Biology subjects

Zou, Y.

Publications and source records attributed to Zou, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Modeling Masticatory Myalgia to Headache-like Referred Pain Triggers Local Gene Plasticity at Referred Pain Sites

Patients with myofascial pain in the head and neck area report widespread and referred pain, including headache. Existing preclinical models fail to replicate this clinical phenotype; therefore, we aimed to develop animal models mimicking referred pain phenomenon and investigate whether referred pain leads to gene plasticity at the referred sites. We modeled masticatory myalgia by stimulation of either the masseter (MM) or temporal muscle (TM) in mice. MM and TM were stimulated with a single high-dose injection of Collagenase-type II (Col), repetitive low-dose Col injections, repetitive gentle MM stimulation, or single or repetitive forceful mouth opening. Referred pain was assessed by measuring mechanical hypersensitivity in the periorbital area (representing headache-like behavior) and another masticatory muscle. Stimulation of the MM, whether through single or repetitive Col injections or mouth opening, produced inconsistent, short-lasting (1-2 days) headache-like behavior in both males and females. In contrast, stimulation of the TM, using different paradigms, triggered mechanical hypersensitivity in both the MM and the periorbital area. Referred headache-like behavior lasted longer in females compared to males, while referred myalgia in the MM was pronouncer in males. The referred pain in the MM and periorbital areas triggered by TM stimulation was associated with significant gene plasticity in the MM and dura mater. Transcriptional changes in the MM following Col injection into the TM resembled those observed after direct MM injections. Presented data imply that referred pain modeled by TM stimulation could be accounted by nociceptive signaling from multiple local sites involved in this referred pain network.

neuroscience↗

bioRxiv2025

scBiopsy-seq: a platform for temporal single-cell RNA-seq analysis

The development of temporal single-cell RNA-seq (scRNA-seq) assay enabled us to systemically investigate the effects of various types of perturbations in a time course with single-cell resolution. However, the existing temporal scRNA-seq technologies have certain limitations in reliability, detection efficacy and detection diversity. In the current study, we develop scBiopsy-seq assay, which combines a synergistic electroosmosis-electrophoresis extraction method for efficient RNA extraction and digital microfluidics with contamination-isolated hydrophobic interface for high-performance sample processing. scBiopsy-seq extracts the cytoplasm at well-controlled volume to detect >10K genes per extraction with 90% successful rate. Functional enrichment analysis revealed that the genes robustly detected by scBiopsy-seq were associated to diverse biological processes, demonstrating its superb diversity of detection. scBiopsy-seq can perform the sequential extraction of the cytoplasm from the same cell multiple times, which allows us to associate the cell phenotypic responses with its transcriptional dynamics. We employed scBiopsy-seq to analyze the temporal response to a BRD4 degrader induced transcriptional suppression, which identified the key roles of fatty acid beta oxidation in this biological process. scBiopsy-seq with similar data quality as scRNA-seq will dramatically expand the application of temporal scRNA-seq towards a broader spectrum of cell biology research.

bioengineering↗

bioRxiv2025

Methylome and transcriptome analysis reveals the impact of psychological stress on the skin

Psychological stress has been shown to impact skin and DNA methylome, but the mechanisms are poorly understood. Here, we collected multi-omics data from 60 stressed and 60 relaxed individuals to test the hypothesis that psychological stress may impact skin via epigenetic modification and downstream altered gene expression. Each volunteer completed a series of questionnaires and assessments to measure their psychological stress levels and skin condition. Suction blister samples were collected for analyze skin cytokines, DNA methylome, and transcriptome. The dataset generated allowed for the identification of 289 differentially methylated probes(DMPs) and 10 differentially expressed genes (DEGs). Integration of methylation and expression data revealed seven functional epigenetic modules(P<0.05), which were involved in the glutamatergic synapse. In line with previous studies on the prefrontal cortex/hippocampus, we found that psychological stress also affects the glutamatergic synapse in the skin. There was no significant group difference in cytokines, while the stressed group had more severe skin darkening (P=7.36x10-6). This study provides important insight into the impact of psychological stress on the skin and contributes a comprehensive multi-omics dataset resource for the healthy epidermis.

