Biology subjects

Gong, T.

Publications and source records attributed to Gong, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Methylmalonic acid: a new target for Hadamard-edited MRS

BackgroundMethylmalonic acidemia (MMAemia) is a genetic metabolic disorder characterized by an accumulation of methylmalonic acid (MMA) and impaired energy metabolism leading to increased lactate (Lac). The signals of MMA (1.23 ppm) and Lac (1.33 ppm) overlap, making their separation using conventional MRS challenging. An MRS method to differentiate the two metabolites could enhance pathophysiological understanding and improve treatment monitoring - Hadamard-edited MRS has the potential to achieve this. PurposeTo develop a Hadamard-encoded J-difference editing approach for independent detection of MMA and Lac at 3T. MethodsA novel Hadamard-encoded editing scheme was implemented and evaluated with density-matrix simulations, phantom and in vivo experiments. The new four-step scheme uses frequency-selective editing pulses, applied at 3.2 ppm and 4.1 ppm to modulate the J-coupled methyl resonances of MMA and Lac, respectively. Hadamard combinations of the four sub-experiments yield the separate difference-edited spectra for each target metabolite. ResultsSimulations and phantom experiments clearly illustrate the separated signals of MMA and Lac. In vivo validation experiments show a Lac signal (but no MMA) in a healthy infant, and both Lac and MMA (separated into their respective Hadamard-combination spectra) in a patient with MMAemia. ConclusionHadamard-encoded editing at 3T can separate MMA and Lac signals and shows promise for studying altered metabolism in patients with MMAemia.

biochemistry↗

bioRxiv2026

Hierarchical X-ray microscopy and mesoscopic diffusion MRI in the same brain reveal the human connectome across scales

We present a multimodal pipeline for 3D imaging of cerebral white-matter archi-tecture across scales, from whole-brain axonal projections down to individual myelinated axons. After diffusion MRI, an adult ex vivo human hemisphere undergoes label-free imaging with Hierarchical Phase-Contrast Tomography (HiP-CT) from 20 {micro}m/voxel in the whole hemisphere to 2 {micro}m/voxel in areas of interest, with intrinsic cross-scale alignment. A 4 cm tissue block extracted from the hemisphere is reimaged with HiP-CT at 0.857 {micro}m/voxel, enabling direct visualisation of single myelinated axons. After osmium staining, micro-CT at 0.364 {micro}m/voxel and electron microscopy at 4 nm/voxel are acquired in biop-sies from the tissue block to validate the presence of myelinated axons in the label-free HiP-CT contrast. Spanning three orders of magnitude in resolution, these co-registered multimodal datasets bridge microscopic wiring and macro-scopic brain organisation, providing a foundation for anatomically grounded whole-brain connectomics.

neuroscience↗

bioRxiv2026

A human subcortical connectome at 400 μm resolution

The fiber pathways of the human subcortex are of particular importance in clinical neuroscience, as they are targeted by neuromodulation therapies, such as deep brain stimulation (DBS) and lesion approaches, in various motor and psychiatric disorders. However, the complexity, size, and position of these pathways make them challenging to image non-invasively with diffusion MRI (dMRI). As a result, recent efforts in atlasing these pathways to guide neuromodulation have resorted to using synthetic data. Here we present the first extensive reconstruction of fiber pathways of the human subcortex with ultra-high-resolution dMRI. We leverage a unique, ex vivo dMRI dataset acquired by the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits (LINC) on the first-of-its-kind Connectome 2.0 scanner. Our contribution is two-fold. First, by showing the feasibility of reconstructing these pathways with non-invasive neuroimaging at the single subject level, we set the stage for future research into reconstructing them in vivo in individual patients. Second, we provide a high-definition atlas of basal-ganglia-thalamocortical circuits that is readily extensible via our publicly released data and annotations. As a first demonstration of its clinical validity, we align our atlas to "hotspots" from previous DBS studies and identify pathways associated with therapeutic or side effects.

neuroscience↗

bioRxiv2026

Multi-dimensional diffusion MRI at ultra-high gradient strength for mapping axonal architecture and microstructure in the primate brain

We present the most comprehensive sampling of the macaque and human brain with diffusion MRI to date. As part of the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits, we leverage ultra-high-gradient MRI systems, including the first-of-its-kind Connectome 2.0, for post-mortem acquisitions. Each sample is imaged for [~]250 hours at multiple spatial resolutions down to 0.25 mm isotropic for whole macaque brains and 0.4 mm isotropic for human hemispheres. Our optimized protocols allow us to sample both species across [~]50 diffusion shells varying in b-value, diffusion time, and echo time, reaching ultra-high b-values up to 64000 s/mm2 with high signal-to-noise ratio. We demonstrate that these multi-dimensional data resolve not only white matter connectional architecture but also cortical and subcortical cytoarchitectonic boundaries, at a level of detail previously inaccessible in whole-brain noninvasive imaging. As such, these data are an important resource for both technical development and basic and clinical neuroscience.

