Preprint Article Version 1 This version is not peer-reviewed

Hierarchical Clustering of DNA K-Mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities

Version 1 : Received: 5 November 2018 / Approved: 7 November 2018 / Online: 7 November 2018 (14:24:53 CET)

A peer-reviewed article of this Preprint also exists.

Kaisers , W.; Schwender, H.; Schaal , H. Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities. Int. J. Mol. Sci. 2018, 19, 3687. Kaisers , W.; Schwender, H.; Schaal , H. Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities. Int. J. Mol. Sci. 2018, 19, 3687.

Journal reference: Int. J. Mol. Sci. 2018, 19, 3687
DOI: 10.3390/ijms19113687

Abstract

We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as an unspecific diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data. Analysis of three Fastq batches downloaded from ArrayExpress indicated experimental effects. Analysis of RNAseq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced in our facility indicate presence of batch effects. The observed batch effects were also present in reads mapped to the human genome and also in reads filtered for high quality (Phred > 30). We propose, that hierarchical clustering of DNA k-mer counts provides an unspecific diagnostic tool and a quality criterion and for RNAseq experiments.

Subject Areas

Hierarchical clustering; DNA; Fastq; HcKmer

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