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Umu, S. U.

Publications and source records attributed to Umu, S. U..

8 recordsLinked to original sources

MirMachine 2: a scalable, evolutionarily informed pipeline for microRNA annotation and comparative genomics across thousands of animal genomes

Genome sequencing is rapidly outpacing the annotation of conserved regulatory elements, limiting the evolutionary and comparative insights that can be extracted from expanding genome collections. MicroRNAs are among the most conserved and phylogenetically informative genes, yet automated annotation has remained difficult to scale while preserving evolutionary interpretability. Here we present MirMachine 2, an evolutionarily informed framework that combines curated reference models, lineage-aware scoring, and adaptive filtering to enable robust genome-wide microRNA annotation at scale. Applying this to thousands of animal genomes reveals that many apparent absences of conserved microRNAs reflect methodological bias rather than biological loss, particularly in underrepresented lineages. By enabling consistent and interpretable comparison of microRNA complements across large datasets, MirMachine 2 establishes scalable microRNA annotation as a practical foundation for genome-scale evolutionary and comparative genomics.

bioinformatics↗

Easydecon: A Lightweight Workflow for Cell Type Analysis in High-Definition Spatial Transcriptomics Assays

The emergence of high-resolution spatial transcriptomics platforms, such as VisiumHD, has enabled transcriptome-wide spatial profiling at near-single-cell resolution. However, existing analysis tools often lack scalability or compatibility with this new resolution, limiting their utility for multimodal cell type analysis. We present Easydecon, a lightweight and modular computational framework for spatial transcriptomics analysis using marker genes from single-cell RNA sequencing datasets. Easydecon uses a two-phase strategy, which firstly detects expression hotspots and then refines cell type assignments with similarity-based methods. We demonstrate its efficacy by resolving cell type subsets with high accuracy. Easydecon supports integration with segmentation tools and outperforms established methods in speed, usability and cell type recovery.

bioinformatics↗

Single cell spatial transcriptomics identifies coordinated cellular programs that are associated with good prognosis in microsatellite stable colorectal cancer

It has been reported that microsatellite instable (MSI) tumors in colorectal cancer (CRC) exhibit stronger anti-tumor responses than microsatellite stable (MSS) tumors. Analyzing scRNA-seq data from 185 CRC patients we found that immune, structural, and cancer cells in MSS tumors with high numbers of tumor-infiltrating CD8 T cells and macrophages (TAMs; CD8hiTAMhi) and CD8lowTAMhi tumors were enriched for pathways associated with anti- and pro-tumor responses, respectively. In CD8hiTAMhi tumors, TAMs expressed an IFN-induced phenotype (e.g. GBP1, CXCL9, IFITM3) and high infiltration of GBP1+ TAMs was associated with better overall survival (n=941). High-resolution spatial transcriptomics (Visium HD) revealed that GBP1+ TAMs clustered with CXCL13+IFNG+PDCD1+ CD8 T cells in tumoral niches, suggesting that GBP1+ TAMs were involved in recruitment and activation of tumor-reactive CD8 T cells. Together, we uncovered coordinated cellular programs across cell types in the microenvironment of MSS tumors, based on simple classification variables, that were strongly associated with prognosis.

cancer biology↗

MirGeneDB 3.0: Improved taxonomic sampling, uniform nomenclature of novel conserved microRNA families, and updated covariance models.

We present a major update of MirGeneDB (3.0), the manually curated animal microRNA gene database. Beyond moving to a new server and the creation of a computational mirror, we have expanded the database with the addition of 33 invertebrate species, including representatives of 5 previously unsampled phyla, and 6 mammal species. MirGeneDB now contains entries for 21, 822 microRNA genes (5, 160 of these from the new species) belonging to 1743 microRNA families. The inclusion of these new species allowed us to refine both the evolutionary node of appearance of a number of microRNA genes/families, as well as MirGeneDBs phylogenetically informed nomenclature system. Updated covariance models of all microRNA families, along with all smallRNA read data are now downloadable. These enhanced annotations will allow researchers to analyze microRNA properties such as secondary structure and features of their biogenesis within a robust phylogenetic context and without the database plagued with numerous false positives and false negatives. In light of these improvements, MirGeneDB 3.0 will assume the responsibility for naming conserved novel metazoan microRNAs. MirGeneDB is part of RNAcentral and Elixir Norway, and is publicly and freely available at master.cloud.mirgenedb.org. Key PointsO_LIMajor update to the manually curated and uniformly named microRNA gene database MirGeneDB C_LIO_LI114 animal species, >1700 microRNA families and [~]20 000 genes searchable, browsable and downloadable C_LIO_LINew features to download all smallRNA read data and updated covariance models for each family C_LI

