Biology subjects

Chen, N.-C.

Publications and source records attributed to Chen, N.-C..

11 recordsLinked to original sources

bioRxiv2025

A multi-modal cell-free RNA language model for liquid biopsy applications

Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multi-modal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pre-trained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise, and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics.

bioinformatics↗

bioRxiv2024

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BackgroundUnderstanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. ResultsWe report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. ConclusionsOur work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

genomics↗

bioRxiv2023

Measuring, visualizing and diagnosing reference bias with biastools

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios, i.e. (a) when the donors variants are known and reads are simulated, (b) when donor variants are known and reads are real, and (c) when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.

bioinformatics↗

bioRxiv2022

A refined characterization of large-scale genomic differences in the first complete human genome

The first telomere-to-telomere (T2T) human genome assembly (T2T-CHM13) release was a milestone in human genomics. The T2T-CHM13 genome assembly extends our understanding of telomeres, centromeres, segmental duplication, and other complex regions. The current human genome reference (GRCh38) has been widely used in various human genomic studies. However, the large-scale genomic differences between these two important genome assemblies are not characterized in detail yet. Here, we identify 590 discrepant regions ([~]226 Mbp) in total. In addition to the previously reported non-syntenic regions, we identify 67 additional large-scale discrepant regions and precisely categorize them into four structural types with a newly developed website tool (SynPlotter). The discrepant regions ([~]20.4 Mbp) excluding telomeric and centromeric regions are highly structurally polymorphic in humans, where copy number variation are likely associated with various human disease and disease susceptibility, such as immune and neurodevelopmental disorders. The analyses of a newly identified discrepant region--the KLRC gene cluster--shows that the depletion of KLRC2 by a single deletion event is associated with natural killer cell differentiation in [~]20% of humans. Meanwhile, the rapid amino acid replacements within KLRC3 is consistent with the action of natural selection during primate evolution. Our study furthers our understanding of the large-scale structural variation differences between these two crucial human reference genomes and future interpretation of studies of human genetic variation.

genomics↗

bioRxiv2022

The complete sequence of a human Y chromosome

The human Y chromosome has been notoriously difficult to sequence and assemble because of its complex repeat structure including long palindromes, tandem repeats, and segmental duplications1-3. As a result, more than half of the Y chromosome is missing from the GRCh38 reference sequence and it remains the last human chromosome to be finished4, 5. Here, the Telomere-to-Telomere (T2T) consortium presents the complete 62,460,029 base pair sequence of a human Y chromosome from the HG002 genome (T2T-Y) that corrects multiple errors in GRCh38-Y and adds over 30 million base pairs of sequence to the reference, revealing the complete ampliconic structures of TSPY, DAZ, and RBMY gene families; 41 additional protein-coding genes, mostly from the TSPY family; and an alternating pattern of human satellite 1 and 3 blocks in the heterochromatic Yq12 region. We have combined T2T-Y with a prior assembly of the CHM13 genome4 and mapped available population variation, clinical variants, and functional genomics data to produce a complete and comprehensive reference sequence for all 24 human chromosomes.

genomics↗

bioRxiv2022

Improved sequence mapping using a complete reference genome and lift-over

Complete, telomere-to-telomere genome assemblies promise improved analyses and the discovery of new variants, but many essential genomic resources remain associated with older reference genomes. Thus, there is a need to translate genomic features and read alignments between references. Here we describe a new method called levioSAM2 that accounts for reference changes and performs fast and accurate lift-over between assemblies using a whole-genome map. In addition to enabling the use of multiple references, we demonstrate that aligning reads to a high-quality reference (e.g. T2T-CHM13) and lifting to an older reference (e.g. GRCh38) actually improves the accuracy of the resulting variant calls on the old reference. By leveraging the quality improvements of T2T-CHM13, levioSAM2 reduces small-variant calling errors by 11.4-39.5% compared to GRC-based mapping using real Illumina datasets. LevioSAM2 also improves long-read-based structural variant calling and reduces errors from 3.8-11.8% for a PacBio HiFi dataset. Performance is especially improved for a set of complex medically-relevant genes, where the GRC references are lower quality. The software is available at https://github.com/milkschen/leviosam2 under the MIT license.

