Zhiping Weng

Longitudinal multimodal cohort studies require data models that can integrate heterogeneous molecular, phenotypic, and contextual data while preserving relationships across participants, biospecimens, study visits, and computational transformations. Here, we present the data model developed for the Multi-Omics for Health and Disease (MOHD) Consortium, a modality-agnostic, provenance-driven framework designed to support an evolving longitudinal biomedical resource. The model uses a compact set of linked entities and a central dataset abstraction to represent experimental, phenotypic and contextual, and computational data within a common framework. Stable identifiers, explicit provenance relationships, and flexible grouping structures enable data to be tracked across biological sources, processing workflows, and releases without requiring modality-specific restructuring of the core model. To address the complexity of participant context, we developed a harmonization framework that transforms branched REDCap survey data into a longitudinal, machine-readable representation with normalized variables and explicit response status. Interoperability with external data models and ontologies is supported through reproducible translation workflows while maintaining a stable canonical representation. We demonstrate the model through its implementation in the first production-scale MOHD data release, encompassing 956 participants, nearly 7,000 datasets, and more than 40,000 files across multiple molecular modalities and study visits. This implementation demonstrates the ability of the model to accommodate heterogeneous and longitudinal data, evolving computational products, participant withdrawal, and distinct data-access environments. Together, the MOHD implementation provides a practical framework and generalizable design principles for building longitudinal multimodal biomedical resources that remain traceable, interoperable, and adaptable as data and analytical requirements evolve.

Small nuclear RNAs (snRNAs) are essential components of the spliceosome and are encoded by large, multicopy gene families. However, their genome-wide identification and quantification have remained challenging due to high sequence similarity among family members. To address this, we utilized RAMPAGE (Rapid Amplification of cDNA Ends) data from the ENCODE project to comprehensively profile nascent transcription of spliceosomal snRNAs across 115 human biosamples. We identified 74 expressed snRNA variants, characterized by canonical promoter features including bidirectional transcription flanking a positioned nucleosome, active histone modifications, and evolutionary conservation- features largely absent from unexpressed variants. These transcriptional events were corroborated by total RNA-seq and Bru-seq data, yet the majority of these variants showed extremely low levels in small RNA-seq, indicating post-transcriptional bottlenecks for snRNA processing and maturation. Our findings reveal new layers of regulation in snRNA variant expression and suggest that selective post-transcriptional processing plays a critical role in shaping the functional snRNA repertoire and its contribution to splicing regulation.

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease ^1–6 . Here we create and evaluate a resource of more than 92 million enhancer–gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional contacts and large-scale genetic perturbations generated by the ENCODE Consortium ⁷ . We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,356 element–gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer–gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer–promoter contacts, additional features that guide enhancer–promoter communication include promoter class and enhancer–enhancer synergy. These genome-wide maps of enhancer–gene regulatory interactions, benchmarking software, predictive models and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

RNA-binding proteins (RBPs) regulate their RNA targets by binding to short sequence motifs, but the underlying mechanisms enabling sequence-specific recognition within the vast transcriptome remain unclear for the majority of human RBPs. Sequence contexts are believed to be a significant contributing factor to RBP binding specificity but are often overlooked. Further, existing motif discovery algorithms do not consider the structure and composition of the motif’s flanking regions in their construction, which represents a consequential shortcoming. Herein, we present a novel linguistics-inspired RBP motif and context discovery algorithm that is consensus-based, deterministic, and flexible. Our algorithm draws multiple parallels between natural language and genomic language and relies on three important k-mer properties that impart lexical, syntactic, and semantic structures and rules to the process of motif and context discovery. Critically, our algorithm integrates information from sequence contexts when constructing RBP motifs. We demonstrate that our algorithm achieves strong discovery accuracy against a ground-truth set, and even outperforms existing methods in primary motif ranking.

Transposable elements (TEs) in the human genome are the heritage of ancient parasitic infections. While most of human DNA comprises TEs and TE-derived elements, their repetitive nature poses technical challenges; thus, little is known about their positional identity and regulatory roles. Here, by integrating long-read and multidimensional transcriptional analyses, we investigate when, where and how TEs become part of a gene. We characterize how TE-derived isoforms change across mouse–human variation and how they are linked to gene regulatory networks controlling cell states during differentiation, organogenesis and health (aging and pathological states). Mechanistically, we identify an RNA degradation-dependent and splicing-dependent quality control mechanism that operates independently of conventional mechanisms of TE suppression, such as DNA methylation and heterochromatinization, and prevents TE-chimera expression and TE-induced cell differentiation. Overall, our findings unveil mechanisms by which viral-derived elements enhance transcriptome plasticity.

