Biology subjects

Nie, F.

Publications and source records attributed to Nie, F..

11 recordsLinked to original sources

bioRxiv2026

A five-dimensional functional state space for fingerprinting disease transcriptomes

High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (5-D) functional state space that represents disease transcriptomes as coordinated activity patterns across major biological systems. The framework maps transcriptomic signals onto five functional systems, 14 subcategories, and a distinct infrastructure layer, and was implemented as a reproducible pipeline for functional scoring, cross-condition profiling, benchmarking, and large language model (LLM)-assisted interpretation. Applied to wound healing, sepsis, colorectal cancer-related datasets, an extended GEO atlas of 38 complete case-control disease fingerprints spanning diverse disease contexts, and a TCGA-COAD/READ stage benchmark, the approach recovered interpretable disease-state patterns and retained progression-related information under strong compression. It also improved the quantitative grounding of LLM-generated summaries. This framework provides a compact and auditable representation for comparing disease transcriptomes and supporting human-AI biological interpretation. TeaserA five-dimensional functional framework turns complex disease transcriptomes into compact, interpretable fingerprints for human-AI analysis.

bioinformatics↗

bioRxiv2025

RefLA: Efficient reference-guided genome assembly from long reads

High-quality de novo genome assembly from long reads remains computationally demanding, while fast assemblers often compromise on contiguity or accuracy. This trade-off between speed and assembly quality presents a persistent challenge in large-scale genomic studies. Fortunately, high-quality reference genomes are now available for a growing number of species. These references can be leveraged to assist the assembly of closely related genomes, enabling efficient reconstruction without sacrificing fidelity--particularly when evolutionary divergence is moderate.We present RefLA, a reference-assisted assembler tailored for thirdgeneration long-read data. By combining alignment-guided structural refinement with localized reassembly, RefLA achieves accuracy comparable to or better than state-of-the-art de novo tools such as Flye and hifiasm, while running significantly faster. It also surpasses rapid assemblers like wtdbg2 and Shasta in both contiguity and base-level correctness.

bioinformatics↗

bioRxiv2025

An accurate phasing tool via high-quality heterozygous variations

Long-read phasing has become a widely adopted approach for reconstructing paternal and maternal haplotypes in diploid genomes. However, most existing phasing methods treat all variants equally, even though low-quality variants frequently introduce switch errors. We present a new phasing method, snpphaser, which first phases high-quality variants and then leverages them to filter out low-quality variants that are inconsistent with the high-confidence haplotypes. We benchmarked snpphaser against state-of-the-art phasing methods across nine diverse datasets. The results demonstrate that snpphaser achieves superior contiguity and accuracy compared to existing approaches.

bioinformatics↗

bioRxiv2025

HapBridge: A Methylation-Guided Approach for Correcting Switch Errors and Bridging Phased Blocks in Long-Read Phasing

Long-read sequencing has substantially advanced haplotype phasing yet continues to face challenges in low-heterozygosity regions and switch errors caused by read noise. DNA methylation exhibits haplotype-specific patterns, providing complementary linkage information, but existing phasing algorithms have not fully leveraged these signals owing to their variability. Here, we present Hap-Bridge, a methylation-guided phasing framework that enhances single-nucleotide variant (SNV)-based phasing by detecting and correcting switch errors and bridging adjacent phased blocks. Evaluations on Oxford Nanopore R9/R10 and PacBio HiFi datasets show that HapBridge reduces switch errors by 3.07-18.72% and improves N50 length by 5.84-68.61% compared with Meth-Phaser, achieving higher phasing accuracy and contiguity. These results demonstrate that integrating methylation with sequence variation can provide a robust and intrinsic linkage signal that effectively improves haplotype continuity in long-read sequencing. HapBridge is publicly available at GitHub https://github.com/Humonex/HapBridge.

bioinformatics↗

bioRxiv2025

LRMD: Reference-Free Misassembly Detection Based on Multiple Features from Long-Read Alignments

Genome assembly serves as the cornerstone of genomics research, with the detection of misassembly playing a crucial role in downstream analyses. Reference-free methods for misassembly detection, leveraging read alignments, enable us to circumvent the need for high-quality reference genomes and broaden their applicability. However, existing methods struggle to effectively utilize alignment data, leading to a noticeable deficiency in sensitivity for detecting misassemblies. We introduce LRMD, a novel reference-free tool for misassembly detection. LRMD integrates depth, clipping, and read pileup information derived from long-read-to-assembly alignments to significantly enhance sensitivity in identifying misassemblies. Experimental evaluations on both simulated and real datasets demonstrate that LRMD consistently outperforms existing tools in terms of sensitivity and F1-score. Notably, its results are closest to the reference-based evaluation tool QUAST. As an evaluation tool, LRMD also outputs metrics such as base quality, assembly size, contig N50, and others. LRMD is public available at http://github.com/sxfss/LRMD.

genomics↗

bioRxiv2024

Multimodal Protein-Ligand Contrastive Pretraining for Effective and Efficient Drug Discovery

