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VariantFormer: A hierarchical transformer integrating DNA sequences with genetic variations and regulatory landscapes for personalized gene expression prediction

1Accurately predicting gene expression from DNA sequence remains a central challenge in human genetics. Current sequence-based models overlook natural genetic variation across individuals, while population-based models are restricted to variants observed within specific cohorts. Here, we present VariantFormer, a 1.2-billion-parameter transformer that predicts gene-level RNA abundance directly from personalized diploid genomes. Trained on 21,004 genome-transcriptome pairs from 2,330 donors, VariantFormer achieves state-of-the-art performance across both sequence- and population-based prediction tasks, while generalizing better to out-of-distribution contexts--including somatic mutation settings in cancer cell lines--and main-taining robustness across ancestries. Beyond expression prediction, VariantFormer improves eQTL effect size estimation compared to prior methods, with notable gains for lower-frequency and ancestry-specific variants. In applications to Alzheimers disease, VariantFormer gene embeddings prioritize likely causal genes and relevant tissue contexts, and in silico mutagenesis of known APOE alleles faithfully recovers known risk modifying effects. Together, these results establish VariantFormer as a scalable, diploid-aware framework for variant interpretation and personalized gene expression modeling across tissues and populations.

genomics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗

Genetic regulation of human aortic smooth muscle cell gene expression and splicing predict causal coronary artery disease genes

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies (GWAS) have identified over 175 loci associated with CAD. The majority of these loci are in non-coding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we hypothesized that a subset of the CAD GWAS risk loci are associated with the regulation of transcription in distinct SMC phenotypes. Here, we measured gene expression in SMCs isolated from the ascending aortas of 151 ethnically diverse heart transplant donors in quiescent or proliferative conditions and calculated the association of their expression and splicing with [~]6.3 million imputed single nucleotide polymorphism (SNP) markers across the genome. We identified 4,910 expression and 4,412 splice quantitative trait loci (sQTL) that represent regions of the genome associated with transcript abundance and splicing. 3,660 of the eQTLs had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 of the eQTLs were SMC- and sex-specific, respectively. To identify the effector transcript(s) regulated by CAD GWAS loci, we used four distinct colocalization approaches and identified 84 eQTL and 164 sQTLs that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. Two CAD loci colocalized with a SMC sex-specific eQTL (AL160313.1 and TERF2IP) and another locus colocalized with SMC-specific eQTL (ALKBH8). Also, 27% and 37% of the sQTLs were unique to quiescent or proliferative SMCs, respectively. The most significantly associated CAD locus, 9p21, was an sQTL for the long non-coding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, these results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.

genetics↗

Tissue-specific and cis-regulatory changes underlie parallel, adaptive gene expression evolution in house mice

