Biology subjects

Zhao, W.

Publications and source records attributed to Zhao, W..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

High-throughput 3D super-resolution ultrasound imaging

Capturing fast hemodynamics in deep organs is essential for understanding microvascular regulation of organ responses to physiological demands and pathological stress. However, non-invasive three-dimensional (3D) imaging of these microscale processes remains challenging due to trade-offs between spatial resolution, imaging speed, and penetration depth. Here, we present fluctuation-based high-order super-resolution acoustic microscope (FLAME), a tracking-free 3D ultrasound imaging technology capable of fast microvascular angiography and flow measurement. Using as few as 30 volumes, FLAME improves 3D ultrasound resolution by 8-fold ([~]50 m) and shortens data acquisition time by over two orders of magnitude, from tens of seconds to tens of milliseconds, compared with conventional tracking-based super-resolution approaches1-6. This high-throughput capability supports high-fidelity imaging of tissue functions under flexible experimental settings. FLAME achieves an unprecedented 3D super-resolution frame rate of [~]40 Hz, capturing transient hemodynamic responses to various physiological and pathological stimuli in different mouse organs, from the microvascular level to the whole-body scale. With open-sourced implementation, FLAME provides an accessible platform for real-time, in-depth hemodynamic monitoring across diverse biomedical applications and clinical translation.

bioengineering↗

bioRxiv2025

Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI. Key architectural modifications included a simplified two-layer residual block without bottleneck layers, selective shortcut connections, and a sigmoid-based classification head for independent prediction of each alteration, designed to reduce computational complexity while maintaining predictive accuracy. The model was trained and evaluated on 495 WSIs from the Sherlock-Lung study (70% training with 10% internal test set for 10-fold cross-validation, and 30% held-out validation set for final evaluation). For the held-out validation data, our model achieved high areas under the receiver operating characteristic curve [AUROC] values =0.84-0.93 for detecting 11 features: EGFR, KRAS, TP53, RBM10 mutations, MDM2 amplification, kataegis, CDKN2A deletion, ALK fusion, whole-genome doubling, and EGFR hotspot mutations (p.L858R and p.E746_A750del). Performance was low to moderate for tumor mutational burden (AUROC=0.67), APOBEC mutational signature (AUROC=0.57), and KRAS hotspot mutations (p.G12C: AUROC=0.74, p.G12V: AUROC=0.55, p.G12D: AUROC=0.43). Compared to results from established architectures such as Inception-v3 on the same WSIs, our model demonstrated significantly improved performance for most features. With further optimization, our model could support triaging for molecular testing and inform precision treatment strategies for NS-LUAD patients.

pathology↗

bioRxiv2025

A prognostic signature for lung adenocarcinoma in people who have never smoked

Knowledge of tumor cell dynamics can inform prognosis and treatment yet is largely lacking for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA-seq data from 684 NS-LUAD and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histological data further characterized the subtypes. Steady, marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver genes alterations, is linked to prolonged survival and low immune evasion. Proliferative shows high proliferation markers, TP53 mutations, and gene fusions. Chaotic, with high epithelial-to-mesenchymal transition markers, has the worst prognosis even within stage I tumors. Lacking known molecular or histological characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the overall classification and strongly predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving NS-LUAD prognostication in clinical settings. SignificanceThe transcriptome of 684 lung adenocarcinomas in people who have never smoked (NS-LUAD) identifies three subtypes with different cellular dynamics, and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a clinically applicable tool for treatment decision making in NS-LUAD patients.

genomics↗

bioRxiv2025

Label-free DIA-NN enabled RIME (DIANNeR) reveals glucocorticoid receptor interaction networks in breast, bladder, and blood across normaluntransformed cells, cancer cell lines, and PDXs

Understanding how transcription factors execute tissue-specific programmes requires defining their protein interaction networks in physiologically relevant contexts. However, profiling normal untransformed cells presents fundamental challenges due to intrinsically limited input material. To meet this challenge we integrated data-independent acquisition (DIA), ion mobility separation, and library-free analysis to achieve a 2-fold increase in detection without loss in enrichment. Applied to the glucocorticoid receptor (GR), a ubiquitously expressed nuclear receptor driving pleiotropic responses to standard-of-care anti-inflammatory therapeutics, DIANNeR (DIA-NN enabled RIME) resolves distinct, context-dependent networks across breast, ureter and blood in normal and transformed contexts. Key findings include: detection of a HOXA5-GR interaction in normal epithelium which was undetected in malignant models; an epithelial-restricted SMARCD3-GR interaction with prognostic relevance; and a FOXP3-BCL11B-GR network in primary CD4+ T cell populations undetected by data dependant acquisition (DDA). By defining normal GR tissue interactomes, DIANNeR provides the essential comparator for interpreting network rewiring that drives disease.

