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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: Apr 12, 2026

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

PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets.

Changjin Hong¹, Solaiappan Manimaran², William Evan Johnson²

¹Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA. ; Cytogenetics/Molecular Genetics Laboratory, Nationwide Children's Hospital, Columbus, Ohio, USA.

Cancer Informatics

|May 19, 2015

Summary

This summary is machine-generated.

PathoQC is a new toolkit for quality control and read preprocessing in high-throughput sequencing data. It combines popular methods to remove technical artifacts, ensuring accurate biological conclusions from genomic screens.

Keywords:

metagenomics parallel processing sequencing quality control sequencing read preprocessing

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Quality control and read preprocessing are essential for high-throughput genomic screening data analysis.
Improper preprocessing can lead to inaccurate biological conclusions.

Purpose of the Study:

To present PathoQC, a toolkit for streamlined quality control and read preprocessing of next-generation sequencing data.
To provide a versatile tool for various high-throughput sequencing applications.

Main Methods:

PathoQC integrates multiple quality control software approaches.
It is available as a module within the PathoScope software suite and as a standalone open-source Python module.
Supports parallel computation for high performance.

Main Results:

PathoQC effectively removes technical sequencing artifacts.
Facilitates robust downstream analysis of genomic data.
Offers a variety of quality control options suitable for diverse applications.

Conclusions:

PathoQC is a high-performance, effective tool for preprocessing next-generation sequencing data.
Its integration into bioinformatics workflows enhances the reliability of genomic analyses.