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Results for “gene expression”

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bioRxiv2016

Functional partitioning of local and distal gene expression regulation in multiple human tissues

Studies of the genetics of gene expression have served as a key tool for linking genetic variants to phenotypes. Large-scale eQTL mapping studies have identified a large number of local eQTLs, but the molecular mechanism of how genetic variants regulate expression is still unclear, particularly for distal eQTLs, which these studies are not well-powered to detect. In this study, we use a heritability partitioning approach to dissect the functional components of gene regulation. We make use of an existing method, stratified LD score regression, that leverages all variants (not just those that pass stringent significance thresholds) to partition heritability across functional categories, and we extend this method to partition local and distal gene expression heritability in 15 human tissues. The top enriched functional categories in local regulation of peripheral blood gene expression included super enhancers (5.18x), coding regions (3.73x), conserved regions (2.33x) and four histone marks (p<3x10-7 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral blood gene expression: super enhancers (1.91x), coding regions (4.47x), conserved regions (4.51x) and two histone marks (p<3x10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues showed that local gene expression regulation is largely shared across tissues, but distal gene expression regulation is highly tissue-specific. Our results elucidate the functional components of the genetic architecture of local and distal gene expression regulation.

Genomics↗

bioRxiv2019

Sex-biased gene expression is masculinized in asexual females

Males and females feature strikingly different phenotypes, despite sharing most of their genome. The resolution of this apparent paradox is through differential gene expression, whereby genes are expressed at different levels in each sex. This resolution, however, is likely to be incomplete, leading to conflict between males and females over the optimal expression of genes. We test the hypothesis that gene expression in females is constrained from evolving to its optimum level due to sexually antagonistic selection on males, by examining changes sex-biased gene expression in five obligate asexual species of stick insect, which do not produce males. We predicted that the transcriptome of asexual females would be feminized as asexual females do not experience any sexual conflict. Contrary to our prediction we find that asexual females feature masculinized gene expression, and hypothesise that this is due to shifts in female optimal gene expression levels following the suppression of sex.

evolutionary biology↗

bioRxiv2019

Differential gene expression and the importance of regulatory ncRNAs in acidophilic microorganisms

Gene expression profiles provide insight into how microorganisms respond to changing environmental conditions. However, few studies have integrated expression profile analyses of both coding genes and non-coding RNAs (ncRNAs) to characterize the functional activity of microbial community members. Here, we defined gene expression profiles from environmental and laboratory-grown acidophilic biofilms using RNASeq. In total, 15.8 million Illumina reads were mapped to the genomes of 26 acidophilic microorganisms and nine viruses reconstructed from the Richmond Mine at Iron Mountain, California. More than 99% of the genome was transcribed in three Leptospirillum species, and > 80% in the archaea G-plasma and Ferroplasma Type II. High gene expression by G-plasma and the Leptospirillum Group II UBA strain correlated with extremely acidic conditions, whereas high transcriptional expression of Leptospirillum Group III and Leptospirillum Group II 5way-CG strain occurred under higher pH and lower temperature. While expression of CRISPR Cas genes occurs on the sense strand, expression of the CRISPR loci occurs on the antisense strand in the Leptospirilli. A novel riboswitch associated with the biosynthetic pathway for the osmolyte ectoine was upregulated when each specific Leptospirillum Group II strain was growing under the conditions most favorable for it. Newly described ncRNAs associated with CO dehydrogenase (CODH) suggest regulation of expression of CODH as a CO sensor in mature biofilms in the Leptospirilli. Results reveal the ways in which environmental conditions shape transcriptional profiles of organisms growing in acidophilic microbial communities and highlight the significance of ncRNAs in regulating gene expression. IMPORTANCEMicroorganisms play important roles in environmental acidification and in metal-recovery based bioleaching processes. Therefore, characterizing how actively growing microbial communities respond to different environments is key to understanding their role in those processes. Microorganisms express their genes, both coding and non-coding, differently depending on environmental factors, thus evaluating community expression profiles inform about the ecology of actively growing microorganisms. Here we used community transcriptomic analyses to characterize gene expression profiles from biofilm communities growing under extremely acidic conditions. Results expand our knowledge of how acidophilic microorganisms respond to changes in their environment and provide insight into possible gene regulation mechanisms.

