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    Project admins: [Photo] Gary Van Domselaar
    [Photo] J.W. Bizzaro
    John S. J. Anderson

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    An international consortium of scientists has analyzed protein-coding genes from nearly 46,000 people, linking rare DNA alterations to type 2 diabetes. The study, one of the largest known of its type, includes data from people of European, African American, Hispanic/Latino, East Asian, and South Asian ancestries.


    June 17-21, 2019
    Berlin, Germany

    Some last seats available!

    The purpose of this intense one week summer course is to get a deep understanding in Next-Generation Sequencing (NGS) with a special focus on bioinformatics issues. Advantages and disadvantages of current sequencing technologies and their implications on data analysis will be discovered. You will be trained on understanding NGS data formats and handling potential problems/errors therein. In the summer school we will use a real-life RNA-seq dataset from the current market leader Illumina.

    All students will be enabled to perform important first tasks of NGS data analysis themselves. The layout of the summer school has been adapted to the needs of beginners in the field of NGS bioinformatics and allows scientists with no or little background in computer science to get a first hands-on experience in this new and fast evolving research topic.

    In the evenings there will be social events, like a conference dinner, or a guided city tour through Berlin. These are always great networking possibilities.


    May 17, 2019 1 pm EDT | 12 noon CDT | 11 am MDT | 10 am PDT/AZ
    Online webinar[...]-VICE

    Join us for:
    Cyverse's Friday Focus Forum Webinar,
    intended to be 30 minus in length

    This webinar will present how to find sequence patterns and visualize data relationships with the PatMatch and Circos applications right in your browser via the Jupyter environment provided by the system or CyVerse's cyberinfrastructure. We will briefly demonstrate each of these tools and how Binder and/or CyVerse's Visual and Interactive Computing Environment (VICE) enable you to use them right now without having any arduous installations. Finally, we will show you how you can easily connect your data to these tools in CyVerse.

    Click here for more information and to register:[...]-VICE



    Scientists at the Stanford University School of Medicine and their collaborators followed a cohort of more than 100 people over several years, tracking the biology of what makes them them. Now, after collecting extensive data on the group's genetic and molecular makeup, the researchers are piecing together a new understanding of what it means to be healthy and how deviations from an individual's norm can flag early signs of disease.
    Events: Recap webinar of Phase 2 of FaceBase
    Submitted by Cris Williams; posted on Wednesday, May 08, 2019

    May 20, 2019
    Online webinar

    FaceBase is a collaborative NIDCR-funded consortium to generate data in support of advancing research into craniofacial development and malformation. It serves as a community resource for understanding the developmental processes that create the face by generating a variety of datasets that spotlight high-throughput genetic, molecular, biological, imaging and computational techniques and making them available to the wider research community via their website, .

    FaceBase has just concluded its second funding year and is hosting a publicly accessible recap webinar on

    ::: Monday, May 20th from 11 am Eastern to 5:30 pm Eastern :::

    During this broadcast, each of the consortium projects will provide a summary of their research results over the past year and how they've contributed to the FaceBase repository.

    If you would like to join us, please register using this form at the following link and we will send you the details the week before the meeting:

    Click here to view the agenda:[...]a.pdf
    Education: Training in Bioinformatics for Beginners @ NIH
    Submitted by Vijayaraj Nagarajan; posted on Monday, April 22, 2019

    June 11-14, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA


    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.


    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.


    • 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 Educational 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: R, Perl, Python
    • Platforms: EMBOSS, UGENE, H2O, Galaxy
    Education: Training course: Introduction to Machine Learning
    Submitted by Carlo Pecoraro; posted on Thursday, April 18, 2019


    June 3-7, 2019
    BGBM/ Freie Universität Berlin, Königin-Luise-Straße 6-8, 14195 Berlin[...]se43/

    Registration deadline: May 4, 2019
    Instructor: Prof. Paolo Frasconi (University of Florence, Italy;


    This workshop is aimed to students and researchers aiming to understand the basic principles of machine learning. It will focus on supervised learning, starting with linear models (regression, logistic regression, support vector machines) and will extend to the basic technologies of deep learning and kernel methods for vector data, signals, and structured data. Basic principles of learning theory that are useful to analyze results of practical applications will be also covered. Finally, there will be practical sessions using scikit-learn, TensorFlow, and Keras. After completing the workshop, students should able to understand the most popular learning algorithms, to apply them to solve simple practical problems, and to analyze and interpret the results. All course materials (including copies of presentations, practical exercises, data files, and example scripts prepared by the instructing team) will be provided electronically to participants.


    This workshop is aimed at all researchers and technical workers with a background in biology, computer science, mathematics, physics or related disciplines who want to understand and apply supervised machine learning algorithms to practical problems. The syllabus has been planned for people with zero or very basic knowledge of machine learning.

    Students are assumed to know calculus, linear algebra, and algorithms and data structures at the undergraduate level. Students should also have sufficient programming skills, and preferably previous knowledge of the Python programming language.


    Session content:[...]um43/
    For more information about the course, please visit our website:[...]se43/
    Here is the full list of our courses and Workshops:


    We developed an in silico tool NeuroPIpred for predicting and designing insect neuropeptides from natural and modified peptides.

    Paper URL:[...]538-x
    Web server :[...]pred/



    Scientists at ETH Zurich have created the first fully computer-generated genome of a living organism. The brand new genome, named Caulobacter ethensis-2.0, was built by essentially cleaning up and simplifying the natural code of a bacterium called Caulobacter crescentus. For now it exists as one large DNA molecule and not a living organism itself, but the team says this is a huge step towards creating completely synthetic life and medicinal DNA molecules.

    May 30-31, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA


    Participants will create and access cloud instances (renting and using a windows or linux machine), set security, configure storage, create snapshots, create images, up or down scaling the resources, monitoring resources, billing and perform routine tasks.


    Cloud offers computers with one to hundreds of cores and megabytes to terabytes of memory, on a hourly basis, from pennies to 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 CPU, GPU, Cluster
    • Standards: GovCloud
    • Pricing: On-demand, spot, reserved
    • Storage: AWS Simple Storage Service (S3)
    • Storage: AWS Elastic Block Store (EBS)
    • Storage: AWS Volumes, Snapshots, Amazon Machine Images (AMI)
    • BigData: Hadoop, MapReduce, DynamoDB, Serverless computing
    • Monitoring: CloudWatch
    • Marketplace: MolBioCloud


    • 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.
    • Training provided by active NIH researchers
    • Cookbook style manual for all exercises
    • Direct, after training support through exclusive forum membership
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