MethMarkerDB: a comprehensive cancer DNA methylation biomarker database.

DNA methylation plays a crucial role in tumorigenesis and tumor progression, sparking substantial interest in the clinical applications of cancer DNA methylation biomarkers. Cancer-related whole-genome bisulfite sequencing (WGBS) data offers a promising approach to precisely identify these biomarkers with differentially methylated regions (DMRs). However, currently there is no dedicated resource for cancer DNA methylation biomarkers with WGBS data. Here, we developed a comprehensive cancer DNA methylation biomarker database (MethMarkerDB, https://methmarkerdb.hzau.edu.cn/), which integrated 658 WGBS datasets, incorporating 724 curated DNA methylation biomarker genes from 1425 PubMed published articles. Based on WGBS data, we documented 5.4 million DMRs from 13 common types of cancer as candidate DNA methylation biomarkers. We provided search and annotation functions for these DMRs with different resources, such as enhancers and SNPs, and developed diagnostic and prognostic models for further biomarker evaluation. With the database, we not only identified known DNA methylation biomarkers, but also identified 781 hypermethylated and 5245 hypomethylated pan-cancer DMRs, corresponding to 693 and 2172 genes, respectively. These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value. We hope that MethMarkerDB will help identify novel cancer DNA methylation biomarkers and propel the clinical application of these biomarkers.

Machine Learning-Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis.

Crystallographic facets in a crystal carry interior properties and proffer rich functionalities in a wide range of application areas. However, rational prediction, on-demand customization, and accurate synthesis of facets and facet junctions of a crystal are enormously desirable but still challenging. Herein, a framework of machine learning (ML)-aided crystal facet design with ionic liquid controllable synthesis is developed and then demonstrated with the star-material anatase TiO2 . Aided by employing ML to acquire surface energies from facet junction datasource, the relationships between surface energy and growth conditions based on the Langmuir adsorption isotherm are unveiled, enabling to develop controllable facet synthetic strategies. These strategies are successfully verified after applied for synthesizing TiO2 crystals with custom crystal facets and facet junctions under tuning ionic liquid [bmim][BF4 ] experimental conditions. Therefore, this innovative framework integrates data-intensive rational design and experimental controllable synthesis to develop and customize crystallographic facets and facet junctions. This proves the feasibility of an intelligent chemistry future to accelerate the discovery of facet-governed functional material candidates.

Surface energies of non-centrosymmetric nanocrystals by the inverse Wulff construction method.

The Wulff construction is a well-known method for studying the morphologies of nanocrystal particles. The underlying Gibbs-Wulff theorem relies on Wulff points or particle centers, which are invalid for non-centrosymmetric crystals. In this study, we extend the method of inverse Wulff construction to study surface free energies of non-centrosymmetric crystals. A nonpolar (4[combining macron]3m) and a polar crystal system (6mm) are selected to show the difficulties and constraints caused by the lack of inversion centers. In addition to analytical and numerical results, we also present a general four-parameter function to simplify calculations of surface free energies from observed micro- or nanoparticle morphologies. The results reveal that non-centrosymmetric crystal morphologies entail the surface thermodynamics of coupling polar surfaces, with certain limitations originating from the combination of geometrical shapes and crystal symmetries.

AraENCODE: A comprehensive epigenomic database of Arabidopsis thaliana.

Arabidopsis is an important model organism in plant biology and genetics, and a large number of a chromatin conformation and epigenomic datasets have been generated to study the biology in Arabidopsis. To make it easier to access the accumulated epigenomic data, a user-friendly and reproducible epigenomic database, AraENCODE was developed. It contains various datasets and resources, including chromatin conformation, epigenomic, and transcriptome data, allowing researchers to investigate the regulation of epigenetic and chromatin interactions in Arabidopsis.