microbiology↗

bioRxiv2025

PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics

Spatial Transcriptomics (ST) integrates histology with spatially resolved gene expression, offering rich insights into tissue architecture and function. However, its clinical and large-scale deployment is hindered by high costs, technical complexity, and limited accessibility. To address this, computational pathology methods have emerged to predict gene expression directly from histology images, typically framing the task as a multi-output regression problem mapping image patches to gene expression profiles. While several Convolutional Neural Network (CNN) models have been proposed, little is known about how performance is influenced by (a) the number of trainable parameters and (b) the patch size used for prediction. Moreover, existing studies rely primarily on quantitative metrics and overlook biological relevance of predictions. In this study, we systematically evaluated multiple convolution based models (including a Vision Transformer (ViT) model) with different patch sizes on the Xenium based Autoimmune Machine Learning Challenge (AMLC) dataset. We assessed model performance on both globally expressed genes and subsets enriched for immune or disease associated pathways. Our findings reveal that compact CNNs trained on larger patches outperform deeper models, offering superior accuracy in predicting gene expression, especially for biologically important genes. These insights provide practical guidance for designing efficient and biologically meaningful models in the emerging field of image-based gene expression prediction. CCS CONCEPTSComputing methodologies [->] Computer vision; Neural networks; * Applied computing [->] Computational genomics; Imaging.

pathology↗

bioRxiv2017

Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

Tuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the worlds population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

genomics↗

bioRxiv2025

Regulatory network topology and the genetic architecture of gene expression

In human populations, most of the genetic variance in gene expression can be attributed to trans-acting expression quantitative trait loci (eQTLs) spread across the genome. However, in practice it is difficult to discover these eQTLs, and their cumulative effects on gene expression and complex traits are yet to be fully understood. Here, we assess how properties of the genetic architecture of gene expression constrain the space of plausible gene regulatory networks. We describe a structured causal model of gene expression regulation and consider how it interacts with biologically relevant properties of the gene regulatory network to alter the genomic distribution of expression heritability. Under our model, we find that the genetic architecture of gene expression is shaped in large part by local network motifs and by hub regulators that shorten paths through the network and act as key sources of trans-acting variance. Further, simulated networks with an enrichment of motifs and hub regulators best recapitulate the distribution of cis and trans heritability of gene expression as measured in a recent twin study. Taken together, our results suggest that the architecture of gene expression is sparser and more pleiotropic across genes than would be suggested by naive models of regulatory networks, which has important implications for future studies of complex traits.

genetics↗

bioRxiv2021

Arsenic hexoxide has differential effects on cell proliferation and genome-wide gene expression in human primary mammary epithelial and MCF7 cells

Arsenic is reportedly a biphasic inorganic compound for its toxicity and anticancer effects in humans [1, 2]. Recent studies have shown that certain arsenic compounds including arsenic hexoxide (AS4O6; hereafter, AS6) induce programmed cell death and cell cycle arrest in human cancer cells and murine cancer models [3, 4]. However, the mechanisms by which AS6 suppresses cancer cells are incompletely understood. In this study, we report the mechanisms of AS6 through transcriptome analyses. In particular, the cytotoxicity and global gene expression regulation by AS6 were compared in human normal and cancer breast epithelial cells. Using RNA-sequencing and bioinformatics analyses, differentially expressed genes in significantly affected biological pathways in these cell types were validated by real-time quantitative polymerase chain reaction and immunoblotting assays. Our data show markedly differential effects of AS6 on cytotoxicity and gene expression in human mammary epithelial normal cells (HUMEC) and Michigan Cancer Foundation 7 (MCF7), a human mammary epithelial cancer cell line. AS6 selectively arrests cell growth and induces cell death in MCF7 cells without affecting the growth of HUMEC in a dose-dependent manner. AS6 alters the transcription of a large number of genes in MCF7 cells, but much fewer genes in HUMEC. Importantly, we found that the cell proliferation, cell cycle, and DNA repair pathways are significantly suppressed whereas cellular stress response and apoptotic pathways increase in AS6-treated MCF7 cells. Together, we provide the first evidence of differential effects of AS6 on normal and cancerous breast epithelial cells, suggesting that AS6 at moderate concentrations induces cell cycle arrest and apoptosis through modulating genome-wide gene expression, leading to compromised DNA repair and increased genome instability selectively in human breast cancer cells.

genetics↗

bioRxiv2018

Convergent evolution of gene expression in two high-toothed stickleback populations

