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

Syllabus of the MOOC “Bioinformatics: Introduction and Methods.”

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).

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).

Dataset for the practice in the data preprocessing and unsupervised learning in the introduction to bioinformatics course

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).

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

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).

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

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.

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

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.

In Brazil, training capable bioinformaticians is done, mostly, in graduate programs, sometimes with experiences during the undergraduate period. However, this formation tends to be inefficient in attracting students to the area and mainly in attracting professionals to support research projects in research groups. To solve these issues, participation in short courses is important for training students and professionals in the usage of tools for specific areas that use bioinformatics, as well as in ways to develop solutions tailored to the local needs of academic institutions or research groups. In this aim, the project “Bioinformática na Estrada” (Bioinformatics on the Road) proposed improving bioinformaticians’ skills in undergraduate and graduate courses, primarily in the countryside of the State of Pará, in the Amazon region of Brazil. The project scope is practical courses focused on the areas of interest of the place where the courses are occurring to train and encourage students and researchers to work in this field, reducing the existing gap due to the lack of qualified bioinformatics professionals. Theoretical and practical workshops took place, such as Introduction to Bioinformatics, Computer Science Basics, Applications of Computational Intelligence applied to Bioinformatics and Biotechnology, Computational Tools for Bioinformatics, Soil Genomics and Research Perspectives and Horizons in the Amazon Region. In the end, 444 undergraduate and graduate students from higher education institutions in the state of Pará and other Brazilian states attended the events of the Bioinformatics on the Road project.

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

Content of the Introduction to Bayesian statistics SIB course of May 2023

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

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

June 2023 Version

This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github:

Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments

Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.

Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.

Slides.zip contains all the slides used in the workshop.

Raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.