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RNA-seq

RNA-seq

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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Related Experiment Video

Updated: Dec 22, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue.

Kevin Litchfield¹, Stacey Stanislaw², Lavinia Spain³

¹Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.

|May 7, 2020

Summary

This summary is machine-generated.

Current tumor sequencing methods are biased by small sample sizes. Representative sequencing (Rep-Seq) uses homogenized tissue for unbiased tumor profiling, significantly reducing misclassification of tumor mutation burden (TMB) and improving clinical utility.

Keywords:

biomarkers homogenization molecular profiling representative sampling tumor hetereogeneity tumor mutational burden tumor sampling tumor sequencing

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Last Updated: Dec 22, 2025

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Thousands of solid tumors are sequenced, but current methods have inherent under-sampling bias.
Fixed-dimension tissue samples (e.g., 6 mm biopsy) are under-powered as tumor volume increases.
This bias leads to misclassification of tumor characteristics like tumor mutation burden (TMB).

Purpose of the Study:

To introduce and validate Representative Sequencing (Rep-Seq) as a novel method for unbiased tumor tissue sampling.
To assess the impact of Rep-Seq on the accuracy of intratumor TMB variability analysis.
To demonstrate the potential of Rep-Seq for improved clinical utility in cancer research.

Main Methods:

Rep-Seq involves homogenizing fixed residual tumor material for next-generation sequencing.
Analysis focused on intratumor TMB variability and misclassification rates compared to single-biopsy methods.
Evaluated misclassification in lung and bladder tumors using both current and Rep-Seq methodologies.

Main Results:

Current single-biopsy methods misclassify TMB in a significant percentage of tumors (20% lung, 52% bladder).
Rep-Seq dramatically reduced misclassification rates to 2% for lung and 4% for bladder tumors.
Demonstrated high intratumor TMB variability that is poorly captured by conventional biopsies.

Conclusions:

Rep-Seq provides a more representative sampling method for comprehensive tumor profiling.
This improved methodology significantly enhances the accuracy of TMB assessment.
Rep-Seq holds substantial potential for advancing clinical utility and precise clonal structure deconvolution in oncology.