The time sequence of RNA and protein synthesis in cellular compartments following an acute dietary challenge with amino acid mixtures.
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Amino Acids↗
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Results for “RNA sequencing”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
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Spatial mutual nearest neighbors for spatial transcriptomics data.
MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).
Algorithms↗
Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.
MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.
Algorithms↗
PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.
SUMMARY: Identifying differentially expressed (DE) genes along cell pseudotime is crucial for understanding dynamic biological processes captured by single-cell RNA sequencing. However, existing DE methods either produce invalid P-values by ignoring the uncertainty in pseudotime inference or struggle to scale with the growing size of modern datasets. To address these limitations, we introduce PseudotimeDE-fast, a scalable method for detecting DE genes along pseudotime with well-calibrated P-values. Through comprehensive simulations and real-data analyses, we demonstrate that PseudotimeDE-fast delivers comparable or superior performance to existing approaches while offering substantial improvements in computational efficiency. AVAILABILITY AND IMPLEMENTATION: PseudotimeDE-fast is implemented in R with Rcpp acceleration and released under the MIT license. The source code is available at: https://github.com/dsong-lab/PseudotimeDE.
Single-Cell Analysis↗
REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research.
SUMMARY: We introduce REACTOR, a computational tool designed to detect differential activity of transcriptional regulators and their target genes (regulons) in single-cell RNA-sequencing data. It expands the currently available framework for regulon analysis by introducing a robust statistical test to detect differential regulon activity between conditions, such as disease versus control, with multiple replicates. By contrasting different conditions, REACTOR enables identification of key condition- and cell type-specific regulons. To demonstrate the use of REACTOR, we illustrate its performance in a publicly available COVID-19 dataset. AVAILABILITY: REACTOR R-package together with an implementation vignette are available at https://www.github.com/elolab/REACTOR.
Regulon↗
Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has transformed transcriptome profiling at cellular resolution, yet accurate reconstruction of full-length transcripts for individual cells remains a central challenge. Emerging scRNA-seq protocols can produce reads that span entire transcripts, enabling isoform-level expression analysis. For example, Smart-seq protocols combine unique molecular identifier (UMI)-linked reads that index and stitch together multiple reads from the same molecule, with internal reads filling coverage gaps. We demonstrate that these read types exhibit markedly different biological and statistical properties in strandness, 5'/3' coverage bias, and genomic locality. Existing assemblers fail to leverage these distinctions, yielding suboptimal assembly. RESULTS: We developed Amaranth, a novel single-cell assembler that discriminatively models UMI and internal reads. Amaranth implements heuristics specifically designed to address the distinct biases of UMI-linked and internal reads, enabling accurate strandness assignment for internal reads, reliable splicing graph refinement, and precise transcript start site determination. We also developed Amaranth-meta, which integrates information across cells to enhance individual cell assemblies. Benchmarked on Smart-seq3 datasets from human HEK293T and mouse fibroblast cells, Amaranth outperformed other state-of-the-art assemblers in assembling individual cells and in meta-assembly. Amaranth advances isoform-level analysis in single-cell transcriptomics, facilitating detailed studies at cellular resolution. AVAILABILITY AND IMPLEMENTATION: Amaranth is implemented in C++ and is freely available at https://github.com/Shao-Group/amaranth under the BSD-3-Clause license. Scripts, documentation, and data for reproducing experiments in this manuscript are available at https://github.com/Shao-Group/amaranth-test.
Single-Cell Gene Expression Analysis↗
FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.
MOTIVATION: Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene-expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. RESULTS: To address this challenge, we introduce FIERCE (Framework for InfERence of the veloCity of Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. AVAILABILITY AND IMPLEMENTATION: The FIERCE R package is available on GitHub at https://github.com/bicciatolab/FIERCE.
Cell Differentiation↗
A heuristic approach for detecting RNA H-type pseudoknots.
MOTIVATION: RNA H-type pseudoknots are ubiquitous pseudoknots that are found in almost all classes of RNA and thought to play very important roles in a variety of biological processes. Detection of these RNA H-type pseudoknots can improve our understanding of RNA structures and their associated functions. However, the currently existing programs for detecting such RNA H-type pseudoknots are still time consuming and sometimes even ineffective. Therefore, efficient and effective tools for detecting the RNA H-type pseudoknots are needed. RESULTS: In this paper, we have adopted a heuristic approach to develop a novel tool, called HPknotter, for efficiently and accurately detecting H-type pseudoknots in an RNA sequence. In addition, we have demonstrated the applicability and effectiveness of HPknotter by testing on some sequences with known H-type pseudoknots. Our approach can be easily extended and applied to other classes of more general pseudoknots. AVAILABILITY: The web server of our HPknotter is available for online analysis at http://bioalgorithm.life.nctu.edu.tw/HPKNOTTER/ CONTACT: cllu@mail.nctu.edu.tw, chiu@cc.nctu.edu.tw
Algorithms↗
Haemophilus parainfluenzae endocarditis: application of a molecular approach for identification of pathogenic bacterial species.
