Biology subjects

Ni, P.

Publications and source records attributed to Ni, P..

At least 19 recordsLinked to original sources

bioRxiv2025

Unimeth: A unified transformer framework for accurate DNA methylation detection from nanopore reads

Nanopore sequencing enables direct detection of DNA modifications from native DNA. However, accurate methylation calling across species, sequence contexts, modification types and chemistries remains challenging. We present Unimeth, a transformer-based framework that jointly processes raw signals and basecalled sequences in read patches and predicts all target methylation sites within each patch. Unimeth uses a three-phase training strategy that combines signal pre-training, methylation fine-tuning and site-level calibration using methylation frequency information. We evaluated Unimeth for 5mC and 6mA detection using public and in-house datasets spanning 14 species, three nanopore chemistries and wild-type, mutant and enzyme-treated samples. Unimeth improved plant 5mC detection in non-CpG contexts, reduced false-positive calls in low-methylation samples and maintained high 5mCpG performance in mammalian datasets. For 6mA, Unimeth reduced background calls while preserving signals for Fiber-seq nucleosome and gene-level analyses. Unimeth provides a unified framework for nanopore-based methylation detection across methylation types and biological contexts.

bioinformatics↗

bioRxiv2025

Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age

Histological images offer a wealth of data. Mining these data holds significant potential for enhancing disease diagnosis and prognosis, though challenges remain, especially in non-cancer contexts. In this study, we developed a statistical framework that links raw histological images and their derived features to the genotype, transcriptome, and chronological age of the samples. We first demonstrated an association between image features and genotypes, identifying 906 image quantitative trait loci (imageQTLs) significantly associated with image features. Next, we identified differentially expressed (DE) genes by stratifying samples into image-similar groups based on image features and performing DE comparisons between groups. Additionally, we developed a deep-learning model that accurately predicts gene expression in specific tissues from raw images and their features, highlighting gene sets associated with observed morphological changes. Finally, we constructed another deep-learning model to predict chronological age directly from raw images and their features, revealing relationships between age and tissue morphology, especially aspects derived from nucleus features. Both models are supported by a computational approach that greatly compresses gigapixel whole-slide images and extracts interpretable nucleus features, integrating both large-scale tissue morphology and smaller local structures. We have made all interpretable nucleus features, imageQTLs, DE genes, and deep-learning models available as online resources for further research. Significance StatementThis study establishes a comprehensive framework that links histological image features to genotype, transcriptome, and chronological age in large-scale healthy tissue datasets, providing valuable insights into tissue morphology. By identifying 906 significant, interpretable imageQTLs and conducting differential expression analysis based on image features, we enhance understanding of genetic and morphological interactions. Additionally, we developed predictive models for both gene expression and chronological age from raw histological images, introducing a novel approach to studying age-related tissue-specific changes and presenting the first model to demonstrate the predictability of age from histological images.

bioinformatics↗

bioRxiv2025

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

bioRxiv2025

Intraductal Papillary Mucinous Neoplasm Cellular Plasticity linked with Repeat Element Dysregulation

