Biology subjects

Zang, C.

Publications and source records attributed to Zang, C..

At least 19 recordsLinked to original sources

bioRxiv2026

GeneKnow: AI-powered literature synthesis for gene-context analysis

Biological function is context dependent, yet synthesizing evidence for a genes function in a defined cell type, disease, or perturbation remains labor-intensive. Here we present GeneKnow, an auditable artificial intelligence (AI) framework for source-grounded biological evidence synthesis. GeneKnow separates evidence-critical operations, including literature retrieval, passage selection, provenance tracking, and bibliography construction, from generative steps that are constrained to semantic analysis and literature synthesis. GeneKnow supports multi-paper discovery and single-paper inspection while preserving links from synthesized claims to source passages, generating trustworthy syntheses without fabricated citations and minimizing hallucinations. Systematic benchmarking showed that GeneKnow achieved higher claim support and citation fidelity than leading general-purpose and scientific AI systems. These results demonstrate that an AI system with controlled division of labor between deterministic and generative components can substantially improve the fidelity and auditability of biomedical literature synthesis.

bioinformatics↗

bioRxiv2026

Cellular stemness identifies high-risk ductal carcinoma in situ and offers a therapeutic interception opportunity

Ductal carcinoma in situ (DCIS) exhibits substantial heterogeneity in its risk of progression to invasive breast cancer, yet the cellular and molecular determinants of high-risk lesions remain incompletely defined. Using spatially resolved single-cell transcriptomic and epigenomic profiling of 43 patient-derived DCIS and DCIS/invasive ductal carcinoma (IDC) samples, we delineate cellular programs, spatial organization, and epigenetic regulatory mechanisms associated with invasive potential. We identify an epithelial population with stemness features within luminal hormone-responsive (LumHR) cells that progressively expands from benign tissue to DCIS and IDC, and is strongly associated with invasive progression and recurrence-linked transcriptional programs. Spatial mapping reveals discrete DCIS niches enriched for stem-like LumHR cells, characterized by elevated CEACAM6 expression and enhanced ligand-receptor interactions, including CEACAM6-EGFR signaling between epithelial and stromal compartments, including cancer-associated fibroblasts, macrophages (APOC1-positive) and perivascular cells. These niches define a microenvironmental context that supports stemness and invasive potential. Epigenomic analyses implicate FOXA1 as a key regulator of these stem-like transcriptional states. Pharmacologic disruption of FOXA1-regulatory network using LSD1 inhibition suppresses stemness-associated transcriptional programs in vitro and significantly restrains tumor growth in vivo. Collectively, these findings define high-risk DCIS as a stemness-driven disease embedded within specialized microenvironments, and identify associated regulatory networks as candidate biomarkers and therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

BART-spatial unravels biologically significant transcriptional regulators from spatial omics data

Transcriptional regulators (TRs) are crucial regulators of cell fate decisions by activating or repressing lineage-specific genes and integrating environmental signals with intrinsic networks. Identifying functional TRs is essential for understanding development, tissue organization, and disease. Emerging spatial transcriptomics and epigenomics technologies now provide near- single-cell resolution mapping of genomic features while preserving information of each cells physical location and microenvironment which influence TR activity. Despite these advances, identifying active TRs in spatial data remains challenging due to low TR expression and the fact that TR activity often does not correlate directly with mRNA levels. Moreover, existing tools mainly designed for non-spatial single-cell data overlook spatial heterogeneity. To bridge this gap, we developed BART-spatial (Binding Analysis for Regulation of Transcription for spatial omics data), an innovative computational method to infer functional TRs from spatial omics data. BART-spatial integrates spatial variability and pseudotemporal information with publicly available TR binding profiles. Applied to multiple spatial datasets from diverse platforms, including 10x Visium, Visium HD, Atera, and spatial RNA-ATAC-seq, BART-spatial consistently outperforms existing methods, identifying state-specific TRs and revealing regulators undetectable by expression alone. Its compatibility with spatial epigenomics data further strengthens its utility and enables cross-validation. Overall, BART-spatial provides a powerful and robust tool for decoding spatially resolved gene regulatory programs.