Haemophilus parainfluenzae is both a human oropharyngeal commensal bacterium and a cause of serious invasive disease. The fastidious growth characteristics of this organism and the poor specificity of traditional methods for species identification are likely to have led to inaccuracies in the diagnosis of infections caused by H. parainfluenzae and related organisms. We report a case of H. parainfluenzae endocarditis in which confusion related to microbial identification was resolved by the analysis of 16S ribosomal RNA sequences. Rapid identification was facilitated by amplification of 16S ribosomal DNA directly from cultured cells with use of the polymerase chain reaction and by direct DNA sequence determination of the amplified product. This procedure is potentially useful for the identification of fastidious bacterial pathogens by reference laboratories.
Adult↗
Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.
BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.
Single-Cell Analysis↗
An improved method for sequencing of RNA templates.
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Avian Myeloblastosis Virus↗
The guide RNA database.
The RNA editing process in protozoan parasites is controlled by small RNA molecules known as guide RNAs (gRNAs). The gRNA database is a comprehensive compilation of published guide RNA sequences from eight different kinetoplastid organisms. In addition to the RNA primary sequences, information on the gene localization, the experimental verification of the transcripts, and literature citations are provided. Accessory information includes the secondary structures of fourTrypanosoma bruceigRNAs as well as a computer modelled three dimensional gRNA structure. The database is made available as a hypertext document accessible via the World Wide Web (WWW) or from the authors in a printed form.
Animals↗
The EMBL nucleotide sequence database.
The EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl. html ) constitutes Europe's primary nucleotide sequence resource. DNA and RNA sequences are directly submitted from researchers and genome sequencing groups and collected from the scientific literature and patent applications (Fig. 1). In collaboration with DDBJ and GenBank the database is produced, maintained and distributed at the European Bioinformatics Institute. Database releases are produced quarterly and are distributed on CD-ROM. EBI's network services allow access to the most up-to-date data collection via Internet and World Wide Web interface, providing database searching and sequence similarity facilities plus access to a large number of additional databases.
Base Sequence↗
Proportionality-based association metrics in count compositional data.
Compositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA sequencing are compositional in nature. In this kind of data, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, an inherent bias arises when these metrics are calculated empirically on count-based measures due to variability in read depths. We quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data and are applied to a mouse embryonic stem cell single-cell sequencing dataset, as well as a pediatric-onset multiple sclerosis metagenomic dataset.
Animals↗
Optimal alphabets for an RNA world.
Experiments have shown that the canonical AUCG genetic alphabet is not the only possible nucleotide alphabet. In this work we address the question 'is the canonical alphabet optimal?' We make the assumption that the genetic alphabet was determined in the RNA world. Computational tools are used to infer the RNA secondary structure (shape) from a given RNA sequence, and statistics from RNA shapes are gathered with respect to alphabet size. Then, simulations based upon the replication and selection of fixed-sized RNA populations are used to investigate the effect of alternative alphabets upon RNA's ability to step through a fitness landscape. These results show that for a low copy fidelity the canonical alphabet is fitter than two-, six- and eight-letter alphabets. In higher copy-fidelity experiments, six-letter alphabets outperform the four-letter alphabets, suggesting that the canonical alphabet is indeed a relic of the RNA world.
Animals↗
A new cleavage method for the study of nucleotide sequences in RNA.
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Basidiomycota↗
Ribosomal RNA nucleotide sequence homologies in plants.
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Journal Article↗
Genetic and biochemical dissection of transgenic RNA-mediated virus resistance.
RNA-mediated virus resistance has been observed in transgenic plants at varying frequencies, suggesting that a nuclear requirement or other pre-condition must be met. This study was undertaken to characterize genetically transgenes that confer a highly resistant state to infection by tobacco etch virus (TEV). Transgenic tobacco line 2RC-6.13, expressing an untranslatable mRNA containing the TEV coat protein open reading frame, had three distinct transgene integration events that segregated as two linkage groups. A genetic series of plants that contained zero, one, two, or all three transgene inserts in both homozygous and heterozygous conditions was produced and examined. Genetic and biochemical data suggested that RNA-mediated virus resistance is a multigenic trait in line 2RC-6.13; three or more transgenes were necessary to establish the highly resistant state. One or two transgene copies resulted in an inducible form of resistance (i.e., recovery). Transcription rates and steady state RNA levels of the transgene-derived transcript present in different members of the genetic series supported a post-transcriptional RNA degradation process as the underlying mechanism for transgene transcript reduction and virus resistance. This degradation process appeared to initiate via cleavage of specific sites within the target RNA sequence, as determined by RNA get blot and primer extension analyses of transgene-derived mRNA from various transgenic plant lines.
Capsid↗