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PubMed2026

Comparative transcriptome analysis reveals ncRNA-mediated regulatory networks associated with muscle crispiness in grass carp.

Non-coding RNAs (ncRNAs) have been demonstrated to be involved in muscle development and to function as key regulators. However, the molecular mechanism underlying muscle crispiness in grass carp (GC) remains poorly understood, and whether these ncRNAs are involved in its regulation is still unknown. In the current investigation, differentially expressed (DE) RNAs (including lncRNAs, circRNAs, miRNAs, and mRNAs) were identified; concomitantly, target genes prediction was conducted, and functional and signaling pathway enrichment analyses were performed. Pathways related to muscle crispiness were identified, and the competitive endogenous RNA (ceRNA) (lncRNA/circRNA-miRNA-mRNA) regulatory network was further constructed. The results showed that a total of 126 DE-lncRNAs, 17 DE-circRNAs, 329 DE-miRNAs, and 442 DE-mRNAs were identified in muscle tissues of both the GC and crisp grass carp (CGC). GO and KEGG enrichment analyses revealed that target genes of DE-ncRNAs were significantly enriched in signaling pathways, including structural constituents of muscle, apoptosis, oxidative phosphorylation, and regulation of actin cytoskeleton, suggesting that these pathways may be involved in muscle texture remodeling. Subsequently, DE-RNAs enriched in related pathways were identified, and a core ceRNA regulation network comprising 3 lncRNAs, 4 circRNAs, 3 miRNAs, and 17 mRNAs was constructed. Additionally, 10 DE-RNAs from randomly selected groups were validated by qRT-PCR. Our findings not only provide scientific evidence elucidating the molecular mechanisms underlying muscle crispiness in GC but also establish a foundation for studying changes in muscle textural qualities across other fish species.

Animals↗

PubMed2026

miRNA-mediated control of TLR-NLR interplay in the uterus: A hidden corner of recurrent pregnancy loss.

Toll-like receptors (TLRs) and NOD-like receptors (NLRs) are crucial pattern recognition receptors that initiate inflammatory responses and immunological activation upon detecting pathogen- or damage-associated molecular patterns (PAMPs/DAMPS) in the female reproductive tract, thereby maintaining homeostasis and supporting pregnancy success. Their signaling pathways play a significant role in reproductive disorders by mediating the immune response to various pathogenic stimuli. Recurrent pregnancy loss (RPL), defined as the natural ending of two or more pregnancies before 24 weeks of gestation, approximately half of these patients remain idiopathic without precise prognostic, diagnostic, and therapeutic plans. Emerging data point that microRNAs are essential for immunological control in the female reproductive tract. MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to mRNA and preventing translation into protein. miRNAs play a role in many biological processes, including the development and differentiation of trophoblasts, the activation and implantation of embryos, immune tolerance, and the receptivity of the endometrium during implantation. Given their capacity to regulate up to 30 % of the human genome, miRNAs offer a promising avenue for understanding the immunopathogenesis of pregnancy complications. Recent research has detected differential expression of specific miRNAs in reproductive system pathologies. This review focuses on microRNAs and their association with idiopathic recurrent miscarriage, a condition characterized by considerable heterogeneity. Future studies identifying the precise mechanisms linking miRNA-mediated immune dysregulation in RPL immunopathogenesis could open the way for novel personalized therapeutic and diagnostic strategies.

Female↗

PubMed2026

The identification of growth-promoting lncRNAs in oral cavity squamous cell carcinoma.

Oral Cavity Squamous Cell Carcinoma (OCSCC) is an aggressive tumor that develops within the mouth of patients. Tumor-suppressor gene loss and genomic arrangements fuel tumorigenesis and transcriptional reprogramming. Understanding how these alterations contribute to OCSCC growth and cell survival may identify new therapeutic vulnerabilities or biomarkers. We profiled the role of long non-coding RNAs (lncRNAs) in the growth of three OCSCC cell lines using a CRISPRi-screen and identified 19 lncRNAs that contribute to OCSCC proliferation. By comparing these lncRNAs to other screens, we find that these lncRNAs are uniquely required in OCSCC and not other malignancies. We show that these lncRNAs are abundantly expressed in OCSCC cells and tumors. Independent testing of candidate lncRNAs confirms their role in supporting OCSCC growth. Our results show that a novel subset of lncRNAs are required for the growth of OCSCC cancer cells and that these lncRNAs are cell lineage specific.

