Literature DB >> 33584559

Gene Annotation in High Schools: Successful Student Pipeline and Teacher Professional Development in Bioscience Using GENI-ACT.

Stephen T Koury1, Shannon Carlin-Menter2, Rama Dey-Rao1, Kimberle Kelly3.   

Abstract

Knowledge of genomics is an essential component of science for high school student health literacy. However, few high school teachers have received genomics training or any guidance on how to teach the subject to their students. This project explored the impact of a genomics and bioinformatics research pipeline for high school teachers and students using an introduction to genome annotation research as the catalyst. The Western New York-based project had three major components: (1) a summer teacher professional development workshop to introduce genome annotation research, (2) teacher-guided student genome annotation group projects during the school year, (3) with an end of the academic year capstone symposium to showcase student work in a poster session. Both teachers and students performed manual gene annotations using an online annotation toolkit known as Genomics Education National Initiative-Annotation Collaboration Toolkit (GENI-ACT), originally developed for use in a college undergraduate teaching environment. During the school year, students were asked to evaluate the data they had collected, formulate a hypothesis about the correctness of the computer pipeline annotation, and present the data to support their conclusions in poster form at the symposium. Evaluation of the project documented increased content knowledge in basic genomics and bioinformatics as well as increased confidence in using tools and the scientific process using GENI-ACT, thus demonstrating that high school students are capable of using the same tools as scientists to conduct a real-world research task.
Copyright © 2021 Koury, Carlin-Menter, Dey-Rao and Kelly.

Entities:  

Keywords:  STEM education and careers; bioinformatics; curriculum development (education); gene annotation; high school (9–12); professional development

Year:  2021        PMID: 33584559      PMCID: PMC7873969          DOI: 10.3389/fmicb.2020.578747

Source DB:  PubMed          Journal:  Front Microbiol        ISSN: 1664-302X            Impact factor:   5.640


  19 in total

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Journal:  Nucleic Acids Res       Date:  2002-01-01       Impact factor: 16.971

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Authors:  Gavin E Crooks; Gary Hon; John-Marc Chandonia; Steven E Brenner
Journal:  Genome Res       Date:  2004-06       Impact factor: 9.043

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Authors:  R L Tatusov; E V Koonin; D J Lipman
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Authors:  C Timothy Beagley
Journal:  Biochem Mol Biol Educ       Date:  2013 Jan-Feb       Impact factor: 1.160

7.  SignalP 5.0 improves signal peptide predictions using deep neural networks.

Authors:  José Juan Almagro Armenteros; Konstantinos D Tsirigos; Casper Kaae Sønderby; Thomas Nordahl Petersen; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen
Journal:  Nat Biotechnol       Date:  2019-02-18       Impact factor: 54.908

8.  PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

Authors:  Nancy Y Yu; James R Wagner; Matthew R Laird; Gabor Melli; Sébastien Rey; Raymond Lo; Phuong Dao; S Cenk Sahinalp; Martin Ester; Leonard J Foster; Fiona S L Brinkman
Journal:  Bioinformatics       Date:  2010-05-13       Impact factor: 6.937

Review 9.  An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology.

Authors:  Jian Li; Aarif Mohamed Nazeer Batcha; Björn Grüning; Ulrich R Mansmann
Journal:  Cancer Inform       Date:  2016-04-10

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Authors:  Sara El-Gebali; Jaina Mistry; Alex Bateman; Sean R Eddy; Aurélien Luciani; Simon C Potter; Matloob Qureshi; Lorna J Richardson; Gustavo A Salazar; Alfredo Smart; Erik L L Sonnhammer; Layla Hirsh; Lisanna Paladin; Damiano Piovesan; Silvio C E Tosatto; Robert D Finn
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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