Biology subjects

Wong, A. O. K.

Publications and source records attributed to Wong, A. O. K..

3 recordsLinked to original sources

bioRxiv2025

Clair-Mosaic: A deep-learning method for long-read mosaic small variant calling

Mosaic variants, defined as postzygotic mutations occurring during an organisms development from zygote to adult, play critical roles in developmental biology, aging, and diseases such as cancer and neurological disorders. However, their accurate detection remains challenging due to low abundance in the genome and low variant allelic fractions (VAF). While current mosaic variant callers are primarily designed for short-read sequencing, no method is available for long-read sequencing, which can generate tens of kilobase-long reads to cover complex genomic regions inaccessible to short reads. To fill the gap, we present Clair-Mosaic, a deep-learning-based method for detecting mosaic small variants from long-read data. Clair-Mosaic was trained on hundreds of millions synthetic variants encompassing diverse read coverages and allelic fractions, enabling it to detect low-VAF mosaic variants with high sensitivity in paired-sample and single-sample modes. In addition to neural network prediction, Clair-Mosaic distinguishes genuine mosaic variants from sequencing artifacts by leveraging their inherent haplotype relationship in phased long reads. Furthermore, a Bayesian mosaic-germline discriminator is introduced to distinguish mosaic variants from germline variants. It also employs multiple post-calling filters, including a mosaic variant database and multiple germline population resources, to tag common germline and mosaic variants. Comprehensive benchmarking on synthetic datasets and real samples demonstrated Clair-Mosaics outstanding performance in ONT and PacBio. Clair-Mosaic is also applicable to short-read data and outperforms methods like MosaicHunter, MosaicForecast, DeepMosaic, and DeepSomatic. Clair-Mosaic is open-source and available at https://github.com/HKU-BAL/Clair-Mosaic.

bioinformatics↗

bioRxiv2025

ClairS-TO: A deep-learning method for long-read tumor-only somatic small variant calling

Accurate identification of somatic variants in tumor is crucial but challenging, and typically requires a matched normal sample for reliable detection, which is often unavailable in real-world research and clinical scenarios, necessitating proficient algorithms to tell real somatic variants from germline variants and background noises. However, existing tumor-only somatic variant callers that were designed for short-read data dont work well with long-read. To fill the gap, we present ClairS-TO, a deep-learning-based method for long-read tumor-only somatic variant calling. ClairS-TO uses an ensemble of two disparate neural networks that were trained from the same samples but for opposite tasks - how likely/not likely a candidate is a somatic variant. ClairS-TO also applies multiple post-calling filters, including 1) nine hard-filters, 2) four public plus any number of user-supplied PoNs, and 3) a module that statistically separates somatic and germline variants using tumor purity and copy number profile. Benchmarks using COLO829 and HCC1395 show that ClairS-TO outperforms DeepSomatic in long-read. ClairS-TO is also applicable to short-read and outperforms Mutect2, Octopus, Pisces, and DeepSomatic. Extensive experiments across various sequencing coverages, VAF ranges, and tumor purities support that ClairS-TO has a broad coverage of usage scenarios. ClairS-TO is open-source, available at https://github.com/HKU-BAL/ClairS-TO.

bioinformatics↗

bioRxiv2024

Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a [~]91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a [~]92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a [~]95% and [~]96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached [~]97% for ONT and [~]98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

bioinformatics↗