An Improved Filtering Algorithm for Big Read Datasets

TL;DR

Bignorm is a new read filtering algorithm that improves speed and quality by incorporating quality scores, significantly reducing dataset size and accelerating genome assembly without compromising assembly quality.

Contribution

We introduce Bignorm, a faster, quality-aware filtering algorithm that outperforms Diginorm in speed while maintaining high assembly quality.

Findings

Removes 97.15% of reads median

Produces high-quality assemblies faster

Maintains competitive assembly quality

Abstract

For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Titus Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their $k$-mers. We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new feature is the use of phred quality scores together with a detailed analysis of the $k$-mer counts to decide which reads to keep. With recommended parameters, in terms of median we remove 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. We conclude that read filtering is a practical method for reducing read data and for speeding up the assembly process. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm.git