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    Education: Training in Bioinformatics for Beginners @ NIH
    Submitted by Vijayaraj Nagarajan; posted on Tuesday, December 04, 2018

    January 15-18, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA
    faes.org/biotech82

    BACKGROUND

    Bioinformatics (Computational Biology) is a must skill required in every modern biomedical research lab. Installing and configuring a wide variety of computational biology tools is a cumbersome task that requires software engineering skills. This hands-on training course will introduce participants to a comprehensive set of bioinformatics concepts, tools and techniques, using a cloud based, all-in-one, fully loaded linux desktop (with windows like graphical user interface) machine, that comes with hundreds of popular computational biology (bioinformatics) tools required for a successful modern biomedical research lab.

    OBJECTIVES

    The participants will be provided with end-to-end hands-on training, along with introduction to basic concepts, in using popular tools and techniques for sequence analysis, structure analysis, function prediction, biological database searching, "omics" data analysis, pathway analysis, data visualization, data curation and integration, linux, R, perl and scripting basics.

    HIGHLIGHTS

    • Participants will use a Graphic User Interface based Linux Desktop environment, specially configured for bioinformatics analysis in the Amazon Cloud.
    • Training provided by experienced active NIH researchers
    • Cookbook style bound manual for all exercises
    • Direct, after training support through exclusive forum membership
    • Continuing Education Credits

    HANDS-ON SKILLS & TOOLS TAUGHT

    • Databases: NCBI-ENTREZ, UniProt, PDB, STRING, Others
    • Sequence analysis and function predictions: EMBOSS suite & others
    • Local Alignment: EMBOSS-WATER
    • Global Alignment: EMBOSS-NEEDLE
    • Similarity search: NCBI BLAST, PSI-BLAST
    • Multiple sequence alignment: Clustal Omega, MUSCLE, MAFFT
    • Phylogenetics: MrBayes, MEGA, FigTree and Dendroscope
    • Motif finding, analysis: MEME suite
    • Structure prediction, visualization & analysis: PyMOL, Chimera, iTASSER
    • Transcriptome analysis: NCBI GEO, Tuxedo tools, R
    • Enrichment analysis: DAVID
    • Pathway analysis: Cytoscape
    • Programming: Linux, R, Perl, Python
    • Platforms: EMBOSS, UGENE, H2O, Galaxy

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