CRISPRi↗

PubMed2026

High-Resolution Chromosome-Level Genome Assembly and Annotation of Triplophysa stewarti, an Endemic Plateau Loach from the Qinghai-Tibet Plateau.

The bottom-dwelling fish Triplophysa stewarti, endemic to the Qinghai-Tibet Plateau, is a valuable model for studying high-altitude adaptation in aquatic ecosystems. However, the lack of a high-quality reference genome has hindered comparative genomic and evolutionary studies within this genus. Here, we present a chromosome-level genome assembly for T. stewarti, generated using PacBio HiFi long-read sequencing and Hi-C scaffolding. The 697.9 Mb assembly is highly continuous (scaffold N50 of 253.58 Mb) and encompasses 25 chromosomes, representing 92.65% of the genome. BUSCO analysis indicated a 98.4% completeness, supporting the high quality of the assembly. We annotated 28,009 protein-coding genes, with 97.04% being functionally assigned across multiple databases (NR, UniProt, KEGG, GO, Pfam and InterPro). Additionally, repetitive elements constituted 42.47% of the genome, and we identified 52,709 non-coding RNAs. This high-quality reference genome provides a fundamental resource for exploring the adaptive evolution, population structure, and conservation genetics of T. stewarti and related species on the Qinghai-Tibet Plateau.

Animals↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗

PubMed2025

MicroRNA-122 overexpression suppresses the colon cancer cell proliferation by downregulating the astrocyte elevated gene-1/metadherin oncoprotein.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that regulate essential cellular functions, such as cell adhesion, proliferation, migration, invasion, and programmed cell death, and therefore, alterations in miRNAs can contribute to carcinogenesis. Previous studies have shown that miRNA-122 is abundant in the liver and regulates cell proliferation, migration, and apoptosis. However, the expression pattern and mechanism of actions of miR-122 remain primarily unknown in colon cancer. METHODS: In this study, we analyzed The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database to assess the clinical significance of astrocyte elevated gene-1 (AEG-1)/metadherin (MTDH) and miR-122 in colon cancer. MiR-122 overexpression studies were performed in HCT116, SW480, and SW620 cell lines. Dual-luciferase assay was carried out to confirm the interaction between AEG-1 and miR-122. In vivo-JetPEI-transfection reagent was used for in-vivo transient transfection of miR-122 in the AOM/DSS-induced colon tumor mouse model. RESULTS: Our results demonstrate that miR-122 was downregulated in colon cancer cells, and it influences the expressions of apoptotic factors and inflammatory cytokines. MiR-122 overexpression in HCT116, SW480, and SW620 cells showed upregulation of Caspase 3, Caspase 9, and BAX and decreased expression of BCL2, which are pro-apoptotic and anti-apoptotic members that maintain a ratio between cellular survival and cell death. In vivo transient transfection of miR-122 mimic in AOM/DSS induced colon tumor mouse model showed less inflammation and disease activity. The TCGA-COAD data indicated that AEG-1 expression was higher in patients with low expression of miR-122 and lower AEG-1 expression in patients with higher expression miR-122. CONCLUSION: Our findings highlight the key role of miR-122 in the high grade of colonic inflammation, and possibly in colon cancer, and the use of miR-122 mimic might be a therapeutic option.

MicroRNAs↗

PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗

PubMed2026

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

RNA, Long Noncoding↗

PubMed2026

Micropeptides encoded by lncRNAs associated with cancer progression reveal novel immunogenic epitopes.

MOTIVATION: Long non-coding RNAs (lncRNAs) regulate gene expression, chromatin organization, and cellular signaling. Recent studies indicate that ∼20% of the ∼36 000 human lncRNA genes harbor small open reading frames (sORFs) capable of producing micropeptides (MPs), whose functions remain largely unknown. Whether these peptides contribute to the cancer immunopeptidome is largely unexplored. RESULTS: We systematically analyzed lncRNAs with strong experimental and computational evidence of MP-encoding potential (∼13% of the initial MP collection). Using The Cancer Genome Atlas (TCGA), we identified 2606 high-confidence lncRNA-derived MPs encoded by 647 genes across 16 cancer types. We then focused on 501 MPs from 124 lncRNA genes whose expression changes significantly across tumor stages and metastatic transitions, representing cancer transitional lncRNAs (Tr-lncRNAs). Dipeptide composition and conservation analyses showed that these MPs differ from a size-matched human coding proteome, supporting their potential as neoantigens. All possible 9-mer peptides were evaluated for predicted binding to prevalent European HLA class I alleles. Approximately 60% of Tr-lncRNA genes and 184 (37%) of derived peptides exhibited strong predicted HLA binding. Peptides from XIST, PCAT7, PVT1, HAND2-AS1 showed broad HLA coverage. Notably, TTN-AS1, encoded an MP (79 aa) generated 33 predicted distinct epitopes spanning all 27 HLA alleles. Our analysis identifies lncRNA-derived MPs as a previously underexplored source of potential cancer neoantigens, highlighting their promise as biomarkers and targets for immunotherapy. AVAILABILITY: Data, code and supplementary materials are available in https://doi.org/10.5281/zenodo.20167452 and GitHub: https://github.com/stavzok1/lncrna_peptide_analysis.

