Galaxy Deployment on Heterogenous Hardware

Talk presented at Galaxy Community Conference 2014, June 30 - July 2, 2014. Video is available at URL: https://wiki.galaxyproject.org/Events/GCC2014/Abstracts/Talks#Galaxy_Deployment_on_Heterogenous_HardwareIndiana University, like many institutions, houses a heterogenous mixture of compute resources. In addition to university resources, the National Center for Genome Analysis Support, the Extreme Science and Engineering Discovery Environment, and the Open Science Grid all provide resources to biologists with NSF affiliations. Such a diverse mixture of compute power and services could be applied to address the equally diverse set of problems and needs in the bioinformatics field. Many software suites are well suited for large numbers of fast CPUS, such as phylogenetic tree building algorithms. De novo assembly problems really crave a machine with lots of RAM to spare. Alignment and mapping problems where each input is a separate invocation lend themselves perfectly to high-throughput, heavily distributed compute systems. Galaxy is a web interface that acts as a mediator between the biologist and the underlying hardware and software - in an ideal setup, Galaxy would be able to delegate work to the best suited underlying infrastructure. We present an instance of Galaxy at Indiana University, installed and maintained by NCGAS, that takes advantage of a variety of compute resources to increase utilization and efficiency. The OSG is a distributed grid through which Blast jobs can be run. IU, NCGAS and XSEDE jointly support Mason, a 512Gb/node system. For IU users, Big Red 2 is the first university-owned petaFLOPS machine. Connecting these resources to Galaxy and using the best tool for the job results in the best performance and utilization - everyone wins.This material is based upon work supported by the National Science Foundation under Grant No. ABI-1062432, Craig Stewart, PI. William Barnett, Matthew Hahn, and Michael Lynch, co-PIs. This work was supported in part by the Lilly Endowment, Inc. and the Indiana University Pervasive Technology Institute. Any opinions presented here are those of the presenter(s) and do not necessarily represent the opinions of the National Science Foundation or any other funding agencie

RSV: OSG Fabric Monitoring and Interoperation with WLCG Monitoring Systems

Presented at CHEP 2009 (Computing in High Energy and Nuclear Physics)

Galaxy based BLAST submission to distributed national high throughput computing resources

To assist the bioinformatic community in leveraging the national cyberinfrastructure, the National Center for Genomic Analysis Support (NCGAS) along with Indiana University's High Throughput Computing (HTC) group have engineered a method to use the Galaxy to submit BLAST jobs to the Open Science Grid (OSG). OSG is a collaboration of resource providers that utilize opportunistic cycles at more than 100 universities and research centers in the US. BLAST jobs make a significant portion of the research conducted on NCGAS resources, moving jobs that are conducive to an HTC environment to the national cyberinfrastructure would alleviate load on resources at NCGAS and provide a cost effective solution for getting more cycles to reduce the unmet needs of bioinformatic researchers. To this point researchers have tackled this issue by purchasing additional resources or enlisting collaborators doing the same type of research, while HTC experts have focused on expanding the number of resources available to historically HTC friendly science workflows. In this paper, we bring together expertise from both areas to address how a bioinformatics researcher using their normal interface, Galaxy, can seamlessly access the OSG which routinely supplies researchers with millions of compute hours daily. Efficient use of these results will supply additional compute time to researcher and help provide a yet unmet need for BLAST computing cycles.This material is based upon work supported by the National Science Foundation under Grant No. ABI-1062432, Craig Stewart, PI. William Barnett, Matthew Hahn, and Michael Lynch, co-PIs. This work was supported in part by the Lilly Endowment, Inc. and the Indiana University Pervasive Technology Institute. Any opinions presented here are those of the presenter(s) and do not necessarily represent the opinions of the National Science Foundation or any other funding agencie

Building a Chemical-Protein Interactome on the Open Science Grid

The Structural Protein-Ligand Interactome (SPLINTER) project predicts the interaction of thousands of small molecules with thousands of proteins. These interactions are predicted using the three-dimensional structure of the bound complex between each pair of protein and compound that is predicted by molecular docking. These docking runs consist of millions of individual short jobs each lasting only minutes. However, computing resources to execute these jobs (which cumulatively take tens of millions of CPU hours) are not readily or easily available in a cost effective manner. By looking to National Cyberinfrastructure resources, and specifically the Open Science Grid (OSG), we have been able to harness CPU power for researchers at the Indiana University School of Medicine to provide a quick and efficient solution to their unmet computing needs. Using the job submission infrastructure provided by the OSG, the docking data and simulation executable was sent to more than 100 universities and research centers worldwide. These opportunistic resources provided millions of CPU hours in a matter of days, greatly reducing time docking simulation time for the research group. The overall impact of this approach allows researchers to identify small molecule candidates for individual proteins, or new protein targets for existing FDA-approved drugs and biologically active compounds