evolutionary biology↗

cellsnake: a user-friendly tool for single cell RNA sequencing analysis

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, non-expert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. ResultsWe have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. ConclusionAs an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

bioinformatics↗

Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine

The annotation of microRNAs, an important class of post-transcriptional regulators, depends on the availability of transcriptomics data and expert knowledge. This led to a large gap between novel genomes made available and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in MirMachine, our new tool for the annotation of conserved microRNA complements from genomes only. We successfully applied MirMachine to a wide range of animal species, including those with very large genomes, additional genome duplications and extinct species, where smallRNA sequencing will be hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field facilitating automated genome annotation pipelines and deeper studies on the evolution of genome regulation, even in extinct organisms. HighlightsO_LIAn annotation pipeline using trained covariance models of microRNA families C_LIO_LIEnables massive parallel annotation of microRNA complements of genomes C_LIO_LIMirMachine creates meaningful annotations for very large and extinct genomes C_LIO_LImicroRNA score to assess genome assembly completeness C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/517654v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1ee2bf1org.highwire.dtl.DTLVardef@18719c1org.highwire.dtl.DTLVardef@abcfb6org.highwire.dtl.DTLVardef@1d1c368_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Circulating small non-coding RNAs associated with age, sex, smoking, body mass and physical activity

Non-coding RNAs (ncRNA) are regulators of cell functions and circulating ncRNAs from the majority of RNA classes, such as miRNA, tRNA, piRNAs, lncRNA, snoRNA, snRNA and miscRNAs, are potential non-invasive biomarkers. Understanding how non-disease traits influence ncRNA expression is essential for assessing their biomarker potential.\n\nWe studied associations of common traits (sex, age, smoking, body mass, physical activity, and technical factors such as sample storage and processing) with serum ncRNAs. We used RNAseq data from 526 donors from the Janus Serum Bank and traits from health examination surveys. We identified associations between all RNA classes and traits. Ageing showed the strongest association with ncRNA expression, both in terms of statistical significance and number of RNAs, regardless of RNA class. Serum processing modifications and storage times significantly altered expression levels of a number of ncRNAs. Interestingly, smoking cessation generally restored RNA expression to non-smoking levels, although for some isomiRs, mRNA fragments and tRNAs smoking-related expression levels persisted.\n\nOur results show that common traits influence circulating ncRNA expression. Therefore it is clear that ncRNA biomarker analyses should be adjusted for age and sex. In addition, for specific ncRNAs identified in our study, analyses should also be adjusted for body mass, smoking, physical activity and serum processing and storage.

molecular biology↗

A comprehensive profile of circulating RNAs in human serum

Non-coding RNA (ncRNA) molecules have fundamental roles in cells and many are also stable in body fluids as extracellular RNAs. In this study, we used RNA sequencing (RNA-seq) to investigate the profile of small non-coding RNA (sncRNA) in human serum. We analyzed 10 billion lllumina reads from 477 serum samples, included in the Norwegian population-based Janus Serum Bank (JSB). We found that the core serum RNA repertoire includes 258 micro RNAs (miRNA), 441 piwi-interacting RNAs (piRNA), 411 transfer RNAs (tRNA), 24 small nucleolar RNAs (snoRNA), 125 small nuclear RNAs (snRNA) and 123 miscellaneous RNAs (misc-RNA). We also investigated biological and technical variation in expression, and the results suggest that many RNA molecules identified in serum contain signs of biological variation. They are therefore unlikely to be random degradation by-products. In addition, the presence of specific fragments of tRNA, snoRNA, Vault RNA and Y_RNA indicates protection from degradation. Our results suggest that many circulating RNAs in serum can be potential biomarkers.

bioinformatics↗