Changes in gene regulation have long been appreciated as a driving force of adaptive evolution, however the relative contributions of cis- and trans-acting changes to gene regulation over short evolutionary timescales remain unclear. Instances of recent, parallel phenotypic evolution provide an opportunity to assess whether parallel patterns are seen at the level of gene expression, and to assess the relative contribution of cis- and trans-changes to gene regulation in the early stages of divergence. Here, we studied gene expression in liver and brown adipose tissue in two wild-derived strains of house mice that independently adapted to cold, northern environments, and we compared them to a strain of house mice from a warm, southern environment. To investigate gene regulatory evolution, we studied expression in parents and allele-specific expression in F1 hybrids of crosses between warm-adapted and cold-adapted strains. First, we found that the different cold-adapted mice showed both unique and shared changes in expression, but that the proportion of shared changes (i.e. parallelism) was greater than expected by chance. Second, we discovered that expression evolution occurred largely at tissue-specific and cis-regulated genes, and that these genes were over-represented in parallel cases of evolution. Finally, we integrated the expression data with scans for selection in natural populations and found substantial parallelism in the two northern populations for genes under selection. Furthermore, selection outliers were associated with cis-regulated genes more than expected by chance; cis-regulated genes under selection influenced phenotypes such as body size, fat content, immune functioning, and skeletal morphology. These results demonstrate that parallel patterns of gene expression in mice that have independently adapted to cold environments are driven largely by tissue-specific and cis-regulatory changes, providing insight into the mechanisms of adaptive gene regulatory evolution at the earliest stages of divergence. Author summaryThe parallel movement of organisms into novel environments provides an opportunity to understand the molecular basis of adaptation and the repeatability of this process. Mutations affecting the expression of genes are known to underlie much of adaptive evolution. Such mutations can arise in cis-(near the gene of interest) or in trans-(at a distant locus), but the relative contribution of these different kinds of changes to adaptation is poorly understood, especially during very recent divergence. Here, we compared evolved gene expression differences between a warm-adapted house mouse strain and two different cold-adapted strains that have independently evolved similar phenotypic traits, such as increased body size and decreased extremity length during the last few hundred years. Using crosses between warm-adapted and cold-adapted mice, we found that mutations acting in a context specific manner (cis-regulatory and tissue-specific changes) predominate expression divergence and are more likely to be involved in parallel evolution. We used population level selection scans in wild animals to identify regions of the genome under selection and combined these findings with the gene expression data to identify candidate genes underlying adaptation to novel environments. Together, our work describes the gene regulatory dynamics of rapid environmental adaptation, and the repeatability of these patterns over multiple instances of adaptation.

evolutionary biology↗

Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors

Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.

bioinformatics↗

Plasmodium falciparum MORC protein modulates gene expression through interaction with heterochromatin

Dynamic control of gene expression is critical for blood stage development of malaria parasites. Here, we used multi-omic analyses to investigate transcriptional regulation by the chromatin-associated microrchidia protein, MORC, during asexual blood stage development of the human malaria parasite Plasmodium falciparum. We show that PfMORC (PF3D7_1468100) interacts with a suite of nuclear proteins, including APETALA2 (AP2) transcription factors (PfAP2-G5, PfAP2-O5, PfAP2-I, PF3D7_0420300, PF3D7_0613800, PF3D7_1107800, and PF3D7_1239200), a DNA helicase DS60 (PF3D7_1227100), and other chromatin remodelers (PfCHD1 and PfEELM2). Transcriptomic analysis of PfMORCHA-glmS knockdown parasites revealed 163 differentially expressed genes belonging to hypervariable multigene families, along with upregulation of genes mostly involved in host cell invasion. In vivo genome-wide chromatin occupancy analysis during both trophozoite and schizont stages of development demonstrates that PfMORC is recruited to repressed, multigene families, including the var genes in subtelomeric chromosomal regions. Collectively, we find that PfMORC is found in chromatin complexes that play a role in the epigenetic control of asexual blood stage transcriptional regulation and chromatin organization.

microbiology↗

Pathway-Level Information ExtractoR (PLIER): a generative model for gene expression data

A major challenge in gene expression analysis is to accurately infer relevant biological insight, such as regulation of cell type proportion or pathways, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification.

genomics↗

A COMPARATIVE ANALAYSIS OF INTER-SITE GENE EXPRESSION HETEROGENICITY OF NORMAL HUMAN BUCCAL MUCOSA WITH NORMAL GINGIVAL MUCOSA

