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Kovarik D.N.,Northwest Association for Biomedical Research | Patterson D.G.,University of Washington | Cohen C.,Cohen Research and Evaluation | Sanders E.A.,University of Washington | And 4 more authors.
CBE Life Sciences Education | Year: 2013

We investigated the effects of our Bio-ITEST teacher professional development model and bioinformatics curricula on cognitive traits (awareness, engagement, self-efficacy, and relevance) in high school teachers and students that are known to accompany a developing interest in science, technology, engineering, and mathematics (STEM) careers. The program included best practices in adult education and diverse resources to empower teachers to integrate STEM career information into their classrooms. The introductory unit, Using Bioinformatics: Genetic Testing, uses bioinformatics to teach basic concepts in genetics and molecular biology, and the advanced unit, Using Bioinformatics: Genetic Research, utilizes bioinformatics to study evolution and support student research with DNA barcoding. Pre-post surveys demonstrated significant growth (n = 24) among teachers in their preparation to teach the curricula and infuse career awareness into their classes, and these gains were sustained through the end of the academic year. Introductory unit students (n = 289) showed significant gains in awareness, relevance, and self-efficacy. While these students did not show significant gains in engagement, advanced unit students (n = 41) showed gains in all four cognitive areas. Lessons learned during Bio-ITEST are explored in the context of recommendations for other programs that wish to increase student interest in STEM careers. © 2013 D. N. Kovarik et al.


Mason C.E.,New York Medical College | Porter S.G.,Digital World Biology | Smith T.M.,Perkin Elmer Corporation | Smith T.M.,Digital World Biology
Advances in Experimental Medicine and Biology | Year: 2014

In today's biology, studies have shifted to analyzing systems over discrete biochemical reactions and pathways. These studies depend on combining the results from scores of experimental methods that analyze DNA; mRNA; noncoding RNAs, DNA, RNA, and protein interactions; and the nucleotide modifications that form the epigenome into global datasets that represent a diverse array of "omics" data (transcriptional, epigenetic, proteomic, metabolomic). The methods used to collect these data consist of high-throughput data generation platforms that include high-content screening, imaging, flow cytometry, mass spectrometry, and nucleic acid sequencing. Of these, the next-generation DNA sequencing platforms predominate because they provide an inexpensive and scalable way to quickly interrogate the molecular changes at the genetic, epigenetic, and transcriptional level. Furthermore, existing and developing single-molecule sequencing platforms will likely make direct RNA and protein measurements possible, thus increasing the specificity of current assays and making it possible to better characterize "epi-alterations" that occur in the epigenome and epitranscriptome. These diverse data types present us with the largest challenge: how do we develop software systems and algorithms that can integrate these datasets and begin to support a more democratic model where individuals can capture and track their own medical information through biometric devices and personal genome sequencing Such systems will need to provide the necessary user interactions to work with the trillions of data points needed to make scientific discoveries. Here, we describe novel approaches in the genesis and processing of such data, models to integrate these data, and the increasing ubiquity of self-reporting and self-measured genomics and health data. © 2014 Springer Science+Business Media New York.


News Article | January 13, 2017
Site: scienceblogs.com

It’s time for the annual blog about the annual Nucleic Acids Research (NAR) database issue. This is the 24th database issue for NAR and the seventh blog for @finchtalk. Like most years I have no idea what I’m going to write about until I start reading the new issue. Something always inspires me. As summarized in the database issue’s introduction, Galperin, Fernández-Suarez, and Rigden tell us this year’s issue has 152 papers. 54 of those describe new databases, 98 provide updates, and 16 are updates of databases that have been published elsewhere.  18 duplicate entries and 30 obsolete database have been removed.  But we are not told how many databases are in the catalog. That is an exercise for the reader. Given that last year the authors stated that there were 1685 databases one would assume that this year’s total would be 1685+54+16-18-30=1707, or 1691 if the 16 updated databases were in the catalog and just described somewhere else. But, since we are not told that, we need to figure it out on our own. Fortunately, the entire list of databases is available, so all you have to do is visit the page and count the entries. Ok, that would be tedious and take forever because you’d have to check your  work  and likely get lost several times doing so. Instead, one can capture the text and write a Perl script to count the entries.  When I did this, I got 1662 for an answer. This is neither 1707 nor 1691. As the catalog is maintained through the year, more databases have likely been removed than were reported in the article. As I counted the entries, I also looked at the titles and descriptions and thought about what could we learn from this information. After all, these 1662 databases are used to develop scientific knowledge. Can we use this data to learn about the kinds of things scientists are interested in? Now my simple Perl script grew from a command line that counted empty lines to a script that had to grab the second line of each entry – triggered by an empty line using a state machine, with an initialization to get the first entry – parse that second line and count the words. For students interested in bioinformatics, this is a common exercise with data. Once that was done, a review of the words indicated some clean up was in order. Common words, that added little value, were removed. Also, plurals were converted to singular forms to avoid duplication of terms. The last step was to use wordle™ to create a tag cloud of terms found in the database descriptions. So, what did I learn? First, database is the most common term. Nearly 25% of the descriptions use that term. The next most frequent term is protein, which is followed by gene, genome, human, sequence, and data. The term structure, something we’re interested in at Digital World Biology, is the eighth most frequent term. It is followed by genomic, interaction, and expression. While DNA sequencing captures attention in the news, understanding how genotypes impact phenotypes requires that we deeply understand the relationship between sequence, structure, and function. Thus, it is not surprising that the most common terms describing biological databases would include words that describe this relationship. The other interesting finding is the sheer number of unique words. The tag cloud above summarizes 150 of 2370 total words. To be listed in the tag cloud a word had to used at least nine times.  Words used only once occurred over 1500 times. These are interesting and instructive too. A few of the words indicate that there are databases that include information on waterfleas, mites, exosomes, leptospira, paramecium, amoebazoa, honey,  plexipus, bananas, and many others. The words used once list also includes misspellings, word fragments, and words that add context to descriptions, many of these are chemical and biochemical terms. The real importance of the number and variety of words used to describe the databases however, is that biological databases store and organize data and information about biology.  And, the complexity of biology cannot be stored in a single source.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2013