Humans↗

PubMed2026

Making multi-axis Gaussian graphical models scalable to millions of cells.

MOTIVATION: Networks underlie the generation and interpretation of many biological datasets: gene networks shed light on the regulatory structure of the genome, and cell networks can capture structure of the tumor micro-environment. However, most methods that learn such networks make the faulty "independence assumption"; to learn the gene network, they assume that no cell network exists. "Multi-axis" methods, which do not make this assumption, fail to scale beyond a few thousand cells or genes. This limits their applicability to only the smallest datasets. RESULTS: We develop a multi-axis method, which learns conditional dependency networks, capable of processing million-cell datasets within minutes. This was previously impossible, and unlocks the use of such methods on modern scRNA-seq datasets, as well as more complex datasets. We apply the method to a new scRNA-seq dataset for neuronal cell development, and compare the result to an existing state of the art method, hdWGCNA. We demonstrate that the new method yields gene networks that have a more focused biological interpretation and that the simultaneously learned cell network has advantages over a conventional kNN-based clustering. Further, our method yields novel biological insights by identifying long non-coding RNAs that potentially have a role in neuronal development. AVAILABILITY AND IMPLEMENTATION: Our methodology is available as a Python package GmGM on PyPI (https://pypi.org/project/GmGM/0.5.3/). The code for all experiments performed in this article is available on GitHub (https://github.com/BaileyAndrew/GmGM-Bioinformatics) and Zenodo (10.5281/zenodo.20384566).

Gene Regulatory Networks↗

PubMed2026

Digital Kennison: A bioinformatics pipeline for rapid mapping of sequences to the Drosophila melanogaster Y chromosome.

The Drosophila melanogaster Y chromosome is currently known to contain 13 single-copy protein-coding genes, six of which are essential for male fertility, as well as several non-coding genes and abundant repetitive DNA. Localization of Y-linked sequences has traditionally relied on labor-intensive crosses using Kennison's translocation strains, which map Y-linked loci by generating flies deficient for each of the six Y-chromosome fertility regions (ks-1, ks-2, kl-1, kl-2, kl-3, and kl-5). Here we present Digital Kennison, a computational pipeline that recasts this classical mapping strategy as a sequence-based analysis. The pipeline queries eight genomic databases derived from Kennison's strains using BLAST and read coverage, assigning sequences to fertility regions or the centromeric region with a calibrated confidence score. We benchmarked the method on 60 Y-linked sequences spanning all seven regions, including single-copy protein-coding genes, Mst77Y family members, non-coding RNAs, and the centromere. Digital Kennison achieved 97% precision while resolving challenging cases, including boundary-spanning genes (PRY and Ppr-Y), fragmented Mst77Y copies, and FDY, which has a closely related autosomal paralog. Beyond validating known localizations, the pipeline localized the unmapped gene CG41561 to the kl-1region and reassigned the transcript CR40629-RC from the kl-2 region to kl-5. It also localized 7 of 16 recently transferred Y-linked sequences, including 4 with high confidence. Applied to 904 R6 scaffolds, Digital Kennison assigned 75% to fertility regions, including five currently annotated as autosomal-pericentromeric. Digital Kennison reduces sequence localization from weeks of genetic crosses to minutes of computation while preserving the power of classical translocation mapping.

Drosophila melanogaster↗

PubMed2025

Constructing epigenetic regulatory landscapes of plant lncRNAs-an exploration utilizing the novel specialized platform PERlncDB.

