Multivariate Cutoff Level Analysis (MultiCoLA) of large community data sets
|Author(s)||Gobet Angelique1, 2, Quince Christopher3, Ramette Alban1|
|Affiliation(s)||1 : Max Planck Inst Marine Microbiol, Microbial Habitat Grp, Bremen, Germany.
2 : Jacobs Univ Bremen GmbH, Bremen, Germany.
3 : Univ Glasgow, Dept Civil Engn, Glasgow G12 8QQ, Lanark, Scotland.
|Source||Nucleic Acids Research (0305-1048) (Oxford Univ Press), 2010-08 , Vol. 38 , N. 15 , P. e155 (9p.)|
|WOS© Times Cited||79|
High-throughput sequencing techniques are becoming attractive to molecular biologists and ecologists as they provide a time- and cost-effective way to explore diversity patterns in environmental samples at an unprecedented resolution. An issue common to many studies is the definition of what fractions of a data set should be considered as rare or dominant. Yet this question has neither been satisfactorily addressed, nor is the impact of such definition on data set structure and interpretation been fully evaluated. Here we propose a strategy, MultiCoLA (Multivariate Cutoff Level Analysis), to systematically assess the impact of various abundance or rarity cutoff levels on the resulting data set structure and on the consistency of the further ecological interpretation. We applied MultiCoLA to a 454 massively parallel tag sequencing data set of V6 ribosomal sequences from marine microbes in temperate coastal sands. Consistent ecological patterns were maintained after removing up to 35-40% rare sequences and similar patterns of beta diversity were observed after denoising the data set by using a preclustering algorithm of 454 flowgrams. This example validates the importance of exploring the impact of the definition of rarity in large community data sets. Future applications can be foreseen for data sets from different types of habitats, e.g. other marine environments, soil and human microbiota.