Emerging single‐cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single‐cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here scCompass is proposed, which provides a comprehensive resource designed to build large‐scale, multi‐species, and model‐friendly single‐cell data collection. By applying standardized data pre‐processing, scCompass integrates and curates transcriptomic data from nearly 105 million single cells across 13 species. Using this extensive dataset, it is able to identify stable expression genes (SEGs) and organ‐specific expression genes (OSGs) in humans and mice. Different scalable datasets are provided that can be easily adapted for AI model training and the pretrained checkpoints with state‐of‐the‐art single‐cell foundation models. In summary, scCompass is highly efficient and scalable database for AI‐ready, which combined with user‐friendly data sharing, visualization, and online analysis, greatly simplifies data access and exploitation for researchers in single‐cell biology ( http://www.bdbe.cn/kun ).

Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. Its architecture, enhanced with supervised contrastive loss and rooted in visual Transformer principles, markedly improves accuracy in identifying complex lithological patterns. To this end, we have collected public datasets and implemented data augmentation, aiming to validate our method using sandstone as a focal point. The results demonstrate that Rock-ViT achieves superior accuracy and effectiveness in the refined lithology identification of sandstone. Rock-ViT presents a new perspective and a feasible approach for detailed lithological analysis, offering fresh insights and innovative solutions in geological analysis.

DarwinTree (http://www.darwintree.cn) provides an integrated bioinformatics platform that supports all phases of the analytical pathway for phylogenetic study from data collections, phylogenetic tree constructions, visualization of the tree of life, and web-based rendering to specific application services & data mining. First, it is a repository for sequence records that form the basic data unit of all phylogenetic studies. Second, it is a workbench that aids the management, quality assurance, and analysis of phylogenetic data. Third, it provides a community of phylogenetic application services with tree reconstruction and related data mining. This paper provides a brief introduction to the key elements of DarwinTree, discusses their functional capabilities, details the database features, phylogeny pipeline, data mining tools, and specific application services available to support it, and concludes with future prospects

Cells in the human body are regulated by sophisticated networks of gene regulation, which allows them to fulfill their cellular destiny and function. Inspired by the advancements in large language models, there have been several attempts focusing on constructing foundation models with single-cell transcriptomic data to decipher gene regulatory networks. However, these models tend to impose a sequential structure on genes within each cell, which may omit intrinsic biological characteristics and lack the utilization of other available prior knowledge. In this paper, we introduce Cell-GraphCompass (CGCompass), the pioneering foundation model that employs graph pre-training to model genes and cells. We use three types of gene-related information as node features for constructing cell graphs and collect data from three perspectives depicting relationships between genes as edge features. We pre-trained the model with over 50 million human cells and then fine-tuned it to a broad spectrum of tasks, such as batch integration, cell type annotation, single-cell gene perturbation and in silico gene knockout predictions, achieving commendable performance. Overall, CGCompass provides a practical architecture for leveraging graph pre-training to incorporate prior knowledge in constructing a foundation model for single-cell analysis.

With the accumulation of phylogenomic data and the growing demand for bioinformatics analyses, it has become increasingly important and complex to construct evolutionary relationships for different research purposes. Therefore, the ability to support multiple scenarios has become an essential need for phylogenetic visualization. In this study, we present PhyloScape, a web-based application for interactive visualization of phylogenetic trees that can be used stand-alone or as a toolkit deployed on the users’ website. The platform supports customizable multiple visualization features and is equipped with a flexible metadata annotation system, providing researchers with publishable, interactive views of trees. PhyloScape extensions include views of amino acid identity, geometry, and protein structure, which are applicable to various areas such as microbial taxonomy, pathogen phylogeny, and plant conservation. Trees published on the website can be efficiently shared and integrated into the users’ own system via a unique address. As a scalable platform, PhyloScape provides a variety of online plug-ins that users can easily combine for specific scenarios. PhyloScape is freely available at http://darwintree.cn/PhyloScape .