BACKGROUNDDescription of heterogeneity of gene expression of various human intraoral sites are not adequate. The aim of this study was to explore the difference of gene expression profiles of whole tissue obtained from apparently normal human gingiva and buccal mucosa (HGM, HBM). MATERIALS AND METHODSGene sets fulfilling inclusion and exclusion criteria of HGM and HBM in gene Expression Omnibus(GEO) database were identified, segregated, filtered and analysed using the ExAtlas online web tool using pre-determined cut-off. The differentially expressed genes were studied for epithelial keratinization related, housekeeping(HKG), extracellular matrix related(ECMRG) and epithelial-mesenchymal transition related genes(EMTRGs). RESULTSIn all 40 HBM and 64 HGM formed the study group. In all there were 18012 significantly expressed genes. Of this, 1814 were over-expressed and 1862 under-expressed HBM genes as compared to HGM. One in five of all studied genes significantly differed between HBM and HGM. For the keratinization genes, 1 in 6 differed. One of every 5 HKG-proteomics genes differed between HBM and HGM, while this ratio was 1-in 4 for all ECMRGs and EMTRGs. DISCUSSIONThis difference in the gene expression between the HBM and HGM could possibly influence a multitude of biological pathways. This result could explain partly the difference in clinicopathological features of oral lesions occurring in HBM and HGM. The innate genotypic difference between the two intra-oral niches could serve as confounding factor in genotypic studies. Hence studies that compare the HBM and HGM should factor-in these findings while evaluating their results.

genomics↗

Gene Expression Meta-Analysis of Colon Rectal Cancer Tumour Cells Reveals Genes in Association With Tumorogenesis.

BackgroundEvery year, more than 12 million people are diagnosed with colorectal cancer(CRC), and more than 600,000 people die from it, making it second most deadly form of cancer.This work analyzes differential gene expression across CRC and other glandular tumour samples to identify expression changes potentially contributing to the development of CRC tumorogenesis. MethodsThis work defines 13 gene signatures representing four CRC tumour and 10 other glandular tumours that are colonic by origin.Gene Set Enrichment Analysis (GSEA) is used to define positive and negative CRC gene panels from GSEA-identified leading-edge genes using two CRC signatures. GSEA then is used to verify enrichment and leading-edge gene membership of CRC panels in two independent CRC gene signatures. Analysis is then extended to four individual and 10 glandular tumour signatures. Genes most associated with CRC tumorogenesis are predicted by intersecting membership of GSEA-identified leading-edges across signatures. ResultsSignificant enrichment is observed between CRC gene identification signatures, from which the positive (55 genes) and negative (77 genes) CRC panels are defined. Non-random significant enrichment is observed between CRC gene panels and verification signatures, from which 54 over- and 72 under-expressed genes are shared across leading-edges. Considering other glandular tumour samples individually and in combination with CRC, significant non-random enrichment is observed across these signatures. Eight solute carrier family genes such as (SLC25A32, SLC22A3, SLC25A20, SLC36A1, SLC26A3,SLC9A2, SLC4A4 and SLC26A2) from the CRC panel were shared commonly across all the gene signatures leading-edges, regardless of the colonic tumour type. ConclusionThis meta-analysis identifies gene expression changes associated with the process of CRC tumorogenesis. These changes may contribute to developing therapeutic treatments available for CRC patients.

bioinformatics↗

Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach

Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. In this paper, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior biological information into the clustering process. We tested our approach on two distinct gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, as well as with a recent deep learning clustering approach. As a result, our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture could generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge could be helpful in the gene expression clustering task.

bioinformatics↗

Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement

Changes in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the Chinese Spring reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in Chinese Spring reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the worlds most important crop.

plant biology↗

Perinatal Lead (Pb) Exposure Increases Mouse Embryonic Weight and Alters Neuronal Gene Expression

Acute and chronic exposure to lead (Pb) during pregnancy is linked to adverse health outcomes, including delayed neurodevelopment in offspring. However, the pathways by which Pb exposure influences long-term health remain poorly understood. To address this, we measured the effects of perinatal Pb exposure on gene expression including imprinted genes, X-linked genes, and sexually dimorphic genes. Female mice were given control or Pb acetate dosed (32 ppm) drinking water two weeks prior to timed mating until embryonic day (E)10-12, upon which whole embryos were collected, weighed, and sexed at E13-15. From a subset of embryo heads (n[&ge;]9 per sex per group), we extracted and sequenced RNA. We used linear regression to assess Pb impacts on embryonic weight and gene expression across all mice and stratified by sex. Among the differentially expressed genes, we identified significantly enriched pathways. Pb-exposed embryos weighed more than controls (p=0.007), across both sexes. Collectively, we identified 2,920 differentially expressed genes (FDR<0.05), including 31 imprinted genes and 120 X-linked genes upon Pb exposure. Pb exposure altered expression in gene pathways related to neuronal structure and function as well as sexually dimorphic genes (44 for females; 76 for males). These findings highlight perinatal Pb-linked alterations that may drive later-life health outcomes.