cancer biology↗

bioRxiv2025

NuMA promotes constitutive heterochromatin compaction by stabilizing linker histone H1 on chromatin

Heterochromatin has been widely recognized to exert pivotal functions of silencing specific genes and maintenance of genome stability. However, the mechanisms underlying heterochromatin formation and maintenance remain to be fully elucidated. Here, we discovered that the critical mitotic regulator NuMA, as a nucleoskeleton protein, is required for constitutive heterochromatin organization at the level of nucleosomes in the interphase. Depletion of NuMA results in shortened nucleosome repeat length (NRL), dispersed nucleosome clutches and increased chromatin accessibility in heterochromatin regions. Afterwards, epigenetic maintenance and transcription repression in constitutive heterochromatin are disrupted upon NuMA-depletion, particularly the up-regulated transcription level of the non-coding long terminal repeat (LTR) elements, indicating the crucial roles of NuMA in cell differentiation and senescence. We revealed that such functions of NuMA rely on its interaction with linker histone H1, which stabilizes H1s binding to chromatin and facilitates nucleosome stacking. We provided direct structural evidence of NuMAs stabilization effect at the highest spatial resolution of nucleosomes through in situ cryo-ET. Notably, we found that NuMA oligomerizes into quasi-meshwork in nucleoplasm and highly co-localizes with H1 on the chromatin, providing the organization basis for NuMA as a nucleoskeleton protein in chromatin architecture regulation. Collectively, our findings illuminate the concerted effect of nucleoskeleton protein and linker histone on chromatin compaction at the level of nucleosomes, which unveil a new layer of mechanisms by which nucleoskeleton regulates heterochromatin formation and maintenance.

biophysics↗

bioRxiv2025

A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India

India is the most populous country globally, yet genetic studies involving Indian individuals remain limited. The Indian population is composed of many founder groups and has a mixed genetic ancestry, including an ancestral component not observed anywhere outside of India. This presents a unique opportunity to uncover novel disease variants and develop more tailored medical interventions. To facilitate genetic research in India, a crucial first step is to create a foundational resource that serves as a benchmark for future population studies and methods development. To this end, we have constructed the largest and most nationally representative linkage disequilibrium and genotype imputation reference panels in India to date, using high-coverage whole-genome sequencing data of 2,680 Indian participants from the Longitudinal Aging Study in India-Harmonized Diagnostic Assessment of Dementia (LASI-DAD). As an LD reference panel, LASI-DAD includes 69.5 million variants, representing 170% and 213% increases relative to the 1000 Genomes Project (1000G) and TOP-LD panels, respectively. Besides serving as an LD lookup panel, LASI-DAD facilitates various statistical analyses that rely on precise LD estimates. In a polygenic risk score (PRS) analysis, LASI-DAD improved the predictive performance of PRS by 2.1% to 35.1% across traits and studies. As an imputation reference panel, LASI-DAD improved the imputation accuracy by 3% to 101% (mean = 38%) compared to the TOPMed panel (Version R3) and by 3% to 73% (mean = 27%) compared to the Genome Asia Pilot (GAsP) panel across different minor allele frequencies. The LASI-DAD reference panel is publicly available to benefit future studies.

genetics↗

bioRxiv2025

Inactivating a β1 subunit of SnRK1 promotes broad-spectrum disease resistance in rice

Rice is a staple crop for over half of the worlds population, and its sustainable production is vital to ensure global food security. However, rice is highly susceptible to several devastating fungal diseases1, including blast disease caused by Magnaporthe oryzae, sheath blight by Rhizoctonia solani, false smut by Ustilaginoidea virens, brown spot by Bipolaris oryzae, bakanae by Fusarium fujikuroi, and head blight by F. graminearum. The mechanisms underlying the susceptibility of rice to these fungal diseases remain unclear. Here, we report that the {beta} subunit of SnRK1, SnRK1{beta}1A, confers broad-spectrum susceptibility to these fungal diseases. Our findings reveal that diverse rice fungal pathogens have convergently evolved an effector, Gas2, which interacts with SnRK1{beta}1A to prevent its ubiquitination-mediated degradation and promotes its nuclear translocation. While SnRK1{beta}1A is expressed at low levels in healthy plants, its expression is markedly induced upon fungal infections, facilitating susceptibility by inhibiting SnRK11, the subunit of SnRK1, whose overexpression has been shown to enhance broad-spectrum resistance in rice2. Notably, rice lines with disrupted SnRK1{beta}1A are resistant to multiple fungal diseases without compromising growth and yield in the field. Together, this study demonstrates that broad-spectrum disease resistance in crops can be achieved by disrupting inducible susceptibility genes whose encoded proteins are targeted by effectors conserved in multiple pathogens.

