AI Article Synopsis
- The COVID-19 pandemic necessitated a transition of the Bioinformatics course from in-person to remote learning, prompting changes in teaching methods and lab activities.
- Students now require foundational knowledge in DNA sequence analysis and are using Jupyter Notebook for practical learning, which allows for coding and results to be integrated into one document.
- This platform enhances student engagement by enabling interaction with code and results, creating a hybrid learning experience for Python scripting and genomics during remote instruction.
Article Abstract
The COVID-19 pandemic has forced the Bioinformatics course to switch from on-site teaching to remote learning. This shift has prompted a change in teaching methods and laboratory activities. Students need to have a basic understanding of DNA sequences and how to analyze them using custom scripts. To facilitate learning, we have modified the course to use Jupyter Notebook, which offers an alternative approach to writing custom scripts for basic DNA sequence analysis. This approach allows students to acquire the necessary skills while working remotely. It is a versatile and user-friendly platform that can be used to combine explanations, code, and results in a single document. This feature enables students to interact with the code and results, making the learning process more engaging and effective. Jupyter Notebook provides a hybrid approach to learning basic Python scripting and genomics that is effective for remote teaching and learning during the COVID-19 pandemic.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/bmb.21746 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Efficient diagnosis of diabetes mellitus using an improved ensemble method.
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
View Article and Find Full Text PDFIntegrating single-cell multimodal epigenomic data using 1D convolutional neural networks.
Bioinformatics
December 2024
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.
Motivation: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using these types of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type.
View Article and Find Full Text PDFMitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism.
Bioinform Adv
November 2024
Aix-Marseille University, CNRS, IBDM UMR7288, Turing Center for Living Systems (CENTURI), Marseille 13009, France.
Motivation: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.
View Article and Find Full Text PDFComputational Methods for Lineage Reconstruction.
Methods Mol Biol
January 2025
Centro Nacional de Análisis Genómico, Barcelona, Spain.
The recent development of genetic lineage recorders, designed to register the genealogical history of cells using induced somatic mutations, has opened the possibility of reconstructing complete animal cell lineages. To reconstruct a cell lineage tree from a molecular recorder, it is crucial to use an appropriate reconstruction algorithm. Current approaches include algorithms specifically designed for cell lineage reconstruction and the repurposing of phylogenetic algorithms.
View Article and Find Full Text PDFProtomix: a Python package for H-NMR metabolomics data preprocessing.
Bioinform Adv
November 2024
Laboratory of Molecular Science and Engineering, Åbo Akademi University, Henrikinkatu 2, Turku 20500, Finland.
Motivation: NMR-based metabolomics is a field driven by technological advancements, necessitating the use of advanced preprocessing tools. Despite this need, there is a remarkable scarcity of comprehensive and user-friendly preprocessing tools in Python. To bridge this gap, we have developed Protomix-a Python package designed for metabolomics research.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!