Plant embryogenesis is a critical process characterized by extensive cellular differentiation and morphogenesis, regulated by complex epigenetic mechanisms. However, dynamic changes of DNA methylation during Arabidopsis early embryo development remain to be elucidated. In this study, Arabidopsis hybrid embryos from 2/4-cell to globular stage were subject to bisulfite sequencing and RNA-seq analysis. Our results revealed a local remodeling of DNA methylation during early embryogenesis, represented by a remarkable gain of CHH methylation and the progressive loss of CHG methylation. Genes with differential methylation were found to participate in cell division, morphogenesis, pattern specification and auxin signaling, indicating their potential role in embryo development. At globular stage, CHH hyper-methylation exhibited a notable association with TE repression. The divergences between allelic methylation primarily lied in CG context which increased with embryogenesis. CHH methylation variations between parental alleles underwent reprogramming. Our results implied a negative relevance between allele-specific expression and methylation at CHG sites in early embryos. This work provides new insights into DNA methylation remodeling and its association with transcriptional changes during Arabidopsis early embryogenesis, laying a foundation for future functional studies.

Horn phenotype is an important trait in sheep breeding, yet the genetic factors contributing to horn variation remain incompletely understood. In this study, we focused on the Corneodesmosin (CDSN) gene to evaluate its potential involvement in horn development and phenotypic divergence. Using RNA-seq data from horn tissue, we observed consistently higher CDSN expression in small-horn individuals compared with large-horn sheep. Analysis of public transcriptomes further confirmed a stable skin- and horn-biased expression pattern across breeds.Whole-genome sequencing datasets were used to identify single-nucleotide polymorphisms (SNPs) within and around CDSN. A total of 29 functional variants were detected, including missense, synonymous, intronic and 3'UTR sites. Several SNPs showed clear allele-frequency differences between horned and polled populations, and multiple sites were significantly associated with horn length under a dominant model. Linkage disequilibrium analysis revealed distinct haplotype blocks encompassing both regulatory and coding variants. Allele-specific expression analysis identified eight cis-acting sites clustered within exon 2, suggesting that this region plays a key regulatory role in controlling CDSN transcription. The convergence of differential expression, associated SNPs, and allele-specific patterns supports CDSN as a promising candidate gene associated with horn-related phenotypic variation in sheep. These results provide useful candidate markers and a foundation for future functional studies of horn biology and breeding applications.

Peach (Prunus persica) and almond (P. dulcis) are closely related species within the Prunus genus that exhibit strikingly different fruit characteristics, particularly in mesocarp expansion and ripening behaviour. To investigate the biological processes driving these differences, we performed a comprehensive transcriptomic analysis of fruit development in the peach cultivar 'Earlygold', the almond cultivar 'Texas', and their interspecific F1 hybrid. Fruit samples were collected at three developmental stages that are key in the different ripening behaviour of peach and almond: initial phase of rapid growth (T1), cell expansion and lignification (T2), and ripening (T3). Global transcriptome profiling revealed almost identical expression patterns irrespective of the reference genome used for the RNA-seq analysis. We found 4,241, 3,862 and 2,922 DEGs between T1 and T2 in 'Earlygold', 'Texas' and F1 hybrid respectively, with most specific changes (55%, 76.6% and 51.3%) occurring during the first half of fruit development. Between T2 and T3, peach-type fruits continued active transcriptional regulation (2,665 DEGs in 'Earlygold', 2,199 in F1 hybrid), whereas almond showed limited late-stage changes (1,032 DEGs), reflecting its non-ripening phenotype. Enrichment analysis showed conserved cell division and photosynthesis-related genes enriched at T1 at both species. Peach displayed unique enrichment in pathways related to auxin signaling, DNA replication, and cyanogenic compound metabolism whereas almond for abscisic acid- and ethylene-related stress pathways. Allele-specific expression (ASE) analysis in the F1 hybrid revealed 79, 99 and 119 peach-biased ASE genes, and 27, 51 and 77 almond-biased ASE genes at T1, T2 and T3, respectively. These findings reveal that peach and almond share conserved early developmental programs but diverge markedly from mid-development. Our data highlight auxin signaling, DNA replication, and ethylene-mediated ripening as central processes driving these developmental differences. The limited number of ASE genes and their parental bias patterns further illuminate cis-regulatory divergence between both species. This study provides new insights into the genetic regulation of fruit development in Prunus species and demonstrates a robust pipeline for cross-species transcriptomic analysis.