BackgroundIntraductal papillary mucinous neoplasms (IPMNs) are preinvasive pancreatic lesions with a spectrum of histologic phenotypes and variable risk in progressing to invasive cancer. Aberrant repetitive element expression has been shown to be functionally linked to cell state changes in pancreatic cancer. ObjectiveThis study utilized spatial transcriptomics with customized repeat element probes to better understand the relationship of histologic subtypes, repeat element dysregulation, and molecular profiles of different cell populations in IPMN. DesignA total of 52 lesions from 18 patients with resected IPMNs of different histologies and degrees of dysplasia were analyzed with whole transcriptome spatial analysis (GeoMx). Of these, 50 lesions from 17 patients were also processed for single-cell spatial molecular imaging (CosMx). Repeat element probes for LINE1, HSATII, HERVK, and HERVH were used for GeoMx and CosMx. ResultsPancreaticobiliary-type IPMN was enriched for basal-like epithelium and infiltration of Treg cells. Intestinal-type IPMN was enriched for classical epithelium and macrophage infiltrates. Gastric-type IPMN was found to have equal basal-like and classical epithelium with a diverse immune infiltrate. Repeat RNAs were expressed at high levels across IPMN phenotypes and enriched in high-grade dysplasia. Single-cell transcriptional trajectory analysis revealed a phylogeny starting from gastric toward intestinal and pancreaticobiliary branches associated with higher-grade dysplasia and repeat RNA expression. ConclusionSpatial transcriptomics of IPMN identified a molecular continuum between histological subtypes supporting a common gastric-type origin that transitions to intestinal and pancreatobiliary phenotypes. This cell state plasticity is linked with repeat element expression that can be a potential biomarker for IPMN progression. What is already known on this topicO_LIMolecular characterization of IPMN subtypes using regional spatial transcriptomics has described the differences between histologies C_LIO_LIRepeat element expression is associated with cell state changes in pancreatic ductal neoplasm C_LI What this study addsO_LISingle cell spatial molecular imaging of IPMN subtypes reveals a cellular and molecular continuum starting from gastric histology with distinct branches to Intestinal and pancreatobiliary subtypes C_LIO_LIRepeat element expression is associated with higher grade IPMN histology C_LIO_LIRepeat element expression is elevated in the histologic and molecular continuum from gastric to intestinal and pancreatobiliary-type IPMN C_LI How this study might affect research, practice, or policyO_LIThese results support a common gastric histology origin of IPMN subtypes with different molecular trajectories towards higher grade disease C_LIO_LIOur findings suggest that repeat RNA expression can be used in conjunction with other transcriptional markers of cell state as a biomarker for IPMN progression C_LI

cancer biology↗

bioRxiv2024

GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing

Accurate and complete gene annotations are indispensable for understanding how genome sequences encode biological functions. For more than twenty years, the GENCODE consortium has developed reference annotations for the human and mouse genomes, becoming a foundation for biomedical and genomics communities worldwide. Nevertheless, collections of important yet poorly-understood gene classes like long non-coding RNAs (lncRNAs) remain incomplete and scattered across multiple, uncoordinated catalogs. To address this, GENCODE has undertaken the most comprehensive lncRNA annotation effort to date. This is founded on the manually supervised computational annotation of full-length targeted long-read sequencing, on matched embryonic and adult tissues, of orthologous regions in human and mouse. Altogether 17,931 human genes (140,268 transcripts) and 22,784 mouse genes (136,169 transcripts) have been added to the GENCODE catalog representing a 2-fold and 6-fold growth in transcripts, respectively - the greatest increase in the number of annotated human genes since the sequencing of the human genome. Our targeted design assigned human-mouse orthologs at a rate beyond previous studies, tripling the number of human disease-associated lncRNAs that have mouse orthologs. Novel lncRNA genes consistently exhibit biological signals of functionality, and they greatly enhance the functional interpretability of the human genome. While poorly expressed in bulk RNA-Seq samples, many of them are highly expressed in specific cell populations, maybe even contributing to cell-type determination. The expanded GENCODE lncRNA annotations mark a critical step toward deciphering the human and mouse genomes.

genomics↗

bioRxiv2024

Dynamic Single Cell Transcriptomics Defines Kidney FGF23/KL Bioactivity and Novel Segment-Specific Inflammatory Targets

