On the impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

Abstract

The ability to detect the identity of a sample obtained from its environment is a cornerstone of molecular ecological research. Thanks to the falling price of shotgun sequencing, genome skimming, the acquisition of short reads spread across the genome at low coverage, is emerging as an alternative to traditional barcoding. By obtaining far more data across the whole genome, skimming has the promise to increase the precision of sample identification beyond traditional barcoding while keeping the costs manageable.While methods for assembly-free sample identification based on genome skims are now available, little is known about how these methods react to the presence of DNA from organisms other than the target species. In this paper, we show that the accuracy of distances computed between a pair of genome skims based on k-mer similarity can degrade dramatically if the skims include contaminant reads; i.e., any reads originating from other organisms. We establish a theoretical model of the impact of contamination. We then suggest and evaluate a solution to the contamination problem: Query reads in a genome skim against an extensive database of possible contaminants (e.g., all microbial organisms) and filter out any read that matches. We evaluate the effectiveness of this strategy when implemented using Kraken-II, in detailed analyses. Our results show substantial improvements in accuracy as a result of filtering but also point to limitations, including a need for relatively close matches in the contaminant database.

Genome Skimming with Nanopore Sequencing Precisely Determines Global and Transposon DNA Methylation in Vertebrates

“…23 The generation of long-reads is crucial to improve alignment over traditional short reads. 27 The further use of nanopore sequencers for genome skimming will enable the acquisition of DNA methylation in parallel to the DNA sequence data for no additional cost.…”

The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

“…Raw data used in the manuscript is deposited in https://github.com/noraracht/kraken_raw_data.git. Additionally, data repositories are stored in zenodo https://doi.org/10.5281/zenodo.3588625 (Rachtman, Balaban, Bafna, & Mirarab, ) and https://doi.org/10.5281/zenodo.3588569 (Rachtman, Balaban, Bafna, & Mirarab, ). The detailed description of genomic datasets used in our experiments, accession numbers of the assemblies and the exact commands used to simulate genome skims are provided in Appendix S1.…”

Contamination detection in genomic data: more is not enough

“…The presence of foreign DNA in metagenomes is an important problem for microbiome studies [ 14 ]. Genomic contamination is also known to be a source of artefacts in genome skimming [ 15 ] or in phylogenomic studies, with emblematic examples of incorrect results in high-profile articles about animal [ 16 , 17 ] and plant evolution [ 18 , 19 ]. Moreover, contaminated sequences have the power to spread into and across databases over time [ 2 , 12 ].…”