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Open AccessArticle

Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities

Department of Anaesthesiology, HELIOS University Hospital Wuppertal, University of Witten/Herdecke, Heusnerstr. 40, 42283 Wuppertal, Germany
Institut fur Virologie, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
Mathematisches Institut, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(11), 3687;
Received: 5 November 2018 / Accepted: 15 November 2018 / Published: 21 November 2018
(This article belongs to the Section Biochemistry)
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 a 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 for RNAseq experiments. Further exploration is required once samples are identified as outliers in HC derived trees. View Full-Text
Keywords: DNA-kmer; Fastq; RNAseq DNA-kmer; Fastq; RNAseq
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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.

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