Dataset Information

Competitive mapping allows for the identification and exclusion of human DNA contamination in ancient faunal genomic datasets.

ABSTRACT:

Background

After over a decade of developments in field collection, laboratory methods and advances in high-throughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data.

Results

Here we investigate whether human contaminating DNA can be found in ancient faunal sequencing datasets. We identify variable levels of human contamination, which persists even after the sequence reads have been mapped to the faunal reference genomes. This contamination has the potential to affect a range of downstream analyses.

Conclusions

We propose a fast and simple method, based on competitive mapping, which allows identifying and removing human contamination from ancient faunal DNA datasets with limited losses of true ancient data. This method could represent an important tool for the ancient DNA field.

SUBMITTER: Feuerborn TR

PROVIDER: S-EPMC7708127 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Competitive mapping allows for the identification and exclusion of human DNA contamination in ancient faunal genomic datasets.

Feuerborn Tatiana R TR Palkopoulou Eleftheria E van der Valk Tom T von Seth Johanna J Munters Arielle R AR Pečnerová Patrícia P Dehasque Marianne M Ureña Irene I Ersmark Erik E Lagerholm Vendela Kempe VK Krzewińska Maja M Rodríguez-Varela Ricardo R Götherström Anders A Dalén Love L Díez-Del-Molino David D

BMC genomics 20201130 1

<h4>Background</h4>After over a decade of developments in field collection, laboratory methods and advances in high-throughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data.<h4>Results</h4>Here we investigate whether human contaminating DNA can be found in ancient faunal sequencing datasets. We identify variable levels of hum ...[more]

PMID: 33256612

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Project description:High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.

Dataset Information

Competitive mapping allows for the identification and exclusion of human DNA contamination in ancient faunal genomic datasets.

Background

Results

Conclusions

Publications

Competitive mapping allows for the identification and exclusion of human DNA contamination in ancient faunal genomic datasets.

Similar Datasets

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