genetics↗

bioRxiv2025

Gut Microbiome Signatures Reflect Within-Host Subtype Diversity of Blastocystis in Non-Human Primates

Blastocystis is a prevalent gut eukaryote intricately associated with the gut microbiota. This genetically diverse protozoan exhibits significant intra-host subtype heterogeneity, yet the implications of this diversity for the host gut microbiome remain poorly understood. Here, we investigated the interactions between Blastocystis and gut microbiota in non-human primates at the level of subtypes, using a comprehensive investigation of gut microbiota for Blastocystis carriers of captive Macaca fascicularis (discovery cohort, n = 100) and Macaca mulatta (validation cohort, n = 26). We identified highly prevalent intra-host co-occurrence patterns of Blastocystis SSU rRNA-based subtypes, primarily dominated by Subtype 1(ST1) or ST3. These patterns were associated with compositional and structural variations in the gut microbiome but were not significantly influenced by host covariates such as sex, age, or BMI. Specifically, Ruminococcaceae-enterotype was enriched in the patterns dominated by ST1, whereas Limosilactobacillus-enterotype was predominantly identified in the patterns dominated by ST3. We discovered that the absolute abundance of Blastocystis was a critical determinant in elucidating this association across concurrent patterns. In vivo experiments in a new cohort (n = 11) demonstrated that lactic acid bacteria, enriched in the Limosilactobacillus-enterotype, were sufficient to reduce Blastocystis load. We validated the strong association between gut microbiome composition and Blastocystis load in M. mulatta, confirming that specific microbial features could quantitatively predict Blastocystis status in both species. Our findings establish a direct link between gut microbial variations, intra-host genetic heterogeneity, and absolute abundance in Blastocystis for non-human primates.

microbiology↗

bioRxiv2025

A Comparative Evaluation of Computational Models for RNA modification detection using Nanopore sequencing with RNA004 Chemistry

Direct RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine ({psi}), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONTs Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with [≥]10% modification ratio and [≥]10X coverage, Dorado has higher recall ([~]0.92) than m6Anet ([~]0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of [~]0.89 for Dorado-truth comparison and [~]0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower ([~]8% for Dorado and [~]33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A ([~]40% for Dorado and [~]80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine ([~]95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.

bioinformatics↗

bioRxiv2025

IQI: Individualized Quantitative trait loci (QTL) Index

There is currently no standard efficient method to precisely detecting and characterizing the difference of quantitative trait loci (QTL) effect among groups and/or populations at individual level. The main reason is that the traditional global regression based QTL analyses can only derive an overall effect of genetic variants on the quantitative trait in a specific group. It is a big challenge to obtain the individual-level QTL effect, which is necessary for accurate testing and precise characterization. Here, we first proposed an individualized QTL index (IQI) that represents the degree of regulation between genetic variants and the trait for each individual. Based on IQI, Differential QTL (DQ) analysis framework was presented to accurately detect the case-control differences in more detail. In addition, IQI can be a new feature/biomarker applied to various accurate bioinformatics analysis, such as differential analysis, disease prediction, and survival analysis. Overall, IQI affords a new perspective on pathogenic mechanisms and will facilitate the study of genetic regulation more detailedly and precisely.

bioinformatics↗

bioRxiv2025

TransBrain: A computational framework for translating brain-wide phenotypes between humans and mice

