Genetic Engineering & Biotechnology News

MAY15 2018

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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For more information, visit our website (, click on New Products, and search through our comprehensive new products database. New Products 26 | MAY 15, 2018 | | Genetic Engineering & Biotechnology News Best of the Web All of the links to the URLs described above are posted on GEN's website, To suggest a website for Best of the Web, please send the URL to Taralyn Tan ( Key Strong Points Weak Points Excellent Ratings Very Good Good 9 – HHHH HHH HH CRISPR Fly Design HHHH 9 Nicely designed, good collection of open-access resources CRISPR has taken the field of biological re- search by storm. As CRISPR has (seemingly overnight) been adopted by many research- ers around the world, it is important for scientists using the technology to share their experiences and resources with the greater scientific community. That is the general idea behind CRISPR Fly Design, an open-access resource for researchers using CRISPR in Dro- sophila. Based at the German Cancer Research Center in Heidelberg, the team behind CRISPR Fly Design posts information about fly stocks (spanning D. melanogaster and three other Drosophila species), guide RNA plasmids, and experimental protocols. The protocols section includes the topics: selecting an appropriate strategy, avoiding off-target effects, making guide RNA, and finding your mutants. The website also includes a blog (though it is somewhat infrequently updated) as well as a list of links to other CRISPR resources. IEDB HHHH 9 Nicely organized, incredibly large database Funded by the National Institute of Allergy and Infectious Diseases, the Immune Epitope Database and Analysis Re- source (IEDB) provides the immunology community a rich collection of experimental data (we're talking hundreds of thousands of assays) reflecting the study of T-cell epitopes and antibodies. To start, users can search the database by epitope, assay type, antigen, MHC classification, host, or dis- ease. These search parameters can also be used in conjunc- tion to filter search results as desired. Results are presented in different tabs corresponding to epitopes, antigens, assays, receptors, and references, and within each tab, the user can further sort the results. Beyond the database itself, the website includes analysis tools like T-cell and B-cell epitope predictors, as well as additional epitope analysis tools to calculate, for ex- ample, the proportion of individuals within a population predicted to respond to a particular epitope. How to Use t-SNE Effectively HHHH 9 Nice interactive graphics, simple design Especially within the context of high-dimensional single-cell RNA- sequencing datasets, biological research articles increasingly include "t-SNE plots"—plots designed to convert multidimensional datasets into lower-dimensional representations based on similarities in the data. For example, data points corresponding to different "cell types" are often depicted as distinct clusters in a 2D plot. While these plots can be very useful, they are accompanied by many caveats that are often unknown to the casual observer. Enter the article, "How to Use t-SNE Effectively" on (a website that provides articles to simply explain concepts in machine learning). This article uses interactive simulations to teach site visi- tors the effects of altering different parameters (e.g., points per cluster, dimensions, and "perplexity") used by the t-SNE clustering algorithm. A nice text explanation and embedded illustrations drive home the main points. NeuroExpresso HHH 9 Customizable data-viewing options, good help section – Few experimental datasets included There's nothing like a shot of gene-expression data to wake you up in the morning! The NeuroExpresso database devel- oped by the Pavlidis Lab at the University of British Columbia contains curated cell type–specific gene-expression data from microarray and single-cell RNA-sequencing experiments. Its goal is the discovery of novel molecular markers for function- ally distinct cell types in the nervous system. The website provides a useful interface to visualize the gene-expression data contained within the database. Via the "gene search" option, users specify a gene, brain region, and experimental platform to view the result- ing data in graphical form. In the graph, gene expression is plotted as a function of different cell types within the selected brain region. Users can restrict the data to particular cell types and can also divide the data into groups and perform a differential gene-expression analysis between the groups. Pharmit HHHH 9 Vast collection of compounds and conformations, good graphics display Phamacological screening of compound libraries in the laboratory is costly and time consuming. So, the creators of this website at the University of Pittsburgh invite re- searcher to "pharm" out the work to the Pharmit virtual- screening platform. The Pharmit visualization environ- ment runs within a web browser and grants users access to six built-in libraries containing close to 100 million compounds in over 1 billion total conformations. Users can search for proteins via their Protein Database (PDB) IDs; however, the site also provides some samples for users to explore when they're just getting started on the website. The graphics display nicely within the browser, with only slight lag when viewing the molecules at high zoom. Within the visualization browser, users can search through specific ligand libraries. Search results appear on the right side of the page, and each ligand can be displayed in its predicted binding location by selecting on the corresponding search result. EPIC HHH 9 Includes videos of raw data and expression trees – Partial resource (only select genes included), work in progress As it is still a work in progress, the EPIC (Expression Patterns in Cae- norhabditis) database may not quite live up to its grandiose name… yet. However, it is a very exciting start to the cataloging—on a cell- by-cell basis—of the expression patterns of single genes in the de- veloping worm embryo. For now, all of the data come from imaging protein fusions in C. elegans, though the website indicates that there are intentions to expand the analysis to another species, C. briggsae, as well. At present, the database contains expression data for over 130 genetic reporters. For each entry, users are provided an expres- sion tree that maps the reporter expression over the cell lineage tree to illustrate the specific lineages and cells in which the particular reporter is expressed. Videos of the complete imaging time series used to construct the expression tree are also provided. Basic details about the experiment, strain, and construct are included for each entry in the database.

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