Estimating individual mtDNA haplotypes in mixed DNA samples by combining MinION and MiSeq



doi: 10.1007/s00414-021-02763-0.


Online ahead of print.

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Hiroaki Nakanishi et al.


Int J Legal Med.


.

Abstract

We tried to estimate individual mtDNA haplotypes in mixed DNA samples by combining MinION and MiSeq. The BAM files produced by MiSeq were viewed using Integrative Genomics Viewer (IGV) to verify mixed bases. By sorting the reads according to base type for each mixed base, partial haplotypes were determined. Then, the BAM files produced by MinKNOW were viewed using IGV. To determine haplotypes with IGV, only mixed bases determined by MiSeq were used as target bases. By sorting the reads according to base type for each target base, each contributor’s haplotype was estimated. In mixed samples from two contributors, even a haplotype with a minor contribution of 5% could be distinguished from the haplotype of the major contributor. In mixed samples of three contributors (mixture ratios of 1:1:1 and 4:2:1), each haplotype could also be distinguished. Sequences of C-stretches were determined very inaccurately in the MinION analysis. Although the analysis method was simple, each haplotype was correctly detected in all mixed samples with two or three contributors in various mixture ratios by combining MinION and MiSeq. This should be useful for identifying contributors to mixed samples.


Keywords:

Contributor; MiSeq; MinION; Mitochondrial DNA; Mixed DNA sample.

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References

    1. Bleka Ø, Storvik G, Gill P (2016) EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci Int Genet 21:35–44. doi.org/10.1016/j.fsigen.2015.11.008



      DOI



      PubMed

    1. Swaminathan H, Grgicak CM, Medard M, Lun DS (2015) NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping. Forensic Sci Int Genet 16:172–180. doi.org/10.1016/j.fsigen.2014.11.010



      DOI



      PubMed

    1. Swaminathan H, Garg A, Grgicak CM, Medard M, Lun DS (2016) CEESIt: a computational tool for the interpretation of STR mixtures. Forensic Sci Int Genet 22:149–160. doi.org/10.1016/j.fsigen.2016.02.005



      DOI



      PubMed

    1. Marsden CD, Rudin N, Inman K, Lohmueller KE (2016) An assessment of the information content of likelihood ratios derived from complex mixtures. Forensic Sci Int Genet 22:64–72. doi.org/10.1016/j.fsigen.2016.01.008



      DOI



      PubMed

    1. Manabe S, Morimoto C, Hamano Y, Fujimoto S, Tamaki K (2017) Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model. PLoS ONE 12(11):e0188183. doi.org/10.1371/journal.pone.0188183



      DOI



      PubMed



      PMC

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