Despite remarkable advances in whole-brain imaging technologies, the lack of quantitative approaches to bridge rodent preclinical and human studies remains a critical challenge. Here we present TransBrain, a computational framework enabling bidirectional translation of brain-wide phenotypes between humans and mice. TransBrain improves human-mouse homology mapping accuracy through: (1) a novel detached region-specific deep neural network trained on integrated multi-modal human transcriptomics to improve cortical correspondence (89.5% improvement over the original transcriptome), which revealed two evolutionarily conserved gradients explaining >50% of cortical organizational variance, and (2) random walk-based graph representation learning to construct a unified cross-species latent space incorporating anatomical hierarchies and structural connectivity. We demonstrated TransBrains utility through three cross-species applications: quantitative assessment of resting-state brain organizational features, inferring human cognitive functions from mouse optogenetic circuits, and translating molecular insights from mouse models to individual-level mechanisms in autism. TransBrain enables quantitative cross-species comparison and mechanistic investigation of both normal and pathological brain functions.

neuroscience↗

bioRxiv2025

The evolutionary dynamics of organellar pan-genomes in Arabidopsis thaliana

In plants, comparative analyses of organellar genomes often depend on draft assemblies, with large-scale investigations into the complex structural rearrangements of mitochondrial genomes remaining scarce. Here, we conducted a comprehensive analysis of the dominant conformations and dynamic heteroplasmic variants of organellar genomes in the model plant Arabidopsis thaliana, utilizing high-quality long-read assemblies validated at read-level resolution from 149 samples. We found that mitochondrial and plastid genomes share common types of structural and small-scale variants driven by similar DNA sequence features. However, rearrangements mediated by repetitive sequences in mitochondrial genomes evolved so rapidly that they often became decoupled from other types of variants. Rare complex events involving elongation and fusion were also observed, contributing to the unalignable regions commonly found at the interspecies level. Additionally, we demonstrated that disrupting and rescuing organellar DNA maintenance could drive the rapid evolution of dominant mitochondrial genome conformations. Our study provides an unprecedentedly detailed view of the dynamics of organellar genomes at pan-genome scale in A. thaliana, paving the way to unlock the full potential of organellar genetic resources. SignificancePlant mitochondrial genomes exhibit rapid rearrangements within species. Large-scale analyses using high-quality assemblies offer detailed insights into their evolutionary dynamics, paving the way for organellar genome engineering and functional research in the future.

evolutionary biology↗

bioRxiv2025

Flipping the switch on some of the slowest mutating genomes: Direct measurements of plant mitochondrial and plastid mutation rates in msh1 mutants

Plant mitochondrial and plastid genomes have exceptionally slow rates of sequence evolution, and recent work has identified an unusual member of the MutS gene family ("plant MSH1") as being instrumental in preventing point mutations in these genomes. However, the eXects of disrupting MSH1-mediated DNA repair on "germline" mutation rates have not been quantified. Here, we used Arabidopsis thaliana mutation accumulation (MA) lines to measure mutation rates in msh1 mutants and matched wild type (WT) controls. We detected 124 single nucleotide variants (SNVs: 49 mitochondrial and 75 plastid) and 668 small insertions and deletions (indels: 258 mitochondrial and 410 plastid) in msh1 MA lines. In striking contrast, we did not find any organelle mutations in the WT MA lines, and reanalysis of data from a much larger WT MA experiment also failed to detect any variants. The observed number of SNVs in the msh1 MA lines corresponds to estimated mutation rates of 6.1{xi}10-7 and 3.2 {xi}10-6 per bp per generation in mitochondrial and plastid genomes, respectively. These rates exceed those of species known to have very high mitochondrial mutation rates (e.g., nematodes and fruit flies) by an order of magnitude or more and are on par with estimated rates in humans despite the generation times of A. thaliana being nearly 100-fold shorter. Therefore, disruption of a single plant-specific genetic factor in A. thaliana is suXicient to erase or even reverse the enormous diXerence in organelle mutation rates between plants and animals.

