Reanalysis of data from [14] using Galaxy and Jupyter.

A. Workflow used in the analysis. As an input, the workflow takes a collection of paired Illumina datasets and outputs an unfiltered list of variable sites. B. Galaxy history showing all steps of these analyses. It only contains 12 steps because we use dataset collections to combine multiple similar datasets into a small number of history entries. This significantly simplifies processing. For example, collection 313 contains all 312 paired-end Illumina datasets generated for this study. This allows us to deal with just one history item instead of 312. The next item in the history is a collection of BAM datasets generated by mapping each read-pair from collection 313 against human genome (hg38) with bwa-mem. These BAM datasets are de-duplicated (collection 627), filtered (by only retaining reads mapping to mitochondrial DNA, with mapping quality of 20 or higher, and mapped in a proper pair; collection 941), realigned to mitigate misalignment around indels or structural variant calls (collection 1098), and used to call variants with Naive Variant Caller [21]. Finally, we use Variant Annotator to process VCF datasets generated by Naive Variant Caller and to create a list of variants (collection 1412) and the concatenation tool to reduce collection 1412 into a single table (dataset 1413). This dataset is used for further processing with Jupyter. C. The relationship of minor allele frequencies for heteroplasmic sites between tissues (panels A and B) and individuals (panels C and D). D. Estimates for bottleneck size with (red) and without (blue) accounting for mitotic segregation.