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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2017

Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data

Identification of co-expressed gene clusters can provide evidence for genetic or physical interactions between genes. Thus, co-expression clustering is a routine step in large-scale analyses of gene expression data. We show that commonly used clustering methods produce results that substantially disagree with each other, and do not match the biological expectations of co-expressed gene clusters. Furthermore, these clusters can contain up to 50% unreliably assigned genes. Consequently, downstream analyses of these clusters (e.g. functional term enrichment analysis) suffer from high error rates. We present clust, an automated method that solves these problems by extracting clusters that match the biological expectations of co-expressed genes. Using 100 datasets from five model organisms we demonstrate that clusters generated by clust are better than those produced by other methods, both numerically and for use in functional analysis. Finally, we show that clust can simultaneously cluster multiple datasets, enabling users to leverage the large quantity of public expression data for novel comparative analysis.

bioinformatics↗

bioRxiv2023

Alternative splicing is coupled to gene expression in a subset of variably expressed genes

Numerous factors regulate alternative splicing of human genes at a co-transcriptional level. However, how alternative splicing depends on the regulation of gene expression is poorly understood. We leveraged data from the Genotype-Tissue Expression (GTEx) project to show a significant association of gene expression and splicing for 6874 (4.9%) of 141,043 exons in 1106 (13.3%) of 8314 genes with substantially variable expression in ten GTEx tissues. About half of these exons demonstrate higher inclusion with higher gene expression, and half demonstrate higher exclusion, with the observed direction of coupling being highly consistent across different tissues and in external datasets. The exons differ with respect to sequence characteristics, enriched sequence motifs, RNA polymerase II binding, and inferred transcription rate of downstream introns. The exons were enriched for hundreds of isoform-specific Gene Ontology annotations, suggesting that the coupling of expression and alternative splicing described here may provide an important gene regulatory mechanism that might be used in a variety of biological contexts. In particular, higher inclusion exons could play an important role during cell division.

genomics↗

bioRxiv2019

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

genomics↗

bioRxiv2024

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database [1]. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.

bioinformatics↗

bioRxiv2023

How host-like signals drive gene expression and gene expression drives capsule expansion in Cryptococcus neoformans

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of five signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-Seq in quadruplicate, yielding 881 RNA-Seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP (cAMP). Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected dataset will be a significant resource for the research community. IMPORTANCECryptococcus neoformans is an opportunistic yeast that kills [~]150,000 people each year. This major impact on human health makes it imperative to understand the basic biology of C. neoformans and the factors that mediate its virulence. One key virulence factor is a polysaccharide capsule that expands greatly during infection. To help define capsule synthesis and fungal biology, we provided cells with many different combinations of host-like signals and sampled the cultures over time for transcriptional analysis. The resulting time resolved data set is by far the largest gene expression resource ever produced for C. neoformans (881 RNA-seq samples), further enriched by accompanying capsule images and measurements. It revealed surprising findings, including that rich medium suppresses capsule size regardless of other signals. This landmark data resource will be enormously valuable to the research community as it continues to define the relationships between environmental signals and cryptococcal gene expression, biology, and virulence.

genomics↗

bioRxiv2025

A Transformer based method for the Cap Analysis of Gene Expression and Gene Expression Tag associated 5' cap site prediction in RNA

5 RNA capping is one of the major post-transcriptional modifications for the mobility and stability of RNA molecules. Measuring 5 caps of RNAs can help quantify expression levels of mRNAs and lncRNAs. One of the most successful RNAseq methods that have used capping as a tool to quantify expression of transcription is Cap Analysis of Gene Expression(CAGE). Computational prediction of capping can therefore be used as a precursor to the prediction of transcriptional expression. Unfortunately, there is hardly any computational technique that has focused purely on predicting 5 capping. We have developed a transformer-based method for computational prediction of capping from DNA sequences. Our Llama and ReLoRA-based pre-training model, and Llama and LoRA-based fine-tuning model predict 5 cap sites. We have used Leave-one-chromosome-out-cross-validation for our model. The average accuracy, and F1-score after fine-tuning the human genome hg19(mouse genome mm9) for sequence classification is 79.12%(78.09%), and 78.11%(76.17%), respectively. We noted attention peak-based motifs having an aggregate Wilcoxon rank-sum p-value of 1.075e-10 between the attention peak region and the entire context window for the predicted positive motifs; an aggregate p-value of 7.17e-18 for the predicted negative motifs; and an aggregate p-value of 6.70e-08 between the attention peaks of the predicted positive and the predicted negative motifs. Our Llama-based approach aims to create a sequence-based framework to identify 5 capping sites corresponding to CAGE peaks. Our analysis reveals statistically significant motifs from the regions of peak attention scores, which demonstrates biological relevance for some through their resident sites matching with known TF motifs.

