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    Resources: Repositive is now the largest portal for human genomic data
    Submitted by Craig Smith; posted on Tuesday, October 18, 2016

    The number of datasets discoverable through the Repositive platform has now reached over one million, making Repositive the world's largest portal for accessing human genomic data.

    Repositive, a software company developing novel tools to improve access to human genomic research data, has expanded the number of datasets now available on its free platform to include 1,006,914 datasets from 32 different global data sources.

    The Repositive team has been expanding the range of datasets available by incorporating public data, privately held data, and has continued to add specialist dataset collections including Chinese control data, Methylation data and Expression data.

    Read more:[...]rtal/




    Scientists have found some toxic DNA lurking inside a virus that infects bacteria. In addition to its own genes, the virus holds a gene for black widow spider venom and DNA from other animals, the researchers found. The findings suggest that either the virus snagged this foreign genetic material or that these other animals have stolen DNA from the virus, the researchers said.

    Future research could find that such swapping across domains of life, from the most complex to the most ancient, is more common than previously thought, scientists say.

    Nov 15-17 ,2016
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD, USA[...]mblab


    Participants will get a brief hands-on introduction to the R platform, followed by hands-on walkthrough for creating several different popular plots, graphs and maps. We will start from formatting data and go all the way to loading data, setting parameters, creating the images and saving the outputs.

    Hands-on Skills/Tools Taught:
    • Heat maps
    • Scatterplots
    • Regression lines
    • Density plots
    • And more


    Participants will work with RStudio. A copy of all the scripts used in the class to create plots, graphs and maps would be provided to the participants at the end of the training. Participants will also receive a cookbook style manual for all the hands-on exercises. After training support is also provided through exclusive members only forum.
    Education: Training in Metagenomics Data Analysis at NIH
    Submitted by Vijayaraj Nagarajan; posted on Friday, October 07, 2016

    November 7-10, 2016
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD[...]mblab


    This training will introduce participants to the end-to-end solutions for analyzing metagenomic data, starting from data quality analysis, alignment, community profiling, taxonomic comparison and novel taxa discovery.

    • Participants will work with a Graphic User Interface based Linux Desktop environment in the Amazon Cloud, that is specially configured to run popular open source metagenomics analysis tools.
    • Participants will be provided with free access to the fully configured Amazon Machine Image for their personal use after the training.
    • Participants will also receive a cookbook style manual for all the hands on exercises.
    • After training support is also provided through exclusive members only forum.
    • Hands-on training provided by NIH researchers active in the field of metagenomics
    Hands-on Skills/Tools Taught:
    • Processing and Analysis: mothur, FLASh
    • Analysis: Permanova
    • Analysis: ANOSIM
    • Marker analysis: LEfSe
    • Marker analysis: QIIME
    • Functional analysis: PICRUSt
    • Metagenomics analysis: A5miseq
    • Contig annotation: MEGAN
    • Functional analysis: bioBakery
    • Advanced visualizations: phyloseq
    • Network analysis: Cytoscape



    In this Viewpoint, John Ioannidis and colleagues review how rarely gene therapy and genomic medicine have led to breakthroughs for patients and call on the NIH to defund preclinical research that fails to deliver on its initial promise.

    For several decades now the biomedical research community has pursued a narrative positing that a combination of ever-deeper knowledge of subcellular biology, especially genetics, coupled with information technology will lead to transformative improvements in health care and human health. In this Viewpoint, we provide evidence for the extraordinary dominance of this narrative in biomedical funding and journal publications; discuss several prominent themes embedded in the narrative to show that this approach has largely failed; and propose a wholesale reevaluation of the way forward in biomedical research.
    Events: Accelerating Biology 2017
    Submitted by vinod jani; posted on Friday, September 30, 2016

    January 17-19, 2017
    Pune University, Pune, India[...]y2017

    Accelerating Biology 2017 is a 3-day symposium (17 January to 19 January 2017) on current trends in bioinformatics. The sessions will comprise invited talks from eminent academicians from esteemed institutes. The talks will address the challenges in the below mentioned focus areas. The symposium is targeted at students and researchers from academic institutes, universities and bioinformatics industries.


