Blakesley et al. BMC Genomics 2010, 11:21 http://www.biomedcentral.com/1471-2164/11/21 Table 3 Comparative-grade finished BAC sequences listed by ENCODE region. ENCODE region Total BACs Total Mb ENm001 ENm003 ENm005 ENm010 ENm012 ENm013 ENm014 ENr111 ENr211 ENr213 ENr221 ENr222 ENr312 ENr323 Totals Size in human (kb) 1,877 500 1,696 500 1,000 1,114 1,163 500 500 500 500 500 500 500 14,850 357 91 256 96 179 184 214 84 89 90 100 98 94 99 2,031 58.9 15.3 42.6 16.1 29.4 30.3 35.4 14.4 15.2 15.4 16.5 16.5 15.7 16.9 338.5 The total number of BACs (Total BACs) and the amount of comparative-grade finished sequence generated (Total Mb) for each genomic region are shown. example, considerable effort was required to resolve misassembled sequences for more than 20% of all BACs from ENCODE pilot project regions ENr231 and ENr333; for the other ENCODE regions, less than 5% of the BACs needed such attention. Similarly, certain spe- cies stand out as being more prone to sequence misas- semblies, regardless of the genomic region being sequenced. For example, baboon BAC sequences typi- cally contain an unusually high frequency of long (2- to 43- kb) inverted and tandem repeats of >90% identity that greatly confound standard sequence-assembly rou- tines. As another example, assembled hedgehog sequences often contained non-contiguous segments that had been incorrectly joined by Phrap. In these cases, more extensive manual effort was required to untangle the misassemblies. We investigated alternative sequence-assembly pro- grams to Phrap that provide ‘read-pair awareness’ as a means of reducing the amount of manual correction of the initial sequence assembly required. Of the many pro- grams evaluated, rPhrap (Geospiza) [27] and Autosort (NFH, unpublished) showed the greatest promise. We thus tested these two sequence-assembly programs by having them assemble the sequence reads generated from 102 BACs that had previously been found to be asso- ciated with Phrap-induced sequence misassemblies. Improved sequence assemblies were generated for 68 of these BACs (with complete resolution for 29 BACs and reduction of minor misassemblies for 39 BACs). Direct comparison of the performance of each program revealed that one generally showed more improvement than the other for any particular BAC, but Autosort overall Page 4 of 15 yielded improved assemblies for twice as many BACs as rPhrap. While these findings represent a positive devel- opment, it is important to note that these alternative sequence-assembly programs are actually less effective than Phrap for assembling average BAC sequences, due primarily to a great increase in the number of contigs and occasional generation of new misassemblies. We also tested whether improved sequence assem- blies could be generated by separately altering certain Phrap parameters. The shotgun sequence reads for ten of the above BACs were assembled using four different sets of Phrap parameters; each resulting assembly was then analyzed for features we have found to serve as a measure of the manual effort required for sequence finishing (specifically, the total number of contigs, mis- joined sequences, uncaptured gaps, contigs which can be manually joined, and groups of reads to remove from contigs). Compared to the assemblies generated with our optimized Phrap parameters, those generated using the ‘repeat stringency’ set at 99 or ‘shatter greedy’ were inferior with respect to the above mea- sures, and would have required substantial additional effort to complete the finishing process. Assemblies generated using ‘revise greedy’ or a ‘forcelevel’ of 3 (instead of 0) were found to be similar to our standard optimized Phrap assemblies. Based on analyses such as this, we have adopted a routine in which shotgun sequence reads are first assembled with Phrap using our optimized parameters; if problematic misassem- blies are noted, the reads are then reassembled with rPhrap and separately with Autosort. The best of the three assemblies is then selected as a better starting point for further refinement, including manual resolu- tion of more complex misassemblies. Frequency of gaps When inspecting a sequence assembly, one of the first features to assess is whether the consensus sequence is in a single piece or multiple pieces (i.e., contigs). An assembly consisting of more than one contig indicates that the sequencing procedure failed to generate suffi- cient sequence reads from the DNA residing between contigs (i.e., gaps). The number of sequence gaps is a first indicator of the amount of effort that will be required to finish an assembled sequence. Early in any sequence-finishing process, each contig must be manu- ally inspected, looking for internal regions of low quality or inappropriately joined segments. As might be antici- pated, contig ends typically contain poor-quality sequence, either due to individual sequence reads extending well beyond the average quality length or due to chimeric reads. The amount of effort required to manually inspect and refine contig ends is directly pro- portional to their number (each gap is associated with two flanking contig ends).