Abstract
Errors in mitochondrial DNA (mtDNA) sequencing can result in unusual patterns of polymorphisms which can be detected by the lack of similarity to the sequences in a database. In this work, an approach to routine data quality review is described that employs cluster analysis to identify conserved sequence similarities. Comparison of new sequences to the clusters identifies unusual polymorphisms and sequence regions that may warrant further attention by the analyst. This approach is accessible, easily automatable and can be tailored specifically to targeted population groups in individual investigations.
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