Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Under the potential outcomes framework, we propose a randomization based estimation procedure for causal inference from split-plot designs, with special emphasis on 22 designs that naturally arise in many social, behavioral and biomedical experiments. Point estimators of factorial effects are obtained and their sampling variances are derived in closed form as linear combinations of the between- and within-group covariances of the potential outcomes. Results are compared to those under complete randomization as measures of design efficiency. Conservative estimators of these sampling variances are proposed. Connection of the randomization-based approach to inference based on the linear mixed effects model is explored. Results on sampling variances of point estimators and their estimators are extended to general split-plot designs. The superiority over existing model-based alternatives in frequency coverage properties is reported under a variety of simulation settings for both binary and continuous outcomes.

The Canadian oil sands underlie 142,000 km2 of the boreal forest in northeastern Alberta. Oil sands production greenhouse gas (GHG) emissions increased from 15 million tonnes (Mt) to 55 Mt between 1990 and 2011. Their production represents the fastest-growing source of GHG emissions in Canada. A large body of studies show that oil sands industries have large environmental impacts, including effects on climate, land, water, and air quality but GHG emissions from oil sands land use disturbance and future land use impacts have yet to be examined in detail and the associated literature is scarce and incomplete. Our paper examines the historical and potential land use change and GHG emissions associated with oil sands development in Canada. Disturbance occurred between 1985 and 2009 from oil sands development were identified using remote sensing technique and mapped onto spatially explicit soil, biomass and peatlands carbon maps. We found that land use and GHG disturbance of oil sands production, especially in-situ technology that will be the dominant technology of choice for future oil sands development, are greater than previously reported. We estimate additional 500 km2 and 2,400 km2 of boreal forest including carbon-rich peatlands would be disturbed from surface mining and in-situ production, respectively, between 2012 and 2030; releasing additional 107–182 million tonnes of GHG from land use alone. Future efforts to monitor land use impacts of in-situ production are needed to reduce landscape impacts and associated GHG emissions. In addition, land reclamation after oil sands projects needs to be enforced for broad ecological benefits together with GHG benefits.

Prime editing enables the precise modification of genomes through reverse transcription of template sequences appended to the 3' ends of CRISPR-Cas guide RNAs¹. To identify cellular determinants of prime editing, we developed scalable prime editing reporters and performed genome-scale CRISPR-interference screens. From these screens, a single factor emerged as the strongest mediator of prime editing: the small RNA-binding exonuclease protection factor La. Further investigation revealed that La promotes prime editing across approaches (PE2, PE3, PE4 and PE5), edit types (substitutions, insertions and deletions), endogenous loci and cell types but has no consistent effect on genome-editing approaches that rely on standard, unextended guide RNAs. Previous work has shown that La binds polyuridine tracts at the 3' ends of RNA polymerase III transcripts². We found that La functionally interacts with the 3' ends of polyuridylated prime editing guide RNAs (pegRNAs). Guided by these results, we developed a prime editor protein (PE7) fused to the RNA-binding, N-terminal domain of La. This editor improved prime editing with expressed pegRNAs and engineered pegRNAs (epegRNAs), as well as with synthetic pegRNAs optimized for La binding. Together, our results provide key insights into how prime editing components interact with the cellular environment and suggest general strategies for stabilizing exogenous small RNAs therein.