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Related Concept Videos

RNA-seq03:21

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 microarray-based...
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Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: Jun 26, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

ReadChop: a high-performance demultiplexer for long-read sequencing data.

Chen Jiang1, Yuanyan Xiong1

  • 1Department of Biochemistry, Key Laboratory of Gene Engineering of the Ministry of Education, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China.

Bioinformatics (Oxford, England)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

ReadChop is a new high-performance demultiplexer for long-read sequencing data. It efficiently handles complex barcode designs and significantly speeds up processing, offering a flexible solution for massive datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long-read sequencing (LRS) offers extended read lengths but faces computational hurdles like high error rates and indel artifacts.
  • Sample multiplexing is crucial for cost-efficiency in LRS, but existing demultiplexing tools struggle with flexibility for non-canonical designs or speed for complex barcodes.

Purpose of the Study:

  • To develop a high-performance demultiplexer for long-read sequencing data.
  • To address the limitations of existing tools in handling complex barcode designs and computational challenges.

Main Methods:

  • Developed ReadChop, a demultiplexer implemented in Rust, utilizing Myers' bit-parallel algorithm for indel-rich error profiles.
  • Employed a streaming architecture for low memory footprint and efficient processing of large datasets.
  • Benchmarked ReadChop on simulated and empirical datasets, including ultra-high multiplexing (13,824-plex) and SARS-CoV-2 amplicons.

Main Results:

  • Achieved over 99.99% classification precision on simulated and empirical data.
  • Demonstrated efficient filtering of in silico chimeras with a 0.1% miss rate.
  • Showcased linear computational scalability on ultra-long templates (up to 100 kb) with memory usage consistently below 200 MB.
  • Exceeded performance benchmarks, being >6x faster than Dorado, >2x faster than Nanoplexer, and >30x faster than Cutadapt.

Conclusions:

  • ReadChop provides a flexible, robust, and high-performance solution for demultiplexing massive long-read sequencing datasets.
  • The tool effectively handles non-canonical experimental designs and complex barcode structures.
  • ReadChop significantly improves processing speed and efficiency compared to existing demultiplexing software.