neuroscience↗

bioRxiv2026

How different AI models understand cells differently

AI single-cell foundation models (scFMs) are believed to be able to learn essential relations in cell transcriptomics with the attention modules in Transformer, but there is no method to reveal what they actually learned. We observed that different models may grasp different aspects of relations. To unravel the mystery, we propose scGeneLens, a framework for dissecting how scFMs perceive cells. We employed a sparse block attention to replace the original attention mechanism to concentrate attentions into a few dominant gene-gene relations, used attention propagation to trace how the relations propagate across Transformer layers, and used integrated gradients to disentangle the relative contributions of gene identity and expression in cell representations. We applied it to scFoundation and scGPT and show that they exhibit pluralistic perceptions of cells: scFoundation emphasizes relations among cell-type marker genes, resulting in stronger cell-type separability, whereas scGPT focuses more on genes involved in shared cellular pathways and core biological activities, leading to representations that generalize across conditions. The framework provides a unified lens for probing what scFMs learn about cells and offers actionable insights for the design of future cellular foundation models. Our code can be seen in https://anonymous.4open.science/r/scGeneLens-B771/.

bioinformatics↗

bioRxiv2026

The modular nature of long-association pathways in the human brain

Human brain functions are organized within large-scale networks, yet the long-range structural pathways that support them remain only partly understood. Diffusion MRI tractography has enabled non-invasive mapping of these connections but its low resolution has also contributed to a view of white matter association bundles as monolithic entities linking distant cortical regions. This contrasts with anatomical studies in non-human primates and humans, which show these bundles act as conduits for complex systems of short- and long-range projections. Here we investigate the dorsal branch of the superior longitudinal fasciculus (SLF-I), the proposed structural backbone of the dorsal attention network, whose anatomy in humans is debated. Using anatomic tracing, polarization-sensitive optical coherence tomography, and in vivo and ex vivo diffusion MRI, we reconstruct the SLF-I across species, modalities, and scales. High resolution tractography (<750 m), consistent with tracer and PS-OCT data, reveals multiple fiber systems of varying lengths rather than uniform parieto-frontal connections observed at more conventional resolution (1.5 mm). Comparative analyses show a consistent organization: the SLF-I carries short-range cortico-cortical projections rostral to area 6 and longer, layered connections extending to parietal regions, while direct parieto-frontal connectivity is mostly supported by the adjacent cingulum bundle. These findings reconcile our understanding of the SLF-I across the scales of non-invasive neuroimaging and invasive anatomic studies and provide an updated framework for future investigations of large-scale networks in health and disease.

neuroscience↗

bioRxiv2025

Long-Term Intestinal Epithelial Remodeling Induced by Acute Protein-Energy Malnutrition

Protein-energy malnutrition (PEM) is a global health burden with lasting effects that extend well beyond the initial nutrient deficiency. To systematically investigate the long-term effects of a single episode of PEM on the structure and function of the intestinal epithelium and its associated microbiota, we employed a comprehensive multi-omics approach, including (spatial) transcriptomics, DNA methylation analysis, fecal metagenomics, and metabolomics. Our findings show that PEM persistently alters the intestinal epithelium by depleting Paneth cells and suppressing antimicrobial gene expression - changes linked to DNA methylation that persist despite dietary recovery. In germ-free mice, the sustained epithelial phenotype after was absent. We identified the microbial lipid metabolite 9-HODE and epigenetically deregulated PPAR-driven GDF15 expression as key molecular drivers of the persistent PEM-induced Paneth cell dysfunction. Targeting microbial lipid production and its link to the host GDF15 pathway could offer novel therapeutic strategies for long-term consequences of malnutrition and other Paneth cell-associated diseases.

immunology↗

bioRxiv2025

Neural signaling contributes to heart formation and growth in the invertebrate chordate, Ciona robusta.