Changes in developmental gene regulatory networks enable evolved changes in morphology. These changes can be in cis regulatory elements that act in an allele-specific manner, or changes to the overall trans regulatory environment that interacts with cis regulatory sequences. Here we address several questions about the evolution of gene expression accompanying a convergently evolved constructive morphological trait, increases in tooth number in two independently derived freshwater populations of threespine stickleback fish (Gasterosteus aculeatus). Are convergently evolved cis and/or trans changes in gene expression associated with convergently evolved morphological evolution? Do cis or trans regulatory changes contribute more to the evolutionary gain of a morphological trait? Transcriptome data from dental tissue of ancestral low-toothed and two independently derived high-toothed stickleback populations revealed significantly shared gene expression changes that have convergently evolved in the two high-toothed populations. Comparing cis and trans regulatory changes using phased gene expression data from F1 hybrids, we found that trans regulatory changes were predominant and more likely to be shared among both high-toothed populations. In contrast, while cis regulatory changes have evolved in both high-toothed populations, overall these changes were distinct and not shared among high-toothed populations. Together these data suggest that a convergently evolved trait can occur through genetically distinct regulatory changes that converge on similar trans regulatory environments.\n\nAuthor SummaryConvergent evolution, where a similar trait evolves in different lineages, provides an opportunity to study the repeatability of evolution. Convergent morphological evolution has been well studied at multiple evolutionary time scales ranging from ancient, to recent, such as the gain in tooth number in freshwater stickleback fish. However, much less is known about the accompanying evolved changes in gene regulation during convergent evolution. Here we compared evolved changes in gene expression in dental tissue of ancestral low-toothed marine fish to fish from two independently derived high-toothed freshwater populations. We also partitioned gene expression changes into those affecting a genes regulatory elements (cis), and those affecting the overall regulatory environment (trans). Both freshwater populations have evolved similar gene expression changes, including a gain of expression of putative dental genes. These similar gene expression changes are due mainly to shared changes to the trans regulatory environment, while the cis changes are largely population specific. Thus, during convergent evolution, overall similar and perhaps predictable transcriptome changes can evolve despite largely different underlying genetic bases.

genomics↗

bioRxiv2024

Impacts of Cell Ranger versions on Chromium gene expression data

In droplet-based single cell gene expression data, cell barcode processing by Cell Ranger (CR) is a standard pipeline. But no systematic evaluation of the impact of CR version on single cell gene expression data has been conducted. To comprehensively evaluate the impact of CR version, we considered six molecular quality criteria, quantified gene expression, and performed downstream analysis for 12 single-cell datasets. Each dataset was processed by 15 versions of CR. We demonstrated that different versions of CR yield different numbers of cell barcodes with significant variation in detected UMIs, features, molecular qualities and average gene expression of protein-coding and lncRNA for the same dataset. Our analysis finds distinction between two diverse categories of cell barcodes: common barcodes unmasked by all versions of CR, and specific barcodes only unmasked/masked by some versions. Surprisingly, we observed variation in molecular read-out between common cell barcodes when called by different versions of CR. The specific barcodes yield skewed gene body coverage and form distinct clusters. The choice of CR version affects scores for quality, average gene expression, clustering results, and top cluster marker genes of the dataset.

bioinformatics↗

bioRxiv2023

Experimental and Phylogenetic Evidence for Correlated Gene Expression Evolution between Dermal and Endometrial Fibroblasts: implications for the evolution of cancer malignancy

Changes in transcriptional gene expression is a dominant mode of evolution, mostly driven by mutations at cis-regulatory regions. Mutations can affect gene expression in multiple cell types if the same cis-regulatory elements are used by different cell types. As a consequence, changes in gene expression in one cell type may be associated with similar gene expression changes in another cell type. Correlated gene expression change can explain correlated character evolution, as for instance the correlation between placental invasion and vulnerability to cancer malignancy. Here we test this hypothesis using a comparative and an experimental data set. Specifically, we investigate gene expression in dermal skin fibroblasts (SF) and uterine endometrial stomal fibroblasts (ESF). The comparative dataset consists of transcriptomes from cultured SF and ESF from 9 mammalian species. We calculated the independent phylogenetic contrasts (PIC) for each gene and cell type. We find that evolutionary changes in gene expression in SF and ESF are highly correlated, supporting the hypothesis that the correlated gene expression changes are a prevalent feature of gene expression evolution. The experimental data set derives from a SCID mouse strain that was selected for slow cancer growth which led to substantial changes in the SF compared to wild type SCID mice. We isolated SF and ESF from wild type and evolved SCID mice and compared their gene expression profiles. We find a significant correlation between the gene expression contrasts of SF and ESF, which supports the hypothesis that gene expression variation in SF and ESF is correlated. We discuss the implications of these findings for the hypothesized correlation between placental invasiveness and vulnerability to metastatic cancer.

evolutionary biology↗

bioRxiv2022

The logic of native enhancer-promoter compatibility and cell-type-specific gene expression variation