bioinformatics↗

bioRxiv2026

BARTsc identifies key transcriptional regulators from single-cell omics data

Inference of transcriptional regulatory mechanisms from single-cell (sc) omics data, such as scRNA-seq, scATAC-seq, and scMultiome, remains an important problem in single-cell biology and functional genomics. Most existing methods for predicting functional transcriptional regulators (TRs) from single-cell data rely on co-expression between regulator and target genes and/or sequence motif enrichment, holding inherent limitations. Here, we present BARTsc, a computational method that accurately predicts functional TRs from clustered single-cell omics data by leveraging a large collection of public ChIP-seq profiles. BARTsc implements a novel framework to infer a cis-regulatory profile from differential genomic features from either unimodal (RNA or ATAC) or bimodal (scMultiome) single-cell profiling data and identify TRs whose binding profiles most associate with the cis-regulatory profile. BARTsc can quantify TR activity across cell clusters and predict key regulators for each cell cluster. We demonstrate that BARTsc can successfully identify active TRs in each cell type and cell-type-defining key regulators across diverse biological systems, including mouse cortex, human peripheral blood mononuclear cells (PBMCs), and human pancreatic ductal adenocarcinoma (PDAC). Using a generative-AI-assisted, literature-supported collection of cell-type key regulators as benchmarks, we show that BARTsc consistently outperforms existing state-of-the-art methods. We apply BARTsc to identify critical regulators in PDAC, including NEFLA, a novel PDAC key regulator, and validate its function in pancreatic tumor proliferation by experiments. As a robust and versatile computational method, BARTsc provides deeper insights into cell-type-specific regulatory programs, facilitating the discovery of key regulators across diverse biological systems.

bioinformatics↗

bioRxiv2026

Differential analysis of image-based chromatin tracing data with Dory

Spatial organization of the genome plays a vital role in defining cell identity and regulating gene expression. The three-dimensional (3D) genome structure can be measured by sequencing-based techniques such as Hi-C usually on the cell population level or by imaging-based techniques such as chromatin tracing at the single-cell level. Chromatin tracing is a multiplexed DNA fluorescence in situ hybridization (FISH)-based method that can directly map the 3D positions of genomic loci along individual chromosomes at single-molecule resolution. However, few computational tools are available for statistical differential analysis of chromatin tracing data, which are inherently high-dimensional, highly variable and contain many missing values. Here, we present Dory, a statistical method for identifying differential spatial patterns between two groups of chromatin traces. Dory quantifies pairwise spatial distances among genomic regions in a chromatin trace and applies multi-level statistical tests to detect significant structural differences between the two groups of traces. It produces a differential score matrix highlighting region pairs with significant distance difference. Applying Dory to multiple chromatin tracing datasets, we found that the detected chromatin structural changes were associated with alterations in A/B compartments and promoter-enhancer interactions correlated with differential gene expression. Dory is a robust and user-friendly computational tool for quantitative analysis of imaging-based 3D genome data that enables systematic exploration of chromatin architecture and its roles in gene regulation.

bioinformatics↗

bioRxiv2025

Temporal single-cell profiling uncovers age-associated delays in immune resolution to respiratory viral infection