Neurons contribute to the complex interplay of signals that mediate heart development and homeostasis. Although a limited set of studies suggest that neuronal peptides impact vertebrate heart growth, the specific contributions of these peptides to cardiomyocyte progenitor differentiation or proliferation have not been elucidated. Here we show that the neuropeptide tachykinin along with canonical Wnt signaling regulate cardiomyocyte progenitor proliferation in the chordate model Ciona robusta. In C. robusta, the heart continues to grow throughout adulthood and classic histological studies indicate that a line of undifferentiated cells may serve as a reserve progenitor lineage. We found that this line of cardiomyocyte progenitors consists of distinct distal and midline populations. Distal progenitors divide asymmetrically to produce distal and midline daughters. Midline progenitors divide asymmetrically to produce myocardial precursors. Through single cell RNA sequencing (scRNA-seq) of adult C. robusta hearts, we delineated the cardiomyocyte progenitor expression profile. Based on this data we investigated the role of Wnt signaling in cardiomyocyte progenitor proliferation and found that canonical Wnt signaling is required to suppress excessive progenitor proliferation. The scRNA-seq data also identified a number of presumptive cardiac neural-like cells. Strikingly, we found that a subset of these neuronal cells appears to innervate the distal cardiomyocyte progenitors. Based on the expression of the tachykinin receptor in these neuronal cells, we blocked tachykinin signaling using pharmacological inhibitors and found that this drove reduced proliferation in the distal progenitor pool. Through targeted CRISPR-Cas9 knockdown we then demonstrated that both extrinsic tachykinin and intrinsic, cardiac tachykinin receptors are required for formation of the myocardial heart tube. This work provides valuable insights into how organisms may deploy neural signals to regulate organ growth in response to environmental or homeostatic inputs.

developmental biology↗

bioRxiv2024

Mechanisms of Meiotic Spindle Initiation in Caenorhabditis elegans Oocytes

In many animal species, the oocyte meiotic spindle, which is required for chromosome segregation, forms without centrosomes. In some systems, Ran-GEF on chromatin initiates spindle assembly. We found that in C. elegans oocytes, endogenously-tagged Ran-GEF dissociates from chromatin during spindle assembly but re-associates during meiotic anaphase. Meiotic spindle assembly occurred after auxin-induced degradation of Ran-GEF but anaphase I was faster than controls and extrusion of the first polar body frequently failed. In search of a possible alternative pathway for spindle assembly, we found that soluble tubulin concentrates in the nuclear volume during germinal vesicle breakdown. We found that the concentration of soluble tubulin in the metaphase spindle region is enclosed by ER sheets which exclude cytoplasmic organelles including mitochondria and yolk granules. Measurement of the volume occupied by yolk granules and mitochondria indicated that volume exclusion would be sufficient to explain the concentration of tubulin in the spindle volume. We suggest that this concentration of soluble tubulin may be a redundant mechanism promoting spindle assembly near chromosomes.

cell biology↗

bioRxiv2024

Language models enable zero-shot prediction of RNA secondary structures including pseudoknots

Current deep learning-based models for predicting RNA secondary structures face challenges in achieving high generalization ability. At the same time, a vast repository of unlabeled non-coding RNA (ncRNA) sequences remains untapped for structure prediction tasks. To address this challenge, we trained RNA-km, a foundation language model that enables zero-shot prediction of RNA secondary structures including pseudoknots. For the end, we incorporated specific modifications into the language model training process, including k-mer masking strategy and relative positional encoding. RNA-km are trained on 23 million ncRNA sequences in a self-supervised manner, gaining the advantages of high generalization ability. For a target RNA sequence, we make a zero-shot secondary structure prediction with the attention maps provided by RNA-km and a specified minimum-cost flow algorithm. Our results on popular benchmark datasets demonstrate that RNA-km exhibits high generalization abilities, excelling in zero-shot predictions for RNA secondary structures. In addition, the attention maps provided by the model capture intricate structural relationships, as evidenced by accurate pseudoknot predictions and precise identification of longdistance base pairs. We anticipate that RNA-km enhances the predictive capacity and robustness of existing models, thereby improving their ability to accurately predict structures for novel RNA sequences.

bioinformatics↗

bioRxiv2024

Characterization of a novel humanized heavy chain antibody targeting endogenous retroviruses with anti-lymphoma activity