A comprehensive review of variant calling tools for RNA-seq: Challenges and advances.

Comparison of single-cell sequencing technologies for allele-specific expression analysis in rabbit spermatids.

The genus Philodendron exhibits exceptional diversity and ornamental value, but the genetic and evolutionary mechanisms driving its speciation and trait variation remain largely unknown. In this study, we constructed a haplotype-resolved, near-complete genome of Philodendron tatei to investigate its evolutionary origins, resolve its phylogenomic placement within Araceae, reconstruct karyotype evolution, and explore genetic clusters and hybridization patterns within Philodendron cultivars. Additionally, the genetic and regulatory mechanisms underlying leaf color variation, a key horticultural trait, were explored. Phylogenomic analysis placed Philodendron within the Araceae family and provided insights into its karyotype evolution. Comparative genomic analyses identified five major genetic clusters across the genus, highlighting extensive hybridization and allele-specific expression as key contributors to Philodendron's diversity. To investigate leaf color variation, variant mining and transcriptome profiling were conducted on samples with diverse pigmentation. Functional validation identified PtSGR1 as a critical regulator of pigmentation formation, with differences in promoter activity driving variation in leaf coloration. Overall, this study provides a comprehensive genomic framework for understanding Philodendron evolution and diversity, tracing the significant role of hybridization in shaping its speciation and identifying key genetic mechanisms underlying ornamental traits. These insights advance our understanding of plant evolution, contribute to horticultural innovation, and enhance the genetic resources available for studying this ecologically and economically important genus.

Most genetic variants in the human genome reside in non-coding regions, where they can perturb regulatory element activity to influence gene expression, thereby contributing to various phenotypes and diseases. However, identifying functionally relevant non-coding genetic variation remains challenging. Here we integrate personal genomics, allele-specific gene regulation, and deep learning predictions to map the impact of non-coding variation in its native allelic and regulatory context. Leveraging whole-chromosome haplotypes and allele-specific analyses, we establish regulatory links within individual human genomes, enabling us to evaluate functional consequences of both common and rare variants. We identify and validate hundreds of cell-type-specific transcription factor binding events disrupted by genetic variants, revealing known and novel mechanisms that underlie allele-specific chromatin accessibility and gene expression. Using this framework, we discovered a rare variant that disrupted an OCT2 binding site within a distal enhancer, thereby modulating the expression of PIK3R5 gene. Our study establishes a generalisable strategy for interpreting non-coding regulatory variation, enabling systematic dissection of variant effects across diverse biological systems and offering a framework to investigate disease mechanisms.

Positive and balancing selection on pattern recognition receptors (PRRs) is widely thought to target ligand-binding domains and affect the specificity of recognition of different pathogens. Alternatively, positive/balancing selection on PRRs could affect general responsiveness by targeting for example signaling domains or cis-regulatory variation. Studies of a wild rodent (the bank vole, Clethrionomys glareolus) have shown that Tlr2-a lipoprotein-binding PRR-is highly polymorphic with divergent haplotypes and signatures of balancing selection, and that Tlr2 genotype is associated with susceptibility to Borrelia afzelii infection in the wild. To investigate what aspect of Tlr2 function has been under selection, we here perform integrated population genetic and functional analyses. Ex vivo infection experiments show that the protective Tlr2 haplotype produces a stronger proinflammatory response to B. afzelii compared to the haplotype associated with susceptibility. Tlr2 genotype has a similar, albeit not statistically significant, effect on responsiveness to the phylogenetically distant pathogen Streptococcus pyogenes. We find that the strongest signature of balancing selection is 4.6 kb upstream of the Tlr2 coding sequence, near a putative enhancer, and that Tlr2 exhibits allele-specific expression such that the protective haplotype is more expressed. Collectively these results indicate that balancing selection has primarily acted on cis-regulatory variation affecting the general responsiveness via Tlr2-signaling rather than on polymorphisms affecting Tlr2 ligand-binding specificity.

