Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences.

By R. Henrik Nilsson, Leho Tedersoo, Kessy Abarenkov, Martin Ryberg, Erik Kristiansson, Martin Hartmann, C. Schoch, Johan A. A. Nylander, J. Bergsten, T. Porter and 9 more. 2012

200 citations recorded by the source corpus.

Molecular data form an important research tool in most branches of mycology. A non-trivial proportion of the public fungal DNA sequences are, however, compromised in terms of quality and reliability, contributing noise and bias to sequence-borne inferences such as phylogenetic analysis, diversity assessment, and barcoding. In this paper we discuss various aspects and pitfalls of sequence quality assessment. Based on our observations, we provide a set of guidelines to assist in manual quality management of newly generated, near-full-length (Sanger-derived) fungal ITS sequences and to some extent also sequences of shorter read lengths, other genes or markers, and groups of organisms. The guidelines are intentionally non-technical and do not require substantial bioinformatics skills or significant computational power. Despite their simple nature, we feel they would have caught the vast majority of the severely compromised ITS sequences in the public corpus. Our guidelines are nevertheless not infallible, and common sense and intuition remain important elements in the pursuit of compromised sequence data. The guidelines focus on basic sequence authenticity and reliability of the newly generated sequences, and the user may want to consider additional resources and steps to accomplish the best possible quality control. A discussion on the technical resources for further sequence quality management is therefore provided in the supplementary material.

Subfields: Cell Biology, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/97df8c67201d662effcf9a10cbfce312c5aa2cd4

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Citations in the corpus

Improving ITS sequence data for identification of plant pathogenic fungi (2014, background): “Improving ITS sequence data for identification of plant pathogenic fungi”
Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi (2015, unknown): “The possibility of identical chimeras occurring in GenBank and thus being interpreted as valid taxa was noted by Nilsson et al. (2012).”
One stop shop: backbones trees for important phytopathogenic genera: I (2014) (2014, background): “There are several caveats to consider when using BLAST analysis to identify isolates to species level to prevent misidentification (Kang et al. 2010; Nilsson et al. 2012).”