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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Related Experiment Video

Updated: Apr 30, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Extraction of Molecular Features through Exome to Transcriptome Alignment.

Prakriti Mudvari1, Kamran Kowsari2, Charles Cole

  • 1McCormick Genomics and Proteomics Center, USA.

Journal of Metabolomics and Systems Biology
|May 3, 2014
PubMed
Summary

This study integrates DNA and RNA sequencing data from the same individual to uncover disease pathways and regulatory elements. New analytical strategies reveal insights beyond individual datasets for systems biology approaches.

Keywords:
Allele Preferential ExpressionAllelic ImbalanceBreast CancerBreast TumorExomeImprintingLOHRNA EditingSNPSomatic MutationsTranscriptome

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) enables integrated DNA and RNA analysis, revealing disease pathways and therapeutic targets.
  • Increasing availability of matched exome, genome, and transcriptome data necessitates advanced exploration strategies.
  • Systems biology approaches integrating genomic and transcriptomic data offer deeper mechanistic and regulatory insights.

Purpose of the Study:

  • To integrate variation and expression data from matched normal and tumor breast samples.
  • To develop and illustrate analytical algorithms for identifying regulatory elements using SNP-centered variant allelic prevalence.
  • To address potential biases and enhance confidence in findings from integrated NGS analyses.

Main Methods:

  • Integrated analysis of four Next-Generation Sequencing (NGS) datasets: exomes and transcriptomes from normal and tumor breast tissues of the same individual.
  • Focus on SNP-centered variant allelic prevalence to identify regulatory elements.
  • Development of analytical algorithms to detect expression advantage, imprinting, loss of heterozygosity (LOH), somatic changes, and RNA editing.

Main Results:

  • Demonstrated analytical algorithms for extracting and validating potential regulatory elements from integrated NGS data.
  • Identified critical factors that can bias integrated analysis outputs.
  • Provided recommendations for maximizing the confidence of findings in integrative genomics studies.

Conclusions:

  • Integrative analysis of genome and transcriptome data provides unique mechanistic and regulatory insights.
  • Advanced analytical strategies are crucial for leveraging comprehensive NGS datasets in systems biology.
  • This approach moves beyond the limitations of analyzing individual datasets to reveal complex biological features.