aligners and assemblers varies greatly across different organisms and experiments, and often the optimal approach is not known beforehand.ResultsHere, we show that the accuracy of transcript reconstruction can be boosted by combining multiple methods, and we present a novel algorithm to integrate multiple RNA-seq assemblies into a coherent transcript annotation. Our algorithm can remove redundancies and select the best transcript models according to user-specified metrics, while solving common artifacts such as erroneous transcript chimerisms.ConclusionsWe have implemented this method in an open-source Python3 and Cython program, Mikado, available on GitHub.

BackgroundSexual reproduction is an obligate phase in the life cycle of most eukaryotes. Meiosis varies among organisms, which is reflected by the variability of the gene set associated to the process. Diatoms are unicellular organisms that belong to the stramenopile clade and have unique life cycles that can include a sexual phase.ResultsThe exploration of five diatom genomes and one diatom transcriptome led to the identification of 42 genes potentially involved in meiosis. While these include the majority of known meiosis-related genes, several meiosis-specific genes, including DMC1, could not be identified. Furthermore, phylogenetic analyses supported gene identification and revealed ancestral loss and recent expansion in the RAD51 family in diatoms. The two sexual species Pseudo-nitzschia multistriata and Seminavis robusta were used to explore the expression of meiosis-related genes: RAD21, SPO11-2, RAD51-A, RAD51-B and RAD51-C were upregulated during meiosis, whereas other paralogs in these families showed no differential expression patterns, suggesting that they may play a role during vegetative divisions. An almost identical toolkit is shared among Pseudo-nitzschia multiseries and Fragilariopsis cylindrus, as well as two species for which sex has not been observed, Phaeodactylum tricornutum and Thalassiosira pseudonana, suggesting that these two may retain a facultative sexual phase.ConclusionsOur results reveal the conserved meiotic toolkit in six diatom species and indicate that Stramenopiles share major modifications of canonical meiosis processes ancestral to eukaryotes, with important divergences in each Kingdom.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1983-5) contains supplementary material, which is available to authorized users.

Next-generation sequencing technologies enable rapid and cheap genome-wide transcriptome analysis, providing vital information about gene structure, transcript expression, and alternative splicing. Key to this is the accurate identification of exon-exon junctions from RNA sequenced (RNA-seq) reads. A number of RNA-seq aligners capable of splitting reads across these splice junctions (SJs) have been developed; however, it has been shown that while they correctly identify most genuine SJs available in a given sample, they also often produce large numbers of incorrect SJs. Here, we describe the extent of this problem using popular RNA-seq mapping tools and present a new method, called Portcullis, to rapidly filter false SJs derived from spliced alignments. We show that Portcullis distinguishes between genuine and false-positive junctions to a high degree of accuracy across different species, samples, expression levels, error profiles, and read lengths. Portcullis is portable, efficient, and, to our knowledge, currently the only SJ prediction tool that reliably scales for use with large RNA-seq datasets and large, highly fragmented genomes, while delivering accurate SJs.