Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions

Marion Ballenghien (1) , Nicolas Faivre (1) , Nicolas Galtier (1, 2, 3, 4, 5)

1 UMR ISEM - Institut des Sciences de l'Evolution de Montpellier
2 CNRS - Centre National de la Recherche Scientifique
3 UM - Université de Montpellier
4 IRD - Institut de Recherche pour le Développement
5 EPHE - École Pratique des Hautes Études

Background: Contamination is a well-known but often neglected problem in molecular biology. Here, we investigated the prevalence of cross-contamination among 446 samples from 116 distinct species of animals, which were processed in the same laboratory and subjected to subcontracted transcriptome sequencing. Results Using cytochrome oxidase 1 as a barcode, we identified a minimum of 782 events of between-species contamination, ~80% of our samples being affected. The analysis of laboratory metadata revealed a strong effect of the sequencing center: nearly all the detected events of between-species contamination involved species that were sent the same day to the same company. We introduce new methods to approach the amount of within-species, between-individual contamination, and to correct for this problem when calling genotypes from base read counts. Conclusions We report evidence for pervasive within-species contamination in this data set, and show that classical population genomic statistics, such as pS, pN/pS, FIT and Tajima's D, are sensitive to this problem to various extents. Control analyses suggest that our published results are probably robust to the problem of contamination. Recommendations on how to prevent or avoid contamination in large-scale population genomics/molecular ecology are provided based on this analysis.

Domaines: Génétique des populations [q-bio.PE]; Systématique, phylogénie et taxonomie; Génomique, Transcriptomique et Protéomique [q-bio.GN]

Marion Ballenghien, Nicolas Faivre, Nicolas Galtier. Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions. BMC Biology, 2017, 15 (1), ⟨10.1186/s12915-017-0366-6⟩. ⟨hal-01815358⟩