Blakesley et al. BMC Genomics 2010, 11:21 http://www.biomedcentral.com/1471-2164/11/21 Page 11 of 15 Table 5 BAC sequence assemblies generated using standard versus copy-control bacterial host cells. BAC sequence [GenBank:] Species ENCODE Region Redundancy variation No. contigs >2 kb No. uncaptured gaps AC19008 AC153092 AC188899 AC190076 AC182792 AC175233 AC186717 AC188506 AC190002 AC187194 AC172296 AC190000 AC188356 Cat ENm011 Platypus ENm009 Owl Monkey ENm006 Owl Monkey ENm011 Shrew ENr331 Shrew ENr322 Platypus ENm006 Rabbit ENm006 Owl Monkey ENr312 Platypus ENr312 Hedgehog ENr211 Owl Monkey ENm007 Dusky Titi ENm006 Standard ++++ ++++ ++++ ++ ++ ++ ++ ++ ++ ++ + + + Copy control Standard - 18 - 15 - 14 - 22 - 22 + 21 - 20 + 11 ++ 10 + 9 - 21 - 10 - 8 Copy control Standard 13 6 8 13 9 6 15 9 11 11 22 9 10 7 6 5 6 5 12 7 13 9 6 2 13 0 Copy control 2 3 3 1 0 10 3 0 4 3 0 0 2 Representative BACs whose generated sequences were associated with atypical characteristics (in terms of large numbers of contigs and uncaptured gaps) were tested. Two exceptions [GenBank: AC190000 and AC188356] represent typical sequence assemblies with respect to redundancy of sequence reads, number of contigs, and number of uncaptured gaps. Only three BACs [GenBank: AC190002, AC187194, and AC172296] belong to the second data set (Tables 2 and 3), while the remaining BACs have sequences orthologous to other ENCODE regions. The variation in the redundancy of sequence reads across all contigs was qualitatively assessed as low (-), medium (+), high (++), or very high (++++); see Figure 4 for illustrative examples. gaps, respectively. In most cases, the sequence assem- blies generated using the copy-control host cells con- tained a greatly reduced total number of gaps (especially uncaptured gaps; see Table 5). In several cases, the total number of uncaptured gaps was reduced to <2, thereby eliminating the challenge of ordering and orienting sequence contigs. Interestingly, a significant reduction in the number of gaps per BAC is associated with decreased variation in sequence-read redundancy; we now use this correlation to predict whether a particular BAC sequence assembly with many gaps would benefit from the extra effort of preparing a supplementary shot- gun library with copy-control competent cells. Several points should be made about using such copy- control host strains for shotgun-library construction. First, because there is some reduction in the yields of purified plasmid DNA (and, therefore, sequencing suc- cess rates and read lengths) associated with the use of EPI400 cells, we only utilize copy-control host strains in special cases. For example, for species (e.g., platypus) whose initial sequence assemblies regularly have large numbers of gaps, we now routinely use copy-control EPI400 cells during the initial shotgun phase; for other species, these cells are used only after the BAC sequence assembly is reviewed and deemed to require such an approach {with the key determinant being a large varia- tion in sequence-read redundancy (as seen in Figure 4) rather than simply the total number of uncaptured gaps}. Finally, implementing the use of a copy-control host strain is straightforward in a high-throughput sequencing pipeline, making it a convenient and practi- cal option on an as-needed basis. In practice, competent cells derived from the copy-control host strain are sim- ply transformed by the original ligation reaction, elimi- nating the need to construct an entirely new library in an alternate cloning vector. Utilization of ‘next-generation’ sequencing technologies Recently, several ‘next-generation’ DNA sequencing technologies have become commercially available [33-35]. These involve the use of completely different methods for generating shotgun-sequence data, with the resulting sequence reads being considerably shorter than those generated by Sanger-based chemistries (but at a fraction of the per-base cost). We investigated whether data produced with one of these new technologies with- out cloning bias might complement our standard San- ger-based shotgun sequence reads in a fashion that reduced the problems encountered during sequence finishing. We selected 12 BACs for this study: 10 whose sequence was previously found to be problematic at the sequence- finishing stage and 2 whose sequence was straightforward to finish (these BACs were derived from 8 different species and 7 different genomic regions). The purified DNA from each BAC was sheared and then ligated with a unique ‘indexing’ linker. All 12 samples were then mixed together, and the resulting pool then used to make a single shotgun sequencing library using the Genome Analyzer instrument (Illumina) [33]. The resulting paired sequence reads (each being 35 bases in length) were sorted by BAC using the