Scaden enables robust cell type deconvolution of complex samples across data types, using a deep learning–based model.

Small RNA molecules play important roles in many biological processes and their dysregulation or dysfunction can cause disease. The current method of choice for genome-wide sRNA expression profiling is deep sequencing. Here we present Oasis 2, which is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment. Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at https://oasis.dzne.de

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGANs outperform existing methods for single cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

Collectible card games are taking up more space in popular culture with traditional paper card games even embracing e-sports. However, longevity in such games is not as common, with some suspecting power creep as a culprit behind why some of these games fail. Yet, Magic: the Gathering has not just survived but thrived for over 25 years with the game’s designers publicly stating their aim to keep curbing power creep. Therefore, it is of interest to determine the rate of power creep in the game. Herein, we formally define a conservative metric power creep and calculate its occurrence in the game of Magic: the Gathering. Although having an increasing rate, power creep appears low with an average of 1.56 strictly better card faces released per year.

Summary: KNIT is a web application that provides a hierarchical, directed graph on how a set of genes is connected to a particular gene of interest. Its primary aim is to aid researchers in discerning direct from indirect effects that a gene might have on the expression of other genes and molecular pathways, a very common problem in omics analysis. As such, KNIT provides deep contextual information for experiments where gene or protein expression might be changed, such as gene knock-out and overexpression experiments. Availability and implementation: KNIT is publicly available at http://knit.ims.bio. It is implemented with Django and Nuxtjs, with all major browsers supported. Supplementary information: Supplementary data are available at Bioinformatics online.

Oasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment. Availability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de