Allele Specific Expression data quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites. Current methodologies of genome-wide sequencing produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Machine learning tools could be employed to explore the data and to estimate missing signatures. We present a two-phase procedure based on Self-Organizing Maps (SOMs), an unsupervised clustering technique, to recover missing allele specific expression data from RNA-seq experiments. Specifically, a SOM trained on a complete population is used to assign a so-called corrupted individual to its most fitting cluster ; then, a completion rule based on allele frequencies within the subpopulation of defined by is employed to reconstruct . To evaluate our approach, we first apply it to purely artificial datasets, in order to have full control over all experimental conditions. After that, we consider a real population of Vitis vinifera, which we also extend by applying a computational framework to generate synthetic individuals from allele expression data. We then introduce two local feature relevance indices in order to assess the influence of specific alleles on the topological placement of corrupted individuals in the SOM structure. Our results, showing promising accuracy in the prediction of missing alleles, suggest that the developed approach could be very useful for recovering incomplete samples in a dataset instead of discarding them, mainly in situations where experiments are challenging.

Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.

Canker disease caused by Pseudomonas syringae pv. actinidiae (Psa) poses a major threat to cultivated kiwifruit, and utilization of wild relatives are key to improve resistance. However, comprehensive comparative genomic analyses between cultivated kiwifruit and their wild relatives with enhanced resistance to Psa remain limited. Here we generate chromosome-scale genome assemblies for eleven wild Actinidia eriantha accessions and one interspecific hybrid between Actinidia eriantha and cultivated Actinidia chinensis var. chinensis. Integrating these with twelve previously released genomes including three Actinidia eriantha and nine Actinidia chinensis var. chinensis, we construct a reference-unbiased graph-based pangenome. These datasets reveal extensive genomic variation, including 31,790,044 SNPs, 13,512,079 InDels and 623,478 structural variations, and provide a landscape of structural variations within and between the two species. Leveraging these datasets, we identify a wild allele showing allele-specific expression, AeNLR25-1, which enhances Psa resistance in cultivated kiwifruit. Genetic and molecular analyses demonstrate that a transposable element-induced structural variation in the AeNLR25-1 promoter introduces a species-specific WRKY binding site, conferring enhanced defense against Psa. Pangenome across cultivated species and wild relatives provides a theoretical framework for accelerating kiwifruit genetic improvement through pangenome-enabled identification of favorable wild alleles.

Allele-specific expression (ASE) of somatic mutations can be caused by cis-activation of the mutant allele or silencing of the wild-type allele and has been investigated by examining the enrichment of mutant allele in RNA relative to DNA. Here we show that this mutation-based approach can be confounded by gene expression differences in tumor and normal cells that coexist in most bulk tumor samples. We model mutant allele expression by incorporating tumor/normal expression difference, mutant allele dosage, tumor purity, and nonsense-mediated decay (NMD) efficiency, projecting that such enrichments can occur without ASE. This confounding effect is exacerbated with low tumor purity and is dependent on mutant allele dosage for NMD-triggering mutations. The model predictions are validated by a pancancer bulk tumor analysis with somatic insertions/deletions (indels) from 9101 The Cancer Genome Atlas (TCGA) samples. A single-cell analysis in five cutaneous squamous cell carcinomas demonstrates the robustness of this model to intratumor heterogeneity. As a byproduct of this confounding effect, we evaluate whether the inverse relationship between mutant allele enrichment in RNA and tumor purity could be leveraged to complement DNA-based somatic mutation detection in low purity samples. Indeed, our de novo somatic indel calling from TCGA RNA-seq increases the TCGA driver indel repertoire by ∼14%, especially in samples with purity less than 0.4, including actionable EGFR indels in lung adenocarcinoma and FLT3 in acute myeloid leukemia. Our study not only reveals confounders in somatic mutant ASE analysis but also demonstrates their utility in RNA-based mutation calling.

