Using local alignment to enhance single-cell bisulfite sequencing data efficiency.

MOTIVATION: Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. RESULTS: We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. AVAILABILITY AND IMPLEMENTATION: The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data.

Motivation: DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging. Results: We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations. Availability and implementation: The CGmapTools software is freely available at https://cgmaptools.github.io/. Contact: guoweilong@cau.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.