bioinformatics↗

bioRxiv2022

MorphNet Predicts Cell Morphology from Single-Cell Gene Expression

Gene expression and morphology both play a key role in determining the types and functions of cells, but the relationship between molecular and morphological features is largely uncharacterized. We present MorphNet, a computational approach that can draw pictures of a cells morphology from its gene expression profile. Our approach leverages paired morphology and molecular data to train a neural network that can predict nuclear or whole-cell morphology from gene expression. We employ state-of-the-art data augmentation techniques that allow training using as few as 103 images. We find that MorphNet can generate novel, realistic morphological images that retain the complex relationship between gene expression and cell appearance. We then train MorphNet to generate nuclear morphology from gene expression using brain-wide MERFISH data. In addition, we show that MorphNet can generate neuron morphologies with realistic axonal and dendritic structures. MorphNet generalizes to unseen brain regions, allowing prediction of neuron morphologies across the entire mouse isocortex and even non-cortical regions. We show that MorphNet performs meaningful latent space interpolation, allowing prediction of the effects of gene expression variation on morphology. Finally, we provide a web server that allows users to predict neuron morphologies for their own scRNA-seq data. MorphNet represents a powerful new approach for linking gene expression and morphology.

bioinformatics↗

bioRxiv2018

Measurement of selective constraint on human gene expression

Gene expression variation is a major contributor to phenotypic variation in human complex traits. Selection on complex traits may therefore be reflected in constraint on gene expression levels. Here, we explore the effects of stabilizing selection on cis-regulatory genetic variation in humans. We analyze patterns of expression variation at copy number variants and find evidence for selection against large increases in gene expression. Using allele-specific expression (ASE) data, we further show evidence of selection against smaller-effect variants. We estimate that, across all genes, singletons in a sample of 122 individuals have approximately 2.5 x greater effects on expression variance than common variants. Despite their increased effect sizes relative to common variants, we estimate that singletons in the sample studied explain, on average, only 5% of the heritability of gene expression from cis-regulatory variants. Finally, we show that genes depleted for loss-of-function variants are also depleted for cis-eQTLs and have low levels of allelic imbalance, confirming tighter constraint on the expression levels of these genes. We conclude that constraint on gene expression is present, but has relatively weak effects on most cis-regulatory variants, thus permitting high levels of gene-regulatory genetic variation.

genomics↗

bioRxiv2025

Confidence: A Web App for Cross-Platform Differential Gene Expression Analysis, Gene Scoring, and Enrichment Analysis

RNA sequencing (RNA-seq) is used to quantify transcript levels through measurement of nucleotide sequences. To evaluate statistically significant changes in gene expression, transcript counts between samples are compared using differential expression analysis methods. However, three of the most pressing challenges in transcriptomics analyses are: 1) analytical packages produce a distinct number of differentially expressed genes with varied P-value and fold-change values; 2) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and 3) there are a lack of intuitive methods to select target genes for further investigation in an unbiased manner. To address these challenges, we developed Confidence, a web-based application to perform simultaneous statistical analysis of RNA-seq count data. Confidence incorporates the Confidence Score (CS), ranging from 1 to 4 to aid in gene prioritization, where 1 represents low confidence and 4 represents high confidence. The Confidence web-based application was designed for rapid and intuitive analysis of standard experimental metadata and gene count inputs. Confidence provided a web-based, wide-net approach to differential gene expression analysis. Gene scoring allows for unbiased gene selection and identification of novel genes strongly associated with disease and treatment models across multiple species. Additionally, pathway analysis tools have been integrated so that highly confident genes can be placed into biological context in terms of functions and pathways. Confidence provides a new strategy for target prioritization in RNA-seq analysis and the generation of publication-quality figures.

