David Buck

An autosomal dominant cardiac arrhythmia syndrome, ST Depression Syndrome, is caused by the de novo creation of a cardiomyocyte enhancer.

The paper identifies a de novo enhancer mutation in KCNB1 causing ST Depression Syndrome, a cardiac arrhythmia, and shows it can be predicted by a deep neural network.

Viral burdens are associated with age and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection related sampling bias.

This study adjusts for sampling bias in SARS-CoV-2 Ct values and finds that viral variant and age affect within-host viral burdens, with variant effects varying by vaccination status.

Using de novo assembly to identify structural variation of complex immune system gene regions

The paper uses long-read sequencing and de novo assembly to identify structural variation in immune system gene regions from a single individual, revealing challenges in current variant calling methods.

Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma

This study evaluates the ability of long-read sequencing to detect somatic variants in a lymphoma patient, highlighting the need for specialized tools and the advantages of long reads for structural variants.

Within-host genomics of SARS-CoV-2

This study analyzes within-host SARS-CoV-2 diversity and transmission patterns using 1390 UK clinical samples, finding low within-host diversity and a narrow transmission bottleneck.

Single cell RNA-seq reveals profound transcriptional similarity between Barretts esophagus and esophageal glands

The paper uses single-cell RNA-seq to compare Barretts esophagus with normal esophageal tissues, finding transcriptional similarities with esophageal cells but not with gastric or duodenal cells.

A practical solution for preserving single cells for RNA sequencing

This paper describes a method using DSP fixation to preserve single cells for RNA sequencing, showing it maintains RNA complexity and reduces technical variability.

Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis.

This paper develops a tool to predict antibiotic resistance in S. aureus and M. tuberculosis from genome sequences with high accuracy and speed.