Recent advancements in genomics and proteomics have identified numerous clinically significant protein targets, with notably 85% categorized as undruggable. These targets present widespread challenges due to their complex structures and dynamics, rendering conventional drug design strategies not always effective. In this study, we introduce Uni-Clip, a contrastive learning framework that incorporates multi-modal features of proteins (structure and residue) and ligands (conformation and graph). Optimized with a specifically designed CF-InfoNCE loss, Uni-Clip enhances the modeling of protein-ligand interactions for both undruggable and druggable proteins. Uni-Clip demonstrates superior performance in benchmark evaluations on widely acknowledged datasets, LIT-PCBA and DUD-E, achieving a 147% and 218% improvements in enrichment factors at 1% compared to baselines. Furthermore, Uni-Clip proves to be a practical tool for various drug discovery applications. In virtual screening for the challenging protein target GPX4 with flat surface, it identified non-covalent inhibitors with an IC50 of 4.17 M, in contrast to the predominantly covalent inhibitors currently known. Through target fishing for benzbromarone, Uni-Clip identified the intrinsically disordered protein c-Myc as a potential target, highlighting benzbromarones potential for repurposing in cancer therapy. Explainable analyses effectively identified binding sites consistent with molecular dynamics and experimental results, even for challenging undruggable targets.

bioinformatics↗

bioRxiv2022

de novo diploid genome assembly using long noisy reads via haplotype-aware error correction and inconsistent overlap identification

The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, a Phased Error Correction and Assembly Tool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly on B. taurus (BisonxSimmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.

bioinformatics↗

bioRxiv2022

Automated assembly of high-quality diploid human reference genomes

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has greatly benefited society1, 2. However, it still has many gaps and errors, and does not represent a biological human genome since it is a blend of multiple individuals3, 4. Recently, a high-quality telomere-to-telomere reference genome, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a duplicate genome, and is thus nearly homozygous5. To address these limitations, the Human Pangenome Reference Consortium (HPRC) recently formed with the goal of creating a collection of high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and automated assembly approaches yields the most complete, accurate, and cost-effective diploid genome assemblies with minimal manual curation. Approaches that used highly accurate long reads and parent-child data to sort haplotypes during assembly outperformed those that did not. Developing a combination of all the top performing methods, we generated our first high- quality diploid reference assembly, containing only [~]4 gaps (range 0-12) per chromosome, most within + 1% of CHM13s length. Nearly 1/4th of protein coding genes have synonymous amino acid changes between haplotypes, and centromeric regions showed the highest density of variation. Our findings serve as a foundation for assembling near-complete diploid human genomes at the scale required for constructing a human pangenome reference that captures all genetic variation from single nucleotides to large structural rearrangements.

genomics↗

bioRxiv2022

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing

Long single-molecular sequencing, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, is advantageous in detecting DNA 5-methylcytosine (5mC) in CpGs, especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence PCR-treated and M.SssI-treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long ([≥]10Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 AUC on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10x reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5mCs using PacBio CCS.

bioinformatics↗

bioRxiv2021

Genome-wide Detection of Cytosine Methylations in Plant from Nanopore sequencing data using Deep Learning

Methylation states of DNA bases can be detected from native Nanopore reads directly. At present, there are many computational methods that can detect 5mCs in CpG contexts accurately by Nanopore sequencing. However, there is currently a lack of methods to detect 5mCs in non-CpG contexts. In this study, we propose a computational pipeline which can detect 5mC sites in both CpG and non-CpG contexts of plant genomes by using Nanopore sequencing. And we sequenced two model plants Arabidopsis thaliana (A. thaliana) and Oryza sativa (O. sativa) by using Nanopore sequencing and bisulfite sequencing. The results of our proposed pipeline in the two plants achieved high correlations with bisulfite sequencing: above 0.98, 0.96, 0.85 for CpG, CHG, and CHH (H indicates A, C or T) motif, respectively. Our proposed pipeline also achieved high performance on Brassica nigra (B. nigra). Experiments also showed that our proposed pipeline can achieve high performance even with low coverage of reads. Moreover, by using Nanopore sequencing, our proposed pipeline is capable of profiling methylation of more cytosines than bisulfite sequencing.

bioinformatics↗

bioRxiv2020

Fast and accurate assembly of Nanopore reads via progressive error correction and adaptive read selection

Although long Nanopore reads are advantageous in de novo genome assembly, applying Nanopore reads in genomic studies is still hindered by their complex errors. Here, we developed NECAT, an error correction and de novo assembly tool designed to overcome complex errors in Nanopore reads. We proposed an adaptive read selection and two-step progressive method to quickly correct Nanopore reads to high accuracy. We introduced a two-stage assembler to utilize the full length of Nanopore reads. NECAT achieves superior performance in both error correction and de novo assembly of Nanopore reads. NECAT requires only 7,225 CPU hours to assemble a 35X coverage human genome and achieves a 2.28-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line showed an NG50 of 29 Mbp. The high-quality assembly of Nanopore reads can significantly reduce false positives in structure variation detection.

bioinformatics↗