Differential display reverse transcriptase polymerase chain reaction (DDRT-PCR) is an extremely powerful method for analyzing differences in gene expression between matched tissue/cell samples. Liang and Pardee originally described the technique in 1992 in their seminal paper (1). DDRT-PCR is now firmly established as a widespread powerful and commonly used method for identifying differentially expressed genes.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1385/1-59259-071-3:459 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.
Nat Methods
January 2025
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner.
View Article and Find Full Text PDFIdentification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.
Sci Rep
January 2025
General Surgery Department, Jiangsu University Affiliated People's Hospital, Zhenjiang, 212000, China.
Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD.
View Article and Find Full Text PDFComparative transcriptome and metabolome analysis reveals the differential response to salinity stress of two genotypes brewing sorghum.
Sci Rep
January 2025
Agricultural College of Inner Mongolia Minzu University, Tongliao, 028000, Inner Mongolia, China.
Salinity tolerance in brewing sorghum is a very important trait, especially in areas that are affected by soil salinity. In order to elucidate the mechanism underlying salt tolerance, we conducted a comparative analysis of the transcriptome and metabolome in two distinct sweet sorghum genotypes, namely the salt-tolerant line NY1298 and the salt-sensitive line MY1176, following exposure to salt treatment. Our initial findings indicate the presence of genotype-specific responses in brewing sorghum under salt stress conditions.
View Article and Find Full Text PDFA quantitative prediction method utilizing whole omics data for biosensing.
Sci Rep
January 2025
BioResource Research Center, RIKEN, 3-1-1, Koyadai, Tsukuba, 305-0074, Ibaraki, Japan.
Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker.
View Article and Find Full Text PDFSTAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.
Nat Commun
January 2025
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!