FGF23 via its coreceptor Klotho (KL) provides critical control of phosphate metabolism, which is altered in rare and very common syndromes, however the spatial-temporal mechanisms dictating renal FGF23 functions remain poorly understood. Thus, developing approaches to modify specific FGF23-dictated pathways has proven problematic. Herein, wild type mice were injected with rFGF23 for 1, 4 and 12h and renal FGF23 bioactivity was determined at single cell resolution. Computational analysis identified distinct epithelial, endothelial, stromal, and immune cell clusters, with differential expressional analysis uniquely tracking FGF23 bioactivity at each time point. FGF23 actions were sex independent but critically relied upon constitutive KL expression mapped within proximal tubule (S1-S3) and distal tubule (DCT/CNT) cell sub-populations. Temporal KL-dependent FGF23 responses drove unique and transient cellular identities, including genes in key MAPK- and vitamin D-metabolic pathways via early- (AP-1-related) and late-phase (EIF2 signaling) transcriptional regulons. Combining ATACseq/RNAseq data from a cell line stably expressing KL with the in vivo scRNAseq pinpointed genomic accessibility changes in MAPK-dependent genes, including the identification of FGF23-dependent EGR1 distal enhancers. Finally, we isolated unexpected crosstalk between FGF23-mediated MAPK signaling and pro-inflammatory TNF receptor activation via NF-{kappa}B, which blocked FGF23 bioactivity in vitro and in vivo. Collectively, our findings have uncovered novel pathways at the single cell level that likely influence FGF23-dependent disease mechanisms. Translational statementInflammation and elevated FGF23 in chronic kidney disease (CKD) are both associated with poor patient outcomes and mortality. However, the links between these manifestations and the effects of inflammation on FGF23-mediated mineral metabolism within specific nephron segments remain unclear. Herein, we isolated an inflammatory pathway driven by TNF/NF-{kappa}B associated with regulating FGF23 bioactivity. The findings from this study could be important in designing future therapeutic approaches for chronic mineral diseases, including potential combination therapies or early intervention strategies. We also suggest that further studies could explore these pathways at the single cell level in CKD models, as well as test translation of our findings to interactions of chronic inflammation and elevated FGF23 in human CKD kidney datasets.

physiology↗

bioRxiv2024

Simultaneous Prediction of Functional States and Types of cis-regulatory Modules Reveals Their Prevalent Dual Uses as Enhancers and Silencers

Our understanding of the precise locations of cis-regulatory elements (CRMs) in the genomes, as well as their functional types (enhancer or silencer), states (active or inactive) and target genes in various cell/tissue types of organisms remains limited, despite recent progresses. To address these challenges, we have recently developed a two-step strategy that first predicts a more complete map of CRMs in the genome, and then predicts the functional states of the CRMs. However, our initial approach lacked the ability to differentiate between the functional types of CRMs. Therefore, we utilized distinct features to simultaneously predict the functional types and states of the CRMs. Applying our method to 107 cell/tissue types with the minimum of required data available, we predicted 868,948 (73.8%) of the CRMs to be active as enhancers or silencers in at least one of these cell/tissue types. In 56 cell/tissue types with required data available for both enhancers and silencers, we predicted that 117,646 (14.8%) and 227,211 (28.6%) CRMs only functioned as enhancers (enhancer-predominant) and silencers (silencer-predominant), respectively, while 83,985 (10.6%) functioned both as enhancers and silencers (dual functional). Thus, both dual functional CRMs and silencers might be more prevalent than previously assumed. Most dual functional CRMs function either as enhancers or silencers in different cell/tissue types (Type I), while some have dual functions regulating different genes in the same cell/tissue types (Type II). Different types of CRMs display different lengths and TFBS densities, reflecting the complexity of their functions. Our two-step approach can accurately predict the functional types and states of CRMs using data of only five epigenetic marks in a cell/tissue type. Author SummaryCRMs function as enhancers and/or silencers to promote and repress, respectively, the transcription of genes in a spatiotemporal manner, thereby playing critical roles in virtually all biological processes. However, despite recent progress, the understanding of CRMs remains limited. Most existing methods are aimed to simultaneously predict the locations and functional states of enhancers in a given cell/tissue type, however, the accuracy of these one-step methods is low. We have recently developed a two-step strategy that first predicts locations of CRMs in the genome, and then predicts their functional states as enhancers in cell/tissue types with high accuracy. However, our initial approach was unable to differentiate between enhancers and silencers. Therefore, in this study, we employ two machine-learning models, so that we can simultaneously predict the functional states and types of our previously predicted 1.2M CRMs in various cell/tissue types. Applying the method to cell/tissue types with the data available, we categorize the CRMs into four types with distinct properties reflecting their functional complexity. Our results indicate that silencers and dual functional CRMs might be more prevalent than previously assumed. The precise prediction of CRM types and states provides opportunities to pinpoint their target genes, thus opening new avenues for research.

