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

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Published on: May 23, 2018

An improved filtering algorithm for big read datasets and its application to single-cell assembly.

Axel Wedemeyer¹, Lasse Kliemann², Anand Srivastav²

¹Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, Kiel, 24118, Germany. axw@informatik.uni-kiel.de.

BMC Bioinformatics

|July 5, 2017

Summary

Bignorm is a new, faster read filtering algorithm that significantly reduces sequencing data size while maintaining high quality. It enables faster and high-quality genome assemblies compared to existing methods like Diginorm.

Keywords:

Bignorm Coverage Diginorm Read filtering Read normalization Singe cell sequencing

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Microfluidics-based High-throughput Circulating Tumor Cell Sorting and Single-cell Sequencing Technology

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-coverage sequencing for single-cell and metagenomic projects generates massive datasets with redundant data.
Pre-assembly read filtering is crucial for managing large sequencing datasets.
Existing algorithms like Diginorm filter reads based on k-mer abundance.

Purpose of the Study:

To introduce Bignorm, a novel, faster, and quality-conscious algorithm for read filtering.
To evaluate Bignorm's effectiveness in reducing data size and improving assembly efficiency.

Main Methods:

Bignorm utilizes Phred quality scores and detailed k-mer count analysis for read selection.
The study identifies and recommends optimal parameters for the Bignorm algorithm.
Performance comparison with Diginorm using the SDAdes assembler.

Main Results:

Bignorm median-filtered 97.15% of reads while preserving high mean Phred scores.
Assemblies generated from Bignorm-filtered data using SDAdes were of high quality.
Assembly time was significantly reduced compared to datasets filtered with Diginorm.

Conclusions:

Read filtering is an effective strategy for data reduction and accelerating genome assembly.
Bignorm offers competitive assembly quality to Diginorm but with substantially increased speed.
Bignorm is applicable to single-cell and metagenomic projects with high-coverage sequencing data.