Notes

Non-Fickian single-file skin pore transport.
Our work shows how the use of improved analysis tools for the study of 5'P degradome can significantly increase the biological information that can be derived from such datasets and facilitate its reproducible analysis.Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially benefit human health. Identifying biosynthetic gene clusters (BGCs) involved in the biosynthesis of SMs can be a costly and complex task, especially due to the genomic diversity of fungal BGCs. Previous studies on fungal BGC discovery present limited scope and can restrict the discovery of new BGCs. In this work, we introduce TOUCAN, a supervised learning framework for fungal BGC discovery. Unlike previous methods, TOUCAN is capable of predicting BGCs on amino acid sequences, facilitating its use on newly sequenced and not yet curated data. It relies on three main pillars rigorous selection of datasets by BGC experts; combination of functional, evolutionary and compositional features coupled with outperforming classifiers; and robust post-processing methods. TOUCAN best-performing model yields 0.982 F-measure on BGC regions in the Aspergillus niger genome. Overall results show that TOUCAN outperforms previous approaches. TOUCAN focuses on fungal BGCs but can be easily adapted to expand its scope to process other species or include new features.Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. selleck Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.DNA methylation is a stable epigenetic modification, extremely polymorphic and driven by stochastic and deterministic events. Most of the current techniques used to analyse methylated sequences identify methylated cytosines (mCpGs) at a single-nucleotide level and compute the average methylation of CpGs in the population of molecules. Stable epialleles, i.e. CpG strings with the same DNA sequence containing a discrete linear succession of phased methylated/non-methylated CpGs in the same DNA molecule, cannot be identified due to the heterogeneity of the 5'-3' ends of the molecules. Moreover, these are diluted by random unstable methylated CpGs and escape detection. We present here MethCoresProfiler, an R-based tool that provides a simple method to extract and identify combinations of methylated phased CpGs shared by all components of epiallele families in complex DNA populations. The methylated cores are stable over time, evolve by acquiring or losing new methyl sites and, ultimately, display high information content and low stochasticity. We have validated this method by identifying and tracing rare epialleles and their families in synthetic or in vivo complex cell populations derived from mouse brain areas and cells during postnatal differentiation. MethCoresProfiler is written in R language. The software is freely available at https//github.com/84AP/MethCoresProfiler/.Influenza A viruses (IAVs) use diverse mechanisms to interfere with cellular gene expression. Although many RNA-seq studies have documented IAV-induced changes in host mRNA abundance, few were designed to allow an accurate quantification of changes in host mRNA splicing. Here, we show that IAV infection of human lung cells induces widespread alterations of cellular splicing, with an overall increase in exon inclusion and decrease in intron retention. Over half of the mRNAs that show differential splicing undergo no significant changes in abundance or in their 3' end termination site, suggesting that IAVs can specifically manipulate cellular splicing. Among a randomly selected subset of 21 IAV-sensitive alternative splicing events, most are specific to IAV infection as they are not observed upon infection with VSV, induction of interferon expression or induction of an osmotic stress. Finally, the analysis of splicing changes in RED-depleted cells reveals a limited but significant overlap with the splicing changes in IAV-infected cells. This observation suggests that hijacking of RED by IAVs to promote splicing of the abundant viral NS1 mRNAs could partially divert RED from its target mRNAs. All our RNA-seq datasets and analyses are made accessible for browsing through a user-friendly Shiny interface (http//virhostnet.prabi.fr3838/shinyapps/flu-splicing or https//github.com/cbenoitp/flu-splicing).Measurements in sequencing studies are mostly based on counts. There is a lack of theoretical developments for the analysis and modelling of this type of data. Some thoughts in this direction are presented, which might serve as a seed. The main issues addressed are the compositional character of multinomial probabilities and the corresponding representation in orthogonal (isometric) coordinates, and modelling distributions for sequencing data taking into account possible effects of amplification techniques.
Homepage: https://www.selleckchem.com/products/pdd00017273.html