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At least 1,567 records · Page 87Linked to original sources
PubMed1989

Sequence analysis of nucleocapsid gene and leader RNA of human coronavirus OC43.

The nucleotide sequence of the 3'-end of the genomic RNA of human coronavirus OC43 (HCV-OC43) was determined from the cDNA clones of the intracellular virus-specific mRNAs. The nucleotide sequence and the predicted amino acid sequence of the main open reading frame (ORF), which represents the nucleocapsid (N) protein, were highly homologous to those of bovine coronavirus (BCV) Mebus strain. This ORF predicts a protein of 448 amino acids. Additional smaller ORFs are also present in a different reading frame. We have also determined the leader sequence present at the 5'-end of HCV-OC43 mRNAs by a primer extension study. This sequence is highly homologous to that of mouse hepatitis virus, particularly in the 3'-end of the leader sequence, which is postulated to be involved in the unique mechanism of leader-primed transcription. These data suggest that HCV-OC43 and BCV might have diverged from each other fairly recently and that the 3'-end of the leader sequence has significant functional roles.

Amino Acid Sequence↗
PubMed2005

Recovery of Swedish Equine arteritis viruses from semen by cell culture isolation and RNA transfection.

Recovery of infectious Equine arteritis virus (EAV) from the semen of persistently infected Swedish stallions was attempted by classical cell culture isolation and by transfection of extracted total RNA. Whereas virus from semen samples stored for several months at -20 degrees C or from extended semen could only be recovered by transfection of extracted RNA, isolation in cell culture was achieved readily with fresh, unextended semen stored at -70 degrees C or directly used after sampling. In parallel, the viruses were examined in the variable region of the large glycoprotein GP5 by nested RT-PCR and direct nucleotide sequencing. The resulting sequences were placed into a large phylogenetic tree from this region, demonstrating that Swedish strains belonged to very diverse phylogenetic groups. This represents the first report of recovery of infectious EAV from archived semen samples by RNA transfection.

Animals↗
PubMed2005

An essential GTPase promotes assembly of preribosomal RNA processing complexes.

Ribosome biogenesis in eukaryotes is a highly regulated process involving hundreds of transiently associated proteins and RNAs. Although most of these assembly factors have been genetically linked to specific step(s) in the biogenesis pathway, their biochemical functions are generally unknown. Bms1, an essential protein in yeast, is the only known GTPase required for biosynthesis of the 40S ribosomal subunit and interacts with Rcl1, an essential protein suggested to be an endonuclease. Here, we show thermodynamic coupling in the binding of Bms1 to GTP, Rcl1, and U3 small nucleolar RNA (snoRNA), an essential RNA that base pairs to pre-rRNA. Rcl1 binding to preribosomes is severely limited in yeast cells expressing a Bms1 mutant defective for Rcl1 binding. Additionally, we provide evidence that the C-terminal domain of Bms1 acts as an intramolecular GTPase-activating protein. Together, these data suggest that Bms1 functions as a GTP-regulated switch to deliver Rcl1 to preribosomes, providing molecular insight into preribosome assembly.

Escherichia coli↗
PubMed1999

Comparative sequence analyses of sixteen reptilian paramyxoviruses.

Viral genomic RNA of Fer-de-Lance virus (FDLV), a paramyxovirus highly pathogenic for reptiles, was reverse transcribed and cloned. Plasmids with significant sequence similarities to the hemagglutinin-neuraminidase (HN) and polymerase (L) genes of mammalian paramyxoviruses were identified by BLAST search. Partial sequences of the FDLV genes were used to design primers for amplification by nested polymerase chain reaction (PCR) and sequencing of 518-bp L gene and 352-bp HN gene fragments from a collection of 15 previously uncharacterized reptilian paramyxoviruses. Phylogenetic analyses of the partial L and HN sequences produced similar trees in which there were two distinct subgroups of isolates that were supported with maximum bootstrap values, and several intermediate isolates. Within each subgroup the nucleotide divergence values were less than 2.5%, while the divergence between the two subgroups was 20-22%. This indicated that the two subgroups represent distinct virus species containing multiple virus strains. The five intermediate isolates had nucleotide divergence values of 11-20% and may represent additional distinct species. In addition to establishing diversity among reptilian paramyxoviruses, the phylogenetic groupings showed some correlation with geographic location, and clearly demonstrated a low level of host species-specificity within these viruses.

Animals↗
PubMed2026

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologically meaningful sequence features for improved lncRNA prediction. A custom BERT-based model was first pre-trained on a large corpus of metazoan RNA sequences using a masked language modelling strategy to learn contextual nucleotide dependencies. The model was subsequently fine-tuned on curated datasets of lncRNAs and protein-coding transcripts and 256-dimensional deep sequence embeddings were extracted. Parallelly, 348 handcrafted features, including ORF characteristics, untranslated region (UTR) properties, nucleotide composition and Fickett scores, were computed. A multi-stage feature selection strategy was applied to identify the most informative features, resulting in optimized hybrid feature sets. Multiple machine learning classifiers were evaluated, with the RF model achieving the best performance. The proposed framework attained an accuracy of 91.30%, F1-score of 91.23% and MCC of 82.60 on an independent validation dataset, outperforming several existing lncRNA prediction tools. Thus, HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction. This approach provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.

RNA, Long Noncoding↗
PubMedDate not supplied

Labeling during cleavage (LDC), a new labeling approach for RNA.

A new and efficient strategy for labeling of RNA sequences prior to their hybridization on high density DNA chip has been developed. Our approach which combines the fragmentation and the labeling is based on the reactivity of the 3'-phosphate of cleaved RNA fragments with a fluorescent molecule bearing aromatic bromomethyl function.

DNA-Directed RNA Polymerases↗
PubMed2002

Computational antisense oligo prediction with a neural network model.

MOTIVATION: The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model. RESULTS: We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10(-8)). This model can predict effective AOs (>50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction.

Algorithms↗
PubMed2003

ddbRNA: detection of conserved secondary structures in multiple alignments.

MOTIVATION: Structured non-coding RNAs (ncRNAs) have a very important functional role in the cell. No distinctive general features common to all ncRNA have yet been discovered. This makes it difficult to design computational tools able to detect novel ncRNAs in the genomic sequence. RESULTS: We devised an algorithm able to detect conserved secondary structures in both pairwise and multiple DNA sequence alignments with computational time proportional to the square of the sequence length. We implemented the algorithm for the case of pairwise and three-way alignments and tested it on ncRNAs obtained from public databases. On the test sets, the pairwise algorithm has a specificity greater than 97% with a sensitivity varying from 22.26% for Blast alignments to 56.35% for structural alignments. The three-way algorithm behaves similarly. Our algorithm is able to efficiently detect a conserved secondary structure in multiple alignments.

Algorithms↗
PubMed2006

Prediction of mRNA polyadenylation sites by support vector machine.

mRNA polyadenylation is responsible for the 3' end formation of most mRNAs in eukaryotic cells and is linked to termination of transcription. Prediction of mRNA polyadenylation sites [poly(A) sites] can help identify genes, define gene boundaries, and elucidate regulatory mechanisms. Current methods for poly(A) site prediction achieve moderate sensitivity and specificity. Here, we present a method using support vector machine for poly(A) site prediction. Using 15 cis-regulatory elements that are over-represented in various regions surrounding poly(A) sites, this method achieves higher sensitivity and similar specificity when compared with polyadq, a common tool for poly(A) site prediction. In addition, we found that while the polyadenylation signal AAUAAA and U-rich elements are primary determinants for poly(A) site prediction, other elements contribute to both sensitivity and specificity of the prediction, indicating a combinatorial mechanism involving multiple elements when choosing poly(A) sites in human cells.

Artificial Intelligence↗
PubMed2019

Profiling the long noncoding RNA interaction network in the regulatory elements of target genes by chromatin in situ reverse transcription sequencing.

Long noncoding RNAs (lncRNAs) can regulate the activity of target genes by participating in the organization of chromatin architecture. We have devised a "chromatin-RNA in situ reverse transcription sequencing" (CRIST-seq) approach to profile the lncRNA interaction network in gene regulatory elements by combining the simplicity of RNA biotin labeling with the specificity of the CRISPR/Cas9 system. Using gene-specific gRNAs, we describe a pluripotency-specific lncRNA interacting network in the promoters of Sox2 and Pou5f1, two critical stem cell factors that are required for the maintenance of pluripotency. The promoter-interacting lncRNAs were specifically activated during reprogramming into pluripotency. Knockdown of these lncRNAs caused the stem cells to exit from pluripotency. In contrast, overexpression of the pluripotency-associated lncRNA activated the promoters of core stem cell factor genes and enhanced fibroblast reprogramming into pluripotency. These CRIST-seq data suggest that the Sox2 and Pou5f1 promoters are organized within a unique lncRNA interaction network that determines the fate of pluripotency during reprogramming. This CRIST approach may be broadly used to map lncRNA interaction networks at target loci across the genome.

Animals↗
PubMed2025

Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Humans↗
PubMed2005

Lack of mutations of the human telomerase RNA gene (hTERC) in myelodysplastic syndrome.

Myelodysplastic syndrome (MDS), considered a pre-leukemic state, has recently been categorized as a subset of bone marrow failure syndromes. Unlike other subtypes of bone marrow failure syndromes, such as aplastic anemia or dyskeratosis congenita, little is known about genetic alterations of human telomerase in MDS, despite the fact that immune cells from patients with MDS frequently exhibit telomere attrition.

Adult↗
PubMed2000

Molecular techniques for determining microbial diversity and community structure in natural environments.

The ability to quantify the number and kinds of microorganisms within a community is fundamental to the understanding of the structure and function of an ecosystem. The simple morphology of most microbes provides few clues for their identification and physiological traits are often ambiguous. In addition, many organisms resist cultivation, which is essential to their characterization. Recombinant DNA techniques have provided a means whereby many of the obstacles associated with cultivation and description can be overcome and subsequently has allowed many new insights into the complexity of natural microbial communities. Molecular approaches based on 16S ribosomal RNA (rRNA) sequence analysis allow direct investigation of the community structure, diversity, and phylogeny of microorganisms in almost any environment, while quantification of the individual types of microorganisms or entire microbial communities may be addressed by nucleic acid hybridization techniques. Furthermore, the use of fluorescently labeled population-specific rRNA probes allows microscopic examination of individual cells in complex microbial assemblages as well as their interactions in situ. In this review, we discuss strategies for characterizing microbial communities without the need for cultivation.

DNA, Ribosomal↗
PubMed2003

High-throughput analysis of total RNA expression profiles by capillary gel electrophoresis.

We studied the expression profiles of total RNA in three types of human tissues. Reverse transcription was performed on total RNA samples extracted from kidney, normal breast, and breast tumor. Then, fluorophor-labeled cDNAs were synthesized by using random hexamers as primers for analysis by capillary gel electrophoresis. By overlaying the electropherograms and by performing multivariate analysis, we found that the RNA patterns were significantly different among the tissue samples. The protocol utilizes only submicroliter volumes of PCR products and is compatible with multiple capillary DNA sequencing instruments. These CE-based RNA patterns provide an alternative to RNA microarrays for expression profiling for understanding the molecular basis of disease.

Breast↗
PubMed2003

Integrated functional and bioinformatics approach for the identification and experimental verification of RNA signals: application to HIV-1 INS.

Regulation of gene expression involves sequence elements in nucleic acids. In promoters, multiple sequence elements cooperate as functional modules, which in combination determine overall promoter activity. We previously developed computational tools based on this hierarchical structure for in silico promoter analysis. Here we address the functional organization of post-transcriptional control elements, using the HIV-1 genome as a model. Numerous mutagenesis studies demonstrate that expression of HIV structural proteins is restricted by inhibitory sequences (INS) in HIV mRNAs in the absence of the HIV-1 Rev protein. However, previous attempts to detect conserved sequence patterns of HIV-1 INS have failed. We defined four distinct sequence patterns for inhibitory motifs (weight matrices), which identified 22 out of the 25 known INS as well as several new candidate INS regions contained in numerous HIV-1 strains. The conservation of INS motifs within the HIV genome was not due to overall sequence conservation. The functionality of two candidate INS regions was analyzed with a new assay that measures the effect of non-coding mRNA sequences on production of red fluorescent reporter protein. Both new INS regions showed inhibitory activity in sense but not in antisense orientation. Inhibitory activity increased by combining both INS regions in the same mRNA. Inhibitory activity of known and new INS regions was overcome by co-expression of the HIV-1 Rev protein.

Computational Biology↗