genetics↗

bioRxiv2024

Female budgerigars prefer males with foraging skills that differ from their own

Foraging skills influence food intake and may therefore also play a role in mate choice decision. Previous empirical work has shown that individuals benefit from being in groups that include individuals with a variety of foraging skills as this increases foraging success. This idea, formalized in the skill-pool hypothesis, may extend to mate choice. Diverse foraging skills can expand the foraging niche of a pair and benefit offspring through enhanced parental provisioning, and exposure to a broader foraging skillset. To test this idea, we trained captive female and male budgerigars to solve one of two different novel foraging puzzle boxes. Then, females simultaneously observed two males that could solve either the same or the other box, and assessed female preferences in a binary mate choice apparatus. Females preferred males with foraging skills that differed from their own, independent of the skill type and the number of times males solved the foraging puzzle. These findings show that foraging skills can influence social preferences, including in a mate choice context, and support intraspecific diversity in foraging skills.

animal behavior and cognition↗

bioRxiv2024

Exploring the Hypothetical Role of Bacteroides Species in Depression Progression: Insights from Metagenomic Analysis

Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of gut microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in gut microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled Genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged to Bactendicating a close link between Bacteroides abundance and the development of depression, suggesting that Bacteroides might be a potential culprit for deroides, ipression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and all Bacteroides MAGs were annotated to nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. Specifically, the mean relative abundance about the genomes of Bacteroides xylanisolvens, Bacteroides caccae, Bacteroides fragilis, Bacteroides stercoris and Bacteroides ovatus showed strong discriminatory power in distinguishing depressed patients from healthy individuals (AUC=0.834). This research shed light on the potential role of gut microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation. ImprotanceThis research highlighted significant differences in the composition and function of gut microbiota between individuals with depression and healthy individuals, particularly the enrichment of Bacteroides MAGs in depression patients. The upregulation of the nitric oxide synthesis pathway associated with these MAGs belong to Bacteroides in the gut of depression patients had also been observed. The mean relative abundance of a specific set of Bacteroides MAGs had been identified, which could accurately classify individuals with depression from healthy individuals (AUC=0.834). Our results suggest the importance of exploring microbial markers as potential diagnostic and therapeutic targets in managing depression.

genomics↗

bioRxiv2024

Decoding Liver Cancer Prognosis: From Multi-omics Subtypes, Prognostic Models to Single Cell Validation

PurposeHepatocellular carcinoma (HCC) is a highly aggressive tumor characterized by significant heterogeneity and invasiveness, leading to a lack of precise individualized treatment strategies and poor patient outcomes. This necessitates the urgent development of accurate patient stratification methods and targeted therapies based on distinct tumor characteristics. Experimental DesignBy integrating gene expression data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO), we identified subtypes through a multi-omics consensus clustering approach amalgamated from 10 clustering techniques. Subsequently, we developed a prognostic model, employing machine learning algorithms, based on subtype classification features. Finally, by analyzing single cell sequencing data, we investigated the mechanisms driving prognostic variations among distinct subtypes. ResultsFirst, we developed a novel consensus clustering method that categorizes liver cancer patients into two subtypes, CS1 and CS2. Second, we constructed a prognostic prediction model, which demonstrated superior predictive accuracy compared to several models published in the past five years. Finally, we observed differences between CS1 and CS2 in various metabolic pathways, biological processes, and signaling pathways, such as fatty acid metabolism, hypoxia levels, PI3K-AKT and MIF signaling pathway.

bioinformatics↗

bioRxiv2024

Astrocytic Ryk signaling coordinates scarring and wound healing after spinal cord injury