bioinformatics↗

bioRxiv2024

Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes

Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.

genetics↗

bioRxiv2024

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized [~]250 disease-risk genes and drug targets with associated cell types. Summary Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/585576v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@70b24org.highwire.dtl.DTLVardef@6c9eddorg.highwire.dtl.DTLVardef@131fae2org.highwire.dtl.DTLVardef@4e0f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimers disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its usage in evaluating how sequence variants affect PPTs, an operation useful for protein design. In addition, we show its superior performance compared to suitable classical benchmarks. Due to the "black-box" nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on Alzheimers disease-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in AD, suggesting a natural defense mechanism. Significance StatementProtein phase transition (PPT) is a physical mechanism associated with both physiological processes and age-related diseases. We present a modeling approach for predicting the protein propensity to undergo PPT, forming droplets or amyloids, directly from its sequence. We utilize a large language model (LLM) and demonstrate how variants within the protein sequence affect PPT. Because the LLM is naturally domain-agnostic, to enhance interpretability, we compare it with a classical knowledge-based model. Furthermore, our findings suggest the possible regulation of PPT by gene expression and transcription factors, hinting at potential targets for drug development. Our approach demonstrates the usefulness of fine-tuning a LLM for downstream tasks where only small datasets are available.

biophysics↗

bioRxiv2023

Underlying causes for prevalent false positives and false negatives in STARR-seq data

STARR-seq and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR peaks are located in repressive chromatins and are not functional in the tested cells. While some of the STARR peaks in repressive chromatins might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. However, the prevalence of and underlying causes for the artifacts are not fully understood. Based on predicted cis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR peaks and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

bioinformatics↗

bioRxiv2022

Spatio-temporal modeling of high-throughput multi-spectral aerial images improves agronomic trait genomic prediction in hybrid maize

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) was measured by a multi-spectral MicaSense camera and ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multi-trait model, a two-stage approach that quantified NDVI local environmental effects (NLEE) was proposed. Firstly, NLEE were separated from additive genetic effects over the growing season using two-dimensional spline (2DSpl), separable autoregressive (AR1) models, or random regression models (RR). Secondly, the NLEE were leveraged within agronomic trait genomic best linear unbiased prediction (GBLUP) either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of NLEE across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to all baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2DSpl NLEE were most correlated to the soil parameters and grain yield 2DSpl effects. Simulation of field effects demonstrated improved specificity for RR models. In summary, NLEE increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

genetics↗

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

PF-D-Trimer, a broadly protective SARS-CoV-2 subunit vaccine: immunogenicity and application

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had and still has a considerable impact on global public health. One of the characteristics of SARS-CoV-2 is a surface homotrimeric spike protein, the primary responsible for the host immune response upon infection. Here we show the preclinical studies of a broad protective SARS-CoV-2 subunit vaccine developed from our Trimer Domain platform using the Delta spike protein, from antigen design to purification, vaccine evaluation and manufacturability. The prefusion trimerized Delta spike protein, PF-D-Trimer, was highly expressed in Chinese hamster ovary (CHO) cells, purified by a rapid one-step anti-Trimer Domain monoclonal antibody immunoaffinity process and prepared as a vaccine formulation with an adjuvant. The immunogenicity studies demonstrated that this vaccine candidate induces robust immune responses in mouse, rat and Syrian hamster models. It also protects K18-hACE2 transgenic mice in a homologous virus challenge. The neutralizing antibodies induced by this vaccine display a cross-reactive capacity against the ancestral WA1 and Delta variants as well as different Omicron, including BA.5.2. The Trimer Domain platform was proven to be a key technology in the rapid production of the PF-D-Trimer vaccine and may be crucial to accelerate the development of updated versions of SARS-CoV-2 vaccines.

