High performance computing workflow for protein functional annotation

Stanberry, Larissa; Liu, Yuan; Rekepalli, Bhanu; Giblock, Paul; Higdon, Roger; Broomall, William

doi:10.1145/2484762.2484809

Cited by 1 publication

(5 citation statements)

References 35 publications

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“…Consequently, 513,923 proteins were assigned to 4525 COG clusters. The results were comparable across the four iterations in terms of the alignment score distribution and the sensitivity and specificity of the classification approach . The rate of cluster expansion varied across COGs and so did the profile computation.…”

Section: Methodsmentioning

confidence: 83%

“…To assess the effect of pre‐clustering on profile compute time and oucomes, we performed a small study. For that, we have selected all clusters that had at least 2000 sequences after the third iteration of the original algorithm . The four selected COGs were pre‐clustered using usearch, and the identified subcluster cores were used to compute the profiles.…”

Section: Methodsmentioning

confidence: 99%

“…The common domain for nonredundant sequences is then built with PSI‐BLAST, and the sequences are aligned with MUSCLE. The algorithm used in the first four iterations is given in Figure 1 of .…”

Section: Methodsmentioning

confidence: 99%

“…In the first four iterations of the original implementation , we have analyzed 800,000 proteins from the newly sequences genomes . Consequently, 513,923 proteins were assigned to 4525 COG clusters.…”

Section: Methodsmentioning

confidence: 99%

“…In , we proposed a new workflow to enable large‐scale functional annotation using high performance computing (HPC) architectures. The workflow uses a Position‐Specific Iterative (PSI)‐BLAST approach and low‐complexity classification method to assign newly sequenced bacterial genomes to existing COGs .…”

Section: Introductionmentioning

confidence: 99%

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Optimizing high performance computing workflow for protein functional annotation

Stanberry

Rekepalli

Liu

et al. 2014

Concurrency and Computation

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Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

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Section: Methodsmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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