bioinformatics↗

bioRxiv2021

LevioSAM: Fast lift-over of alternate reference alignments

MotivationAs more population genetics datasets and population-specific references become available, the task of translating ("lifting") read alignments from one reference coordinate system to another is becoming more common. Existing tools generally require a chain file, whereas VCF files are the more common way to represent variation. Existing tools also do not make effective use of threads, creating a post-alignment bottleneck. ResultsLevioSAM is a tool for lifting SAM/BAM alignments from one reference to another using a VCF file containing population variants. LevioSAM uses succinct data structures and scales efficiently to many threads. When run downstream of a read aligner, levioSAM completes in less than 13% the time required by an aligner when both are run with 16 threads. Availabilityhttps://github.com/alshai/levioSAM Contacttmun1@jhu.edu, langmea@cs.jhu.edu

bioinformatics↗

bioRxiv2021

Improving variant calling using population data and deep learning

Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

bioinformatics↗

bioRxiv2020

Profiling Germline Adaptive Immune Receptor Repertoire with gAIRR Suite

Genetic profiling of germline adaptive immune receptor repertoire (AIRR), including T cell receptor (TR) and immunoglobulin (IG), is imaginably relevant to numerous immune-related conditions, but currently insurmountable due to high genetic complexity. Our gAIRR Suite comprises three modules. gAIRR-seq, a probe capture-based targeted sequencing pipeline, profiles AIRR from individual DNA samples. gAIRR-call and gAIRR-annotate call alleles from gAIRR-seq reads and annotate whole-genome assemblies respectively. We gAIRR-seqed TRV and TRJ of seven Genome in a Bottle (GIAB) DNA samples with 100% accuracy, and discovered novel alleles. We also gAIRR-seqed and gAIRR-called a subject from both the peripheral blood mononuclear cells (PBMC) and oral mucosal cells. The calling results from these two cell types have a high concordance (99% for all known AIRR alleles). We gAIRR-annotated 36 genomes to cumulatively unearth 325 novel TRV alleles and 29 novel TRJ alleles. We could further profile the flanking sequences, including the recombination signal sequence (RSS). We validated two structural variants for HG002. We uncovered substantial conflicts of AIRR genes in references GRCh37 and GRCh38. The gAIRR Suite can potentially benefit future genetic study and clinical applications of various immune-related phenotypes.

genomics↗

bioRxiv2020

Reducing reference bias using multiple population reference genomes

Most sequencing data analyses start by aligning sequencing reads to a linear reference genome. But failure to account for genetic variation causes reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the "reference flow" alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance, but with 14% of the memory footprint and 5.5 times the speed.

bioinformatics↗

bioRxiv2020

Characterization of the dimeric interactions of dimeric and tetrameric conformations of the PvNV protrusion-domain using a mixed DFT/QTAIM approach

The protrusion-domain (P-domain) of Penaeus vannamei nodavirus (PvNV) exists as two dimer-dimer conformations: one is a protein dimer and the other is a protein tetramer. We undertook a theoretical study to gain a clear understanding of the nature of the stabilizing interactions at the dimeric interfaces of the dimeric and tetrameric conformations of the PvNV P-domain (PvNVPd) using the quantum theory of atoms in molecules (QTAIM) and natural-bond orbital (NBO) analyses in the framework of the density-functional theory (DFT) approach. The QTAIM analysis characterized the presence of multiple hydrogen bonds of common types with strength ranging from electrostatic to the covalent limit inside the PvNVPd dimer-dimer interfaces. Val257-Lys335, Phe294-Val330, Gln296-Thr328, Glu296-Thr329, Thr328-Gln297, Val330-Ala293, Lys335-Asp256 and Lys335-Val257 pairs are critical residue pairs of all three dimeric interfaces of PvNVPd. They preserve these dimeric interfaces through charge-charge, charge-dipole, dipole-dipole, hydrophobic and hydrogen bond interactions. The strongest intermolecular dimer-dimer interactions belong to the dimeric interface between subunits A and B of PvNVPd in the tetrameric conformation.

biochemistry↗