bioinformatics↗

bioRxiv2017

Differential Proportionality - A Normalization-Free Approach To Differential Gene Expression

Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

bioinformatics↗

bioRxiv2017

Limits of noise for autoregulated gene expression

Gene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang, Kurtz and Popovic, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.

systems biology↗

bioRxiv2021

Probing 3'UTRs as modular regulators of gene expression

Genes are commonly abstracted into a coding sequence and cis-regulatory elements (CREs), such as promoters and terminators, and short sequence motifs within these regions. Modern cloning techniques allow easy assembly of synthetic genetic constructs from discrete cis-regulatory modules. However, it is unclear how much the contributions of CREs to gene expression depend on other CREs in the host gene. Using budding yeast, we probe the extent of composability, or independent effects, of distinct CREs. We confirm that the quantitative effect of a terminator on gene expression depends on both promoter and coding sequence. We then explore whether individual cisregulatory motifs within terminator regions display similar context dependence, using transcriptomewide datasets of mRNA decay. To test the extent of composability, we construct reporter genes consisting of combinations of motifs within various terminator contexts, paired with different promoters. Our results show that the effect of a motif on RNA abundance depends both on its host terminator, and also on the associated promoter sequence. This emphasises the need for improved motif inference that includes both local and global context effects, which in turn could aid in the accurate use of CREs for the engineering of synthetic genetic constructs.

systems biology↗

bioRxiv2023

Energy metabolism modulates the regulatory impact of activators on gene expression

Gene expression is a regulated process fueled by ATP consumption. Therefore, regulation must be coupled to constraints imposed by the level of energy metabolism. Here, we explore this relationship both theoretically and experimentally. A stylized mathematical model predicts that activators of gene expression have variable impact depending on metabolic rate. Activators become less essential when metabolic rate is reduced and more essential when metabolic rate is enhanced. We find that in the Drosophila eye, expression dynamics of the yan gene are less affected by loss of EGFR-mediated activation when metabolism is reduced, and the opposite effect is seen when metabolism is enhanced. The effects are also seen at the level of pattern regularity in the adult eye, where loss of EGFR-mediated activation is mitigated by lower metabolism. We propose that gene activation is tuned by energy metabolism to allow for faithful expression dynamics in the face of variable metabolic conditions.

developmental biology↗

bioRxiv2024

Promoter motifs facilitate as well as impede the emergence and evolution of gene expression

Gene regulation is essential for life and controlled by regulatory DNA. Mutations can modify the activity of regulatory DNA, and also create new regulatory DNA, a process called regulatory emergence. Non-regulatory and regulatory DNA contain motifs to which transcription factors may bind. In prokaryotes, gene expression requires a stretch of DNA called a promoter, which contains two motifs called -10 and -35 boxes. However, these motifs may occur in both promoters and non-promoter DNA in multiple copies. They have been implicated in some studies to improve promoter activity, and in others to repress it. Here, we ask whether the presence of such motifs in different genetic sequences influences promoter evolution and emergence. To understand whether and how promoter motifs influence promoter emergence and evolution, we start from 50 "promoter islands", DNA sequences enriched with -10 and -35 boxes. We mutagenize these starting "parent" sequences, and measure gene expression driven by 240000 of the resulting mutants. We find that the probability that mutations create an active promoter varies more than 200-fold, and is not correlated with the number of promoter motifs. For parent sequences without promoter activity, mutations created over 1500 new -10 and -35 boxes at unique positions in the library, but only [~]0.3% of these resulted in de-novo promoter activity. Only [~]13% of all -10 and -35 boxes contribute to de-novo promoter activity. For parent sequences with promoter activity, mutations create new -10 and -35 boxes in 11 specific positions that partially overlap with preexisting ones to modulate expression. We also find that -10 and -35 boxes do not repress promoter activity. Overall, our work demonstrates how promoter motifs influence promoter emergence and evolution. It has implications for predicting and understanding regulatory evolution, de-novo genes, and phenotypic evolution.

