Targeted decontamination of sequencing data with CLEAN
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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 https://github.com/hoelzer/clean under a BSD3 license.
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As an rRNA reference, we provide the rRNA database from SortMeRNA, a tool commonly used to filter rRNA from metatranscriptomic data. The database contains representative rRNA sequences from the Rfam and SILVA databases (see https://github.com/biocore/sortmerna/blob/master/data/rRNA_databases/README.txt).
Can this be turned off? I can imagine for e.g. metagenomics one might not want these removed.
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Homo sapiens (Ensembl release 99), Mus musculus (Ensembl release 99), Gallus gallus (Ensembl release 99), Escherichia coli (Ensembl release 45), Chlorocebus sabeus (NCBI GCF_000409795.2), and Columba livia (NCBI GCF_000337935.1)
Just curious if you've thought of benchmarking with T2T assemblies. I think it could be useful and cool (although perhaps beyond the scope of this preprint) to see how much contam changes based on the completeness of the contam reference.
I'm also curious if these genomes needed to be masked at all (repeats, or ribosomal or other highly conserved sequences) so that off-target mapping doesn't occur. https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/37175-introducing-removehuman-human-contaminant-removal
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we also offer a kmer-based option with bbduk
Can you change some of the early language to reflect this? The bbduk method is very different from mapping -- especially depending on the k-mer size that you end up using. It also will have very different output files (no BAM!), which is great, as BAMs are huge and a lot of people like to avoid them.
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Figure 1.
This figure is a bit blurry, would it be possible to update to a higher resolution image? Additionally, this overview is much more useful than the one currently in the GitHub readme. Would you be willing/able to swap out the README figure and place this one in instead?
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