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Summary Tree graphs are used routinely in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte Carlo methods tend to rely on local moves, often leading to poor mixing. A promising approach is to instead directly sample spanning trees on an auxiliary graph. Current spanning tree samplers, such as the celebrated Aldous–Broder algorithm, rely predominantly on simulating random walks that are required to visit all the nodes of the graph. Such algorithms are prone to getting stuck in certain subgraphs. We formalize this phenomenon using the bottlenecks in the random walk’s transition probability matrix. We then propose a novel fast-forwarded cover algorithm that can break free from bottlenecks. The core idea is a marginalization argument that leads to a closed-form expression that allows for fast-forwarding to the event of visiting a new node. Unlike many existing approximation algorithms, our algorithm yields exact samples. We demonstrate the enhanced efficiency of the fast-forwarded cover algorithm, and illustrate its application in fitting a Bayesian dendrogram model on a Massachusetts crime and community dataset. 

Abstract Discovery of off-target CRISPR–Cas activity in patient-derived cells and animal models is crucial for genome editing applications, but currently exhibits low sensitivity. We demonstrate that inhibition of DNA-dependent protein kinase catalytic subunit accumulates the repair protein MRE11 at CRISPR–Cas-targeted sites, enabling high-sensitivity mapping of off-target sites to positions of MRE11 binding using chromatin immunoprecipitation followed by sequencing. This technique, termed DISCOVER-Seq+, discovered up to fivefold more CRISPR off-target sites in immortalized cell lines, primary human cells and mice compared with previous methods. We demonstrate applicability to ex vivo knock-in of a cancer-directed transgenic T cell receptor in primary human T cells and in vivo adenovirus knock-out of cardiovascular risk genePCSK9in mice. Thus, DISCOVER-Seq+ is, to our knowledge, the most sensitive method to-date for discovering off-target genome editing in vivo. 

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.