Genome Biology最新文献

Plasmids play a pivotal role in the emergence of multidrug-resistant and pathogenic bacteria, posing significant clinical challenges. However, the rapidly growing number of unannotated plasmids necessitates comprehensive characterization of their diverse properties. Here, we present PlasRAG, a tool that integrates multi-faceted property characterization of query plasmids and plasmid DNA retrieval based on textual queries. PlasRAG employs a bidirectional multi-modal information retrieval model that aligns DNA sequences with textual data, effectively overcoming the limitations of traditional approaches. Rigorous experiments demonstrate that PlasRAG delivers robust performance and enhanced analytical capabilities, underscoring the effectiveness of its architectural design.

Background: The inference of population structure in domestication studies is prone to biases whenever sampling is unbalanced and effective population sizes (N_e) differ across populations. Such biases can lead to the misclassification of large ancestral populations as admixed, particularly under single-origin domestication scenarios.

Results: We propose a novel parameterization strategy for the STRUCTURE software, combining the F model and alternative ancestry prior (along with a smaller initial ALPHA value), and simulations demonstrate that the strategy mitigates unbalanced sampling and unequal population size biases. We apply our strategy to the domestication history of the common walnut (Juglans regia), using whole-genome resequencing data from 298 individuals from across its range. The results support an origin of J. regia in South Asia, where walnut populations are characterized by high genetic diversity, extensive private allele content, low mutation load, and demographic stability. Building on this demographic framework, we further identify genomic regions under recent positive selection and candidate domestication genes involved in shell structure, pollen development, and lipid transport.

Conclusions: Our results clarify the long-standing debate on the geographic origin of walnut domestication and demonstrate that an optimized, model-aware use of STRUCTURE can substantially improve population-genetic inference in domestication studies and other systems characterized by complex demography.

Background: DNA sequence deep learning models can accurately predict epigenetic and transcriptional profiles, enabling analysis of gene regulation and genetic variant effects. While large-scale models like Enformer and Borzoi are trained on abundant data, they cannot cover all cell states and assays, necessitating training new model to analyze gene regulation in novel contexts. However, training models from scratch for new datasets is computationally expensive.

Results: In this study, we systematically develop and evaluate a transfer learning framework based on parameter-efficient fine-tuning for supervised regulatory sequence models. Using the state-of-the-art model Borzoi, our framework enables accurate model transfer while significantly reducing runtime and memory requirements. Across bulk and single cell RNA-seq datasets, the transferred models effectively predict held-out gene expression changes, identify regulatory drivers in perturbation conditions, and predict cell-type-specific variant effects. We further demonstrate that transferring Borzoi to relevant cell types facilitates mechanistic interpretation of fine-mapped GWAS variants.

Conclusions: Our framework offers a scalable and practical solution for extending large sequence models to novel biological contexts, enabling mechanistic insight into gene regulation and variant effects.

Imaging-based spatial transcriptomics enables high-resolution spatial mapping of RNA species. A key challenge in imaging-based spatial transcriptomics is accurate cell segmentation to assign each RNA molecule to the right cell. Here, we present RNA2seg, a novel segmentation algorithm trained on over 4 million cells from MERFISH and CosMx datasets across seven organs using a teacher-student training scheme. RNA2seg integrates RNA point clouds and all available membrane and nuclear stainings. Validation on manually annotated data shows superior performance including in zero-shot and few-shot settings.

Background: Neutrophil differentiation is a well-orchestrated process that involves coordinated changes in the chromatin accessibility, transcription factor (TF) binding, 3D genome-structure and transcription. However, despite significant advances in understanding the later stages of neutrophil maturation, the initial molecular events that trigger and drive the commitment to neutrophil lineage remain poorly characterized, especially the functional roles of master TFs in orchestrating the earliest stages of lineage specification.