plant biology↗

bioRxiv2025

Examining the Role of Extrachromosomal DNA in 1,216 Lung Cancers

The role of extrachromosomal DNA (ecDNA) in lung cancer, particularly in subjects who never smoked (LCINS), remains unclear. Examination of over 1200 whole-genome-sequenced lung cancers identified ecDNA in 18.9% of patients. Recurrent amplification of MDM2 and other oncogenes via ecDNA possibly drives a LCINS subset. Tumors harboring ecDNA showed worse overall survival than tumors harboring other focal amplifications. A strong association with whole-genome doubling suggests most ecDNA reflects genomic instability in treatment-naive lung cancer.

bioinformatics↗

bioRxiv2025

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalks capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalks capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways.

systems biology↗

bioRxiv2025

Deconvolution of multiplexed peptidoform mass spectra enables high-resolution profiling of complex protein modification patterns

Combinatorial patterns of post-translational modifications (PTMs), like the histone code, play critical roles in regulating protein functionality. However, in bottom-up proteomics, isobaric modification peptidoforms (IMPs) frequently co-elute and co-fragment during liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), resulting in multiplexed mass spectra that cannot be properly analyzed by conventional protein identification and quantification methods. Existing solutions are either limited to highly narrow scenarios or unavailable for public use. Here, we introduce PTMdecoder, a robust and versatile software tool for discriminating and quantifying IMPs by deconvoluting their multiplexed MS/MS spectra and ion chromatograms without requiring a spectral library of individual peptidoforms. Tested on 34 samples of synthesized IMPs, PTMdecoder achieved an overall sensitivity of 95.7% for IMP identification and root mean square errors mostly <0.05 for quantification. In human histone samples, it detected and quantified 36.7-44.8% more IMPs than conventional methods, providing a higher-resolution view of histone modification patterns.

bioinformatics↗

bioRxiv2025

Regulation of translational fidelity by repression of C/D box SNoRNPs during UPR

Endoplasmic reticulum (ER) is the major cellular compartment where the folding and maturation of secretory and membrane proteins take place. When protein folding demands exceed the protein handling capacity of ER, the unfolded protein response (UPR) pathway is activated. UPR reduces the client protein load in the ER by inhibiting protein translation and increases protein handling capacity by upregulating genes encoding ER-resident molecular chaperones. The main pathway for translational repression in response to ER stress has been the phosphorylation of eIF2 by PERK, which also suppresses rRNA synthesis. rRNA methylation and pseudouridylation can fine-tune protein synthesis. Small nucleolar ribonucleoproteins (SnoRNPs) are mainly separated into two subtypes: C/D box and H/ACA box. C/D box SNoRNPs guides rRNA 2-O-methylation, while H/ACA SNoRNPs catalyze the pseudouridylation modifications of rRNAs. We hypothesize that the UPR may regulate translational fidelity by regulating the expression of core proteins of C/D and H/ACA box SnoRNPs. In this study, we show that the expressions of NHP2L1, NOP56 and FBL were significantly downregulated by UPR. In addition, FBL knockdown reduced cell proliferation and colony formation. Furthermore, we show the effects of UPR and FBL knockdown on rRNA methylation and translation fidelity, including alterations in nonsense suppression, frameshifts, ribosome pausing and translation initiation by IRES. In conclusion, this study reveals the regulation of translational fidelity during UPR by change in rRNA methylation due to reduced function of C/D box SnoRNPs.

cell biology↗

bioRxiv2025

Saddle Curvature Association of nsP1 Facilitates the Replication Complex Assembly of Chikungunya Virus in Cells