Influence of separated vortex on aerodynamic noise of an airfoil blade

In order to clarify the mechanism by which aerodynamic noise is generated from separated flow around an airfoil blade, the relation between the attack angle and the aerodynamic noise of the blade was analyzed using a wind tunnel experiment and a CFD code. In the case of rear surface separation, the separated vortex which has a large-scale structure in the direction of the blade chord is transformed into a structure that concentrates at the trailing edge with an increase in the attack angle. The aerodynamic noise level then becomes small according to the vortex scale in the blade chord. When the flow is separated at the leading edge, a separated vortex of low pressure is formed at the vicinity of the trailing edge. The pressure fluctuations on the blade surface at the vicinity of the trailing edge become large due to the vortex in the wake. It is considered that the aerodynamic noise level increases when the flow is separated at the leading edge because the separated vortex is causing the fluctuations due to wake vortex shedding

Engineering Model Development of HIBARI: MicroSatellite for Technology Demonstration of Variable-Shape Attitude Control

We are developing a 40kg class microsatellite “HIBARI”. The main technical mission is demonstration a novel attitude control method called “Variable Shape Attitude Control (VSAC)” proposed by Matunaga, Tokyo Institute of Technology. This VSAC is based on an idea to utilize a reaction torque generated by changing the shape of satellites, for example driving solar array paddles by actuators. HIBARI is planned to be launched in fiscal year 2021 under “Innovative Satellite Technology Demonstration Program” led by JAXA. We are developing EM of HIBARI and describes those in this paper. Specifically, the results of missions, systems, and various tests are shown and the validity is derived

The utility and limitations of an ultrasonic miniprobe in the staging of gastric cancer

To determine the utility and limitations of an ultrasonic miniprobe (UMP) in the staging of gastric cancer, we evaluated 46 patients who underwent endoscopic ultrasonography (EUS) using an UMP and who were histologically determined to have gastric cancers. In every case, UMP findings were compared with histopathological findings after treatment. The total accuracy of UMP relative to the depth of tumor invasion was71.7% (33/46 cases). Accuracy with respect to T1-m tumor diagnosis was 75.7% (22/29cases), and for T1-sm, 76.9% (10/13 cases), but accuracy for T2 tumor diagnosis was low, due to ultrasound attenuation. When the analysis was carried out based on the size of tumor, the accuracy for UMP was 50.0% (9/18cases) for all tumors over 20mm and 85.7%(24/28 cases) for all tumors smaller than 20mm. We conclude that UMP is suitable for investigation of tumor extension when the lesion is superficial and / or small gastric cancers which do not cause ultrasonic attenuation, but not when the tumor is large or located in certain sites, although conventional EUS is useful in some of these cases

Factors Influencing the Return Rates in Mail Surveys : Effects of Paper Size, Number of Pages, and Cover Letter Appeals

郵送調査の返送率に影響を及ぼす要因を究明するため3つ研究が行われた。第1の研究では、片面刷、2ページ見開き、2枚から成る2種類のサイズの質問紙が返送率に及ぼす効果を検討した。一方の質問紙はA3判（297×420mm）の用紙に、他方の質問紙はB4判（257×364mm）の用紙に印刷され、それぞれ353名の調査対象者に郵送された。返送率は60.5％（A3）と55.0％（B4）で、この差は統計的に有意でなかった。第2の研究では、質問紙の枚数が返送率に及ぼす効果を検討した。質問紙は、B5判（182×257mm）の用紙に片面印刷された6枚のものとB4判の用紙に見開き2ページで片面印刷された3枚のものであった。B5判の質問紙は308名の調査対象者に、B4判の質問紙は307名の調査対象者に郵送された。B5判の質問紙に対する返送率（62.1％）は、B4判の質問紙に対する返送率（56.1％）と有意差がなかった。第3の研究では、4種類の協力依頼状の要請表現が返送率に及ぼす効果を調べた｡第1条件は標準的な協力依頼状、第2条件は、前回の調査結果がマスコミに取り上げられたことを付記した協力依頼状、第3条件は、調査報告書を送呈することを付記した協力依頼状､第4条件は､第2と第3の条件を併記した協力依頼状であった。4条件の返送率（60.7％、55.7％、61.0％、55.8％）に有意な差がなかった。Three studies investigated factors that can influence return rates in mail surveys. The first study examined the effect of two sizes of paper using a questionnaire consisting of two single-sided pages with two columns of questions per page. One version was printed on A3 (297 x 420mm) paper and mailed to 353 persons, A second version was on B4 (257 x 364mm) paper and mailed to 353 persons. The return rates were 60.5% (A3) and 55.0% (B4) but this difference was not statistically significant. In the second study the effect of number of pages was examined. The questionnaire was either printed on 6 single-sided B5 (182 x 257mm) sheets or 3 single-sided, two column B4 sheets. 308 B5 and 307 B4 questionnaires were mailed. The return rate for B5 (62.1%) was not statistically different than that for B4 (56.1%) . The third study investigated the effect of four cover letter appeals. The first condition consisted of a standard cover letter. The second was the same as the first with the mention that the results of previous study were discussed in the media. The third was the same as the first with an offer of a copy of the research report. The fourth was the standard cover letter with both additions of the second and third conditions. The return rates of the four conditions (60.7%, 55.7%, 61.0%, 55.8%) were statistically equal

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research