Targeted decontamination of sequencing data with CLEAN
Abstract
Background
Many biological and medical questions are answered based on the analysis of sequence data. However, we can find contaminations, artificial spike-ins, and overrepresented rRNA sequences in various read collections and assemblies; complicating data analysis and making interpretation difficult. In particular, spike-ins used as controls, such as those known from Illumina (PhiX phage) or Nanopore data (DNA CS lambda phage, yeast enolase ENO2), are often not considered as contaminants and also not appropriately removed during bioinformatics analyses.
Findings
To address this, we developed CLEAN, a pipeline to remove unwanted sequence data from both long and short read sequencing techniques from a wide range of use cases. While focusing on Illumina and Nanopore data and removing of their technology-specific control sequences, the pipeline can also be used for everyday tasks, such as host decontamination of metagenomic reads and assemblies, or the removal of rRNA from RNA-Seq data. The results are the purified sequences and the sequences identified as contaminated with statistics summarized in an HTML report.
Conclusions
The decontaminated output files can be used directly in subsequent analyses, resulting in faster computations and improved results. Although decontamination is a task that seems mundane, many contaminants are routinely overlooked, cleaned by steps that are not fully reproducible or difficult to trace by the user. CLEAN will facilitate reproducible, platform-independent data analysis in genomics and transcriptomics and is freely available at<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/hoelzer/clean">https://github.com/hoelzer/clean</ext-link>under a BSD3 license.
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