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RNA-seq03:21

RNA-seq

11.8K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Viruses with RNA Genomes01:29

Viruses with RNA Genomes

848
RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
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Genomics02:02

Genomics

39.8K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
39.8K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.4K
Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

36.9K
Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
36.9K
RNA Stability01:53

RNA Stability

35.6K
Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Related Experiment Video

Updated: Jan 21, 2026

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
18:30

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells

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scVAR: integrating genomics and transcriptomics from single-cell RNA-seq -insights from leukemia case studies.

Ludovica Celli1, Samuele Manessi1, Matteo Barcella2

  • 1Institute of Biomedical Technologies, National Research Council (ITB-CNR), Segrate, Italy.

Frontiers in Genetics
|January 20, 2026
PubMed
Summary

scVAR integrates genetic variation from single-cell RNA sequencing (scRNA-seq) data, improving the identification of cell subpopulations in complex diseases like leukemia. This computational framework enhances disease diagnosis and therapy by combining transcriptomic and genomic insights.

Keywords:
genetic heterogeneityleukemiamulti-omics integrationsingle-cell RNA sequencingvariational autoencoder

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Transcriptome Analysis of Single Cells
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Transcriptome Analysis of Single Cells

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Last Updated: Jan 21, 2026

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Transcriptome Analysis of Single Cells
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Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • High-throughput technologies, including single-cell RNA sequencing (scRNA-seq), are revolutionizing biomedical research by enabling detailed analysis of cellular heterogeneity.
  • Complex diseases like Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) exhibit significant genetic and phenotypic diversity, complicating diagnosis and treatment.
  • While DNA sequencing is standard for genetic variation, the transcriptome also holds valuable genomic information.

Purpose of the Study:

  • To introduce scVAR, a novel computational framework designed to learn and integrate genetic variation directly from scRNA-seq data.
  • To enhance the detection of subtle cellular differences by unifying transcriptomic and variant-derived information using a cross-attention mechanism.
  • To demonstrate the utility of scVAR in leukemia case studies for improved cell identity and subpopulation discovery.

Main Methods:

  • Development of scVAR, a computational framework utilizing variational autoencoders.
  • Implementation of a paired encoder-decoder architecture with a cross-attention-based fusion layer.
  • Application of scVAR to analyze scRNA-seq data from leukemia case studies.

Main Results:

  • scVAR successfully integrates transcriptomic and variant data, revealing cell identities missed by analyzing each data type separately.
  • The framework identified 20%-30% more subpopulations compared to transcriptomic analysis alone, even with limited variant coverage.
  • Variant detection was optimized within captured regions for 3' scRNA-seq, maximizing information extraction.

Conclusions:

  • scVAR effectively bridges the gap between transcriptomics and genomics for single-cell analysis.
  • The framework provides a broadly applicable platform for the integrative characterization of cell states and disease processes.
  • Integrating variant information with scRNA-seq data significantly enhances the resolution of cellular heterogeneity in complex diseases.