Wound healing after spinal cord injury involves highly coordinated interactions among multiple cell types, which is poorly understood. Astrocytes play a central role in creating a border against the non-neural lesion core. To do so, astrocytes undergo dramatic morphological changes by first thickening the processes and then elongating and overlap them. We show here show that the expression of a cell-surface receptor, Ryk, is induced in astrocytes after injury in both rodent and human spinal cord. Astrocyte-specific knockout of Ryk dramatically elongated the reactive astrocytes and accelerated the formation of the border and reduced the size of the scar. Astrocyte-specific knockout of Ryk also accelerated the injury responses of multiple cell types, including the resolution of neuroinflammation. Single cell transcriptomics analyses revealed a broad range of changes cell signaling among astrocytes, microglia, fibroblasts, endothelial cell, etc, after astrocyte-specific Ryk knockout, suggesting that Ryk not only regulates the injury response of astrocytes but may also regulate signals which coordinate the responses of multiple cell types. The elongation is mediated by NrCAM, a cell adhesion molecule induced by astrocyte-specific conditional knockout of Ryk after spinal cord injury. Our findings suggest a promising therapeutic target to accelerate wound healing and promote neuronal survival and enhance functional recovery.

neuroscience↗

bioRxiv2024

Identification and Validation of Mitophagy-Related Genes in Diabetic Retinopathy

BackgroundDiabetic retinopathy is one of the common chronic complications of diabetes, characterized by retinal microvascular and neurodegenerative impairment, and it is the primary cause of vision impairment and blindness in adults. Many studies have demonstrated that mitophagy plays a significant role in the pathological mechanism of DR. however, its mechanism is not yet fully clear and requires further research. MethodsWe obtained relevant datasets of diabetic retinopathy from the GEO database and used R language to screen for differentially expressed genes. We intersected these genes with mitophagy-related genes and identified differentially expressed mitophagy-related genes. We performed GO and KEGG analysis on the differentially expressed mitophagy-related genes, followed by PPI network analysis. Using Cytoscape software, we selected mitophagy hub genes. Finally, we further validated the expression of the mitophagy hub genes in an in vitro cell culture high-glucose model using quantitative real-time polymerase chain reaction (qRT-PCR). ResultsWe identified 27 differentially expressed genes related to mitophagy by using R language, with 10 genes upregulated and 17 genes down regulated. We performed GO and KEGG enrichment analysis using R software to further study the potential biological functions of differentially expressed genes. Through PPI network analysis and Cytoscape software, we selected 10 hub genes associated with mitophagy. Finally, through qRT-PCR validation of these 10 hub genes, we found that the mRNA expression differences of MFN1, BNIP3L, GABARAPL1, and PINK1 genes were consistent with our bioinformatics analysis results. ConclusionWe consider that MFN1, BNIP3L, GABARAPL1, and PINK1 may serve as potential biomarkers for diabetic retinopathy. The upregulation and downregulation of these genes provide new insights for further exploration of the role of mitophagy in the pathological mechanism of diabetic retinopathy. These genes can serve as new potential therapeutic targets for the treatment of diabetic retinopathy.

bioinformatics↗

bioRxiv2024

Wilting Wildflowers and Bummed-Out Bees: Climate Change Threatens U.S. State Symbols

Species designated as state symbols in the United States carry cultural importance and embody historical heritage. However, they are threatened by climate change and even face the risk of local or global extinction. The responses of these species to climate change have received little attention. In this study, we examine the effects of climate change on 64 state flowers and 68 state insects in the United States by employing correlative species distribution models (SDMs). We select a variety of commonly used SDM algorithms to construct an ensemble forecasting framework aimed at predicting the potential climatic habitats for each species under both historical (1981-2010) and future (2071-2100) climate scenarios (SSP1-2.6 and SSP5-8.5), and how these changes might influence the habitat suitability of flower and insect species within their symbolic states and across the United States. Our results indicate that 30 - 66% of state flowers and 18 - 51% of state insects are projected to experience substantial losses of climatically suitable habitat within their symbolic states. Under the high-emissions scenario (SSP5-8.5), ten state flowers and three state insects are likely to face local extinction by the 2080s. Although most of these species may find suitable habitats in other states, only two are projected to have such areas located adjacent to their current symbolic states, potentially limiting natural dispersal. Nationally, 85% of flower species and 71 - 79% of insect species are expected to shift their suitable habitat both poleward and uphill, with the magnitude of latitudinal and elevational shifts significantly greater under SSP5-8.5 than under SSP1-2.6. These findings highlight the vulnerability of culturally significant species to climate change and underscore the urgency of integrating climate adaptation into conservation planning. Proactive, forward-looking conservation and management strategies may be critical for preserving cultural heritage and maintaining ecosystem resilience.

