Zhenxian Zheng

Somatic haplotype reconstruction and variant recalibration from tumor-only long-read sequencing

LongPhase-TO reconstructs somatic haplotypes from tumor-only long-read sequencing, improving variant calling accuracy and haplotype block size.

ClairS: a deep-learning method for long-read somatic small variant calling

ClairS is a deep-learning method for long-read somatic variant calling that outperforms existing tools on synthetic and short-read datasets.

Client-server interfaces enable efficient agent-driven variant calling

The paper re-architected a variant caller into a client-server system to improve agent-driven variant calling efficiency and reliability.

Leveraging ONT move table values for signal aware variant calling

Clair3 v2 improves variant calling accuracy for Oxford Nanopore Technologies sequencing by using move table data and dwelling time, achieving higher F1-scores for SNPs and Indels.

A near-complete human pancreatic cancer genome

This paper presents near-complete, haplotype-resolved assemblies of a pancreatic cancer genome and normal tissue, identifying extensive somatic variants and epigenetic changes in previously excluded regions.

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

Clair3-RNA is a deep learning variant caller for long-read RNA sequencing data that achieves high accuracy in SNP detection across different platforms.

LongPhase-S: purity estimation and variant recalibration with somatic haplotying for long-read sequencing

LongPhase-S improves somatic variant accuracy by jointly estimating tumor purity and recalibrating variants using somatic haplotyping in long-read sequencing.

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

ClairS-TO is a deep-learning method for long-read tumor-only somatic variant calling that outperforms existing tools and works with both long-read and short-read data.

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

Clair-Mosaic detects mosaic small variants from long-read sequencing data using deep learning, achieving high sensitivity and accuracy in both paired and single-sample modes.

Guess till correct: Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power

Gungnir is a DNA storage codec that corrects errors through hash matching and biochemical constraints, achieving full data recovery from 20% erroneous bases without redundancy or high-coverage sequencing.

ClusterV-Web: A User-Friendly Tool for Profiling HIV Quasispecies and Generating Drug Resistance Reports from Nanopore Long-Read Data

ClusterV-Web provides a user-friendly web interface for analyzing Nanopore long-read data to profile HIV quasispecies and generate drug resistance reports.

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP

This study improves variant calling by integrating Oxford Nanopore and Illumina data using Clair3-MP, showing benefits in difficult genomic regions.

Symphonizing pileup and full-alignment for deep learning-based long-read variant calling

This paper introduces Clair3, a deep learning-based variant caller that combines pile-up and full-alignment methods, achieving faster performance and better accuracy, especially at lower coverage.

Evaluation of Mycobacterium Tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling

This study compares Oxford Nanopore adaptive sequencing and PCR amplicon sequencing for detecting Mycobacterium Tuberculosis in metagenomic samples and calls variants, with an open-source analysis framework.

Clair3-Trio: high-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks

Clair3-Trio improves variant calling in family trios using Nanopore long-reads with a Trio-to-Trio neural network and a novel loss function.

ECNano: A Cost-Effective Workflow for Target Enrichment Sequencing and Accurate Variant Calling on 4,800 Clinically Significant Genes Using a Single MinION Flowcell