Lymphomas continue to pose therapeutic challenges, with a considerable portion of patients facing refractory disease. This study focuses on Diffuse Large B-cell Lymphoma (DLBCL), the most prevalent lymphoma type. Within the human genome, transposable elements (TEs), particularly Human Endogenous Retroviruses (HERVs), constitute a significant yet understudied portion. Among HERVs, the HERV-K family, specifically HERV-K113 and HERV-K115, has intact open reading frames. Epigenetic regulation tightly controls HERV expression, and aberrant expression has been observed in various cancers, including lymphomas. This research investigates the potential of HERV-K as a therapeutic target in DLBCL. The study encompasses comprehensive methods, including RNA extraction, PCR detection, flow cytometry, immunoblotting, peptide prediction, phage display, surface plasmon resonance, ELISA, antibody-dependent cell-mediated cytotoxicity, internalization assays, and bioinformatic analysis. Results reveal the presence and expression of HERVs in lymphoma patients and cell lines, with the HERV-K envelope protein identified as a crucial contributor to lymphoma cell growth. Moreover, the study identifies immunogenic regions of HERV-K, leading to the development of a humanized camelid nanobody (FF-01) with potential therapeutic applications. Furthermore, bioinformatic analysis differentiates DLBCL subgroups based on TE expression, providing insights into prognostic variations. Patients with high HERV-K113 expression show activation of pathways related to antiviral responses, suggesting a viral mimicry state. In conclusion, the study highlights the clinical relevance of HERVs in lymphomas, proposing them as novel therapeutic targets. The newly developed nanobody FF-01 demonstrates anti-lymphoma activity through antibody-dependent cellular cytotoxicity and internalization. This research opens avenues for exploring endogenous retroviruses as targets for immunotherapy in lymphomas, showcasing the potential of FF-01 as a promising candidate for further investigation.

cancer biology↗

bioRxiv2023

OrtR regulates the oxidative stress response of Streptococcus mutans

Dental caries is the most common chronic infectious diseases around the world and disproportionately affects the marginalized socioeconomic group. Streptococcus mutans, considered a primary etiological agent of caries, must depend on the coordinated physiological response to tolerate the oxidative stress generated by commensal species within dental plaque, which is a critical aspect of its pathogenicity. Here, we identified and characterized a novel TetR family regulator SMU_1361c, encoded by the TnSmu2 operon, which appears to be acquired by the bacteria via horizontal genes transfer. Surprisingly, smu_1361c functions as a transcriptional repressor to regulate gene expression outside its operon, involved in the oxidative stress response of S. mutans. The smu_1361c overexpression strain UA159/pDL278-1361c was more susceptible to oxidative stress and less competitive against hydrogen peroxide generated by commensal species Streptococcus gordonii and Streptococcus sanguinis. Transcriptomics analysis revealed that smu_1361c overexpression resulted in the significant downregulation of 22 genes mainly belonging to three gene clusters responsible for the oxidative stress response. The conversed DNA binding motif of SMU_1361c was determined by electrophoretic mobility shift and DNase I footprinting assay with purified SMU_1361c protein, therefore, smu_1361c is directly involved in gene transcription related to the oxidative stress response. Crucially, our finding provides a new understanding of how S. mutans deals with the oxidative stress that is required for pathogenesis and will facilitate the development of new and improved therapeutic approaches for dental caries.

microbiology↗

bioRxiv2023

Recombinant outer membrane vesicles coupled with cytokines act as high-performance adjuvants against Helicobacter pylori infection in mice

The widespread prevalence of Helicobacter pylori (H. pylori) infection remains a great challenge to the human health. The existing vaccines are not ideal for preventing H. pylori infection; thus, exploring highly effective adjuvants may improve the immunoprotective efficacy of H. pylori vaccines. In a previous study, we found that outer membrane vesicles (OMVs), a type of nanoscale particle spontaneously produced by Gram-negative bacteria, could act as adjuvants to boost the immune response to vaccine antigens. In the present study, we explored the potential application of OMVs as delivery vectors for adjuvant development. We constructed recombinant OMVs containing cytokines interleukin 17A or interferon-{gamma} and evaluated their function as adjuvants in combination with inactivated whole-cell vaccine (WCV) or UreB as vaccine antigens. Our results showed that recombinant OMVs as adjuvants could induce stronger humoral and mucosal immune responses in mice than wild-type H. pylori OMVs and the cholera toxin (CT) adjuvant. Additionally, the recombinant OMVs significantly promoted Th1/Th2/Th17-type immune responses. Furthermore, the recombinant OMV adjuvant induced more potent clearance of H. pylori than CT and wild-type OMVs. Our data suggest that the recombinant OMVs coupled with cytokines may become potent adjuvants for development of novel and effective vaccines against H. pylori infection. ImportanceHelicobacter pylori (H. pylori) is one of the important risk factors for gastric cancer, and its vaccine is crucial for its prevention and control. However, to date, no effective vaccine has been approved. Exploring novel and effective vaccine adjuvants may provide new perspectives and ideas for the development of H. pylori vaccines. Outer membrane vesicles (OMVs) deserve more attention as a novel form of vaccine and adjuvant. We have long focused on OMVs as vaccine adjuvants to enhance efficacy by delivering eukaryotic plasmid expressing immune promoting cytokines in wild-type OMVs. Our results are expected to provide a new adjuvant form for the development of H. pylori vaccine, and this adjuvant design strategy can also be used in the development of vaccines for other types of pathogens, including bacteria and even viruses.

