A collection of similar but different presentations I've made aimed at introducing bioinformatics to bench biologists.

Modules showing how the NCBI database classifies and organizes information on DNA sequences, evolutionary relationships, and scientific publications. And a module working to identify a nucleotide sequence from an insect endosymbiont by using BLAST

The key role of bioinformatics in explaining biological phenomena calls for the need to rethink didactic approaches at high school aligned with a new scientific reality. Despite several initiatives to introduce bioinformatics in the classroom, there is still a lack of knowledge on their impact on students’ learning gains, engagement, and motivation. In this study, we detail the effects of four bioinformatics laboratories tailored for high school biology classes named “Mining the Genome: Using Bioinformatics Tools in the Classroom to Support Student Discovery of Genes” on literacy, interest, and attitudes on 387 high school students. By exploring these laboratories, students get acquainted with bioinformatics and acknowledge that many bioinformatics tools can be intuitive for beginners. Furthermore, introducing comparative genomics in their learning practices contributed for a better understanding of curricular contents regarding the identification of genes, their regulation, and how to make evolutionary assumptions. Following the intervention, students were able to pinpoint bioinformatics tools required to identify genes in a genomics sequence, and most importantly, they were able to solve genomics-related misconceptions. Overall, students revealed a positive attitude regarding the integration of bioinformatics-based approaches in their learning practices, reinforcing their added value in educational approaches.

Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs — Course Based Undergraduate Research Experiences — evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.

The 4th German Crop BioGreenformatics Network (GCBN, https://www.denbi.de/gcbn) user training provided a hands-on introduction to useful bioinformatics tools for biologists with little or no previous knowledge. The training enabled biologists to process their own small and large datasets using R and Linux based methods and is entirely computer-based with interspersed lectures. The first part started with an introduction into the use and basic administration (software installation) of the Linux distribution Ubuntu and demonstrated the first steps into the use the R software (trainer Andrea Bräutigam, folder AB). In part two the use of Blast+ in the command line version, of simple Linux commands like 'cut', of Perl scripts and the graphical user interface of the phylogeny tool 'seaview' were demonstrated (trainer Uwe Scholz, folder US). The third session introduced basic concepts and practical tools for processing biological datasets in Linux. In particular, 'awk' and 'sed' were used. Moreover, 'SAMtools' and 'BEDTools' were applied (trainer Martin Mascher, folder MM). In the fourth part 'Introduction to Databases' a quick start guide to use relational databases was presented. By providing easy examples, this lesson set the fundamentals to motivate to use relational database systems as daily bioinformatics tool to store, retrieve and even analyze -omics data in the big data age (trainer Matthias Lange, folder ML). The last part introduced basic statistics to biologist, teach some commonly used statistical methods and demonstrated the creation of graphical visualizations with software R (trainer Yusheng Zhao, folder YZ).

Presentation on teaching introductory bioinformatics with Jupyter notebook-based active learning at the 2019 Great Lakes Bioinformatics Conference

Contemporary biology is moving towards heavy reliance on computational methods to manage, find patterns, and derive meaning from large-scale data, such as genomic sequences. Biology teachers are increasingly compelled to prepare students with skills to meet these challenges. However, introducing biology students to more abstract concepts associated with computational thinking remains a major challenge. Analogies have long been used in science classrooms to help students comprehend complex concepts by relating them to familiar processes. Here I present a multi-step procedure for introducing students to large-scale data analysis (bioinformatics workflows) by asking them to describe a common daily task: making toast. First, students describe the main steps associated with this procedure. Next, students are presented with alternative scenarios for materials and equipment and are asked to extend the analogy to accommodate them. Finally, students are led through examples of how the analogy breaks down, or fails to accurately represent, a bioinformatics analysis. This structured approach to student exploration of analogies related to computational biology capitalizes on diverse student experiences to both clarify concepts and ameliorate possible misconceptions. Similar methods can be used to introduce many abstract concepts in both biology and computer science.

Data and conda software environment file for the chapter 'Introduction to Python and Pandas' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).

Introductory bioinformatics exercises often walk students through the use of computational tools, but often provide little understanding of what a computational tool does "under the hood." A solid understanding of how a bioinformatics computational algorithm functions, including its limitations, is key for interpreting the output in a biologically relevant context. This introductory bioinformatics exercise integrates an introduction to web-based sequence alignment algorithms with models to facilitate student reflection and appreciation for how computational tools provide similarity output data. The exercise concludes with a set of inquiry-based questions in which students may apply computational tools to solve a real biological problem.

In the module, students first define sequence similarity and then investigate how similarity can be quantitatively compared between two similar length proteins using a Blocks Substitution Matrix (BLOSUM) scoring matrix. Students then look for local regions of similarity between a sequence query and subjects within a large database using Basic Local Alignment Search Tool (BLAST). Lastly, students access text-based FASTA-formatted sequence information via National Center for Biotechnology Information (NCBI) databases as they collect sequences for a multiple sequence alignment using Clustal Omega to generate a phylogram and evaluate evolutionary relationships. The combination of diverse, inquiry-based questions, paper models, and web-based computational resources provides students with a solid basis for more advanced bioinformatics topics and an appreciation for the importance of bioinformatics tools across the discipline of biology.