BackgroundAllele Specific Expression analysis is an important tool for integrating genome and transcriptome data. It quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites, and is a powerful tool to estimate cis-regulatory diversity of alleles. Clustering algorithms can be used to identify patterns or groups of genes/samples based on their expression profiles. Depending on the structure of the data, different existing clustering algorithm can be adapted to allele specific expression data. However, no ad-hoc procedure has been developed.ResultsIn this work, we begin defining an expression matrix capturing allele expressions from an RNA-sequencing experiment. On this matrix, we develop a novel two-phase unsupervised clustering procedure, built on top of a spectral clustering algorithm, whose aim is to partition the population into groups of similar individuals, according to their allelic expression. As case-studies, the approach is used to cluster 98 cultivars representative of the variability observed in Vitis vinifera, starting from read counts of genes of chromosome 1 of leaves, and to analyze allele-specific count data from a CASTxMRL F1 hybrid mice dataset.ConclusionUsing the above mentioned real case-studies as well as generated synthetic data, we see that our algorithm shows significant robustness and outperforms other standard clustering techniques.

Metformin is a first-line therapy for type 2 diabetes, yet individual response varies significantly, with over 30 % of patients failing to achieve optimal glycemic control. The specific regulatory mechanisms of this phenomenon remain poorly understood and genetic variants involved are mainly undiscovered. There are multiple lines of evidence that the leading role in determining the variance in phenotypic outcome belongs to regulatory SNPs (rSNPs) as they directly modify gene expression. Therefore, the genome-wide search for such functional variants and deciphering associated phenotypes stands as a fundamental challenge. Previously, based on the results of bioinformatics analysis of allele-specific expression and binding landscape in human peripheral blood mononuclear cells, we have established an original panel of 14 796 rSNPs within promotors of 5132 genes. Aiming to pinpoint functional variants most likely linked to metformin hepatic response and impacts on liver gluconeogenesis, we analyzed the relevant open-access data as well as rSNPs from our panel and the corresponding genes. 1196 genes reported to be regulated by metformin in human hepatocytes and 115 genes involved in gluconeogenesis and/or its regulation via Gene Ontology annotations were intersected. Free R software and STRING v.11 tools were used for functional annotation. A number of genes harboring rSNPs within promotor regions were found to be particularly implicated in the mechanisms of metformin's action. Functional enrichment analyses revealed enrichment in critical pathways including FoxO, TNF-α and TGF-β signaling, also implicated in diabetes complications. Among these, six genes (ARPP19, ATF4, NR3C1, PFKFB3, TCF7L2, and WDR5) were strongly associated with regulation of gluconeogenesis, and may be modulated by metformin in the liver. We conclude that metformin therapy response may be influenced by the newly identified functional SNPs including rSNPs within the promotors of genes for gluconeogenic enzymes and transcription regulators.

Single-cell and haplotype-resolved transcriptomics reveal molecular signatures of orchid floral structures.

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis -regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis -regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

Structural variants (SVs) are a major source of genetic diversity, yet how they impact cell types in complex brain diseases remains largely unexplored, partially due to limitations of short-read sequencing. Here, we addressed this fundamental question in Parkinson's disease (PD). generating long-read whole-genome sequencing (WGS) data for 100 post-mortem brain samples from a PD cohort, constructing a high-confidence catalog of 74,552 SVs. To resolve their functional impact, we integrated single-nucleus RNA-sequencing data from two brain regions from the same samples and focused functional analyses on SVs proximal to genes previously nominated as cis-regulated, potential causal targets of PD-associated GWAS loci. Using expression quantitative trait locus and allele-specific expression analyses, we uncovered SVs significantly associated with expression in specific cell types as well as effects shared across cell types. This study demonstrates the power of uniting long-read WGS with transcriptomics to uncover SVs underlying complex disease architecture with cell type resolution.

