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Updated: Mar 9, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data.

Markus Lux¹, Jan Krüger², Christian Rinke³

¹Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Universitätsstr. 25, Bielefeld, 33615, Germany. mlux@techfak.uni-bielefeld.de.

BMC Bioinformatics

|December 22, 2016

Summary

This summary is machine-generated.

Contamination in single-cell sequencing poses a challenge, but acdc offers a novel solution. This tool uses advanced machine learning for reliable, reference-free contaminant detection and removal, improving genome assembly quality.

Keywords:

Binning Clustering Contamination detection Machine learning Quality control Single-cell sequencing

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Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell sequencing is hampered by foreign DNA contamination.
Current methods rely on reference-based detection, limiting contaminant identification.
A reference-free approach is crucial due to fragmented genomic coverage across species.

Purpose of the Study:

To introduce acdc, a novel tool for genomic sequence data quality control.
To develop a reliable method for detecting both known and de novo contaminants.
To provide a reference-free solution for contaminant identification in single-cell genomics.

Main Methods:

Combines supervised (16S rRNA gene prediction, ultrafast alignment) and unsupervised (machine learning, dimensionality reduction, clustering) approaches.
Employs bootstrapping for statistically sound confidence values in contaminant detection.
Features an interactive user interface and a command-line application for workflow integration.

Main Results:

Acdc accurately and quickly identifies contamination in diverse sequencing projects.
The tool successfully detects both known and novel (de novo) contaminants.
Unsupervised methods enable the identification of contaminants lacking close reference species.

Conclusions:

Acdc reliably detects contamination in single-cell genome data.
It complements existing tools by offering unsupervised detection of de novo contaminants.
Acdc has the potential to significantly reduce resource expenditure in quality assurance for rapidly growing single-cell genomics data.