Mapping the query cells onto a well-constructed reference atlas, known as reference mapping, enables robust, reproducible interpretation of new single-cell RNA-seq data in the context of curated and annotated cell subtypes and states. However, existing methods often rely on complex integration frameworks or require re-access to raw data, limiting their applicability and reproducibility. To address this, we introduce ProjectSVR, a machine learning-based framework that formulates reference mapping as a multi-target regression task. By leveraging ensemble support vector regression (SVR) to learn the relationship between gene set activity scores and low-dimensional reference embeddings, ProjectSVR enables platform-agnostic and integration-independent mapping. Benchmarking across diverse biological contexts-including immune responses, developmental trajectories, and disease states-demonstrates that ProjectSVR achieves comparable accuracy and robustness to state-of-the-art methods, with reduced dependence on data-specific preprocessing. Our findings demonstrate that ProjectSVR is a valuable tool for reference mapping, considerably simplifying the analysis of scRNA-seq data when well-constructed reference atlases are available.

With the advances of next-generation sequencing technology, the field of disease research has been revolutionized. However, pinpointing the disease-causing variants from millions of revealed variants is still a tough task. Here, we have reviewed the existing linkage analysis tools and presented PedMiner, a web-based application designed to narrow down candidate variants from family based whole-exome sequencing (WES) data through linkage analysis. PedMiner integrates linkage analysis, variant annotation and prioritization in one automated pipeline. It provides graphical visualization of the linked regions along with comprehensive annotation of variants and genes within these linked regions. This efficient and comprehensive application will be helpful for the scientific community working on Mendelian inherited disorders using family based WES data.

SUMMARY: Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research. AVAILABILITY AND IMPLEMENTATION: CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. RESULTS: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. CONCLUSION: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .