Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “transcriptome 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45Linked to original sources
PubMed2025

Genome topology analysis and transcriptomics of human osteoclasts reveals enhancer-promoter interactions at loci for bone traits and diseases.

Genome-wide association studies (GWAS) relevant to osteoporosis have identified hundreds of loci; however, understanding how these variants influence the phenotype is complicated because most reside in non-coding DNA sequence that serves as transcriptional enhancers and repressors. To advance knowledge on these regulatory elements in osteoclasts (OCs), we performed Micro-C analysis, which informs on the genome topology of these cells and integrated the results with transcriptome and GWAS data to further define loci linked to BMD. Using blood cells isolated from 4 healthy participants aged 31-61 yr, we cultured OC in vitro and generated a Micro-C chromatin conformation capture dataset. We characterized chromatin loops (CLs) in OC from among more than 69 million chromatin interactions identified in the genome. Of the CL identified in OC, >16 000 were unique compared to precursor cells. When sentinel single nucleotide polymorphisms from osteoporosis and bone-related GWAS and those in linkage disequilibrium at r 2 > 0.6 were mapped to CL for OC, 12 588 of these variants were observed within chromatin contact regions. Notable in differential gene ontology enrichment analyses of the topology data for OC and precursors were pathways regulating pluripotency of stem cells, Wnt signaling, nucleotide-binding oligomerization domain (NOD)-like receptor signaling and chemokine signaling. These data, in combination with other 3D genome architecture and epigenetic data (eg, histone modifications and chromatin accessibility), will be useful in modeling to predict genome-wide, which enhancers regulate which genes in OC. This data will therefore also be informative for resolving GWAS hits. In conclusion, we have generated a high-resolution genome topology dataset for human OC and have used this to identify CLs relevant to studies of the genetics of osteoporosis. This data will serve as a powerful resource to inform future functional studies of OC biology.

BMD↗
PubMed2003

DNA microarrays in vaccine research.

DNA microarrays represent the technology platform for a wide variety of analytical methods built around the detection of sequence-specific nucleic acid hybridization. They allow the analysis of complex biological systems on the basis of the genome and transcriptome with high simplicity, efficiency, sensitivity and specificity. This positions DNA microarrays as ideal tools for novel approaches in biomedical research and explains the tremendous pace at which they are being applied to an enormous variety of systems and projects. Recently, vaccine researchers began to use DNA microarrays for the identification of vaccine candidates against bacterial, parasitic and viral pathogens, as well as tumors. Here, DNA microarrays as a technology platform in these areas of vaccine research are reviewed and their limitations, as well as their potential for future applications in vaccinology, critically evaluated.

Bacterial Vaccines↗
PubMed2004

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

Ammonia↗
PubMed2025

Identification of marginal zone B cells in head and neck cancer with immunomodulatory characteristics.

INTRODUCTION: Recently we observed high numbers of marginal zone B cells (MZBs) within murine head and neck squamous cell carcinoma (HNSCC) with immunosuppressive potential. To date, MZBs have not been linked to tumor development or tumor prevention. OBJECTIVES: Based on our previous findings the present study aimed to validate the presence of MZB in HNSCC and to investigate their possible implications in tumorigenesis and prognosis. METHODS: Flow cytometry was used to uncover MZB within tumors and blood of HNSCC patients. A single-cell RNA sequencing cohort of 118 HNSCC patients across different disease stages and 6 healthy donors (HDs) was compiled. Comparative transcriptomic profiling of B lymphocytes between HNSCC and HDs were performed. Downstream analysis, such as pathway enrichment, cell-cell communication, pseudotime trajectory inference, survival correlation, and spatial transcriptomics were applied. RESULTS: Two MZB subsets were revealed in tissues and blood of HNSCC patients and HDs. The tumor-associated MZBs were featured with hypoxia stress and viral-related hallmark genes. MZB-2, characterized by elevated expression of activation markers and immune-regulatory genes, displayed strong interactions with CD4+ T cells and antigen-presenting cells. These interactions were supported by costimulatory signals in HDs but were absent in HNSCC patients. Co-localization of MZB-2, germinal center B cell (GCB), and CD4+ follicular helper T cell (Tfh) was detected in HNSCC, suggesting the presence of an intratumoral MZB-Tfh-GCB axis. Clinically, MZB-2 abundance was associated with favorable prognosis in early-stage HNSCC, but not in advanced disease. Immunosuppressive gene signatures were not exclusive to MZBs, indicating that they do not represent a purely regulatory B cell phenotype. CONCLUSION: Our findings demonstrate an immunomodulatory role of MZBs in tumor immunity, balancing antigen presentation, cytokine signaling, and immune suppression. The association of MZB-2 with improved prognosis in early-stage HNSCC highlights its potential as a beneficial regulator of antitumor immunity during early tumor progression.

Humans↗
PubMed2026

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗
PubMed2007

Discovery of differentially expressed genes: technical considerations.

Identification and characterization of differentially expressed genes may be an important first step toward the understanding of both normal physiology and disease. A multitude of techniques belonging to two main categories have been developed to identify the differences in gene expression between samples from different biological origin: selection techniques and global techniques. Whereas the selection techniques strive to identify specific differentially expressed genes, the global techniques analyze the total transcriptome or a major part of the RNA population in a defined biological material. By exploiting the known sequences of the adaptors used in suppressive subtraction hybridization technique, a strategy named novel rescue-suppression-subtractive hybridization was developed. It should facilitate the discovery of differentially expressed genes.

Cloning, Molecular↗
PubMed2026

Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.

Temporal lobe epilepsy (TLE) is the most common acquired epilepsy, causing refractory seizures and cognitive deficits. We performed single-nucleus RNA sequencing on hippocampal tissue from mice 3 and 6 weeks following pilocarpine-induced status epilepticus, a robust model of TLE. Epilepsy samples showed reductions in Cck and Lamp5-Lhx6 interneuron subclusters, alongside increases in Cajal-Retzius cells, dentate granule (DG) cell precursors, and a mature DG cell subcluster. Among glia, an astrocyte subcluster and a markedly expanded microglia sublcuster were increased. We term this microglia population epilepsy-associated microglia (EAM). The transcriptomic profile of EAM overlaps with microglia described in models of Alzheimer's disease and traumatic brain injury, including enrichment of Myo1e and Igf1. EAM display amoeboid morphology, can be found in clumps around pyramidal and granule cell body layers, and exhibit enlarged vesicles and mitochondria. Cell-cell interaction analysis predicts DG cells as their primary interaction partners. This dataset defines transcriptomic programs underlying key cellular alterations in TLE, enabling mechanistic dissection of epileptogenesis.

TLE↗
PubMed2006

Expressed sequence tags from Madagascar periwinkle (Catharanthus roseus).

The Madagascar periwinkle (Catharanthus roseus) is well known to produce the chemotherapeutic anticancer agents, vinblastine and vincristine. In spite of its importance, no expressed sequence tag (EST) analysis of this plant has been reported. Two cDNA libraries were generated from RNA isolated from the base part of young leaves and from root tips to select 9,824 random clones for unidirectional sequencing, to yield 3,327 related sequences and 1,696 singletons by cluster analysis. Putative functions of 3,663 clones were assigned, from 5,023 non-redundant ESTs to establish a resource for transcriptome analysis and gene discovery in this medicinal plant.

ATP-Binding Cassette Transporters↗
PubMed2026

RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort.

PURPOSE: We aim to better define the genotype and phenotype spectrum of RNU4ATAC-opathy, demonstrate the utility of RNA sequencing (RNA-seq) for variant classification, and highlight the challenges in detecting variants in this noncoding gene. METHODS: Sixty individuals with molecularly confirmed RNU4ATAC-opathy were recruited from multiple clinical and research centers internationally. RNA-seq was available for 7 affected individuals. RESULTS: We report the clinical and molecular findings of 60 individuals, including 42 not previously described, and 33 distinct RNU4ATAC variants, 13 of which are novel. Core features in this cohort-present in most individuals assessed and varying in severity-include microcephaly, short stature, skeletal anomalies, developmental delay, cerebral anomalies, skin conditions, and immune deficiency. Additional findings, such as diabetes, holoprosencephaly, and the absence of various core features in some individuals, highlight the broad phenotypic spectrum. All individuals who underwent RNA-seq showed a consistent pattern of minor intron retention. In 6 individuals, RNA-seq enabled the reclassification of variants of uncertain significance as likely pathogenic. Although RNU4ATAC variants are generally covered by clinical exomes, they are often overlooked in analysis because of their noncoding nature. CONCLUSION: This study highlights the variability of phenotypes and genotypes associated with RNU4ATAC-opathy. Laboratories should ensure RNU4ATAC and other noncoding genes are appropriately assessed by their analysis pipelines.

Lowry-Wood syndrome↗
PubMed2002

Gene expression profiles in young adult Ciona intestinalis.

Comparison of 12,230 expressed sequence tags (ESTs) of 3' ends of cDNA clones derived from young adults of Ciona intestinalis allowed us to categorize them into 976 independent clusters. When the 5'-end sequences of 10,400 ESTs of the 976 clusters were compared with the sequences in databases, 406 of the clusters showed significant matches ( P < E-15) with reported proteins with defined functions, while 117 showed matches with putative proteins for which there is not enough information to categorize their function, and 453 had no significant sequence similarities to known proteins. The 406 clusters with sequence similarity to proteins with defined functions consisted of 304 clusters related to proteins with functions common to many kinds of cells, 73 related to proteins associated with cell-cell communication and 29 related to transcription factors. Spatial expression of all of the 976 clusters was examined by a newly improved whole-mount in situ hybridization method. A total of 430 clusters did not show distinct in situ hybridization signals, while 122 clusters showed ubiquitous distribution of signals, and 253 clusters showed signals in multiple tissues. The remaining 171 clusters showed signals specific to a certain organ or tissue: 16 showed epidermis-specific expression, 3 were specific to the neural complex, 1 to heart, 6 to body-wall muscle, 94 to pharyngeal gill, 3 to esophagus, 26 to stomach, 1 to intestine and 21 to endostyle. Many of these organ-specific genes encode proteins with no sequence similarity to known proteins. The present analysis thus highlights characteristic gene expression profiles of Ciona young adults and provides not only molecular markers for organs and tissues but also transcriptomic information useful for further genomic analyses of this model organism.

Animals↗
PubMed2006

New perspectives on host-parasite interplay by comparative transcriptomic and proteomic analyses of Schistosoma japonicum.

Schistosomiasis remains a serious public health problem with an estimated 200 million people infected in 76 countries. Here we isolated ~ 8,400 potential protein-encoding cDNA contigs from Schistosoma japonicum after sequencing circa 84,000 expressed sequence tags. In tandem, we undertook a high-throughput proteomics approach to characterize the protein expression profiles of a number of developmental stages (cercariae, hepatic schistosomula, female and male adults, eggs, and miracidia) and tissues at the host-parasite interface (eggshell and tegument) by interrogating the protein database deduced from the contigs. Comparative analysis of these transcriptomic and proteomic data, the latter including 3,260 proteins with putative identities, revealed differential expression of genes among the various developmental stages and sexes of S. japonicum and localization of putative secretory and membrane antigens, enzymes, and other gene products on the adult tegument and eggshell, many of which displayed genetic polymorphisms. Numerous S. japonicum genes exhibited high levels of identity with those of their mammalian hosts, whereas many others appeared to be conserved only across the genus Schistosoma or Phylum Platyhelminthes. These findings are expected to provide new insights into the pathophysiology of schistosomiasis and for the development of improved interventions for disease control and will facilitate a more fundamental understanding of schistosome biology, evolution, and the host-parasite interplay.

Amino Acid Sequence↗
PubMed2006

Introduction of silencing-inducing transgenes does not affect expression of known transcripts.

While the RNA interference (RNAi) mechanism has only been discovered a decade ago, RNAi is now often used to study gene function by sequence-specific knockdown of gene expression. However, it is still unknown whether introduction of silencing-inducing transgenes alters the transcriptome. To address this question, genome-wide transcriptional changes in silenced and non-silenced backgrounds were monitored through microarray analysis. No significant transcriptional changes were detected when compared to the non-silenced control. This result was confirmed by real-time polymerase chain reaction analysis of genes known to be involved in RNA silencing. In conclusion, introduction of silencing-inducing constructs does not affect expression of known transcripts in other genes than in those homologous to the targeted ones. Consequently, when gene function is studied by RNAi, the transcriptional changes detected will specifically be the result of knockout of the gene of interest, at least for the genes present on the array used in our study.

Arabidopsis↗
PubMed2000

Conceptual modelling of genomic information.

MOTIVATION: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Computational Biology↗
PubMed2006

Signatures from tissue-specific MPSS libraries identify transcripts preferentially expressed in the mouse inner ear.

