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  • aabi
    Member
    • May 2016
    • 35

    Training in Cloud Computing for Biomedical Researchers @ NIH, USA



    Hands-on Training in Cloud Computing for Biomedical Researchers
    January 17-19, 2017

    Where?

    National Institutes of Health
    9000 Rockville Pike
    Bethesda, MD 20892, USA

    Background
    Cloud offers computers with hundreds of cores and terabytes of memory, on a hourly basis, for a mere couple of dollars and on-demand, for anyone who can use a computer and internet. This democratizes the high performance computing that everybody can use.

    Hands-on Skills/Tools Taught
    • Cloud platform: Amazon Web Services (AWS)
    • Cloud platform: Google Cloud Platform
    • Cloud platform: Azure - Microsoft Cloud Platform
    • Computing: AWS Elastic Cloud Compute (EC2)
    • Computing: Secure Shell, Secure File Transfer (SSH/SFTP)
    • Computing: AWS Identity and Access Management(IAM)
    • Computing: AWS GPU, CPU, Cluster
    • Standards: GovCloud
    • Pricing: On-demand, spot, reserved
    • Storage: AWS Simple Storage Service (S3)
    • Storage: AWS Elastic Block Store (EBS)
    • Storage: AWS Volumens, Snapshots, Amazon Machine Images
    • BigData: Hadoop, MapReduce, DynamoDB
    • Monitoring: CloudWatch

    Highlights
    • Participants will be provided with step-by-step walk through instructions to setup, configure, secure, monitor and access computing resources rented in public cloud infrastructures.
    • Linux based cloud image with comprehensive collection of bioinformatics software freely provided to participants
    • Training provided by active NIH researchers
    • Cookbook style bound manual for all exercises
    • Direct, after training support through exclusive forum membership
    • Continuing Educational Credits

    For more information and registration, please visit the following page;
    Information and Registration

Latest Articles

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  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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  • SEQadmin2
    Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by SEQadmin2


    I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

    Here are nine questions we think about, in roughly the order they matter, before...
    06-18-2026, 07:11 AM

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