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Updated: Jun 6, 2025

3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

AmpSeqR: an R package for amplicon deep sequencing data analysis.

Jiru Han^1,2, Jacob E Munro^1,2, Melanie Bahlo^1,2

¹Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia.

|November 25, 2024

Summary

This summary is machine-generated.

This study introduces AmpSeqR, an R package for processing amplicon sequencing data. It enhances accuracy in detecting low-frequency variants in infectious disease samples.

Keywords:

R package amplicon sequencing data visualization summary report

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Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

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Published on: April 14, 2016

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Amplicon sequencing (AmpSeq) is crucial for high-depth sequencing of targeted genomic regions.
Existing bioinformatics pipelines struggle with noise reduction and downstream analysis for AmpSeq data.
Applications span cancer, infectious diseases, and brain mosaicism studies.

Purpose of the Study:

To present AmpSeqR, a novel R package for processing deep short-read amplicon sequencing data.
To improve the accuracy of sequence data by filtering reads and reducing PCR errors and artifacts.
To facilitate the detection of very low-frequency clones in mixed samples, particularly for infectious disease research.

Main Methods:

Development of the AmpSeqR R package, integrating existing R packages with new functions.
Implementation of optimal read filtering for noise reduction and sequence accuracy.
Inclusion of functions for data pre-processing, amplicon sequence variant (ASV) estimation, and post-processing.
Automated generation of comprehensive Rmarkdown reports for results visualization and reporting.

Main Results:

AmpSeqR effectively processes deep short-read amplicon sequencing data.
The package enhances the accuracy of detected sequences by minimizing errors and artifacts.
It enables sensitive detection and quantification of low-frequency variants and minor clones in mixed samples.
Automated reporting facilitates integration into scientific publications.

Conclusions:

AmpSeqR provides a robust and user-friendly pipeline for amplicon sequencing data analysis.
The package improves the reliability of sequence variant detection, especially for low-frequency elements.
AmpSeqR is particularly valuable for infectious disease studies requiring high sensitivity and accuracy.