genetics↗

Drift and directional selection are the evolutionary forces driving gene expression divergence in eye and brain tissue of Heliconius butterflies

Investigating gene expression evolution over micro- and macroevolutionary timescales will expand our understanding of the role of gene expression in adaptation and speciation. In this study, we characterized which evolutionary forces are acting on gene expression levels in eye and brain tissue of five Heliconius butterflies with divergence times of ~5-12 MYA. We developed and applied Brownian motion and Ornstein-Uhlenbeck models to identify genes whose expression levels are evolving through drift, stabilizing selection, or a lineage-specific shift. We find that 81% of the genes evolve under genetic drift. When testing for branch-specific shifts in gene expression, we detected 368 (16%) shift events. Genes showing a shift towards up-regulation have significantly lower gene expression variance than those genes showing a shift leading towards down-regulation. We hypothesize that directional selection is acting in shifts causing up-regulation, since transcription is costly. We further uncover through simulations that parameter estimation of Ornstein-Uhlenbeck models is biased when using small phylogenies and only becomes reliable with phylogenies having at least 50 taxa. Therefore, we developed a new statistical test based on Brownian motion to identify highly conserved genes (i.e., evolving under strong stabilizing selection), which comprised 3% of the orthoclusters. In conclusion, we found that drift is the dominant evolutionary force driving gene expression evolution in eye and brain tissue in Heliconius. Nevertheless, the higher proportion of genes evolving under directional than under stabilizing selection might reflect species-specific selective pressures on vision and brain necessary to fulfill species-specific requirements.

evolutionary biology↗

Characterization, modelling and mitigation of gene expression burden in mammalian cells

Despite recent advances in genome engineering, the design of genetic circuits in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here we demonstrate that competition for limited transcriptional and translational resources dynamically couples otherwise independent co-expressed exogenous genes, leading to diminished performance and contributing to the divergence between intended and actual function. We also show that the expression of endogenous genes is likewise impacted when genetic payloads are expressed in the host cells. Guided by a resource-aware mathematical model and our experimental finding that post-transcriptional regulators have a large capacity for resource redistribution, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the novel use of endogenous miRNAs as integral components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.

synthetic biology↗

Sequential compression across latent space dimensions enhances gene expression signatures

BackgroundUnsupervised compression algorithms applied to gene expression data extract latent, or hidden, signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically-appropriate latent dimensionality. In practice, most researchers select a single algorithm and latent dimensionality. We sought to determine the extent by which using multiple dimensionalities across ensemble compression models improves biological representations.\n\nResultsWe compressed gene expression data from three large datasets consisting of adult normal tissue, adult cancer tissue, and pediatric cancer tissue. We compressed these data into many latent dimensionalities ranging from 2 to 200. We observed various tradeoffs across latent dimensionalities and compression models. For example, we observed high model stability between principal components analysis (PCA), independent components analysis (ICA), and non-negative matrix factorization (NMF). We identified more unique biological signatures in ensembles of denoising autoencoder (DAE) and variational autoencoder (VAE) models in intermediate latent dimensionalities. However, we captured the most pathway-associated features using all compressed features across algorithms and dimensionalities. Optimized at different latent dimensionalities, compression models detect generalizable gene expression signatures representing sex, neuroblastoma MYCN amplification, and cell types. In two supervised machine learning tasks, compressed features optimized predictions at different latent dimensionalities.\n\nConclusionsThere is no single best latent dimensionality or compression algorithm for analyzing gene expression data. Instead, using feature ensembles from different compression models across latent space dimensionalities optimizes biological representations.