The replication of positive-sense RNA viruses, including the pathogenic SARS-CoV-1 and -2, DENV, and ZIKV, often takes place in curved cellular membrane compartments of tens to hundreds of nanometers in diameter within host cells. The non-structural proteins (nsPs) of viruses are found to critically control the formation and maintenance of such unique nanoscale membrane structures. However, the molecular mechanism underlying the association and assembly of nsPs around curved membranes has remained elusive, due to the technical difficulties of imaging the nanoscale interaction in live cells. In this study, we leveraged vertically aligned nanostructures to define membrane curvatures in live cells and investigated the assembly of viral nsPs on these curved membranes. Using Chikungunya virus as a model system, we found that its nsP1 was preferentially bound to and stabilized around positively curved membrane sites and the preference became more apparent as the radius of curvature decreased to 150 nm or smaller. This preferential accumulation was mainly attributed to the hydrophobic residues on recently identified membrane association loops (MA loops) of each nsP1 monomer. Molecular dynamics simulations further confirmed the improved binding kinetics and stability of nsP1 on positively curved membranes, especially when a 12-mer ring was formed by the nsP1 dodecamer. More interestingly, a saddle curvature association was enabled by the 3D coordination of nsP1 monomers along the dodecamer ring, where the MA loops of individual nsP1 monomers sensed a wide range of the positively curved membrane in the x-z plane while the rigid nsP1 dodecamer ring stabilized the negative curvature in the x-y plane. Its strong coupling to the saddle curvature fulfilled the need to constrain the neck of the membrane spherule for viral replication. Strikingly, productive CHIKV replication exhibited strong enrichment on patterned nanostructure array, proving the effectiveness of membrane curvature-guided assembly of the functional replication complex. Our findings revealed that the cell membrane facilitated the local enrichment of viral nsPs in a curvature- dependent manner. It opens up membrane curvature modulation as a new dimension to dissect the formation and regulation of membrane compartments for viral replication.

biophysics↗

bioRxiv2025

Control of leaflet movement pattern by a novel PP2C phosphatase DLM1 in Medicago truncatula

Sessile plants exhibit diverse movement behaviors that have long intrigued the scientists. The legume plants display a rhythmic leaflet movement pattern characterized by horizontal opening during the day and vertical closure at night. However, the underlying mechanisms remain largely enigmatic. Here, we isolated a mutant designated as dlm1 (downward leaflet movement1) from Medicago truncatula that displays leaflets downward opening during daytime while upward closure at night. Cellular analyses reveal that this aberrant phenotype correlates with abnormal volume changes of motor cells within the pulvinus. DLM1 exhibits high expression level in the motor organ pulvinus and encodes an unreported nuclear-localized PP2C phosphatase. Notably, phylogenetic analysis demonstrates that species exhibiting rhythmic leaflet movements consistently retain DLM1 homologs, suggesting the possibility of its functionally conserved role in regulating leaflets movement pattern. Structural characterization reveals that DLM1 possesses both phosphatase and kinase domains. Functional complementation assays demonstrate that the phosphatase domain is necessary and sufficient for maintaining the leaflet movement pattern. Collectively, our work uncovers a novel PP2C protein that governs the leaflet movement, providing mechanistic insights into this intriguing plant behavior.

plant biology↗

bioRxiv2025

Ancient DNA Reveals Hominoid Evolution: Intermediate DNA Sequences and Advances in Molecular Paleontology

AbstractFossils and ancient crude pottery vessels function as physical "DNA containers," preserving oriDNA (original, in situ DNA) and accumulating eDNA (environmental DNA) over time. Ancient DNA (aDNA) serves as molecular fossils, chronicling evolutionaryhistory. Using "nano-affinitybead technology", we extracted and sequenced DNAfrom Lycoptera fossils in the Jehol region and a "round-bellied jar (Jar)" from the Erlitou period in Guangwu Town, yielding 236,545 primate sequences from the fossils and 86,908 from the pottery. We observed that the AFF value of DNA sequences negatively corre lates with species divergence time1, offering a quantitative measure of DNA preservation and host divergence. Some fragments distinct from modern genomic sequences, termed "intermediate DNAsequences" (IDS), have been identified. Many IDS exhibit an upper age limit, preserving characteristics of the last common ancestor (LCA) of the Hominidae and offering molecular insights into "Darwins puzzle". Among IDS, the SRRA subtype (Sequence Reversal and Rearrangement), identified in mRNA-coding exonic sequences, arises from the incorporation of a complementary antisense strand upstream of the sense strand, either adjacent to it or separated bya sequence interval. This introduces a novel post-transcriptional regulatory mechanism at the mRNA level, driven by SRRA, which presets hairpin structures and Indels in the UTR or CDS of exonic sequences, modulating gene variation. We propose: "SRRAs played a critical trial-and-error role in early Hominidae evolution, facilitating adaptive genomic changes, with some SRRA sequences later excised from exons", positioning SRRAas an evolutionary "genetic switch". Additionally, seven species -specific fragments (SSFs) of non-human primates (NHPs) linked to Asian Homo erectus were identified in the fossils, and pottery DNA reveale d sequences from tropical species (e.g., zebrass, oil palms), providing evidence of climate-driven local extinction and supporting paleo-ecological and paleo-environmental reconstruction. This method of analyzing aDNA from non-skeletal materials opens new avenues in paleontology, archaeology, and geology, guiding the tracing of ancient migration patterns and fossil searches. DNAfragments preserved within "DNA containers " exhibit an "old-few, new-many" turnover pattern, with many aDNA fragments displaying non-deamination. This evidence challenges prevailing perspectives, the authenticity criteria for aDNA, and the capabilities and scope of the traditional research method, necessitating a thorough reevaluation of the relevant knowledge framework. Furthermore, this study opens new opportunities for frontier research.

