A new method for decontamination ofde novotranscriptomes using a hierarchical clustering algorithm

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Abstract

Motivation:

The identification of contaminating sequences in a de novo assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination.

Results:

We present a new decontamination method based on a hierarchical clustering algorithm called MCSC. This method uses frequent patterns found in sequences to create clusters. These clusters are then linked to the target species or tagged as contaminants using classic alignment tools. The main advantage of this decontamination method is that it allows sequences to be tagged correctly even if they are unknown or misaligned to a database.

Availability and Implementation:

Scripts and documentation about the MCSC decontamination method are available at https://github.com/Lafond-LapalmeJ/MCSC_Decontamination.

Contact

: benjamin.mimee@agr.gc.ca

Supplementary information:

Supplementary data are available at Bioinformatics online.

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