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BackgroundProtein-coding gene detection in prokaryotic genomes is considered a much simpler problem than in intron-containing eukaryotic genomes. However there have been reports that prokaryotic gene finder programs have problems with small genes (either over-predicting or under-predicting). Therefore the question arises as to whether current genome annotations have systematically missing, small genes.ResultsWe have developed a high-performance computing methodology to investigate this problem. In this methodology we compare all ORFs larger than or equal to 33 aa from all fully-sequenced prokaryotic replicons. Based on that comparison, and using conservative criteria requiring a minimum taxonomic diversity between conserved ORFs in different genomes, we have discovered 1,153 candidate genes that are missing from current genome annotations. These missing genes are similar only to each other and do not have any strong similarity to gene sequences in public databases, with the implication that these ORFs belong to missing gene families. We also uncovered 38,895 intergenic ORFs, readily identified as putative genes by similarity to currently annotated genes (we call these absent annotations). The vast majority of the missing genes found are small (less than 100 aa). A comparison of select examples with GeneMark, EasyGene and Glimmer predictions yields evidence that some of these genes are escaping detection by these programs.ConclusionsProkaryotic gene finders and prokaryotic genome annotations require improvement for accurate prediction of small genes. The number of missing gene families found is likely a lower bound on the actual number, due to the conservative criteria used to determine whether an ORF corresponds to a real gene.

The graphics processing unit (GPU) has emerged as a computational accelerator that dramatically reduces the time to discovery in high-end computing (HEC). However, while today's state-of-the-art GPU can easily reduce the execution time of a parallel code by many orders of magnitude, it arguably comes at the expense of significant power and energy consumption. For example, the NVIDIA GTX 280 video card is rated at 236 watts, which is as much as the rest of a compute node, thus requiring a 500-W power supply. As a consequence, the GPU has been viewed as a "nongreen" computing solution.This paper seeks to characterize, and perhaps debunk, the notion of a "power-hungry GPU" via an empirical study of the performance, power, and energy characteristics of GPUs for scientific computing. Specifically, we take an important biological code that runs in a traditional CPU environment and transform and map it to a hybrid CPU+GPU environment. The end result is that our hybrid CPU+GPU environment, hereafter referred to simply as GPU environment, delivers an energy-delay product that is multiple orders of magnitude better than a traditional CPU environment, whether unicore or multicore.

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