Beginning with the bacteriophage φX174 genome in 1977 by Sanger et al. [1], microbes have spearheaded technological advances in genome sequencing. This is almost certainly because their small size and limited genomic complexity (at least compared to Eukaryotes) makes genomic analysis especially tractable, and because of their founding role and long history in the study of molecular biology. Microbial genomics also yields enormous and otherwise inaccessible informational gains compared to the relatively few easily observable phenotypes of microbes (at least compared to macroorganisms). This preponderance shows no signs of abating; the current Genomes Online Database (GOLD; http://www.genomesonline.org/cgi-bin/GOLD/bin/gold.cgi; accessed August 15, 2011) lists 8212 bacterial and 308 archaeal genome projects either completed or ongoing. These statistics do not include a myriad of sequenced viruses or projects not registered in GOLD.

For approximately 30 years following its original publication in 1977 [2], the Sanger dideoxy terminator method was the standard for DNA sequencing. Whereas this technology is now fully mature and capable of routinely yielding reads > 800 bp long, its reliance on capillary electrophoresis created a ceiling for the volume and speed of sequences that can be generated inexpensively by this approach [3]. So-called "next-" or "second-generation" sequencing approaches emerged in 2005 following the release of the 454 pyrosequencing platform (Roche), followed closely thereafter by the Illumina/Solexa (Illumina) and SOLiD (Applied Biosciences/Life Technologies) systems [3–6]. Second-generation sequencing platforms all produce reads shorter than those achieved by modern Sanger sequencing, although new PacBio and Roche FLX Titanium XL+ technologies may offer alternatives. Regardless, short-read sequencing remains the most cost-effective on a per-base basis (L. A. Pannacchio, unpublished data), leading to the dominance of these technologies (especially Illumina) in terms of market share http://www.genomicslawreport.com/wp-content/uploads/2011/04/JP-Morgan-NGS-Report.pdf.

Next-generation sequence assembly is an inherently challenging computational problem, stemming from the relatively low amount of data contained within each sequencing read, the huge volumes of data produced, and platform-specific error frequencies and profiles [7] including difficulties with regions of high %GC bias [8] and homopolymeric regions leading to frame-shifts [9]. Simulations suggest that completely unbroken coverage of a bacterial genome is impossible using short read lengths [10, 11], although to some extent this can be compensated for using (more expensive) paired-end approaches [12]. Improving a draft-quality genome to completion is typically costly and laborious due to its general requirement for targeted PCR and Sanger sequencing. Fragmented genomes should therefore be the expectation from modern genome sequencing projects, at least for the foreseeable future, given the current economic reality and the market dominance of short-read sequencing platforms.

Consequently, data occurring between genomic contig ends are omitted from draft-quality genomes. This affects comparative analyses using phylogenetic techniques; because the state of these missing characters cannot be otherwise estimated, their phylogenetic analysis is impossible. In contrast, analyses based on presence-absence of particular open reading frame (ORF) types, e.g., using BLAST [13], can be performed using ORF fragments. These estimations are primarily susceptible to two types of errors: false-negatives, where ORFs that should be present are not annotated; and false-positives, where multiple fragments actually belonging to the same ORF are annotated separately. In the present work, we attempt to understand the extent and consequences of the biases partial ORFs introduce into annotation analyses. Furthermore, we attempt to mitigate these biases by linking fragmented ORFs based on their relatedness to homologs in closely-related organisms. We acknowledge that ORF fragmentation can introduce other biases into comparative analyses, e.g., due to the increased difficulty of correct ORF modeling [14] or potentially, misannotation due to the lower informational content of ORF fragments relative to full-length sequences. However, these errors fall outside the scope this current work.

Next-generation DNA genome sequencing has irrevocably moved microbiology (and much of biology, for that matter) towards extensive data volumes and the concomitant challenges of their analyses. These advances are not without attendant compromises, especially the economic trade-off between dataset breadth and quality which appears to be increasingly towards the former at the expense of the latter. Emerging technologies such as PacBio and 454 FLX Titanium XL+ offer enhanced read lengths, but these formats still cannot match the per-base costs of shorter-read sequencing platforms. Cheaper short read techniques therefore seem poised to dominate the sequencing market for the foreseeable future, especially given that human genome re-sequencing, the main economic driver for innovation in genome sequencing technologies, is focused on clinical personal genomics where short reads appear largely sufficient for reference assembly and SNP detection.

Given this technological reality, draft genomes are likely to dominate microbial genomic data for the immediate future. Here, we have attempted to address some of the limitations of this data type for comparative genomics analysis. We find that ORF fragmentation has the potential to dramatically cause false-negatives in some annotation types, especially those relying on relatively small stretches of sequence (e.g., Pfam domains; Table 1). These biases will be difficult to address without improvements in genome quality. Reciprocally, we also find that ORF fragmentation can cause false-positive annotations of multiple ORF fragments belonging to the same complete ORF where sufficiently sensitive detection methods exist (e.g., KEGG; Table 1). However, we also suggest that these problems can likely be at least partially mitigated by linkage of protein fragments based on their complete homologs in related organisms (Table 1, Figure 3). The impact of a well-sampled variable genome on ORF fragment linkage is especially intriguing (Figure 3), and suggests that sampling multiple related strains will not only more accurately represent biological diversity but will also improve the data quality of all these strains by unmasking otherwise obscured homologies among ORF fragments. This is likely to be particularly important for Streptomyces and other similar organisms, for which the most biotechnologically interesting genes involved in secondary metabolism are both variable between strains [22] and highly fragmented (Figure 2), and assembly is particularly challenged by low per-read information content due to %GC bias.

Although our analyses indicate the potential for biases, they in no way diminish the value of draft bacterial genome sequencing. Indeed, as described above, taking advantage of low cost-sequencing is essential to begin generating sufficient representation of the vast microbial diversity present in nature. With this increased sampling we can begin to differentiate between neutral and adaptive (or at least conserved) genome contents. However, we must also be aware of the limitations of these data types. For example, if > 80% of the predicted ORFs in a given bacterial genome are likely to be fragmented, analyses that can accommodate ORF fragmentation, even if imperfectly, will be crucial. Extensively fragmented draft genomic data will be insufficient for some applications, even with downstream in silico correction. This will necessitate proper resource allocation to ensure data usability for a given experimental objective. Furthermore, data analysis requirements scale even faster than data accumulation, e.g., all-versus-all comparisons of n sequences most simply require n ² comparisons. It may therefore be necessary to use judicious subsets of a global dataset when attempting fragment linkage. In the end, there is no substitute for well designed, tractable research; draft sequencing, for all its promise and pitfalls, is no different.

Draft genome quality will be the dominant form of microbial genomic data produced for the immediate and foreseeable future. This approach produces highly fragmented genomes, which concomitantly translates into abundant predicted ORF fragments. These fragmented ORFs lessen annotation efficacy, especially of repeating, multi-modular proteins. However, this problem can be somewhat ameliorated by increased sampling of a species' variable genome. Therefore, despite their great utility in many applications, there exist disadvantages to draft genome sequencing that need to be addressed by appropriate experimental design and deployment of sequencing resources to effectively answer the study questions posed.