    • Emerging areas in computational drug design
    • Molecular dynamics simulations: witnessing rare events
    • Next Generation Sequencing: disease Implications
    • Metabolic networks and systems biology
    • Big Data driven biology
    Resources: Protein sequence alignment display/analysis with MSAReveal.Org
    Submitted by Eric Martz; posted on Monday, September 26, 2016


    MSAReveal.Org is a new, free, open-source website for display and analysis of your protein sequences and sequence alignments:
    1. Paste in protein sequences or alignments in FASTA format.
    2. Genus and species, gene names, and UniProt IDs are extracted from the headers and tabulated. UniProt IDs are linked to UniProt.
    3. Checkboxes control coloring of amino acids/groups.
    4. Touching an amino acid reports its 3-letter abbreviation and sequence number in a tooltip. You don't have to memorize all 20 one-letter amino acid codes.
    5. The sequence listing can be horizontally scrolled, or wrapped at a specified number of amino acids.
    6. A sequence numbering offset can be specified with "start=N" in the header of each sequence. A negative number enables numbering to start at 1 for the mature protein, after a negatively numbered signal sequence.
    7. A consensus is reported with the frequencies of amino acids in each column.
    8. Statistics are tabulated including lengths (excluding gaps), percentages identity, percentages in gaps, percentages charged, percentages aromatic, net charge at neutral pH, etc. etc..
    9. Minimum, average, and maximum are given for each column in Statistics.
    10. The Statistics Table can be sorted on any column.
    11. A search mechanism finds and highlights sequence fragments/motifs regardless of gaps. It accepts multiple amino acid possibilities at a given position.
    12. Search hits are listed with links so you can jump to any hit instantly.
    13. Numerous irregularities or warnings are reported, including the ambiguous codes B, J, O, U, X, Z and illegal characters, which can be located easily with the search mechanism.
    14. A description of each alignment, added to the first header, will be displayed.
    15. A description of each sequence, added to its header, will be displayed when its taxon is touched.
    16. A Protein Data Bank ID, when added to the header (e.g. "PDB=2ace") will be tabulated and linked to display the 3D model in FirstGlance.Jmol.Org.
    17. New in September, 2016.
    A more detailed overview with snapshots:[...]rview

    Demonstration and test alignments are built in. Instructions are provided for downloading sequences from UniProt, and aligning them with free, easy, quick Jalview (using MAFFT, TCOFFEE, MUSCLE, etc.). Displays any FASTA alignments, e.g. from the ConSurf Server. MSAReveal.Org works in all popular browsers, Windows or OS X. Tested up to a total of one million amino acids, and with alignments containing hundreds of sequences.



    The challenge for Illumina, as with any other large company, is to continue to innovate while maintaining a grip on its core business. The company recently took a page from Alphabet by announcing a series of "moonshot" ventures, called Helix and Grail, which will create an App Store model for DNA informatics and bring early cancer screening tests into every doctor's office. And veteran Apple executive Phil Schiller joined Illumina's board this summer, giving the biotech company an injection of consumer marketing know-how.



    In an important breakthrough for the forensic science community, researchers have developed the first-ever biological identification method that exploits the information encoded in proteins of human hair.

    Scientists from Lawrence Livermore National Laboratory (LLNL) and a Utah startup company have developed the groundbreaking technique, providing a second science-based, statistically validated way to identify people and link individuals to evidence in addition to DNA profiling.



    According to [Ewan] Birney, the act of sharing data has been a tradition going back to the beginning of genetics. He points to innovations in the past century, such as the Protein Data Bank, the ENA Bank (formerly EMBL), and GenBank nucleotide collections, and the Human Genome Project as examples of instances where data sharing worked to positive, successful results. Birney is no stranger to the data sharing debate; he was appointed the joint Director of EMBL-EBI in 2015, and awarded the Benjamin Franklin Award for Open Access in the Life Sciences in 2005.

    Ewan's Blog:
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