Moving Large Data to Galaxy

Talk given at the IU Bioinformatics Clinic July 2014This 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

RNA-Seq Demo on Galaxy

Talk given at the IU Bioinformatics Clinic, July 2014.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

RNA-Seq Demo on Galaxy

Workshop given during the 2015 Bioinformatics Clinic at Indiana University, August 2015This 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 agencies

Galaxy for Data Provenance

Talk given at the IU Bioinformatics Clinic, July 2014This 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

Automating work in Galaxy

Workshop given during the 2015 Bioinformatics Clinic at Indiana University, August 2015This 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 agencies

The FUV to Near-IR Morphologies of Luminous Infrared Galaxies in the GOALS Sample

We compare the morphologies of a sample of 20 LIRGs from the Great Observatories All-sky LIRG Survey (GOALS) in the FUV, B, I and H bands, using the Gini (G) and M20 parameters to quantitatively estimate the distribution and concentration of flux as a function of wavelength. HST images provide an average spatial resolution of ~80 pc. While our LIRGs can be reliably classified as mergers across the entire range of wavelengths studied here, there is a clear shift toward more negative M20 (more bulge-dominated) and a less significant decrease in G values at longer wavelengths. We find no correlation between the derived FUV G-M20 parameters and the global measures of the IR to FUV flux ratio, IRX. Given the fine resolution in our HST data, this suggests either that the UV morphology and IRX are correlated on very small scales, or that the regions emitting the bulk of the IR emission emit almost no FUV light. We use our multi-wavelength data to simulate how merging LIRGs would appear from z~0.5-3 in deep optical and near-infrared images such as the HUDF, and use these simulations to measure the G-M20 at these redshifts. Our simulations indicate a noticeable decrease in G, which flattens at z >= 2 by as much as 40%, resulting in mis-classifying our LIRGs as disk-like, even in the rest-frame FUV. The higher redshift values of M20 for the GOALS sources do not appear to change more than about 10% from the values at z~0. The change in G-M20 is caused by the surface brightness dimming of extended tidal features and asymmetries, and also the decreased spatial resolution which reduced the number of individual clumps identified. This effect, seen as early as z~0.5, could easily lead to an underestimate of the number of merging galaxies at high-redshift in the rest-frame FUV.Comment: Accepted for publication in the Astronomical Journal. The total page count is 15 pages with 13 figures and 1 Tabl

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

Cyberinfrastructure resources enabling creation of the loblolly pine reference transcriptome

This paper was presented at XSEDE 15 conference.Today's genomics technologies generate more sequence data than ever before possible, and at substantially lower costs, serving researchers across biological disciplines in transformative ways. Building transcriptome assemblies from RNA sequencing reads is one application of next-generation sequencing (NGS) that has held a central role in biological discovery in both model and non- model organisms, with and without whole genome sequence references. A major limitation in effective building of transcriptome references is no longer the sequencing data generation itself, but the computing infrastructure and expertise needed to assemble, analyze and manage the data. Here we describe a currently available resource dedicated to achieving such goals, and its use for extensive RNA assembly of up to 1.3 billion reads representing the massive transcriptome of loblolly pine, using four major assembly software installations. The Mason cluster, an XSEDE second tier resource at Indiana University, provides the necessary fast CPU cycles, large memory, and high I/O throughput for conducting large-scale genomics research. The National Center for Genome Analysis Support, or NCGAS, provides technical support in using HPC systems, bioinformatic support for determining the appropriate method to analyze a given dataset, and practical assistance in running computations. We demonstrate that a sufficient supercomputing resource and good workflow design are elements that are essential to large eukaryotic genomics and transcriptomics projects such as the complex transcriptome of loblolly pine, gene expression data that inform annotation and functional interpretation of the largest genome sequence reference to date.This work was supported in part by USDA NIFA grant 2011- 67009-30030, PineRefSeq, led by the University of California, Davis, and NCGAS funded by NSF under award No. 1062432

Spitzer infrared spectrometer 16μm observations of the GOODS fields

We present Spitzer 16μm imaging of the Great Observatories Origins Deep Survey (GOODS) fields. We survey 150 arcmin^2 in each of the two GOODS fields (North and South), to an average 3σ depth of 40 and 65 μJy, respectively. We detect ~1300 sources in both fields combined. We validate the photometry using the 3–24μm spectral energy distribution of stars in the fields compared to Spitzer spectroscopic templates. Comparison with ISOCAM and AKARI observations in the same fields shows reasonable agreement, though the uncertainties are large. We provide a catalog of photometry, with sources cross-correlated with available Spitzer, Chandra, and Hubble Space Telescope data. Galaxy number counts show good agreement with previous results from ISOCAM and AKARI with improved uncertainties. We examine the 16–24μm flux ratio and find that for most sources it lies within the expected locus for starbursts and infrared luminous galaxies. A color cut of S_(16)/S_(24) > 1.4 selects mostly sources which lie at 1.1 < z < 1.6, where the 24μm passband contains both the redshifted 9.7 μm silicate absorption and the minimum between polycyclic aromatic hydrocarbon emission peaks. We measure the integrated galaxy light of 16μm sources and find a lower limit on the galaxy contribution to the extragalactic background light at this wavelength to be 2.2 ± 0.2 nW m^(−2) sr^(−1)