title: Optimizing high performance computing workflow for protein functional annotation.
abstract: 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.
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory and High-Throughput Analysis Core, Seattle Children's Research Institute (SCRI), DELSA Global, Seattle, WA 98101, USA.
authors.forename: Larissa
authors.initials: L
authors.lastname: Stanberry
authors.affiliation: Joint Institute for Computational Sciences, University of Tennessee - Oak Ridge National Laboratory (JICS UT - ORNL), DELSA Global, Oak Ridge, TN, USA.
authors.forename: Bhanu
authors.initials: B
authors.lastname: Rekepalli
authors.affiliation: Joint Institute for Computational Sciences, University of Tennessee - Oak Ridge National Laboratory (JICS UT - ORNL), DELSA Global, Oak Ridge, TN, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Cisco Systems, San Jose, CA 95134, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Giblock
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory and High-Throughput Analysis Core, Seattle Children's Research Institute (SCRI), DELSA Global, Seattle, WA 98101, USA.
authors.forename: Roger
authors.initials: R
authors.lastname: Higdon
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory and High-Throughput Analysis Core, Seattle Children's Research Institute (SCRI), DELSA Global, Seattle, WA 98101, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Montague
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory and High-Throughput Analysis Core, Seattle Children's Research Institute (SCRI), DELSA Global, Seattle, WA 98101, USA.
authors.forename: William
authors.initials: W
authors.lastname: Broomall
authors.affiliation: Bioinformatics & High-Throughput Analysis Laboratory and High-Throughput Analysis Core, Seattle Children's Research Institute (SCRI), DELSA Global, Seattle, WA 98101, USA.
authors.forename: Natali
authors.initials: N
authors.lastname: Kolker
authors.affiliation: Bioinformatics & High-throughput Analysis Laboratory, SCRI, High-throughput Analysis Core, SCRI, Predicitive Analytics, Seattle Children's Hospital, Departments of Pediatrics and Biomedical Informatics & Medical Education, University of Washington, DELSA Global.
authors.forename: Eugene
authors.initials: E
authors.lastname: Kolker
keywords: BLAST; COG; HSPp-BLAST; PS; PSI-BLAST; XSEDE; computational bioinformatics; data-enabled life sciences; petascale; protein annotation; protein sequence universe; science gateways; sequence similarity