Since DNA sequencing alone faces challenges in variant interpretation during genetic diagnosis, RNA sequencing has recently gained attention in resolving these diagnostic gaps. This study aimed to evaluate the advantages of liver tissue RNA sequencing in the diagnosis of genetic liver diseases. Liver tissue RNA sequencing was performed on 147 patients with prior DNA sequencing. We evaluated the role of RNA sequencing by analyzing aberrant gene expression, splicing, allele-specific expression, transcript-level similarity, and mosaic variants. Liver RNA-seq supported the molecular diagnoses in 56 patients diagnosed by DNA sequencing alone. Among 91 previously undiagnosed patients, incorporating RNA sequencing established a diagnosis in 17 (18.68%) patients. Among the 33 patients with indicative clinical phenotypes or prioritized variants, diagnosis was established in 15 (45.45%) patients with the help of RNA sequencing. This improvement was primarily (16/17) driven by the detection of aberrant splicing and allele-specific expression, instead of aberrant expression. RNA sequencing revealed ±50bp of cryptic splicing sites as hotspot regions, characterized allele-specific expression at both the gene and variant levels, and revealed shared transcriptomic features in low-GGT cholestasis. While DNA sequencing demonstrates superior sensitivity in detecting clinically relevant variants, liver RNA sequencing significantly enhances genetic diagnosis, mainly by revealing aberrant splicing and allele-specific expression. These findings suggest that RNA sequencing is an essential complement to DNA sequencing.

Single-cell allele-specific expression (ASE) provides valuable insights into gene regulatory mechanisms. However, its utility is limited by the lack of dedicated computational tools. We present DAESC + , a dual-module end-to-end software package for the processing and analysis of single-cell ASE. The preprocessing module, DAESC-P, is a user-friendly bioinformatics pipeline to obtain ASE counts from multiplexed scRNA-seq data. The analysis module, DAESC-GPU, is a scalable tool for differential ASE analysis powered by graphics processing units (GPUs). We demonstrated that DAESC-P is more accurate than the existing SALSA pipeline. DAESC-GPU is dozens of times faster than our previous method (DAESC) and scalable to over a million cells. Applying DAESC + to a subset of the OneK1K cohort, we identified 15 genes exhibiting differential regulatory patterns between naïve and central memory CD4 + T cells, and 2 genes between naïve and memory B cells.

Deserts are among the most extreme environments on Earth. High temperatures and a lack of water impose powerful selective pressures on desert species, offering an opportunity to investigate the genetic basis of local adaptation. Despite the unique challenges of desert living, house mice (Mus musculus domesticus), a species native to Western Europe, have recently colonized the Sonoran Desert in North America within the last 400-600 generations. House mice from the Sonoran Desert show phenotypic differences consistent with adaptation to water scarcity, including maintaining weight better under water stress than non-desert mice. To investigate the genetic basis of the physiological responses to water deprivation, we compared gene expression responses of desert house mice and an interfertile non-desert dwelling subspecies (M. m. musculus) and their F1 hybrids after 72 hours without water access. First, we show that desert and non-desert mice exhibit highly divergent transcriptional responses to water deprivation across three tissues (hypothalamus, liver, and kidney). Then, by surveying allele-specific expression in intersubspecific hybrids between desert and non-desert mice, we uncover cis-regulatory differences driving changes in the transcriptional response to dehydration (e.g., cis-by-environment interactions). These cis-regulatory changes were highly tissue-specific, consistent with modular regulatory changes shaping expression divergence. Intriguingly, we find that genes with cis-regulatory differences induced by water access were involved in the arachidonic acid pathway, a primary adaptation pathway across many desert species, and lipid metabolism. Finally, our results highlight several candidate genes of interest for understanding rapid adaptation to desert living. Together, our results identify context-dependent cis-regulatory evolution as a key contributor to variation in dehydration response and a potential mechanism facilitating rapid adaptation to extreme environments.