Blakesley et al. BMC Genomics 2010, 11:21 http://www.biomedcentral.com/1471-2164/11/21 Table 4 Repeat content of total sequences and gap sequences. Repeat Type All Repeats Simple LTR SINE LINE DNA Total Sequence 36.9 (0.4) 1.6 (0.0) 4.7 (0.1) 9.3 (0.2) 18.6 (0.3) 2.7 (0.0) Total Gaps 48.9 (1.4) 4.6 (0.7) 5.6 (0.7) 8.8 (0.7) 27.9 (1.3) 2.0 (0.2) Captured Uncaptured Gaps Gaps 45.1 (1.7) 49.9 (1.7) 10.3 (1.0) 2.9 (0.5) 2.8 (0.5) 6.4 (0.8) 9.6 (1.0) 8.6 (0.5) 20.6 (1.6) 29.9 (1.8) 1.8 (0.4) 2.1 (0.4) Sequences of the 541 BACs (Table 1) were analyzed in aggregate by RepeatMasker. The percentages of bases represented by the indicated types of sequence repeats are reported for the total sequence, total gaps, captured gaps, and uncaptured gaps. Numbers in parenthesis are the one sigma error values. each other if two or more sequence reads in one contig connect to their plasmid ‘mates’ in the other contig; in such cases, the intervening gap is considered ‘captured’ by the spanning subclones. Furthermore, the calculated minimum distance between read pairs should not greatly exceed the average insert size of the subclone library. Sequence gaps not spanned by at least two appropriately spaced sequence read-pairs are deemed ‘uncaptured’ and add no spatial information to the sequence contig map. An assembly containing several uncaptured gaps can thus be quite challenging to finish, with other data required to deduce contig order and orientation; substantial additional effort is frequently involved during the finishing process, particularly in cases where the auxiliary data are weak or limited. Furthermore, generating the sequence of regions within captured gaps (e.g., during human-grade sequence fin- ishing) is most readily achieved by sequencing the span- ning plasmid subclones; in contrast, generating the sequence of regions within uncaptured gaps requires sequencing alternative templates (e.g., PCR products or purified BAC DNA), which is more costly and labor- intensive. We analyzed the comparative-grade finished sequence generated for the 2,031 BACs in the second data set for the presence of captured and uncaptured gaps. In aggre- gate, these sequences contain roughly 30 gaps per Mb, with approximately two-thirds of these gaps being cap- tured. This result is generally expected for shotgun sequence assemblies with at least eightfold sequence redundancy. However, the characteristics of the sequence gaps vary among genomic regions and among species (Figure 3). For example, the average total number of gaps within the sequences generated for these 14 ENCODE pilot project regions ranges from 20 per Mb (ENr213) to 43 per Mb (ENm003), while the fraction of uncaptured gaps varies from 26% (ENm010) to 50% (ENr312) (Figure 3A). None of these are among the worst genomic regions with respect to gaps that we have Page 8 of 15 encountered; the sequences generated for ENCODE pilot project regions ENr231, ENr232, and ENr333 (included in Figure 3A for comparison purposes, but not actually part of the second data set) contain more gaps per Mb (56, 68, and 48, respectively) than any of the other ana- lyzed regions. Similar differences can be seen among the sequences from 21 species in the second data set, with the average total number of gaps ranging from 19 per Mb (galago) to 58 per Mb (platypus), while the fraction of uncaptured gaps varies from 21% (hedgehog) to 50% (elephant and tenrec) (Figure 3B). The effort required for establishing the order and orientation of sequence contigs–a key part of the sequence-finishing process–directly relates to the num- ber of uncaptured gaps in the sequence assembly. While total gaps per Mb is an important metric to monitor, in practice, it is the presence of two or more uncaptured gaps in a given BAC sequence assembly that presents a significant challenge during sequence finishing. Within the second data set, the fraction of BAC sequence assem- blies containing two or more uncaptured gaps ranged from 16% (ENr213) to 47% (ENm003) among the differ- ent ENCODE pilot project regions and 12% (vervet) to 60% (platypus) among the different species. BAC sequences from ENCODE regions ENm003, ENm005, ENr111, ENr211 and ENr312 (as well as ENr333 and ENr231) have routinely required extra finishing effort, mostly because over 40% of the sequence assemblies con- tained two or more uncaptured gaps. Meanwhile, in the case of vervet and squirrel BACs, nearly all sequence assemblies contained fully ordered and oriented contigs, since few of these BAC sequences had two or more uncaptured gaps. In contrast, platypus BAC sequence assemblies had an average of over eight gaps per BAC, with more than 44% of those gaps being uncaptured; this characteristic (in addition to other features of platypus sequence mentioned above) has made the finishing of platypus sequence more challenging than that of any other species’ sequence encountered to date. Strategies for reducing sequence gaps In recent years, we have implemented various steps to reduce the number of gaps in our BAC sequence assem- blies. For example, in the case of sequence assemblies containing two or more uncaptured gaps, we often will construct fosmid shotgun libraries from the starting BAC DNA, and then generate a modest number (e.g., 96) of sequence read-pairs from fosmid insert-ends. The resulting fosmid clones often capture the DNA residing in previously uncaptured gaps, thereby helping to order and orient the sequence contigs. While effective, such a fosmid-based approach is labor-intensive and expensive; it also introduces delays in finishing a BAC sequence because of the manual nature of fosmid-library con- struction and subclone-DNA purification. Of note, this