Resource Allocation in Humanitarian Operations
This project studies inventory management in the context of supplementary food and disease progression in a finite time horizon. In addition to presenting structural properties, the researcher explores the efficacy of pragmatic heuristics in this context. MSI resources are used to computationally evaluate the performance of different heuristics.
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The Users Bulletin provides a summary of new policies, procedures, and events of interest to MSI users. It is published quarterly.
To request technical assistance with your MSI account, please contact email@example.com.
1. New Storage Limits: Early this year, MSI started a campaign to help groups with big data requirements manage their data using MSI's Second Tier storage. The campaign is helping groups get below and stay below the new 20 TB limit on primary storage. MSI staff are also helping groups move older data (greater than one year) to our Second Tier if they are using between 5 and 20 TB. These efforts have freed up space on our Primary Storage system and have made it possible for us to grant new storage allocations to 39 user groups.
See the Storage Allocations page of the MSI website for complete information on MSI storage limits.
2. Stratus: This spring, MSI has been testing a locally hosted cloud environment, called Stratus, designed to store and analyze protected data, such as dbGaP data. Stratus is isolated from other MSI storage and compute resources in order to meet the data use requirements of some of our funding agencies. Stratus will be available starting July 1, 2017 under a fee-for-service model with a rate structure similar to popular commercial cloud providers. For example, a basic annual subscription will cost $626 for 16 vCPUs and 2 TB of storage. Additional storage can be purchased as needed.
See the Stratus page on the MSI website for more details concerning MSI’s cloud environment.
3. New Archive Storage: MSI has been testing a big data archive storage solution that will give researchers a robust, secure, and inexpensive place to store very large datasets for five years or more. Think of this as a good alternative to purchasing a bunch of USB hard drives to back up important data or to archive data that you don't have to access on a regular basis. The new archive system will go into production starting July 1, 2017. The cost of MSI archival storage is $456.12 for 6 TB of replicated storage for five years. Replication means that data are written to two tapes, so that data are not lost if one tape fails. Access to the archive storage will be available via Globus and some other tools to help automate workflows.
4. Summer Tutorials: The Summer tutorial schedule is posted on the MSI website.
5. Acknowledgment of MSI in Publications: Please acknowledge MSI in your published works (e.g., posters, research reports, journal articles, abstracts, and talks) where MSI resources (computing, data storage, visualization, staff, etc.) contributed to your published research results.
- You can either list MSI in your affiliations in the byline, or cite MSI in the acknowledgments section (including, at a minimum, "Minnesota Supercomputing Institute (MSI)” and "University of Minnesota").
- On posters or in slide presentations, you can use the MSI wordmark; please contact Tracey Bartlett (firstname.lastname@example.org) to get a copy of the wordmark. Please do not use old MSI logos or wordmarks, as these do not meet current branding standards.
The following is a more complete example of how you could acknowledge MSI:
The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: www.msi.umn.edu
This text can also be found in a couple of locations on the MSI website: FAQ - How do I properly cite MSI to acknowledge the use I have made of MSI’s resources for my research? and the Acknowledgments page in the Research @ MSI section.
6. Jobs at MSI:
7. Useful Webpages: Looking for help with MSI? One of these pages may have the information you need:
c. MSI systems
Functional Genomics of Fusarium graminearum, the Wheat and Barley Scab Fungus
Fusarium head blight or scab caused by Fusarium graminearum is a destructive disease of wheat and barley. Infested cereals are reduced in yield and contaminated with harmful mycotoxins. In the past decade, the disease has resulted in billions of dollars of economic loss to United States agriculture. Better understanding of F. graminearum pathogenesis and differentiation is critical because effective fungicides and highly resistant plant varieties are not available for controlling the disease. This group’s goals are to identify and characterize genes important for plant infection and colonization, secondary metabolism, sporulation, and sexual development of F. graminearum by using transcriptome analysis and targeted mutation of selected genes.
One objective of this research is to analyze gene expression profiles of F. graminearum in different infection and colonization stages, in mutants defective in plant infection or toxin production, and in different developmental stages. Genes differentially expressed during specific infection or development processes or in response to mutants will be identified by high throughput cDNA sequencing (RNAseq). The second objective is to experimentally determine the biological functions of selected candidate genes identified in gene expression experiments. Targeted deletion mutants will be generated for genes chosen on the basis of expression profiles and bioinformatics analyses. A third objective will be to assess the presence of Fusarium species and total fungal content of environmental samples using a metagenomic approach. MSI resources are used for storage and analysis of RNAseq data as well as DNA sequence storage and metagenomic analysis of fungi from environmental samples.
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PICRUSt (pronounced “pie crust”) is a bioinformatics software package designed to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes.
PICRUSt primarily consists of two workflows: gene content inference (detailed in Genome Prediction Tutorial) and metagenome inference (detailed in Metagenome Prediction Tutorial). Users working with 16S data can use pre-computed gene content information, and as a result don’t need to be concerned with the gene content inference workflow. The following sections describe these workflows. Each section links to detailed tutorials that illustrate the exact commands that can be applied as well as example data that you can use to test and learn PICRUSt.
Detailed information can be found at http://picrust.github.io/picrust/
PICRUSt is available as a module load.
module load picrust
All dependencies are loaded and environment variables are set.