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Showing 1–6 of 6 results
Advanced filters: Author: Angel On Ki Wong Clear advanced filters

  • Identifying genetic variants using long-read RNA sequencing (lrRNA-seq) is useful but hampered by high error rates and biological complexities. Here, the authors developed Clair3-RNA, a deep-learning tool that improves variant calling accuracy in lrRNA-seq data and accurately identifies RNA editing sites.

    • Zhenxian Zheng
    • Xian Yu
    • Ruibang Luo
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-19

  • The accurate detection of somatic variants in cancer without matched normal controls remains challenging, particularly for long-read sequencing data. Here, the authors develop ClairS-TO, a deep learning method for long-read tumour-only somatic variant calling that outperforms similar algorithms and can also work with short-read sequencing data.

    • Lei Chen
    • Zhenxian Zheng
    • Ruibang Luo
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-15

  • This report from the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations; hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites, can be found in each individual.

    • Gil A. McVean
    • David M. Altshuler (Co-Chair)
    • Gil A. McVean
    ResearchOpen Access
    Nature
    Volume: 491, P: 56-65

  • 1000 Genomes imputation can increase the power of genome-wide association studies to detect genetic variants associated with human traits and diseases. Here, the authors develop a method to integrate and analyse low-coverage sequence data and SNP array data, and show that it improves imputation performance.

    • Olivier Delaneau
    • Jonathan Marchini
    • Leena Peltonenz
    Research
    Nature Communications
    Volume: 5, P: 1-9