Phytophthora cinnamomi is an oomycete pathogen of global relevance. It is considered as one of the most invasive species, which has caused irreversible damage to natural ecosystems and horticultural crops. There is currently a lack of a high-quality reference genome for this species despite several attempts that have been made towards sequencing its genome. The lack of a good quality genome sequence has been a setback for various genetic and genomic research to be done on this species. As a consequence, little is known regarding its genome characteristics and how these contribute to its pathogenicity and invasiveness. In this work we generated a high-quality genome sequence and annotation for P. cinnamomi using a combination of Oxford Nanopore and Illumina sequencing technologies. The annotation was done using RNA-Seq data as supporting gene evidence. The final assembly consisted of 133 scaffolds, with an estimated genome size of 109.7 Mb, N50 of 1.18 Mb, and BUSCO completeness score of 97.5%. Genome part Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington's disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins. In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversamplrently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http//47.100.136.418081/.The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http//47.100.136.418081/. The lack of an understanding about the genomic architecture underpinning parental behaviour in subsocial insects displaying simple parental behaviours prevents the development of a full understanding about the evolutionary origin of sociality. selleck chemicals Lethrus apterus is one of the few insect species that has biparental care. Division of labour can be observed between parents during the reproductive period in order to provide food and protection for their offspring. Here, we report the draft genome of L. apterus, the first genome in the family Geotrupidae. The final assembly consisted of 286.93 Mbp in 66,933 scaffolds. Completeness analysis found the assembly contained 93.5% of the Endopterygota core BUSCO gene set. Ab initio gene prediction resulted in 25,385 coding genes, whereas homology-based analyses predicted 22,551 protein coding genes. After merging, 20,734 were found during functional annotation. Compared to other publicly available beetle genomes, 23,528 genes among the predicted genes were assigned to orthogroups of which 1664 were in species-specific groups. Additionally, reproduction related genes were found among the predicted genes based on which a reduction in the number of odorant- and pheromone-binding proteins was detected. These genes can be used in further comparative and functional genomic researches which can advance our understanding of the genetic basis and hence the evolution of parental behaviour.These genes can be used in further comparative and functional genomic researches which can advance our understanding of the genetic basis and hence the evolution of parental behaviour. Sucrose nonfermenting-1 (SNF1)-related protein kinases (SnRKs) play important roles in regulating metabolism and stress responses in plants, providing a conduit for crosstalk between metabolic and stress signalling, in some cases involving the stress hormone, abscisic acid (ABA). The burgeoning and divergence of the plant gene family has led to the evolution of three subfamilies, SnRK1, SnRK2 and SnRK3, of which SnRK2 and SnRK3 are unique to plants. Therefore, the study of SnRKs in crops may lead to the development of strategies for breeding crop varieties that are more resilient under stress conditions. In the present study, we describe the SnRK gene family of barley (Hordeum vulgare), the widespread cultivation of which can be attributed to its good adaptation to different environments. The barley HvSnRK gene family was elucidated in its entirety from publicly-available genome data and found to comprise 50 genes. Phylogenetic analyses assigned six of the genes to the HvSnRK1 subfamily, 10 to HvSnRK2 and) and HvSnRK3 (34 members), showing differential positive and negative responses to ABA. Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that the network structure stays constant across time, while the network parameters are time-dependent. The objective is then to learn the network structure along with changepoints that divide the time series into time segments. An uncoupled model learns the parameters separately for each segment, while a coupled model enforces the parameters of any segment to stay similar to those of the previous segment. In this paper, we propose a new consensus model that infers for each individual time segment whether it is coupled to (or uncoupled from) the previous segment. The results show that the new consensus model is superior to the uncoupled and the coupled model, as well as superior to a recently proposed generalized coupled model. The newly proposed model has the uncoupled and the coupled model as limiting cases, and it is able to infer the best trade-off between them from the data.