Enhanced genome assembly and a new official gene set for Tribolium castaneum.

BACKGROUND: The red flour beetle Tribolium castaneum has emerged as an important model organism for the study of gene function in development and physiology, for ecological and evolutionary genomics, for pest control and a plethora of other topics. RNA interference (RNAi), transgenesis and genome editing are well established and the resources for genome-wide RNAi screening have become available in this model. All these techniques depend on a high quality genome assembly and precise gene models. However, the first version of the genome assembly was generated by Sanger sequencing, and with a small set of RNA sequence data limiting annotation quality. RESULTS: Here, we present an improved genome assembly (Tcas5.2) and an enhanced genome annotation resulting in a new official gene set (OGS3) for Tribolium castaneum, which significantly increase the quality of the genomic resources. By adding large-distance jumping library DNA sequencing to join scaffolds and fill small gaps, the gaps in the genome assembly were reduced and the N50 increased to 4753kbp. The precision of the gene models was enhanced by the use of a large body of RNA-Seq reads of different life history stages and tissue types, leading to the discovery of 1452 novel gene sequences. We also added new features such as alternative splicing, well defined UTRs and microRNA target predictions. For quality control, 399 gene models were evaluated by manual inspection. The current gene set was submitted to Genbank and accepted as a RefSeq genome by NCBI. CONCLUSIONS: The new genome assembly (Tcas5.2) and the official gene set (OGS3) provide enhanced genomic resources for genetic work in Tribolium castaneum. The much improved information on transcription start sites supports transgenic and gene editing approaches. Further, novel types of information such as splice variants and microRNA target genes open additional possibilities for analysis.

Multifaceted biological insights from a draft genome sequence of the tobacco hornworm moth, Manduca sexta.

Manduca sexta, known as the tobacco hornworm or Carolina sphinx moth, is a lepidopteran insect that is used extensively as a model system for research in insect biochemistry, physiology, neurobiology, development, and immunity. One important benefit of this species as an experimental model is its extremely large size, reaching more than 10 g in the larval stage. M. sexta larvae feed on solanaceous plants and thus must tolerate a substantial challenge from plant allelochemicals, including nicotine. We report the sequence and annotation of the M. sexta genome, and a survey of gene expression in various tissues and developmental stages. The Msex_1.0 genome assembly resulted in a total genome size of 419.4 Mbp. Repetitive sequences accounted for 25.8% of the assembled genome. The official gene set is comprised of 15,451 protein-coding genes, of which 2498 were manually curated. Extensive RNA-seq data from many tissues and developmental stages were used to improve gene models and for insights into gene expression patterns. Genome wide synteny analysis indicated a high level of macrosynteny in the Lepidoptera. Annotation and analyses were carried out for gene families involved in a wide spectrum of biological processes, including apoptosis, vacuole sorting, growth and development, structures of exoskeleton, egg shells, and muscle, vision, chemosensation, ion channels, signal transduction, neuropeptide signaling, neurotransmitter synthesis and transport, nicotine tolerance, lipid metabolism, and immunity. This genome sequence, annotation, and analysis provide an important new resource from a well-studied model insect species and will facilitate further biochemical and mechanistic experimental studies of many biological systems in insects.

Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii.

BACKGROUND: Differential expression (DE) analysis of RNA-seq data still poses inferential challenges, such as handling of transcripts characterized by low expression levels. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on transcripts characterized by a small number of read counts, so-called low-count transcripts, as motivated by an ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem exhibits qualitative phenotypic divergence consistent with enhanced drought tolerance, plausibly associated with transcripts of low expression levels. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are classified as low-count) collected from leaf tissue of individual plants of big bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the inferential performance of recently developed statistical methods for DE analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. RESULTS: Both DE methods seemed to properly control family-wise type 1 error on low-count transcripts, whereas edgeR robust showed greater power and DESeq2 showed greater precision and accuracy. However, specification of the degree of freedom parameter under edgeR robust had a non-trivial impact on inference and should be handled carefully. When properly specified, both DE methods showed overall promising inferential performance on low-count transcripts, suggesting that ad-hoc data filtering steps at arbitrary expression thresholds may be unnecessary. A note of caution is in order regarding the approximate nature of DE tests under both methods. CONCLUSIONS: Practical recommendations for DE inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications focused on transcripts of low expression levels.

A massive expansion of effector genes underlies gall-formation in the wheat pest Mayetiola destructor.

Gall-forming arthropods are highly specialized herbivores that, in combination with their hosts, produce extended phenotypes with unique morphologies [1]. Many are economically important, and others have improved our understanding of ecology and adaptive radiation [2]. However, the mechanisms that these arthropods use to induce plant galls are poorly understood. We sequenced the genome of the Hessian fly (Mayetiola destructor; Diptera: Cecidomyiidae), a plant parasitic gall midge and a pest of wheat (Triticum spp.), with the aim of identifying genic modifications that contribute to its plant-parasitic lifestyle. Among several adaptive modifications, we discovered an expansive reservoir of potential effector proteins. Nearly 5% of the 20,163 predicted gene models matched putative effector gene transcripts present in the M. destructor larval salivary gland. Another 466 putative effectors were discovered among the genes that have no sequence similarities in other organisms. The largest known arthropod gene family (family SSGP-71) was also discovered within the effector reservoir. SSGP-71 proteins lack sequence homologies to other proteins, but their structures resemble both ubiquitin E3 ligases in plants and E3-ligase-mimicking effectors in plant pathogenic bacteria. SSGP-71 proteins and wheat Skp proteins interact in vivo. Mutations in different SSGP-71 genes avoid the effector-triggered immunity that is directed by the wheat resistance genes H6 and H9. Results point to effectors as the agents responsible for arthropod-induced plant gall formation.