Biology subjects

Fu, Z.-C.

Publications and source records attributed to Fu, Z.-C..

3 recordsLinked to original sources

bioRxiv2026

ANCHOR: haplotype-aware allelic and isoform inference from single-cell long-read RNA sequencing with de novo variant calling

Long-read RNA sequencing enables haplotype- and isoform-resolved allelic analysis of transcriptomes, yet extending this capability to single cells and distinct cell types remains computationally challenging due to sparse coverage, sequencing errors, incomplete variant information, and reference-biased transcript assignment. Here we present ANCHOR, a haplotype-aware framework for single-cell long-read RNA sequencing that performs de novo expressed-variant discovery, molecule-level haplotype assignment and isoform-resolved allelic quantification. ANCHOR combines a signed-graph variant caller, pair hidden Markov modelling and beta-binomial UMI aggregation to infer parental allele counts for genes and splice-resolved isoforms, without requiring a pre-existing phased genotype or deep learning. In human single-cell long-read RNA benchmarks, ANCHOR improved variant-calling performance over tested long-read RNA callers at single-cell and low-to-moderate coverage, and its beta-binomial model reduced depth-driven false positives in allele-specific expression testing. Applied to newly generated single-cell long-read RNA-seq data from reciprocal mouse crosses during gastrulation, ANCHOR resolved cell-type- and isoform-specific parent-of-origin imprinting and identified an antagonistic maternally biased Sgce isoform. ANCHOR provides a general framework for allele-and isoform-resolved analysis of diploid single-cell long-read transcriptomes.

bioinformatics↗

bioRxiv2023

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

We develop a stepwise computational framework, called DEMINING, to directly detect expressed DNA and RNA mutations in RNA deep sequencing data. DEMINING incorporates a deep learning model named DeepDDR, which facilitates the separation of expressed DNA mutations from RNA mutations after RNA-seq read mapping and pileup. When applied in RNA-seq of acute myeloid leukemia patients, DEMINING uncovered previously-underappreciated DNA and RNA mutations, some associated with the upregulated expression of host genes or the production of neoantigens. Finally, we demonstrate that DEMINING could precisely classify DNA and RNA mutations in RNA-seq data from non-primate species through the utilization of transfer learning.

bioinformatics↗

bioRxiv2021

No observable guide-RNA-independent off-target mutation induced by prime editor

Prime editor (PE) has been recently developed to induce efficient and precise ontarget editing, whereas its guide RNA (gRNA)-independent off-target effects remain unknown. Here, we used whole-genome and whole-transcriptome sequencing to determine gRNA-independent off-target mutations in cells expanded from single colonies, in which PE generated precise editing at on-target sites. We found that PE triggered no observable gRNA-independent off-target mutation genome-wide or transcriptome-wide in transfected human cells, highlighting its high specificity.

biochemistry↗