paleontology↗

bioRxiv2025

Multi-modal single-cell foundation models via dynamic token adaptation

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in applying deep learning in genomics include DNA-language and single-cell foundation models. However, these models take only one data type as input. We introduce dynamic token adaptation and demonstrate how it combines these models to predict gene regulation at the single-cell level in different genetic contexts. Although the method is generalisable, we focus on an illustrative example by training an adapter from DNA-sequence embeddings to a single-cell foundation models token embedding space. As a qualitative evaluation, we assess the impact of DNA sequence changes on the models learned gene regulatory networks by mutating the transcriptional start site of the transcription factor GATA4 in silico, observing predicted expression changes in its target genes in fetal cardiomyocytes.

bioinformatics↗

bioRxiv2025

Detecting cell-level transcriptomic changes of Perturb-seq using Contrastive Fine-tuning of Single-Cell Foundation Models

Genome-scale perturbation cell atlases are an exciting new resource to understand the transcriptomic and phenotypic impact of single-gene activation or knockdown. However, in terms of differentially expressed genes identified, the signal detected in these data atlases is low, leading to the exclusion of most data from downstream analyses. Recent advances in single-cell foundation models have shown promise in capturing complex biological insights. However, their application to perturbation analysis, especially in predicting perturbed single-cell transcriptomes, remains limited. In this paper, we focus on learning representations of single-cell transcriptomes that capture subtle, yet important, transcriptome-wide changes, and we propose a novel fine-tuning strategy using contrastive learning to leverage single-cell foundation models for this task. We pre-train a single-cell foundation model and fine-tune on a genome-scale perturbation dataset using a contrastive loss, which minimises the distance between cell embeddings from unperturbed cells while maximising the distance between perturbed and unperturbed cells. We validate and test the model on unseen perturbations, demonstrating its ability to identify global biologically meaningful transcriptional changes not captured by traditional differential expression methods. Our approach provides a novel framework for analysing single-cell perturbation data and offers a more effective means of identifying perturbations that drive systemic gene expression changes.

bioinformatics↗

bioRxiv2025

Advanced coarse-grained model for fast simulation of nascent polypeptide chain dynamics within the ribosome

The nascent polypeptide exit tunnel (NPET) is a sub-compartment of the ribosome that constrains the dynamics of nascent polypeptide chains during protein translation. Simulating these dynamics has been limited due by the spatial scale of the ribosome and the temporal scale of elongation. Here, we present an automated pipeline to extract the geometry of the NPET and the ribosome surface at high resolution from any ribosome structure. We further convert this into a coarse-grained (CG) bead model that can be used in molecular simulations. This CG model more accurately captures NPET geometry than previous representations and allows for the simulation of co- and post-translational processes that are computationally prohibitive with all-atom approaches. In particular, we illustrate how the CG model may be used to simulate the elongation dynamics of the nascent polypeptide and its escape post-translation, as well as evaluate free energy landscapes and examine the influence of electrostatics on the nascent polypeptide escape. SIGNIFICANCEThe translation of nascent polypeptide chains is mediated by the ribosome, with interactions between the protein and the nascent polypeptide exit tunnel (NPET) impacting the process. However, modeling and simulating protein elongation and its escape from the NPET remain challenging due to computational limitations in spatial and temporal resolution. Here, we develop a computational pipeline for generating coarse-grained (CG) models of the NPET and ribosome surface from any ribosome structure that allow for both effective and accurate computer simulations. We demonstrate how the model can be implemented for various simulations, including the elongation dynamics of the nascent polypeptide and escape of the chain post-translation, as well as in estimating free energy landscapes and examining the impact of the charged environment on the escape time.

biophysics↗

bioRxiv2025

The GSA Family in 2025: A Broadened Sharing Platform for Multi-Omics and Multimodal Data

The Genome Sequence Archive Family (GSA family) provides a suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), the open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to the 2021 version, the GSA family has significantly expanded by introducing a new database member, the OBIA, and through comprehensive upgrades to the existing ones. Notable enhancements to the existing members include broadening the range of accepted data types, enhancing the quality control systems, fine-tuning the data retrieval system, and refining the data-sharing management mechanisms.

genomics↗