Aging is a major risk factor for increased morbidity and mortality following acute respiratory virus infections. To elucidate the immune determinants underlying viral pathogenesis and delayed lung repair in the aged lung, a comprehensive time-course study was conducted. Single-cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were utilized to compare lungs from young and aged mice infected with influenza A virus (IAV). Aged hosts displayed diminished alveolar macrophage (AM) and dendritic cell (DC) but elevated monocyte-derived macrophage (MoM) and interstitial macrophage (IM) presence following infection. Additionally, enhanced accumulation of adaptive immune cells, including CD4+ tissue-resident helper (TRH) cells, CD8+ tissue-resident memory (TRM) cells, and a B cell subset resembling age-associated B cells, was observed in the memory phase. Pathway analysis revealed that elevated type I and II interferon (IFN/{gamma}) signaling, especially in MoM/IM subsets, distinguished the aged hosts from the young. Inhibition of IFN/{gamma} signaling after viral clearance improved long-term respiratory outcomes and reduced both IM and TRH populations in aged mice. These findings highlight the pivotal role of IFN/{gamma} signaling, likely within MoM/IM subsets, in driving the exuberant persistence of adaptive immune cells and chronic immunopathology in the aged lung following acute viral infection.

immunology↗

bioRxiv2025

Transcriptional condensates at super-enhancers mediate pH-dependent transcriptional control in innate immunity

Tissue acidification is a common feature of hypoxia, inflammation and solid tumor. Acidic pH regulates innate immune response in macrophages by weakening BRD4-containing transcriptional condensates. Yet how disruption of transcriptional condensates leads to gene-specific regulation of immune programs remain unclear. Here, we integrated ATAC-seq, ChIP-seq, and RNA-seq of primary murine macrophages and performed integrative epigenomics analyses to identify transcriptional regulators (TRs) with pH-sensitive regulatory potential and association to BRD4-dependent transcriptional condensates. We determined pH-dependent super-enhancers (SEs) by extended profiles of BRD4 binding and h3K27ac marks. We found RELA, IRF family, and STAT family as candidate TRs enriched at BRD4-associated, pH-sensitive SE regions. RELA and IRF3 preferentially occupied BRD4-associated and pH-sensitive SEs, and displayed markedly reduced binding under acidic conditions, aligning with BRD4 occupancy change. Correspondingly, immune-response genes within BRD4-associated, pH-sensitive SE regions, including Ch25h, Acp2, Gda, and Ifit family, were significantly higher expressed at pH 7.4 than at pH 6.5. Together, these results reveal a set of TRs involved in BRD4-associated, pH-sensitive transcriptional condensates that coordinate macrophage gene activation under physiological conditions, providing mechanistic insight into how acidic stress modulates transcriptional condensates and immune responses.

immunology↗

bioRxiv2025

PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5s preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open-chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open-chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open-chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

bioinformatics↗

bioRxiv2025

CCAAT-enhancer binding protein delta functions as a tumor suppressor gene in acute myeloid leukemia

There is a continued need for identification of novel disease drivers of acute myeloid leukemia as many patients experience relapse and have poor clinical outcomes. Analyses from our study and publicly available datasets predicted CEBPD as a novel tumor suppressor gene in acute myeloid leukemia. Consistent with the analyses, CEBPD knockdown experiments showed activation of MAPK signaling with concomitant increase in cell growth rate, while upregulation experiments suggested induction of myeloid differentiation marker CD14 expression in AML cell lines OCI-AML2 and OCI-AML5. Consistent with a previous report, our genomics analyses and azacytidine treatment experiments suggested a role for DNA methylation in downregulation of CEBPD expression during AML pathogenesis. Altogether, our results provide experimental evidence for a tumor suppressor function of CEBPD in AML.

cancer biology↗

bioRxiv2025

Super-resolution microscopy reveals distinct epigenetic states regulated by estrogen receptor activity

