BAllC and BAllCools: efficient formatting and operating for single-cell DNA methylation data.

Motivation: With single-cell DNA methylation studies yielding vast datasets, existing data formats struggle with the unique challenges of storage and efficient operations, highlighting a need for improved solutions.

Results: BAllC (Binary All Cytosines) emerges as a tailored format for methylation data, addressing these challenges. BAllCools, its complementary software toolkit, enhances parsing, indexing, and querying capabilities, promising superior operational speeds and reduced storage needs.

PyComplexHeatmap: a Python package to visualize multimodal genomics data.

Python has emerged as a robust programming language increasingly employed in genomics data analysis, largely due to its comprehensive deep learning libraries and proficiency in handling large-scale data, such as single-cell multi-omics datasets. Although Python has become a prominent data science ecosystem for bioinformatics, there remains a growing demand for advanced heatmap visualization and assembly tools, which are not sufficiently addressed by existing Python-based data visualization libraries. We present PyComplexHeatmap, an all-inclusive Python library for heatmap visualization, inspired by the ComplexHeatmap package currently available in R.

Brain-wide correspondence of neuronal epigenomics and distant projections.

Single-cell analyses parse the brain's billions of neurons into thousands of 'cell-type' clusters residing in different brain structures. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain.

BAllC and BAllCools: Efficient Formatting and Operating for Single-Cell DNA Methylation Data.

Motivation: With single-cell DNA methylation studies yielding vast datasets, existing data formats struggle with the unique challenges of storage and efficient operations, highlighting a need for improved solutions.

Results: BAllC (Binary All Cytosines) emerges as a tailored binary format for methylation data, addressing these challenges. BAllCools, its complementary software toolkit, enhances parsing, indexing, and querying capabilities, promising superior operational speeds and reduced storage needs.

Genetics and Environment Distinctively Shape the Human Immune Cell Epigenome.

The epigenomic landscape of human immune cells is dynamically shaped by both genetic factors and environmental exposures. However, the relative contributions of these elements are still not fully understood. In this study, we employed single-nucleus methylation sequencing and ATAC-seq to systematically explore how pathogen and chemical exposures, along with genetic variation, influence the immune cell epigenome.

Comparative epigenome analysis using Infinium DNA methylation BeadChips.

The arrival of the Infinium DNA methylation BeadChips for mice and other nonhuman mammalian species has outpaced the development of the informatics that supports their use for epigenetics study in model organisms. Here, we present informatics infrastructure and methods to allow easy DNA methylation analysis on multiple species, including domesticated animals and inbred laboratory mice (in SeSAMe version 1.16.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse.

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks.

Autonomous climbing: An effective exercise mode with beneficial outcomes of aerobic exercise and resistance training.

Aims: To assess the effects of three specific exercise training modes, aerobic exercise (A), resistance training (R) and autonomous climbing (AC), aimed at proposing a cross-training method, on improving the physical, molecular and metabolic characteristics of mice without many side effects.

Materials And Methods: Seven-week-old male mice were randomly divided into four groups: control (C), aerobic exercise (A), resistance training (R), and autonomous climbing (AC) groups. Physical changes in mice were tracked and analysed to explore the similarities and differences of these three exercise modes.

Co-occurrence and Mutual Exclusivity Analysis of DNA Methylation Reveals Distinct Subtypes in Multiple Cancers.

Co-occurrence and mutual exclusivity (COME) of DNA methylation refer to two or more genes that tend to be positively or negatively correlated in DNA methylation among different samples. Although COME of gene mutations in pan-cancer have been well explored, little is known about the COME of DNA methylation in pan-cancer. Here, we systematically explored the COME of DNA methylation profile in diverse human cancer.

DNMIVD: DNA methylation interactive visualization database.

Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression-methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis.

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning.

In Vitro Modeling of Human Germ Cell Development Using Pluripotent Stem Cells.

Due to differences across species, the mechanisms of cell fate decisions determined in mice cannot be readily extrapolated to humans. In this study, we developed a feeder- and xeno-free culture protocol that efficiently induced human pluripotent stem cells (iPSCs) into PLZF+/GPR125+/CD90+ spermatogonium-like cells (SLCs). These SLCs were enriched with key genes in germ cell development such as MVH, DAZL, GFRα1, NANOS3, and DMRT1.