Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

MOTIVATION: Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. AVAILABILITY: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

The contribution of transgenic plants to better health through improved nutrition : opportunities and constraints

Malnutrition is a prevalent and entrenched global socioeconomic challenge that reflects the combined impact of poverty, poor access to food, inefficient food distribution infrastructure, and an over-reliance on subsistence mono-agriculture. The dependence on staple cereals lacking many essential nutrients means that malnutrition is endemic in developing countries. Most individuals lack diverse diets and are therefore exposed to nutrient deficiencies. Plant biotechnology could play a major role in combating malnutrition through the engineering of nutritionally enhanced crops. In this article, we discuss different approaches that can enhance the nutritional content of staple crops by genetic engineering (GE) as well as the functionality and safety assessments required before nutritionally enhanced GE crops can be deployed in the field. We also consider major constraints that hinder the adoption of GE technology at different levels and suggest policies that could be adopted to accelerate the deployment of nutritionally enhanced GE crops within a multicomponent strategy to combat malnutrition