Changes in gene expression regulated by ligand-dependent transcription factors such as estrogen receptor- (ER) involves the recruitment of coactivators including p300 that acetylates histone H3 at lysine 27 (H3K27ac). While H3K27ac marks active enhancers, the detailed chromatin architecture of enhancers remains unclear. Using super-resolution microscopy, we reveal distinct structural states of H3K27ac modified chromatin in response to ER activation. In estradiol (E2)-treated cells, H3K27ac modified chromatin adopts open, elongated structures, while ER inhibition induces compact, spherical H3K27ac modified chromatin conformations. Using MED1, a core subunit of the Mediator complex that bridges transcription factors with RNA polymerase II (Pol II), we demonstrate that larger H3K27ac structures are preferentially associated with active enhancers, whereas more compact structures show reduced MED1 association, consistent with a less active or inactive state. A constitutively active ER mutation linked to endocrine therapy resistance in breast cancer maintains open chromatin states independent of ligand, suggesting sustained transcriptional activity. Our findings provide the first direct visualization of H3K27ac associated chromatin structural dynamics, challenging the assumption that H3K27ac modification alone is sufficient to lead to enhancer activation. By demonstrating that H3K27ac architecture is dynamically regulated by ER, we establish a new paradigm for understanding epigenetic regulation and highlight potential therapeutic targets for endocrine therapy resistant cancers.

cancer biology↗

bioRxiv2025

TEAD1 condensates are transcriptionally inactive storage sites on the pericentromeric heterochromatin

TEA domain transcription factor 1 (TEAD1), a Hippo pathway transcription factor important in cellular homeostasis and development, is increasingly implicated in cancer biology. Here, we reveal a novel role for TEAD1 in organizing nuclear condensates, independent of active transcription. Using high-resolution imaging, ChIP-seq, RNA-seq and proximity-based proteomics, we demonstrate that in patient-derived renal cell carcinoma cells, TEAD1 forms micron-sized condensates by binding to the heterochromatic pericentromeric regions using its DNA-binding domain. TEAD-specific MCAT motifs selectively enrich and cluster in the pericentromeric region, specifically seeding TEAD1 condensates. TEAD1 condensates do not activate transcription but instead serve as depots for excess TEAD1, and disrupting TEAD1 condensates leads to increases in YAP/TEAD target gene expression. This organization of TEAD1 contrasts with that observed in other genomic regions of both RCC and normal kidney cells, in which TEAD1 associates with markers of active transcription. Our findings provide a mechanistic framework for TEAD1s dual regulatory roles, offering new insights into its contribution to transcriptional dysregulation and tumor progression.

cell biology↗

bioRxiv2025

Single-Cell Multimodal Profiling Highlights Persistent Aortic Smooth Muscle Cell Changes in Diabetic Mice Despite Glycemic Control

BackgroundType 2 diabetes (T2D) is associated with accelerated vascular complications like hypertension and atherosclerosis. "Phenotypic switching" of vascular smooth muscle cells (SMC), a major driver of these complications, is enhanced in diabetes. Despite adequate glycemic control, SMC dysfunction can persist due to "metabolic memory" of prior hyperglycemia. However, the mechanisms of hyperglycemic memory associated with persistent SMC dysfunction are unclear. Here, leveraging single-cell (sc) multi-omics, we examined the effect of glucose normalization on transcriptomic and epigenomic changes associated with SMC phenotypic transition in T2D mice. MethodsWe treated T2D db/db mice with the antidiabetic drug dapagliflozin (DAPA) (db/dbDAPA) or vehicle (db/db), and non-diabetic control db/+ mice with vehicle for 6 weeks. Dissected aortas were subjected to scRNA-seq, scATAC-seq, and spatial transcriptomics (Xenium) to determine single-cell changes in gene expression and chromatin accessibility. ResultsDAPA treatment conferred effective glycemic control in db/db mice, with significant reductions in blood glucose/hemoglobin A1c. scRNA and scATAC-seq analysis of aortas identified major cell populations, including SMC, fibroblasts, endothelial and immune cells. SMC were further clustered into 9 subtypes, including contractile and fibromyocyte-like cells. Cell composition analysis revealed decreases in contractile SMC and increases in vascular remodeling associated fibromyocyte-like SMC in db/db versus db/+ mice. Interestingly, DAPA did not reverse diabetes-induced decreases in contractile markers but reversed changes in several fibromyocyte markers in db/db mice. Pseudotime trajectory analysis revealed increased activities of fibromyocyte enriched transcription factors (TFs) during contractile to fibromyocyte transition. Furthermore, increased expression of TFs regulating fibromyocyte phenotype (e.g. Atf4, Bach1, Hand2, Fosl2) in db/db were partially reversed by DAPA, whereas reduced contractile TF (Mef2c) expression was unchanged. Spatial transcriptomics analysis further mapped aortic cell types within intact aortas and confirmed that DAPA reversed alterations in key fibromyocyte but not contractile genes in db/db mouse aortas. ConclusionsPersistent epigenetic changes may contribute to sustained vascular remodeling and dysfunction in T2D. T2D reduced contractile SMC gene expression and related chromatin accessibility and promoted phenotypic transition to fibromyocytes. These changes are only partially reversed by a widely used antidiabetic drug like DAPA, underscoring the need for more effective therapies that target hyperglycemic memory.

