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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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Updated: Jun 5, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

QClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples.

Eloi Schmauch^1,2,3, Johannes Ojanen^1,2,3, Kyriakitsa Galani^1,2

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA.

Nucleic Acids Research

|December 10, 2024

Summary

This summary is machine-generated.

Quality Clustering (QClus) is a new algorithm for filtering droplets in single-nuclei RNA sequencing. It improves data quality for challenging human tissue samples by accurately identifying and removing empty or contaminated droplets.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Single-nuclei RNA sequencing (snRNA-seq) is crucial for understanding human tissue heterogeneity.
Challenges in snRNA-seq include background noise and contamination, masking true cell-type signals.
Existing droplet filtering methods struggle with complex or contaminated samples.

Purpose of the Study:

To develop an advanced droplet filtering algorithm for snRNA-seq.
To enhance the reliability and accuracy of snRNA-seq data analysis, especially for difficult samples.
To provide a robust solution for processing diverse snRNA-seq datasets.

Main Methods:

Introduced Quality Clustering (QClus), a novel droplet filtering algorithm.
QClus utilizes cell-type-specific marker gene expression for enhanced clustering and filtering.
Algorithm was benchmarked against seven existing methods across 252 datasets (>1.9 million nuclei).

Main Results:

QClus demonstrated superior performance, achieving the highest quality across the most samples.
The algorithm successfully filtered empty and contaminated droplets, even in challenging samples.
QClus showed robust retention of nuclei counts and had no processing failures.

Conclusions:

QClus offers a high-quality, automated, and robust solution for snRNA-seq droplet filtering.
The algorithm's flexibility and user-adjustability make it suitable for various experimental needs.
QClus significantly improves the reliability of snRNA-seq analysis for human tissues.