This is an introduction to bioinformatics using hemoglobin as an example. The worksheets introduce students to resources to explore the DNA, RNA and polypeptide linear structure with a brief introduction to the quaternary structure of hemoglobin.

In order to introduce students to the concept of molecular diversity, we developed a short, engaging online lesson using basic bioinformatics techniques. Students were introduced to basic bioinformatics while learning about local on-campus species diversity by 1) identifying species based on a given sequence (performing Basic Local Alignment Search Tool [BLAST] analysis) and 2) researching and documenting the natural history of each species identified in a concise write-up. To assess the student’s perception of this lesson, we surveyed students using a Likert scale and asking them to elaborate in written reflection on this activity. When combined, student responses indicated that 94% of students agreed this lesson helped them understand DNA barcoding and how it is used to identify species. The majority of students, 89.5%, reported they enjoyed the lesson and mainly provided positive feedback, including “It really opened my eyes to different species on campus by looking at DNA sequences”, “I loved searching information and discovering all this new information from a DNA sequence”, and finally, “the database was fun to navigate and identifying species felt like a cool puzzle.” Our results indicate this lesson both engaged and informed students on the use of DNA barcoding as a tool to identify local species biodiversity.

Primary Image: DNA Barcoded Specimens. Crane fly, dragonfly, ant, and spider identified using DNA barcoding.

Computational_Genomics_ Instructor: Name: Dr. Rodolfo Aramayo, PhD Email address: raramayo@tamu.edu Location: Department of Biology Room 412A, Biological Sciences Building West (BSBW) Texas A&M University College Station, TX 77843-3258 Description: This repository contains materials used to teach Computational Genomics in the Spring 2023. This course was heavily based on materials extracted from and/or adapted from: ENSEMBL, and ENSEMBL Tutorials and Examples. Genomes. 2nd edition Current Topics in Genome Analysis Galaxy Training Materials Course Topics: History of Bioinformatics History of Genomics Cloning Basics The Carbon Clarke Formula Introduction to Galaxy Genome Files: FASTA Format Uploading Data into Galaxy Introduction to Text Manipulations Introduction to Regular Expressions Introduction to Gene Models and Tables: GFF3 Files Introduction to Genome Annotation Cyverse User Portal Introduction to Genome Browsers (ENSEMBL) Introduction to Comparative Genomics Working with Genome Files Introduction to Sequence Analysis Computational Arithmetics Author: Rodolfo Aramayo (raramayo@tamu.edu) License: All content produced in this site is licensed by: CC BY-NC-SA 4.0

Data and conda software environment file for the chapter 'Introduction to R and the Tidyverse' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).

This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023. Event description Computational workflows are invaluable resources for research communities. They help us standardise common analyses, collaborate with other researchers, and support reproducibility. Bioinformatics workflow developers invest significant time and expertise to create, share, and maintain these resources for the benefit of the wider community and being able to easily find and access workflows is an essential factor in their uptake by the community. Increasingly, the research community is turning to workflow registries to find and access public workflows that can be applied to their research. Workflow registries support workflow findability and citation by providing a central repository and allowing users to search for and discover them easily. This workshop will introduce you to workflow registries and support attendees to register their workflows on the popular workflow registry, WorkflowHub. We’ll kick off the workshop with an introduction to the concepts underlying workflow findability, how it can benefit workflow developers, and how you can make the most of workflow registries to share your computational workflows with the research community. You will then have the opportunity to register your own workflows in WorkflowHub with support from our trainers. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. 2023-03-21_Workflows_slides (PDF): A copy of the slides presented during the workshop Materials shared elsewhere: A recording of the first part of this workshop is available on the Australian BioCommons YouTube Channel: https://youtu.be/2kGKxaPuQN8

Introductory curriculum for high school students (grades 9-12) that explores genetic research and bioinformatics. Posted on-line October 2012. Funded by NSF grant DRL-0833779

The German Crop BioGreenformatics Network (GCBN, http://www.denbi.de/gcbn) user training provided a hands-on introduction to useful bioinformatics tools for biologists with little or no previous knowledge. The training enabled biologists to process their own small and large datasets using R and Linux based methods and is entirely computer-based with interspersed lectures. - Introduction into Linux and R (folder AB) - Sequence analysis using Blast and simple phylogenies (folder US) - Data processing with Linux tools (folder MM) - Introduction to databases (folder ML) - Statistics and figures with R )folder YZ)

This record contains the data files used in exercises in the NBIS course "Introduction to Data Management Practices".

Data and conda software environment file for the chapter 'Introduction to the Command Line' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).

File used for the Introduction to bioinformatics (IBT) Linux practical session course.

A brief introduction to the concept, vision and challenges associated with Biodiversity Informatics.