biochemistry↗

bioRxiv2025

A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics

Spatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for understanding complex tissue architectures, such as those found in cancers. Seurat, by far the most popular tool for analyzing ST data, uses the Wilcoxon rank-sum test by default for differential expression analysis. However, as a nonparametric method that disregards spatial correlations, the Wilcoxon test can lead to inflated false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we propose a Generalized Estimating Equations (GEE) framework as a robust solution for differential gene expression analysis in ST. We conducted a comprehensive comparison of the GEE-based tests with existing methods, including the Wilcoxon rank-sum test and z-test. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE test with robust standard error, referred to as the Independent GEE, demonstrated superior Type I error control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed poor calibration of the p-values and potential false positive findings from the Wilcoxon rank-sum test. Our comparative study based on simulations and real data applications suggests that the Independent GEE test is well-suited for ST data, offering more accurate identification of biologically relevant gene expression changes and complementing the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub. Author SummarySpatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for studying complex tissue architectures and disease etiology. Seurat, a widely used software tool for analyzing ST data, relies on the Wilcoxon rank-sum test for differential expression analysis. However, this test ignores spatial correlations, leading to inaccurate control of false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we have proposed a Generalized Estimating Equation (GEE) framework as a robust solution for differential gene expression analysis in ST. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE-based test demonstrated superior false positive rate control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed potential false positive findings from the Wilcoxon rank-sum test. We recommend the GEE method to be a useful complement to the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub.

bioinformatics↗

bioRxiv2024

Regulation of epigenetics and chromosome structure by human ORC2

We report a multi-omics study in a human cell line with mutations in three subunits of Origin Recognition Complex (ORC). The ORC subunits bind to DNA independent of each other in addition to as part of a common six-subunit ORC. DNA-bound ORC2 compacts chromatin and attracts repressive histone marks to focal areas of the genome, but ORC2 also activates chromatin at many sites and protects the genes from repressive marks. The epigenetic changes regulate hundreds of genes, including some epigenetic regulators, adding an indirect mechanism by which ORC2 regulates epigenetics without local binding. DNA-bound ORC2 also prevents the acquisition of CTCF at focal sites in the genome to regulate chromatin loops and indirectly affect epigenetics. Thus, our study reveals the genes and ORC1 regions bound by individual ORC subunits and suggests their role as epigenetics and chromosome structure regulators, independent of the role of the six-subunit ORC in DNA replication.

genomics↗

bioRxiv2024

TUSCAN: Tumor segmentation and classification analysis in spatial transcriptomics

