No abstract

DESS is a formulation widely used to preserve DNA in biological tissue samples. Although it contains three ingredients, dimethyl sulfoxide (DMSO), ethylenediaminetetraacetic acid (EDTA) and sodium chloride (NaCl), it is frequently referred to as a DMSO-based preservative. The effectiveness of DESS has been confirmed for a variety of taxa and tissues, however, to our knowledge, the contributions of each component of DESS to DNA preservation have not been evaluated. To address this question, we stored tissues of three aquatic taxa, Mytilus edulis (blue mussel), Faxonius virilis (virile crayfish) and Alitta virens (clam worm) in DESS, each component of DESS individually and solutions containing all combinations of two components of DESS. After storage at room temperature for intervals ranging from one day to six months, we extracted DNA from each tissue and measured the percentage of high molecular weight (HMW) DNA recovered (%R) and normalized HMW DNA yield (nY). Here, HMW DNA is defined as fragments >10 kb. For comparison, we also measured the % R and nY of HMW DNA from extracts of fresh tissues and those stored in 95% EtOH over the same time intervals. We found that in cases where DESS performed most effectively (yielding � 20%R of HMW DNA), all solutions containing EDTA were as or more effective than DESS. Conversely, in cases where DESS performed more poorly, none of the six DESS-variant storage solutions provided better protection of HMW DNA than DESS. Moreover, for all taxa and storage intervals longer than one day, tissues stored in solutions containing DMSO alone, NaCl alone or DMSO and NaCl in combination resulted in %R and nY of HMW DNA significantly lower than those of fresh tissues. These results indicate that for the taxa, solutions and time intervals examined, only EDTA contributed directly to preservation of high molecular weight DNA.

Gain-of-function (GOF) variants yield increased or novel protein function while loss-of-function (LOF) variants yield diminished protein function. GOF and LOF variants can result in markedly varying phenotypes even when occurring in the same gene. Experimental approaches for identifying GOF and LOF are slow and costly, and computational tools cannot accurately discriminate between GOF and LOF variants. We developed LoGoFunc, an ensemble machine learning method for predicting pathogenic GOF, pathogenic LOF and neutral variants, trained on 672 protein, gene, variant, and network annotations describing diverse biological characteristics. We analyzed features in terms of protein structure and function, splice disruption, and phenotypic associations, revealing previously unreported relationships between various biological phenomena and variant functional outcomes. For example, GOF and LOF variants demonstrate contrasting enrichments in protein structural and functional regions, and LOF variants are more likely to disrupt canonical splicing as indicated by splicing-related features employed by the model. Furthermore, by performing phenome-wide association studies (PheWAS), we identified strong associations between relevant phenotypes and high-confidence predicted GOF and LOF variants. LoGoFunc performs well on an independent test set of GOF and LOF variants, and we provide precomputed genome-wide GOF and LOF predictions for 71,322,505 missense variants.

Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.