Global‐scale distributed I/O with ParaMEDIC

Balaji, Pavan; Feng, Wu-chang; Lin, Heshan; Archuleta, Jeremy; Matsuoka, Satoshi; Warren, Andrew S.; Setúbal, João Carlos; Lusk, Ewing L.; Thakur, Rajeev; Foster, Ian; Jha, Shantenu; Shinpaugh, Kevin; Coghlan, Susan; Reed, Daniel A.

doi:10.1002/cpe.1590

Cited by 1 publication

(4 citation statements)

References 23 publications

(23 reference statements)

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“…In order to find the missing genes using all fully-sequenced prokaryotic genomes we have used an innovative high-performance methodology [ 10 ]. That methodology can be potentially used for other genome database-wide surveys of sequence data.…”

Section: Discussionmentioning

confidence: 99%

“…That methodology can be potentially used for other genome database-wide surveys of sequence data. The performance of both mpiBLAST [ 13 ] and ParaMEDIC [ 10 ] theoretically scales well if the number of processors that are used scales as the square of the number of replicon sequences.…”

Section: Discussionmentioning

confidence: 99%

“…Our basic idea is to compare all Open Reading Frames (ORFs) greater than or equal to a minimum length from all fully-sequenced genomes using BLAST [ 9 ] based on a novel high-performance approach [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…The BLASTP search was performed using mpiBLAST on Virginia Tech's System X supercomputer [ 12 ]. mpiBLAST parallelizes BLAST using database fragmentation, query segmentation [ 13 ], parallel input-output [ 14 ], and advanced scheduling [ 15 ]; further details are given in [ 10 ]. The BLAST search used approximately 10,000 CPU hours on System X with 15 groups of 9 CPUs running continuously between 48 and 96 hours.…”

Section: Methodsmentioning

confidence: 99%

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Missing genes in the annotation of prokaryotic genomes

et al. 2010

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%