The identification of tumor cells is pivotal to understanding tumor heterogeneity and the tumor microenvironment. Recent advances in spatially resolved transcriptomics (SRT) have revolutionized the way that transcriptomic profiles are characterized and have enabled the simultaneous quantification of transcript locations in intact tissue samples. SRT is a promising alternative method of studying gene expression patterns in spatial domains. Nevertheless, the precise detection of tumor regions within intact tissue remains a great challenge. The common way of identifying tumor cells is via tumor-specific marker gene expression signatures, which is highly dependent on marker accuracy. Another effective approach is through aneuploid copy number events, as most types of cancer exhibit copy number abnormalities. Here, we introduce a novel computational method, called TUSCAN (TUmor Segmentation and Classification ANalysis in spatial transcriptomics), which constructs a spatial copy number variation profile to improve the accuracy of tumor region identification. TUSCAN combines the gene information from SRT data and the hematoxylin-and-eosin-staining image to annotate tumor sections and other benign tissues. We benchmark the performance of TUSCAN and several existing methods through the application to multiple datasets from different SRT platforms. We demonstrate that TUSCAN can effectively delineate tumor regions, with improved accuracy compared to other approaches. Additionally, the output of TUSCAN provides interpretable clonal evolution inferences that may lead to novel insights into disease development and potential druggable targets.

bioinformatics↗

bioRxiv2024

Does early-life food shortage alter the effect of elevated temperature on female life history?

O_LIGlobal warming is reducing prey availability in many aquatic systems, raising questions about the combined effects of higher temperatures and lower food availability on fish life histories and reproductive output. C_LIO_LIIn ectotherms, higher temperatures accelerate growth and promote an earlier onset of reproduction. However, when fish have less food during development, resource depletion might constrain these temperature-driven processes. C_LIO_LIWe manipulated water temperature (24 or 28{degrees}C) and early-life food availability (control or restricted) for female guppies (Poecilia reticulata). We measured how both factors affected key life history traits (growth, reproduction, survival, self-maintenance). C_LIO_LIHigher temperature significantly affected female life histories. Females at 28{degrees}C matured at a larger size, but then grew more slowly and produced fewer, smaller offspring than females at 24{degrees}C. The effect of temperature on reproduction persisted even after controlling for body size, suggesting there was a shift in the fecundity-size relationship. C_LIO_LIAdult mortality was greater at 28{degrees}C. Higher temperature also resulted in a longer gut, potentially enhancing resource acquisition, but a higher temperature did not affect immunity or telomere length of the surviving females. C_LIO_LIEarly-life food shortage affected very few traits, except for a weak interaction with temperature that affected total fecundity. At 28{degrees}C, females that experienced early-life food restriction produced fewer offspring than females with continual food supply. No such diet effect occurred at 24{degrees}C. C_LIO_LIOur results suggest that tropical fish may be severely impacted by increased temperatures (i.e., decreased reproduction with increased morality), but are likely to be resilient to brief periods of food limitations during early development. C_LIO_LIInterestingly, early-life food shortage caused a reduction in total offspring number but only at 28{degrees}C, suggesting that global prey decline might exacerbate the negative effects of a warming climate on stock recruitment of tropical fish. C_LI

ecology↗

bioRxiv2024

TreeCompR: Tree competition indices for inventory data and 3D point clouds

O_LIIn times of more frequent global-change-type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices leave users with the agony of choice, as no single competition index has proven universally superior. C_LIO_LITo help users with the choice and computation of appropriate indices, we present the open-source TreeCompR package, which can handle 3D point clouds in various formats as well as classical forest inventory data and serves as a centralized platform for exploring and comparing different competition indices (CIs). Within a common interface, users can efficiently select the most suitable CI for their specific research questions. The package facilitates the integration of both traditional distance-dependent and novel point cloud-based indices. C_LIO_LITo evaluate the package, we used TreeCompR to quantify the competition situation of 308 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. C_LIO_LIThe compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well-structured output, our package can easily be integrated into existing data-analysis workflows both for 3D point cloud and classical forest inventory data. C_LI

ecology↗

bioRxiv2023

DARDN: Identifying transcription factor binding motifs from long DNA sequences using multi-CNNs and DeepLIFT

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the models output in terms of the contributions of its input features [1], for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDNs ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identified potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

bioinformatics↗