Microalgal Chlorella has been demonstrated to process wastewater efficiently from piggery industry, yet optimization through genetic engineering of such a bio-treatment is currently challenging, largely due to the limited data and knowledge in genomics. In this study, we first investigated the differential growth rates among three wastewater-processing Chlorella strains: Chlorella sorokiniana BD09, Chlorella sorokiniana BD08 and Chlorella sp. Dachan, and the previously published Chlorella sorokiniana UTEX 1602, showing us that BD09 maintains the best tolerance in synthetic wastewater. We then performed genome sequencing and analysis, resulting in a high-quality assembly for each genome with scaffold N50 >2 Mb and genomic completeness ≥ 91%, as well as genome annotation with 9,668, 10,240, 9,821 high-confidence gene models predicted for BD09, BD08, and Dachan, respectively. Comparative genomics study unravels that metabolic pathways, which are involved in nitrogen and phosphorus assimilation, were enriched in the faster-growing strains. We found that gene structural variation and genomic rearrangement might contribute to differential capabilities in wastewater tolerance among the strains, as indicated by gene copy number variation, domain reshuffling of orthologs involved, as well as a ∼1 Mb-length chromosomal inversion we observed in BD08 and Dachan. In addition, we speculated that an associated bacterium, Microbacterium chocolatum , which was identified within Dachan, play a possible role in synergizing nutrient removal. Our three newly sequenced Chlorella genomes provide a fundamental foundation to understand the molecular basis of abiotic stress tolerance in wastewater treatment, which is essential for future genetic engineering and strain improvement.

End-to-end omics analysis requires more than selecting tools: a usable agent must bind data correctly, execute long workflows without blocking, preserve provenance and recover the biological conclusions that motivate an analysis. Existing LLM-driven bioinformatics agents automate parts of this process, but their operational dependencies and conclusion-level validity are often unclear. Here we present MetaClaw, an auditable agent that maps a user request to a registered workflow, executes standardized upstream processing on FlowHub, and runs study-specific downstream analyses in network-isolated OpenClaw containers. A YAML registry and an explicit plan-submit-poll-finalise lifecycle record file bindings, parameters, scripts, environments and outputs in per-job bundles. Across the full cohorts of four published studies (769 metagenomic profiles), MetaClaw recovered 4/4 sorghum marker groups, 3/3 RRMS features, 4/5 canonical CRC markers among the top 20 classifier features and 5/5 permafrost marker groups. In 45 model-by-prompt runs, upstream completion was consistent whereas downstream validity depended on the backend and instruction detail; three decoy-tested endpoints showed no significant differences. In 48 ablation sessions, removing the registry, planning loop or manifest caused distinct losses, with registry removal increasing time, tool calls and token cost. MetaClaw therefore connects standardized upstream execution, local analytical flexibility and conclusion-level validation in a rerunnable framework for metagenomic and microbiome multi-omics analysis.

Breast cancer is one of the most common cancers, accounting for about 30% of female cancers and a mortality rate of 15%. The 5-year survival rate is most commonly used to assess cancer progression and guide clinical practice. We used the CatBoost model to systematically construct a five-year mortality risk prediction model based on two independent data sets (BRCA_METABRIC, BRCA_TCGA). The model input data are the somatic genomic variants (copy number variation, SNP locus, cumulative mutation number of genes) and phenotype data of cancer samples. The optimal model combined all the above characteristics, and the AUC reached 0.70 in an independent external data set. At the same time, we also conducted a biological analysis of the characteristics of the model and found some potential biomarkers (TP53, DNAH11, MAP3K1, PHF20L1, etc.). The results of model risk stratification can be used as a guide for the prognosis of breast cancer.

Birth defect, not only poses a major challenge for infant health but also attracts the attention of countless people in the world. Chromosome abnormality directly results in diverse birth defects which are generally deleterious and even lethal. Therefore, gaining molecular regulatory insights into these diseases is important and necessary for effective prenatal screening. Recently, with the advance of next-generation sequencing (NGS) techniques, a myriad of treatises and data associated with these diseases are now constantly produced from different laboratories across the world. To meet the increasing requirements for birth-related data resources, we developed a birth defect multi-omics database (BDdb), freely accessible at http://t21omics.cngb.org and consisting of multi-omics data, circulating free DNA (cfDNA) data, as well as diseases biomarkers. Omics data sets from 138 GSE samples, 5271 GSM samples and 328 entries, and more than 2000 biomarkers of 22 birth-defect diseases in 5 different species were integrated into BDdb, which provides a user-friendly interface for searching, browsing and downloading selected data. Additionally, we re-analyzed and normalized the raw data so that users can also customize the analysis using the data generated from different sources or different High-Throughput Sequencing (HTS) methods. To our knowledge, BDdb is the first comprehensive database associated with birth-defect-related diseases. which would benefit the diagnosis and prevention of birth defects.

Background: The specification and differentiation of neocortical projection neurons is a complex process under precise molecular regulation; however, little is known about the similarities and differences in cerebral cortex development between human and mouse at single-cell resolution. Results: Here, using single-cell RNA-seq (scRNA-seq) data we explore the divergence and conservation of human and mouse cerebral cortex development using 18,446 and 7,610 neocortical cells. Systematic cross-species comparison reveals that the overall transcriptome profile in human cerebral cortex is similar to that in mouse such as cell types and their markers genes. By single-cell trajectories analysis we find human and mouse excitatory neurons have different developmental trajectories of neocortical projection neurons, ligand-receptor interactions and gene expression patterns. Further analysis reveals a refinement of neuron differentiation that occurred in human but not in mouse, suggesting that excitatory neurons in human undergo refined transcriptional states in later development stage. By contrast, for glial cells and inhibitory neurons we detected conserved developmental trajectories in human and mouse. Conclusions: Taken together, our study integrates scRNA-seq data of cerebral cortex development in human and mouse, and uncovers distinct developing models in neocortical projection neurons. The earlier activation of cognition -related genes in human may explain the differences in behavior, learning or memory abilities between the two species.