immunology↗

bioRxiv2023

sLASER and PRESS Perform Similarly at Revealing Metabolite-Age Correlations

PurposeTo compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate. MethodsMRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale (CSO) and posterior cingulate cortex (PCC) regions. Acquisition parameters included TR/TE 2000/30 ms; 96 transients; 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. Signal-to-noise ratio (SNR), full-width-half-maximum linewidth of tCr, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. ResultsSNR and linewidth were significantly higher (p<0.05) for sLASER than PRESS. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. Metabolite measures were significantly correlated (p<0.05) for most metabolites between the two methods except GABA, Gln and Lac in CSO and GSH, Lac and NAAG in PCC. Metabolite-age relationships were consistently identified using both PRESS and sLASER. Similar CVs were observed for most metabolites. ConclusionThe study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in CSO and PCC data acquired at 3T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in good brain regions at clinical field strength.

neuroscience↗

bioRxiv2023

Metabolite T1 relaxation times differ across the adult lifespan

PurposeTo investigate the age-dependence of metabolite T1 relaxation times at 3T in both gray- and white-matter-rich voxels. MethodsThis manuscript analyzes publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3T using PRESS localization. Voxels were placed in posterior cingulate cortex and centrum semiovale in 102 healthy volunteers across 5 decades of life (20s to 60s). All spectra were analyzed in Osprey v2.4.0. To estimate T1 relaxation times for tNAA2.0 and tCr3.0, the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single inversion recovery signal equation. Correlations between T1 and subject age were evaluated. ResultsSpearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.43; p < 0.001) and tCr3.0 (rs = -0.23; p = 0.021) decreased significantly with age in white-matter-rich CSO, and less steeply (and not significantly) for tNAA2.0 (rs = -0.15; p = 0.136) and tCr3.0 (rs = -0.10; p = 0.319) in gray-matter-rich PCC. ConclusionThe analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

neuroscience↗

bioRxiv2023

DPHL v2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v1, a previous pan-human library built from 1096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying 10,943 proteins. However, a major limitation of DPHL v1 is the lack of semi-tryptic peptides and protein isoforms, which are abundant in clinical specimens. Here, we generated DPHL v2 from 1608 DDA-MS data acquired using Orbitrap mass spectrometers. The data included 586 DDA-MS newly acquired from 17 tissue types, while 1022 files were derived from DPHL v1. DPHL v2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v2 to include semi-tryptic peptides and protein isoforms. DPHL v2 was then applied to a publicly available colorectal cancer dataset with 286 DIA-MS files. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v1. Our findings show that the increased human proteome coverage of DPHL v2 provides larger pools of potential protein biomarkers.

bioinformatics↗

bioRxiv2022

RACK1 may participate in placental development via regulating proliferation and migration of trophoblast cell in pigs following intrauterine growth restriction

Intrauterine growth restriction (IUGR) is a severe complication in swine production. Placental insufficiency is responsible for inadequate fetal growth, but the specific etiology of placental dysfunction-induced IUGR in pigs remains poorly understood. In this work, placenta samples supplying the lightest-weight (LW) and mean-weight (MW) pig fetuses in the litter at day 65 (D65) of gestation were collected, and the relationship between fetal growth and placental morphologies and functions was investigated using histomorphological analysis, RNA sequencing, quantitative polymerase chain reaction, and in-vitro experiment in LW and MW placentas. Results showed that the folded structure of the epithelial bilayer of LW placentas followed a poor and incomplete development compared with that of MW placentas. A total of 632 differentially expressed genes (DEGs) were screened out between the LW and MW placentas, and RACK1 was found to be downregulated in LW placentas. The DEGs were mainly enriched in translation, ribosome, protein synthesis, and mTOR signaling pathway according to GO and KEGG enrichment analyses. In-vitro experiments indicated that the decreased RACK1 in LW placentas may be involved in abnormal development of placental folds (PFs) by inhibiting the proliferation and migration of porcine trophoblast cells. Taken together, these results revealed that RACK1 may be a vital regulator in the development of PFs via regulating trophoblast ribosome function, proliferation, and migration in pigs.

genetics↗

bioRxiv2022

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of Knot-Fold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using a self-attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially-designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely-used dynamic programming algorithms, thus facilitating the identification of pseudoknots. Using a total of 1605 RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy significantly higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

bioinformatics↗