evolutionary biology↗

bioRxiv2025

Disentangling the cost of gene expression

Gene expression is essential for biological functions but also incurs a fitness cost. Although the fitness cost can be experimentally measured as the relative reduction in growth rate, it remains unclear how the cost quantitatively depends on different limiting factors. In this work, we establish a resource competition model and disentangle the fitness cost into components arising from limiting resources, including ribosomes, RNA polymerases, and transcription factors. Comparing our model predictions with experimental data for Saccharomyces cerevisiae, we demonstrate that ribosome competition dominates the translation cost, and that transcription factor competition dominates the transcription cost. Our model reveals that the fitness costs originate from the processes of transcription and translation, rather than from the products. The model also systematically connects the fitness cost to genetic and environmental properties, making quantitative predictions consistent with various experimental observations. Our work establishes a systematic framework for gene expression cost, guiding synthetic biology to optimize genetic design.

systems biology↗

bioRxiv2019

Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. Studies of protein-protein interaction (PPI) networks of differentially methylated (DMGs) and expressed genes (DEGs) showed that gene expression and methylation consistently targeted the same gene pathways associated with cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks are integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic dependence between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes are probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes, reflecting a coordinated response of methylation and gene-expression regulatory systems. These results support the identification of reliable and stable biomarkers for cancer diagnosis and prognosis.

cancer biology↗

bioRxiv2021

FoxP3 associates with enhancer-promoter loopsto regulate Treg-specific gene expression

Gene expression programs are specified by higher-order chromatin structure and enhancer-promoter loops (EPL). T regulatory cells (Treg) identity is dominantly specified by the transcription factor FoxP3, whose mechanism of action is unclear. We applied proximity-ligation with chromatin immunoprecipitation (HiChIP) in Treg and closely related conventional CD4+ T cells (Tconv). EPL identified by H3K27Ac HiChIP showed a range of connection intensity, with some super-connected genes. TF-specific HiChIP showed that FoxP3 interacts with EPLs at a large number of genes, including some not differentially expressed in Treg vs Tconv, but enriched at the core Treg signature loci that it upregulates. FoxP3 association correlates with heightened H3H27Ac looping, as ascertained by analysis of FoxP3-deficient Treg-like cells. There was marked asymmetry in the loci where FoxP3 associated at the enhancer- or the promoter-side of EPLs, with enrichment for different transcriptional cofactors. FoxP3 EPL intensity distinguished gene clusters identified by single-cell ATAC-seq as co-varying between individual Tregs, supporting a direct transactivation model for FoxP3 in determining Treg identity. One Sentence SummaryFoxP3 is associated with enhancer-promoter loops in Treg cells, and correlates with heightened enhancer-promoter cross-talk

immunology↗

bioRxiv2025

snATAC-Express infers Gene Expression from Prioritized Chromatin Accessibility Peaks using Machine Learning

BackgroundSingle cell multi-omic investigation opens-up new opportunities to understand mechanisms of gene regulation. Existing methods for inferring transcript abundance from chromatin accessibility fail to prioritize the most relevant peaks and tend to assume positive associations between ATAC peaks and RNA counts. We hypothesize that gene regulation can be modeled as a function of combined positive and negative interactions among peaks and that causal regulatory variants are enriched in the vicinity of the most critical peaks. ResultsA machine learning pipeline leveraging single nuclear multiomic transcriptome and chromatin accessibility data is developed to model gene expression as a function of ATAC peak intensity. Multiome data was available for 18 immune cell types from 29 donors, 19 with Crohns disease. The pipeline aggregates results from three machine learning approaches (random forest regression, XGBoost, and Light GBM) as well as linear regression to identify which ATAC peaks contribute to explaining variation among donors and cell types in pseudobulk gene expression. The coefficient of determination with cross-validation was used to identify robust models which typically explain between 5% and 40% of transcript abundance, utilizing on average 47% of the ATAC peaks, representing a significant gain in predictive accuracy. The most important peaks are enriched in GWAS variants for inflammatory bowel disease and the autoimmune disease systemic lupus erythematosus, but not for rheumatoid arthritis. ConclusionAtlanta Plots visualize the proportion of ATAC peaks contributing to a predictive model of gene expression as well as the proportion of variance explained by the model. Software implementing our pipeline, "snATAC-Express", is freely available on GitHub.

bioinformatics↗