Long non-coding RNAs (lncRNAs), once overlooked as transcriptional byproducts, are now recognized for their crucial roles in plant growth, development, and stress responses, with increasing focus on their epigenetic regulation. However, studies investigating epigenomic signals to explore the functions of lncRNAs in plants remain relatively limited. This study collected a comprehensive dataset of over 160 000 high-quality lncRNAs from 19 representative plant species and integrated 6715 ChIP-seq, BS-seq, and RNA-seq datasets to analyze epigenomic patterns at lncRNA loci. Results showed elevated DNA methylation in lncRNA regions. The highest levels occurred in transposable element-associated lncRNAs. Additionally, activating histone modifications at lncRNA loci showed tissue specificity, with epigenetic preferences differed from those at protein-coding gene (PCG) loci. Differential site analysis in epigenetic mutants further highlighted the selective regulation of lncRNA loci by specific epigenetic factors. To facilitate research, we developed PERlncDB, a platform that provides species-specific lncRNA browsing, epigenetic annotation, cross-species conservation analysis, and visualization of epigenomic landscapes. Case studies on MARS and LINC-AP2 emphasized the platform's utility. Conserved epigenetic mechanisms regulating lncRNAs across species, exemplified by a syntenic conserved MET1-regulated lncRNA pair in Arabidopsis and tomato, suggested the stability of regulatory mechanisms underlying lncRNA functions. This work provides critical insights and resources for understanding plant lncRNA epigenetic regulation.

RNA, Long Noncoding↗

PubMed2026

Comparative transcriptome analysis of Qinchuan and Wagyu cattle reveals lnc11599 as a negative regulator of intramuscular fat deposition.

BACKGROUND: Intramuscular fat (IMF) content is a critical factor determining beef quality, influenced by various factors including breed and age. However, the regulatory role of long non-coding RNAs (lncRNAs) in IMF deposition remains unclear. METHODS: This study investigated IMF deposition in the longissimus dorsi muscle of one- and two-year-old Qinchuan and Wagyu cattle through histological examination and fat content measurement. Based on transcriptome sequencing data of intramuscular fat tissue, differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify lncRNAs associated with IMF deposition. The effects of a key candidate lncRNA on the adipogenic differentiation of cattle intramuscular preadipocytes were further examined. RESULTS: Results showed that Wagyu cattle exhibited stronger IMF deposition capacity than Qinchuan cattle across all age groups, with IMF content increasing with age in both breeds. We identified 7,910 lncRNAs from intramuscular fat tissue transcriptome data, including 6,455 novel lncRNAs. Through integrated differential expression analysis and WGCNA, 88 lncRNAs closely associated with IMF deposition were screened from two-year-old Qinchuan and Wagyu cattle. Notably, lnc11599 was significantly upregulated in Qinchuan cattle intramuscular fat tissue, but its expression decreased during intramuscular preadipocyte differentiation. Functional experiments demonstrated that lnc11599 knockdown enhanced adipogenic differentiation capacity, manifested as a highly significant increase in lipid accumulation, upregulation of key adipogenic genes at the mRNA level, together with increases in total fatty acid content and unsaturated fatty acid proportion. CONCLUSIONS: This study established the lncRNA expression profiles in intramuscular fat tissue of Qinchuan and Wagyu cattle across different developmental stages, and demonstrated that lnc11599 acts as a negative regulator of intramuscular fat deposition. These findings provide new directions for elucidating the mechanisms of cattle IMF deposition and offer potential targets for genetic improvement of beef quality.

Animals↗

PubMed2026

Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint.

BACKGROUND: The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. METHODS: Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. RESULTS: In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. CONCLUSIONS: Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. TRIAL REGISTRATION: COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570 .

Female↗

PubMed2026

Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.

Fusion transcripts are hybrid RNA molecules generated through genomic rearrangements or RNA-level fusion mechanisms. They represent important molecular features of many cancers and can function as oncogenic drivers, diagnostic biomarkers, prognostic indicators, and therapeutic targets. Since the discovery of the BCR::ABL1 fusion in chronic myeloid leukemia, numerous cancer-associated fusion transcripts have been identified across hematologic malignancies and solid tumors. These fusion events encompass diverse biological mechanisms, including constitutively active kinases, aberrant transcription factors, epigenetic regulators, and non-coding fusion RNAs. This review summarizes current knowledge of the mechanisms underlying fusion transcript formation, including genomic rearrangement-dependent and rearrangement-independent processes, as well as fusion circular RNAs. The functional roles of fusion transcripts in cancer biology and their clinical relevance as diagnostic, prognostic, and predictive biomarkers are discussed. In addition, recent advances in fusion transcript detection and characterization are reviewed, including next-generation sequencing, long-read sequencing, single-cell approaches, artificial intelligence-assisted computational methods, and CRISPR/Cas9-mediated strategies for functional modeling and functional validation of fusion transcripts. Despite the rapid expansion of fusion transcript catalogs, the biological and clinical significance of most identified fusion events remains incompletely understood. Future progress will depend on integrating advanced sequencing technologies, artificial intelligence-assisted computational prioritization, and systematic functional validation to distinguish clinically actionable fusion transcripts from biologically neutral events. Such multidisciplinary approaches will be essential for translating fusion transcript research into precision oncology and improving cancer diagnosis, patient stratification, and targeted therapy.