The innovation evaluates the feasibility of developing a tablet-based suite of applications that allow students to interrogate relationships between molecular sequences, molecular structures, and their biological functions. One of the most difficult concepts for students to understand is why certain mutations affect the function of a protein and others do not. Students are better able to understand this relationship when they compare molecular structures from different allelic forms of a protein, locate the mutation site, and identify structural changes that occur as a consequence of a mutation. Few students have the opportunity to carry out these kinds of investigations because they lack suitable software tools and their teachers lack both experience and relevant instructional materials. These problems will be addressed by creating a tool kit to support molecular investigations. The tool kit will be designed for tablet-based computers and will contain an application for viewing and manipulating structures, an interactive digital lab manual that supports bioinformatics investigations related to genetic disease, an instructor guide, and a data set with superimposed and annotated structures. The broader impact/commercial potential of this project relates to the potential in this suite of tools for interesting students in careers related to science, technology, engineering, or math (STEM) and in increasing student understanding of the connections between genetics, proteins, and protein function. The ease of interacting with molecular objects through touch with tablet-based computers and the availability of instructional materials that guide students through the process of discovery will lower the barriers to learning about protein structure and genetics and make student-driven investigations possible for a wider group of students in both high school and college courses. The instructor guide will assist instructors in implementing these materials by describing example work-flows and providing guidance in using different types of applications to achieve learning outcomes by helping students carry out these new types of laboratory investigations. The technology developed through this project will generate business opportunities through licensing fees and by providing opportunities to develop additional molecular investigations and learning materials.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 155.00K | Year: 2013

The innovation evaluates the feasibility of developing a tablet-based suite of applications that allow students to interrogate relationships between molecular sequences, molecular structures, and their biological functions. One of the most difficult concepts for students to understand is why certain mutations affect the function of a protein and others do not. Students are better able to understand this relationship when they compare molecular structures from different allelic forms of a protein, locate the mutation site, and identify structural changes that occur as a consequence of a mutation. Few students have the opportunity to carry out these kinds of investigations because they lack suitable software tools and their teachers lack both experience and relevant instructional materials. These problems will be addressed by creating a tool kit to support molecular investigations. The tool kit will be designed for tablet-based computers and will contain an application for viewing and manipulating structures, an interactive digital lab manual that supports bioinformatics investigations related to genetic disease, an instructor guide, and a data set with superimposed and annotated structures.

The broader impact/commercial potential of this project relates to the potential in this suite of tools for interesting students in careers related to science, technology, engineering, or math (STEM) and in increasing student understanding of the connections between genetics, proteins, and protein function. The ease of interacting with molecular objects through touch with tablet-based computers and the availability of instructional materials that guide students through the process of discovery will lower the barriers to learning about protein structure and genetics and make student-driven investigations possible for a wider group of students in both high school and college courses. The instructor guide will assist instructors in implementing these materials by describing example work-flows and providing guidance in using different types of applications to achieve learning outcomes by helping students carry out these new types of laboratory investigations. The technology developed through this project will generate business opportunities through licensing fees and by providing opportunities to develop additional molecular investigations and learning materials.


Digital World Biology | Entity website

Our products help teachers teach modern biology with the same software tools, data, and information resources that are used by science researchers throughout the world. We overcome the many challenges associated with introducing computer-based classroom activities through novel approaches that combine interactive iPad apps with data that are packaged into fun and engaging classroom activities ...


Digital World Biology | Entity website

The Amgen Biotech Experience (ABE) curriculum includes a series of cloning and protein purification experiments using red fluorescent protein (RFP). Fluorescent proteins have become a valuable tool in recent years among scientists in many different fields of biology ...


Digital World Biology | Entity website

Molecule World: DNA Binding Lab is a stand-alone lab activity witha set of "unknown structures" that students explore to learn about DNA structure and the interactions between DNA and other molecules. This lab is appropriate for both high school and college students ...


Digital World Biology | Entity website

The Amgen Biotech Experience (ABE) curriculum includes a series of cloning and protein purification experiments using red fluorescent protein (RFP). Fluorescent proteins have become a valuable tool in recent years among scientists in many different fields of biology ...


Digital World Biology | Entity website

Overview of Genetic Testing Unit The Bio-ITEST genetic testing curriculum is designed for high school students in grades 9-12. In a series of eight lessons, students are introduced to the idea of genetic testing and the implications for managing information about their personal health ...

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