bioinformatics↗

Coordinated shifts in gene expression and regulation during mole-rat evolution

Changes in gene expression and regulation are central to mammalian phenotypic evolution. Yet, distinguishing adaptive gene expression shifts from neutral divergence remains challenging. Here, we integrate comparative transcriptomics and regulatory genomics to investigate how natural selection has shaped gene expression in African mole-rats, a group of subterranean rodents with phenotypic adaptations to underground environments. Using RNA-seq from liver and heart in two mole-rat species (naked mole-rat and Damaraland mole-rat) and two rodent outgroups (mouse and guinea pig), we leveraged phylogenetic models of expression evolution and identified hundreds of genes whose transcriptional levels have experienced accelerated evolution in each tissue and mole-rat species. These lineage-specific shifts account for only a small fraction of gene expression differences identified by classical differential expression analysis between species, underscoring the importance of phylogeny-aware inference to disentangle accelerated evolution from drift. To connect expression divergence with regulatory evolution, we integrated transcriptomic profiles with cis-regulatory landscapes. Genes with lineage-specific expression shifts displayed concordant changes in cis-regulatory activity, particularly at promoters, and the magnitude of expression divergence increased with the number of shifted cis-regulatory elements. Our results demonstrate that adaptive shifts in mole-rat gene expression are mirrored by regulatory evolution, providing genome-wide coordinated evidence of accelerated evolution on expression and regulation during mammalian evolution. Our approach thereby prioritises candidate loci that may have shaped adaptations specific to mole-rat physiology, including metabolic rewiring and stress responses.

evolutionary biology↗

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

genomics↗

Analysis of cell-type-specific chromatin modifications and gene expression in Drosophila neurons that direct reproductive behavior

Examining the role of chromatin modifications and gene expression in neurons is critical for understanding how the potential for behaviors are established and maintained. We investigate this question by examining Drosophila melanogaster fru P1 neurons that underlie reproductive behaviors in both sexes. We developed a method to purify cell-type-specific chromatin (Chromatag), using a tagged histone H2B variant that is expressed using the versatile Gal4/UAS gene expression system. Here, we use Chromatag to evaluate five chromatin modifications, at three life stages in both sexes. We find substantial changes in chromatin modification profiles across development and fewer differences between males and females. We generated cell-type-specific RNA-seq data sets, using translating ribosome affinity purification (TRAP), and identify actively translated genes in fru P1 neurons, revealing novel stage- and sex-differences in gene expression. We compare chromatin modifications to the gene expression data and find patterns of chromatin modifications associated with gene expression. An examination of the genic features where chromatin modifications resides shows certain chromatin modifications are maintained in the same genes across development, whereas others are more dynamic, which may point to modifications important for cell fate determination in neurons. Using a computational analysis to identify super-enhancer-containing genes we discovered differences across development, and between the sexes that are cell-type-specific. A set of super-enhancer-containing genes that overlapped with those determined to be expressed with the TRAP approach were validated as expressed in fru P1 neurons. Author SummaryDifferences in male and female reproductive behaviors are pervasive in nature and important for species propagation. Studies of sex differences in the fruit fly, Drosophila melanogaster, have been ongoing since the early 1900s, with many of the critical molecular and neural circuit determinates that create sexually dimorphic behavior identified. This system is a powerful model to understand fundamental principles about the underpinnings of complex behavior at high resolution. In this study, we examine the gene expression and chromatin modification differences specifically in a set of neurons that direct male and female reproductive behaviors in Drosophila. We describe differences across development and between the sexes with the goal of understanding how the potential for behavior is created and maintained.

genomics↗