bsgenova: an accurate, robust, and fast genotype caller for bisulfite-sequencing data.

Background: Bisulfite sequencing (BS-Seq) is a fundamental technique for characterizing DNA methylation profiles. Genotype calling from bisulfite-converted BS-Seq data allows allele-specific methylation analysis and the concurrent exploration of genetic and epigenetic profiles. Despite various methods have been proposed, single nucleotide polymorphisms (SNPs) calling from BS-Seq data, particularly for SNPs on chromosome X and in the presence of contaminative data, poses ongoing challenges.

A data integration approach unveils a transcriptional signature of type 2 diabetes progression in rat and human islets.

Pancreatic islet failure is a key characteristic of type 2 diabetes besides insulin resistance. To get molecular insights into the pathology of islets in type 2 diabetes, we developed a computational approach to integrating expression profiles of Goto-Kakizaki and Wistar rat islets from a designed experiment with those of the human islets from an observational study. A principal gene-eigenvector in the expression profiles characterized by up-regulated angiogenesis and down-regulated oxidative phosphorylation was identified conserved across the two species.

Single-cell multi-omics profiling reveals key regulatory mechanisms that poise germinal vesicle oocytes for maturation in pigs.

The molecular mechanisms controlling the transition from meiotic arrest to meiotic resumption in mammalian oocytes have not been fully elucidated. Single-cell omics technology provides a new opportunity to decipher the early molecular events of oocyte growth in mammals. Here we focused on analyzing oocytes that were collected from antral follicles in different diameters of porcine pubertal ovaries, and used single-cell M&T-seq technology to analyze the nuclear DNA methylome and cytoplasmic transcriptome in parallel for 62 oocytes.

MUREN: a robust and multi-reference approach of RNA-seq transcript normalization.

Background: Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.

The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation.

Background: A key problem in systems biology is the determination of the regulatory mechanism corresponding to a phenotype. An empirical approach in this regard is to compare the expression profiles of cells under two conditions or tissues from two phenotypes and to unravel the underlying transcriptional regulation. We have proposed the method BASE to statistically infer the effective regulatory factors that are responsible for the gene expression differentiation with the help from the binding data between factors and genes.