MOTIVATION: DNA copy number variations (CNVs) exert a profound impact on major genetic disorders in humans. Although multiple sequencing technologies have become the first line of molecular diagnosis for CNVs, existing tools are unable to resolve the pathogenicity of CNVs directly from raw sequencing data. RESULTS: We developed CNVSeeker, a one-stop and easy-to-use pipeline that provides comprehensive analysis from raw sequencing data to variant interpretation reports, and supports multiple types of sequencing data including short-read data such as whole genome sequencing data and whole exome sequencing data, and long-read sequencing data from Pacific Biosciences HiFi platform or Oxford Nanopore Technologies platform. Through extensive benchmarking, CNVSeeker demonstrated comparable enhancement over the state-of-the-art methods for CNV calling. Moreover, CNVSeeker enables significantly precise variant classification with an accuracy of ∼87%. By applying CNVSeeker to 1946 individuals with autism spectrum disorder (ASD), a total of 133 ASD-associated CNVs in 122 patients were identified, yielding a diagnostic yield of ∼6.3%. Additionally, we have also provided a user-friendly webserver for intuitive visualization of results. This study highlights the potential of CNVSeeker to benefit clinicians and geneticists with limited bioinformatic skill by aiding them interpret CNVs directly from various types of raw sequencing data for auxiliary disease diagnosis. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at https://genemed.tech/cnvseeker and the open-source code can be found at https://github.com/lovelycatZ/CNVSeeker.

The PS3 (Pathogenic Functional Evidence) and BS3 (Benign Functional Evidence) criteria in the American College of Medical Genetics and Genomics (ACMG) guidelines are critical for genetic variant classification. However, manual evaluation is time-consuming and prone to inter-laboratory inconsistencies, limiting the clinical interpretation of Variants of Uncertain Significance (VUS). Large Language Models (LLMs) offer potential for automated assessment, but their performance validation is hindered by the lack of standardized, high-quality datasets. This study introduces the PS3/BS3 Full-Chain Evidence Benchmark Dataset (PBD), the first manually curated dataset comprising 77 peer-reviewed publications, covering 266 cDNA variants (including duplicates) and their PS3/BS3 rating results. Adhering to ClinGen Sequence Variant Interpretation (SVI) standards, PBD includes structured, comprehensive evidence chains spanning genes, diseases, variants, experiments, and evidence ratings, designed to evaluate LLM capabilities in functional evidence extraction. We detail the dataset construction process, including literature screening, data extraction, standardization, and quality control, and developed a Python-based automated evaluation pipeline for reproducible, standardized analysis. Experiments using DeepSeek models$(1.5 \mathrm{b} / 7 \mathrm{b} / 14 \mathrm{b})$demonstrate PBD's potential in supporting automated variant interpretation. PBD provides a vital resource for bioinformatics and precision medicine, facilitating the development and standardization of variant classification tools. Data examples are available at https://github.com/User8588/PBD, with full data and code to be released upon paper acceptance.

We present a video-based method to monitor blood pressure by analyzing pulse transit time (PTT). The PTT is determined from ballistocardiogram (BCG) and photoplethysmogram (PPG), which are recorded simultaneously using a single camera. The measured blood pressure changes are compared with those measured with a cuff-based reference blood pressure monitor. In addition to blood pressure, premature ventricular contraction (PVC) is also observed from both PPG and BCG waveforms. The method provides a non-contact solution towards real-time blood pressure and abnormal heart rhythm monitoring of an individual under normal living conditions without his/her conscious attention.