Results: Here, we examine changes in the genome topology, transcriptome, and chromatin accessibility during the neutrophil differentiation process. We demonstrate striking changes in 3D genome structure and chromatin accessibility as early as 4 h after all-trans-retinoic acid treatment, which accommodate and regulate gene expression to guide neutrophil lineage differentiation. Analysis of early transcriptional changes confirmed CEBPA as a key TF. To further elucidate the relationships among CEBPA binding, chromatin accessibility, 3D genomic organization, and gene expression, we perform CEBPA HiCut, a technology that simultaneously profiles TF- DNA binding sites and TF-mediated 3D genome interactions. Our first TF-mediated HiCut experiment in neutrophils revealed the synergistic relationship and sequential regulation cascade between this core TF and chromatin accessibility, 3D genomic organization, and gene expression.

Conclusions: Our work systematically investigates coordinated chromatin accessibility, TF binding, 3D genome-structure and transcriptional changes during neutrophil differentiation, especially at initialization. Our study highlights the sequential interplay between the initial changes of chromatin state, 3D genome organization and pioneer TF s such as CEBPA.

Background: Metagenomics combined with High-throughput Chromosome Conformation Capture (Hi-C) provides a powerful approach to study microbial communities by linking genomic content with spatial interactions. Hi-C complements shotgun sequencing by revealing taxonomic composition, functional interactions, and genomic organization within a single sample. However, aligning Hi-C reads to metagenomic contigs is challenging due to variable insert sizes of Hi-C paired-end reads, multi-species complexity, and gaps in assemblies. Although several benchmark studies have evaluated general alignment tools and Hi-C data alignment, none have specifically focused on metagenomic Hi-C data.

Results: We evaluated seven alignment strategies commonly used in Hi-C analyses: BWA MEM -5SP, BWA MEM default, BWA aln default, Bowtie2 default, Bowtie2 -very-sensitive-local, Minimap2 default, and Chromap Hi-C default. We benchmarked these tools on one synthetic dataset and seven real-world environments. Performance was assessed based on the number of inter-contig Hi-C read pairs and their impact on downstream tasks, such as binning quality.

Conclusions: We show that BWA MEM -5SPgenerally outperformed all other tools across most environments in terms of inter-contig read pairs and binning quality, followed by BWA MEM default. Chromap and Minimap2, while less effective in these metrics, demonstrated the highest computational efficiency.

Phase separation is an important process in biology associated with formation of membraneless organelles but possibly related to the emergence of solid inclusions. TDP-43 is a largely studied paradigmatic case, as it forms neuronal cytoplasmic inclusions in neurodegenerative diseases and is an essential component of many membraneless organelles. Here, we review the physicochemical fundamentals of liquid-liquid phase separation (LLPS) of TDP-43 and its fragments in vitro, showing that full-length TDP-43 requires RNA or chaperones to form stable liquid droplets. We describe TDP-43-containing membraneless organelles and the debate on whether these assemblies represent reservoirs for pathological solid inclusion formation.

Background: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize.

Results: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies.

Conclusion: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.

Background: Genomic alterations are a hallmark of cancer, and extrachromosomal DNA (ecDNA) has emerged as a key source of oncogene selection, tumor growth, and drug resistance. The intratumor heterogeneity and clonal selection of ecDNA is, however, poorly understood.

Results: In this study, we pursue a computational approach that leverages allelic imbalance and outlier expression from standard single-cell RNA sequencing (scRNA-seq) to deconvolve the tumor heterogeneity of ecDNA at the single-cell level (ecSingle). Using this approach, we identify oncogene-carrying ecDNAs in tumor samples at the single-cell level, which we validate using genome sequencing. Moreover, we show the superiority of using single-molecule long-read sequencing in resolving ecDNA. ecDNAs displayed extensive intratumor heterogeneity, including subclonal oncogene-carrying ecDNA in primary tumor cells that segregate with distinct transcriptional cell states. Importantly, we show that a rare ecDNA+ clone in the primary tumor can expand to form dominant clones in relapse tumors.

Conclusions: Our study introduces a novel approach to studying ecDNA at the single-cell level, enabling both clonal evolution and transcription cell state analysis. We apply this approach to cancer samples to gain deeper insights into the role of ecDNA in intratumor heterogeneity and cellular plasticity.

Background: In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance.

Results: Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin.

Conclusions: Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.