Methylscaper: an R/Shiny app for joint visualization of DNA methylation and nucleosome occupancy in single-molecule and single-cell data.

SUMMARY: Differential DNA methylation and chromatin accessibility are associated with disease development, particularly cancer. Methods that allow profiling of these epigenetic mechanisms in the same reaction and at the single-molecule or single-cell level continue to emerge. However, a challenge lies in jointly visualizing and analyzing the heterogeneous nature of the data and extracting regulatory insight. Here, we present methylscaper, a visualization framework for simultaneous analysis of DNA methylation and chromatin accessibility landscapes. Methylscaper implements a weighted principal component analysis that orders DNA molecules, each providing a record of the chromatin state of one epiallele, and reveals patterns of nucleosome positioning, transcription factor occupancy, and DNA methylation. We demonstrate methylscaper's utility on a long-read, single-molecule methyltransferase accessibility protocol for individual templates (MAPit-BGS) dataset and a single-cell nucleosome, methylation, and transcription sequencing (scNMT-seq) dataset. In comparison to other procedures, methylscaper is able to readily identify chromatin features that are biologically relevant to transcriptional status while scaling to larger datasets. AVAILABILITY AND IMPLEMENTATION: Methylscaper, is implemented in R (version > 4.1) and available on Bioconductor: https://bioconductor.org/packages/methylscaper/, GitHub: https://github.com/rhondabacher/methylscaper/, and Web: https://methylscaper.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis.

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

A rare midbrain infarction presenting with plus-minus lid syndrome with ataxia: a case report.

INTRODUCTION: We present the case of a patient with midbrain infarction with an unusual clinical presentation, where clinical diagnosis and anatomical localization were valuable tools in deciding treatment. CASE PRESENTATION: Our patient was a 59-year-old, right-handed Caucasian man with hypertension who presented to our facility with acute diplopia that persisted until he developed complete right-sided ptosis. He also had difficulty walking and coordinating movements of his upper extremities bilaterally, but this was worse on his left side. CONCLUSIONS: Plus-minus lid syndrome with ataxia is a rare presentation of midbrain infarction with a unique localization and anatomical description. This case highlights the importance of clinical skills for making a diagnosis in the absence of imaging to confirm the findings.

Increased recombination between active tRNA genes.

Transfer RNA genes are distributed throughout eukaryotic genomes, and are frequently found as multicopy families. In Saccharomyces cerevisiae, tRNA gene transcription by RNA polymerase III suppresses nearby transcription by RNA polymerase II, partially because the tRNA genes are clustered near the nucleolus. We have tested whether active transcription of tRNA genes might also suppress recombination, since recombination between identical copies of the repetitive tRNA genes could delete intervening genes and be detrimental to survival. The opposite proved to be the case. Recombination between active tRNA genes was elevated, but only when both genes are transcribed. We also tested the effects of tRNA genes on recombination between the direct terminal repeats of a neighboring retrotransposon, since most Ty retrotransposons reside next to tRNA genes, and the selective advantage of this arrangement is not known.