Specialization in cell function and morphology is influenced by the differential expression of mRNAs, many of which are expressed at low abundance and restricted to certain cell types. Detecting such transcripts in cDNA libraries may require sequencing millions of clones. Massively parallel signature sequencing (MPSS) is well suited to identifying transcripts that are expressed in discrete cell types and in low abundance. We have made MPSS libraries from microdissections of three inner ear tissues. By comparing these MPSS libraries to those of 87 other tissues included in the Mouse Reference Transcriptome online resource, we have identified genes that are highly enriched in, or specific to, the inner ear. We show by RT-PCR and in situ hybridization that signatures unique to the inner ear libraries identify transcripts with highly specific cell-type localizations. These transcripts serve to illustrate the utility of a resource that is available to the research community. Utilization of these resources will increase the number of known transcription units and expand our knowledge of the tissue-specific regulation of the transcriptome.

Animals↗
PubMed2006

Differentially profiling the low-expression transcriptomes of human hepatoma using a novel SSH/microarray approach.

BACKGROUND: The main limitation in performing genome-wide gene-expression profiling is the assay of low-expression genes. Approaches with high throughput and high sensitivity for assaying low-expression transcripts are urgently needed for functional genomic studies. Combination of the suppressive subtractive hybridization (SSH) and cDNA microarray techniques using the subtracted cDNA clones as probes printed on chips has greatly improved the efficiency for fishing out the differentially expressed clones and has been used before. However, it remains tedious and inefficient sequencing works for identifying genes including the great number of redundancy in the subtracted amplicons, and sacrifices the original advantages of high sensitivity of SSH in profiling low-expression transcriptomes. RESULTS: We modified the previous combination of SSH and microarray methods by directly using the subtracted amplicons as targets to hybridize the pre-made cDNA microarrays (named as "SSH/microarray"). mRNA prepared from three pairs of hepatoma and non-hepatoma liver tissues was subjected to the SSH/microarray assays, as well as directly to regular cDNA microarray assays for comparison. As compared to the original SSH and microarray combination assays, the modified SSH/microarray assays allowed for much easier inspection of the subtraction efficiency and identification of genes in the subtracted amplicons without tedious and inefficient sequencing work. On the other hand, 5015 of the 9376 genes originally filtered out by the regular cDNA microarray assays because of low expression became analyzable by the SSH/microarray assays. Moreover, the SSH/microarray assays detected about ten times more (701 vs. 69) HCC differentially expressed genes (at least a two-fold difference and P < 0.01), particularly for those with rare transcripts, than did the regular cDNA microarray assays. The differential expression was validated in 9 randomly selected genes in 18 pairs of hepatoma/non-hepatoma liver tissues using quantitative RT-PCR. The SSH/microarray approaches resulted in identifying many differentially expressed genes implicated in the regulation of cell cycle, cell death, signal transduction and cell morphogenesis, suggesting the involvement of multi-biological processes in hepato-carcinogenesis. CONCLUSION: The modified SSH/microarray approach is a simple but high-sensitive and high-efficient tool for differentially profiling the low-expression transcriptomes. It is most adequate for applying to functional genomic studies.

Carcinoma, Hepatocellular↗
PubMed2025

Transcriptome-wide root causal inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm has been designed to discover root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously determines the sequence in which gene expression changes propagate through the system to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Algorithms↗
PubMed2026

Exploiting Omic Data to Advance Predictive Ecotoxicology.

Predicting species-specific chemical sensitivity using in silico approaches has the potential to transform environmental risk assessment, conservation, and biomonitoring, while reducing, and ultimately replacing, animal testing. Genomic and transcriptomic data capture extensive sensitivity-relevant variation, including differences in molecular targets, xenobiotic metabolism, and damage mitigation pathways. Large-scale sequencing initiatives therefore offer an unprecedented opportunity to address ecotoxicology's "too many species" problem. Although existing omic-based predictive tools provide proof of concept, they have so far been applied to a narrow set of relatively straightforward prediction scenarios. To achieve broader applicability, current and future tools must be firmly grounded in the diverse molecular mechanisms underlying differential chemical responses. Here, we critically evaluate the emerging field of predicting species sensitivity using molecular variation inferred from omic data. We analyze the strengths and limitations of current omic-based approaches and identify major sequence and ecotoxicological data gaps, as well as critical bioinformatic challenges. We then review the current knowledge of how molecular biology underlies differential chemical sensitivity, outlining research paths to allow the next generation of sensitivity prediction tools to exploit ever expanding omic data.

Ecotoxicology↗