ecology↗

bioRxiv2024

Efficiently Constructing Complete Genomes with CycloneSEQ to Fill Gaps in Bacterial Draft Assemblies

BackgroundCurrent microbial sequencing relies on short-read platforms like Illumina and DNBSEQ, favored for their low cost and high accuracy. However, these methods often produce fragmented draft genomes, hindering comprehensive bacterial function analysis. CycloneSEQ, a novel long-read sequencing platform developed by BGI-Research, its sequencing performance and assembly improvements has been evaluated. FindingsUsing CycloneSEQ long-read sequencing, the type strain produced long reads with an average length of 11.6 kbp and an average quality score of 14.4. After hybrid assembly with short reads data, the assembled genome exhibited an error rate of only 0.04 mismatches and 0.08 indels per 100 kbp compared to the reference genome. This method was validated across 9 diverse species, successfully assembling complete circular genomes. Hybrid assembly significantly enhances genome completeness by using long reads to fill gaps and accurately assemble multi-copy rRNA genes, which unable be achieved by short reads solely. Through data subsampling, we found that over 500 Mbp of short-read data combined with 100 Mbp of long-read data can result in a high-quality circular assembly. Additionally, using CycloneSEQ long reads effectively improves the assembly of circular complete genomes from mixed microbial communities. ConclusionsCycloneSEQs read length is sufficient for circular bacterial genomes, but its base quality needs improvement. Integrating DNBSEQ short reads improved accuracy, resulting in complete and accurate assemblies. This efficient approach can be widely applied in microbial sequencing.

genomics↗

bioRxiv2024

Survival of hepatocytes from executioner caspase activation promotes liver regeneration by enhancing JAK/STAT3 activity

Activation of executioner caspases, which is a key step in the apoptotic process, has been reported to promote tissue regeneration by sending pro-proliferation signals to the surrounding cells. However, whether executioner caspase activation (ECA) has cell-autonomous effect on tissue regeneration is not clear. Here, by generating transgenic mice carrying a lineage tracing system for cells that have experienced ECA, we demonstrate that transient ECA occurs in hepatocytes during liver regeneration after partial hepatectomy (PHx) or carbon tetrachloride (CCl4) treatment. Instead of committing apoptotic cell death, the majority of hepatocytes with ECA survive and proliferate to contribute to liver regeneration. Interestingly, inhibition of ECA in livers results in reduced hepatocyte proliferation and impaired regeneration, whereas increasing ECA to a level sufficient to kill hepatocytes also impedes regeneration, suggesting that ECA needs to be precisely controlled during liver regeneration. Mechanistic studies show that ECA promotes hepatocyte proliferation during regeneration through enhancing JAK/STAT3 activity. Our work reveals an essential role of survival of hepatocytes from ECA in liver regeneration.

developmental biology↗

bioRxiv2024

DMRIntTk: integrating different DMR sets based on density peak clustering

BackgroundIdentifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. ResultsHere, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into DMRs by using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, DMRIntTk was applied to different scenarios, including different tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. The results show that DMRIntTk can effectively trim the regions with small methylation differences in the original DMR sets and therefore it can enhance the proportion of DMRs with higher methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and the functional analysis indicates the integrated disease-related DMR sets are significantly enriched in biological pathways, which are associated with the pathological mechanisms of the diseases. ConclusionsConclusively, DMRIntTk can help researchers obtaining a reliable and comprehensive DMR set from many prediction methods.

bioinformatics↗