Cis-regulatory enhancers are essential for differential expression of developmental and housekeeping genes. However, the specificity of native mammalian enhancers and how it shapes cell-type-specific gene expression landscapes remain largely unknown. We show that endogenous enhancers are broadly compatible with the promoters of developmental and housekeeping genes. Broad enhancer compatibility affords retrofitting new regulatory capabilities to housekeeping genes that evolved before the advent of enhancers. This enables cell-type-specific tuning of ubiquitously expressed genes. Segregation between enhancer-dependent and -independent type regulation is blurred. Within the same cell type, a single promoter can be activated by enhancers and non-enhancer promoter-regulatory elements (PREs). It is the tunable and integrated strengths of enhancers and PREs that quantitatively shape gene expression landscapes, within and across cell types. Our findings have broad implications for understanding cell-type-specific quantitative gene expression variation, as well as the emergence and rewiring of gene regulatory networks in disease and organismal evolution.

genomics↗

bioRxiv2019

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets in 7 model organisms ranging from bacteria to Human. We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we show that the correct combination of all regulatory regions must be established in order to accurately control gene expression levels. Therefore, the holistic system that spans the entire gene regulatory structure is required to analyse, understand, and design any future gene expression systems.

systems biology↗

bioRxiv2019

Bayesian inference of the gene expression states of single cells from scRNA-seq data

In spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters. Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

bioinformatics↗

bioRxiv2019

Framework for determining accuracy of RNA sequencing data for gene expression profiling of single samples

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of -0.05. ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.

genomics↗

bioRxiv2023

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinsons disease, and 30% of heritability for Alzhemiers disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness. SummaryWe examine the impact of genetic ancestry on gene expression and DNA methylation of admixed African/Black Americans, highlighting how genetic and environmental background affect risk for brain illness.

genetics↗

bioRxiv2020

DEBay: a computational tool for deconvolution of quantitative PCR data for estimation of cell type-specifc gene expression in a mixed population

The expression of a gene is commonly estimated by quantitative PCR (qPCR) using RNA isolated from a large number of pooled cells. Such pooled samples often have subpopulations of cells with different levels of expression of the target gene. Estimation of gene expression from an ensemble of cells obscures the pattern of expression in different subpopulations. Physical separation of various subpopulations is a demanding task. We have developed a computational tool, Deconvolution of Ensemble through Bayes-approach (DEBay), to estimate cell type-specific gene expression from qPCR data of a mixed population. DEBay estimates Normalized Gene Expression Coefficient (NGEC), which is a relative measure of the expression of the target gene in each cell type in a population. NGEC has a direct algebraic correspondence with the normalized fold change in gene expression measured by qPCR. DEBay can deconvolute both time-dependent and -independent gene expression profiles. It uses the Bayesian method of model selection and parameter estimation. We have evaluated DEBay using synthetic and real experimental data. DEBay is implemented in Python. A GUI of DEBay and its source code are available for download at SourceForge (https://sourceforge.net/projects/debay).

bioinformatics↗

bioRxiv2021

The evolution of ovary-specific gene expression in Hawaiian Drosophilidae

1With detailed data on gene expression accessible from an increasingly broad array of species, we can test the extent to which our developmental genetic knowledge from model organisms predicts expression patterns and variation across species. But to know when differences in gene expression across species are significant, we first need to know how much evolutionary variation in gene expression we expect to observe. Here we provide an answer by analyzing RNAseq data across twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that over evolutionary time, there exists a cohort of ovary specific genes that is stable and that largely corresponds to described expression patterns from laboratory model Drosophila species. Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovary-biased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.

evolutionary biology↗

bioRxiv2021

Role of polycomb repressive complex 2 in regulation of human transcription factor gene expression

The study described here was pursued with the idea that information about control of human gene expression might result from comparing the promoter regions of a highly expressed gene population with that of a related population that is weakly expressed. Systematic differences in the two promoter populations would be candidates for a role in causing the distinct expression levels observed. The results reported here were obtained with human genes encoding transcription factors (TF). Among TF genes, those with wide tissue transcription are strongly expressed while those expressed in one or only a few tissues are weakly transcribed in most cases. The promoter regions of the two populations were compared using the results of ChIP-seq experiments. Results demonstrated that the promoter region of broadly expressed TF genes is enriched in binding sites for POLR2A, a component of RNA polymerase II while promoters of tissue targeted genes are enriched in EZH2, a subunit of polycomb repressive complex 2 (PRC2). It was rare to observe promoters with binding sites for both POLR2A and EZH2 or for neither one. The findings are interpreted to indicate that strong expression of broadly expressed TF genes is due to the presence of RNA polymerase II at the promoter while weak expression of tissue targeted promoters results from the presence of PRC2. Finally, transcription factor families were compared in the proportion of broadly expressed and tissue targeted genes. The results demonstrated that most families possess both broadly expressed and tissue targeted members. For instance, this was the case with 16 of 20 TF families. The results are interpreted to indicate that while individual TFs such as EZH2 may be specific for broadly expressed or tissue targeted genes, this is not a property of most TF families. Most have both broadly expressed and tissue targeted members.

genomics↗