Humans↗

PubMed2026

Deletion of the MALAT1 RNA 3' end promotes transcript decay and inhibits proliferation in gastric and breast cancer cells.

The long non-coding RNA MALAT1 is a conserved oncogenic driver whose function relies on a 3' triple-helix motif. While its biochemistry is well-characterized in vitro, the endogenous requirement for this motif in regulating the stability of the transcript and other genes residing in its locus remains unclear. In this study, we employed a dual-sgRNA CRISPR-Cas9 approach to systematically excise triple-helix-forming sequences from the native MALAT1 locus in gastric (AGS) and breast (MCF7) cancer cells. Our findings demonstrate that the 3' end strongly contributes to MALAT1 stability. Perturbations ranging from genomic deletions to a single-base changes trigger transcript collapse and rapid exonucleolytic decay, while the biogenesis of the small RNA mascRNA (a byproduct of MALAT1, also involved in cancer) remains decoupled and unaffected. In cellulo, DMS probing reveals that edited transcripts retain structural complexity in the 3' region. Phenotypically, structural disruption of the 3' end significantly impairs proliferation of both cancer cellular models. These results identify the 3' triple-helix as a determinant of MALAT1 stability and provide endogenous validation for its role in the analyzed AGS and MCF7 cells.

Cancer↗

PubMed2026

The dark genome in cardiovascular medicine.

Only ∼1%-2% of the human genome directly codes for proteins. The remainder consists of non-coding DNA, often referred to as the 'dark genome'. This includes regulatory elements, transposable and repetitive sequences, structural genomic features, pseudogenes, intronic and intergenic regions, and non-coding RNA (ncRNA) genes. These components are increasingly recognized as major regulators of gene expression, cell identity, and disease susceptibility. Currently, dark genome elements, particularly ncRNAs are increasingly recognized as important regulators of cardiovascular health and disease. Advances in genome analysis technologies have greatly improved our understanding of these non-coding regions and revealed clearer connections between the dark genome and cardiovascular traits. This review highlights major parts of the dark genome involved in cardiovascular disease, with emphasis on those for which mechanistic understanding and translational relevance are beginning to emerge. As mechanistic insight into individual and collective components of the dark genome advances, it increasingly enables the development of new opportunities for targeted therapeutics for cardiovascular prevention and disease management.

Humans↗

PubMed2026

A modular class-aware workflow for small RNA sequencing analysis using mouse sperm as a case study.

BACKGROUND: Small RNA sequencing analysis is challenging because RNA classes differ in biogenesis, sequence redundancy, genomic organization, and annotation reliability. Integrated workflows accommodating these constraints remain limited, particularly for fragment-level and cluster-level analysis. METHODS: We present a reproducible, containerized, class-aware workflow for small RNA sequencing analysis, using mouse sperm as a case study. The workflow combines standardized preprocessing with complementary annotation and quantification strategies for microRNAs (miRNAs), transfer RNA-derived small RNAs (tsRNAs), ribosomal RNA-derived small RNAs (rsRNAs), and PIWI-interacting RNA (piRNA)-enriched genomic clusters. Using sperm small RNA data from offspring of lipopolysaccharide (LPS)-exposed male mice, we compared integrated-reference mapping, multi-class annotation, fragment-level tsRNA profiling, and genome-based piRNA cluster analysis, with custom modules for locus-aware harmonization and condition-specific cluster analysis. RESULTS: Integrated-reference mapping aligned 88.17% of reads and retained 690 features after filtering. It identified 11 differentially expressed miRNAs between LPS and controls, while other classes showed limited signal. Fragment-level profiling improved tsRNA resolution. piRNA cluster analysis identified 958 control and 940 LPS clusters, with 18 control-specific and no LPS-specific clusters. CONCLUSION: This workflow supports transparent, reproducible, class-aware interpretation of small RNA sequencing data while emphasizing cautious interpretation of piRNA-enriched signals from total small RNA sequencing.

Small non-coding RNA analysis↗