immunology↗

bioRxiv2022

Development and application of an uncapped mRNA platform

A novel uncapped mRNA platform was developed. Five lipid nanoparticle (LNP)-encapsulated mRNA constructs were made to evaluate several aspects of our platform, including transfection efficiency and durability in vitro and in vivo and the activation of humoral and cellular immunity in several animal models. The constructs were eGFP-mRNA-LNP (for enhanced green fluorescence mRNA), Fluc-mRNA-LNP (for firefly luciferase mRNA), S{delta}T-mRNA-LNP (for Delta strain SARS-CoV-2 spike protein trimer mRNA), gDED-mRNA-LNP (for truncated glycoprotein D mRNA coding ectodomain from herpes simplex virus type 2 (HSV2)) and gDFR-mRNA-LNP (for truncated HSV2 glycoprotein D mRNA coding amino acids 1[~]400). Quantifiable target protein expression was achieved in vitro and in vivo with eGFP-and Fluc-mRNA-LNP. S{delta}T-mRNA-LNP, gDED-mRNA-LNP and gDFR-mRNA-LNP induced both humoral and cellular immune responses comparable to those obtained by previously reported capped mRNA-LNP constructs. Notably, S{delta}T-mRNA-LNP elicited neutralizing antibodies in hamsters against the Omicron and Delta strains. Additionally, gDED-mRNA-LNP and gDFR-mRNA-LNP induced potent neutralizing antibodies in rabbits and mice. The mRNA constructs with uridine triphosphate (UTP) outperformed those with N1-methylpseudouridine triphosphate (N1m{psi}TP) in the induction of antibodies via S{delta}T-mRNA-LNP. Our uncapped, process-simplified, and economical mRNA platform may have broad utility in vaccines and protein replacement drugs.

bioengineering↗

bioRxiv2022

A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

Mouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome. Here, by integrating binding peaks in 9,060 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into a cis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC sets. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strongly evolutionary constraints, indicating that they are likely cis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikely cis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively. Validation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans.

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↗

bioRxiv2022

Hbxip (Lamtor5) is essential for embryogenesis and regulates embryonic stem cell differentiation through activating mTORC1

Hbxip, also named Lamtor5, has been well characterized as a transcriptional coactivator in various cancers. However, the role of Hbxip in normal development remains unexplored. Here, we demonstrated that homozygous knockout of Hbxip leads to embryonic lethality, with retarded growth around E7.5. Using Hbxip knockout embryonic stem cells (ESCs), we showed that depletion of Hbxip compromises the self-renewal of ESCs, with reduced expression of pluripotency genes, reduced cell proliferation, and decreased colony forming capacity. In addition, Hbxip-/- ESCs are defective in differentiation, particularly ectodermal and mesodermal differentiation. Consistently, Hbxip-/- epiblast fails to differentiate properly, indicated by sustained expression of Oct4 in E8.5 Hbxip-/- epiblast. Mechanistically, in ESCs, Hbxip interacts with other components of the Ragulator complex, which is required for mTORC1 activation by amino acids. Importantly, ESCs depleted of Ragulator subunits, Lamtor3 or Lamtor4, display differentiation defects similar to those of Hbxip-/- ESCs. Moreover, Hbxip-/-, p14-/-, and p18-/- mice, lacking subunits of the Ragulator complex, also share similar phenotypes, embryonic lethality and retarded growth around E7-8. Thus, we conclude that Hbxip plays a pivotal role in the development and differentiation of the epiblast, as well as the self-renewal and differentiation of ESCs, through activating mTORC1 signaling.

developmental biology↗