The assembly-based analysis is limited to detection of variations that occur within contigs. To test for biological variations that might lie in assembly gaps, we identified genome locations that were well covered in data sets that mixed reads from diverse individuals, but were undercovered in multiple NA12878 data sets. First, we gathered two diverse sets of Illumina HiSeq sequencing data aligned to HG19: the first from 39 individuals (198-fold total, data set A2) from the 1000 Genomes Project [32] sequenced with version 2 chemistry and the second from 71 individuals (253-fold total, data set A3) from the 1000 Genomes Project sequenced with version 3 chemistry (see Data for SRA accession numbers). Any reference base with relative coverage of at least 0.5 in either diverse data set was considered 'well covered'. Second, we gathered three NA12878 Illumina HiSeq data sets aligned to HG19: 152-fold from HiSeq v2 chemistry (the Phusion, Phusion + betaine, and AccuPrime data discussed previously, data sets 10 to 12), 110-fold from version 3 chemistry using low-input Fisher et al. library construction (data set 13 with four additional lanes from data set A1), and 120-fold from version 3 chemistry with Kapa-based library construction (the previously discussed 'Kapa' data set 14). Any reference base with less than 0.1 relative coverage in all three NA12878 data sets was considered 'undercovered'. This was a subset of the bases that are undercovered in the HiSeq 'Kapa' data: if a base was not undercovered in one of the other two data sets, then we assumed that its bad performance in the 'Kapa' data might be due to technology rather than biology.
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