The potential disadvantage to LNA substitutions in 2 0 -Omethyl RNA oligonucleotides is that discrimination against mismatches containing an LNA may be less than with a complete 2 0 -O-methyl RNA backbone. This was clearly true for three of the four cases where such direct comparisons were made. Nevertheless, internal mismatches with LNA nucleotides are considerably destabilizing, averaging a penalty of 4.1 kcal/mol at 37 C (Table 5) , which translates to almost a 1000-fold weaker binding due to a single mismatch. When LNAs flanked an A M -G mismatch, the mismatch penalty at 37 C was 4.4 kcal/mol compared with 3.3 kcal/mol in the absence of LNAs. Such an effect may reflect enhanced rigidity due to LNA, which thereby prevents a mismatch from adopting a favorable conformation. Thus, it may be advantageous to use LNAs to flank nucleotides likely to give small mismatch penalties. Draft versus finished sequence data for DNA and protein diagnostic signature development Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10(−3)–10(−5) (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. Draft sequencing requires that the order of base pairs in cloned fragments of a genome be determined usually at least four times (4· depth of coverage) at each position for a minimum degree of draft accuracy. This information is assembled into contigs, or fragments of the genome that cannot be joined further due to lack of sequence information across gaps between the contigs. To generate high-quality draft, usually $8· coverage is optimal (1). Finished sequence, without gaps or ambiguous base calls, usually requires 8· to 10· coverage, along with additional analyses, often manual, to orient the contigs relative to one another and to close the gaps between them in a process called finishing. In fact, it has been stated that 'the defining distinction of draft sequencing is the avoidance of significant human intervention' (1) , although there are computational tools that may also be capable of automated finishing in some circumstances (2) .