RNA-binding proteins (RBPs) are essential modulators in the regulation of mRNA processing. The binding patterns, interactions, and functions of most RBPs are not well-characterized. Previous studies have shown that motif context is an important contributor to RBP binding specificity, but its precise role remains unclear. Despite recent computational advances to predict RBP binding, existing methods are challenging to interpret and largely lack a categorical focus on RBP motif contexts and RBP–RBP interactions. There remains a need for interpretable predictive models to disambiguate the contextual determinants of RBP binding specificity in vivo. Here, we present a novel and comprehensive pipeline to address these knowledge gaps. We devise a natural language processing–based method to deconstruct sequences into entities comprising a target k -mer and its flanking regions, then use this representation to formulate RBP binding prediction as a weakly supervised multiple instance learning problem. To interpret our predictions, we introduce a deterministic motif discovery algorithm to leverage our data structure, recapitulating the established motifs of numerous RBPs as validation. Importantly, we characterize the binding motifs and binding contexts for 71 RBPs in HepG2 and 74 RBPs in K562, with many of them being novel. Finally, through feature integration, transitive inference, and a new cross-prediction approach, we propose novel cooperative and competitive RBP–RBP interaction partners and hypothesize their potential regulatory functions. In summary, we present a complete framework for investigating the contextual determinants of specific RBP binding, and we demonstrate the significance of our findings in delineating RBP binding patterns, interactions, and functions.

RNA-binding proteins (RBPs) are critical regulators of the human transcriptome, but the binding patterns of most RBPs are insufficiently characterized. While sequence context facilitates RBP binding specificity, its precise contribution remains unclear. Existing computational methods to decipher RBP binding patterns are limited by their architecture-dependence, challenging interpretability, and, importantly, lack of focus on context. We present a novel comprehensive approach to address the aforementioned knowledge gaps. We first introduce a natural language-based representation to model RNA sequences using lexical, syntactic, and semantic forms, then devise a sequence decomposition method based on these structures to deconstruct RNA sequences into regions, each containing a target k -mer and its flanking contexts. We leverage this linguistic conceptualization to predict RBP binding under a Multiple Instance Learning (MIL) framework, which we solve using a novel method of significant region extraction termed “iterative relabeling”. We demonstrate that our bottom-up approach discovers key regions contributing to RBP binding in an architecture-dependent, accurate, and interpretable manner.

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression ¹ . Previously, the ENCODE consortium mapped biochemical signals across hundreds of cell types and tissues and integrated these data to develop a registry containing 0.9 million human and 300,000 mouse candidate cis -regulatory elements (cCREs) annotated with potential functions ² . Here we have expanded the registry to include 2.37 million human and 967,000 mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays such as STARR-seq ³ , massively parallel reporter assay ⁴ , CRISPR perturbation ^5,6 and transgenic mouse assays ⁷ have profiled more than 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer and silencer roles in different cellular contexts. Integrating the registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by the identification of KLF1 as a novel causal gene for red blood cell traits. This expanded registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

The Functional Annotation of Variants Online Resource (FAVOR), http://favor.genohub.org, is a whole genome variant annotation database and portal that provides comprehensive variant functional annotations of all possible variants across the genome. It can facilitate the analysis of whole-genome sequencing studies, support the interpretation of variant functional impacts, and help prioritize causal variants of diseases or traits. To support the growing popularity and expand the scope of FAVOR, we present here a substantial platform update. The new release features dramatically expanded annotations, a completely redesigned infrastructure powered by a newly implemented application programming interface (FAVOR-API), and a revamped web interface with advanced data-visualization capabilities and enhanced query performance. Key expansions include much more comprehensive variant annotations, including global, tissue- and cell-type–specific variant annotations; gene and protein annotations; support for both hg38 and hg19 reference genomes; and an interactive genome-browser for visualization of multi-faceted variant annotations. The updated platform also includes FAVOR-GPT, a large language model-powered interface for navigating the FAVOR database and interpreting results. FAVOR continues to evolve to keep pace with advances in research on interpreting the functional and phenotypic impact of genomic variation.