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GLBIO 2021 | May 10-13, 2021 | Virtual Event | Workshops and Tutorials

WORKSHOPS AND TUTORIALS



First Time Conference Attendance

Conferences are an essential component to the computational biology and bioinformatics communities. However, for first-time attendees, conferences may be intimidating and challenging to navigate. GLBIO has a long tradition of encouraging student/trainee participation.  In fact, for many such attendees, GLBIO may be their first experience with an academic conference. The aim for this tutorial would be to provide first time conference attendees with additional information on how to navigate the meeting in order get the most out of the experience, and to build a cohort of trainees that will reduce isolation and provide ongoing support and social opportunity. Participants will work in small groups to practice talking about their work and navigating casual conversations at the conference. With much or all of the 2021 GLBIO meeting likely to be virtual, the importance of building tools for engagement is even more critical. Ultimately, we hope to increase and enhance participation in the meeting by first time attendees, in particular undergraduate students.

Organizers:

  • Layla Oesper, This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Getiria Onsongo, This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Robin Shields-Cutler, This email address is being protected from spambots. You need JavaScript enabled to view it.

URL: https://sites.google.com/macalester.edu/glbio2021tutorial/home

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Introduction to Deep Learning and Creating Neural Networks in Python and R


Recently, deep neural networks have become a powerful tool in bioinformatics and computational biology, which enables researchers to work with large and complex medical datasets (DNA/RNA/protein sequences, medical imaging, etc.) that can be challenging to analyze with simpler machine learning models. It is with the addition of neural networks to our computational toolkits that we have been able to make predictions from big data (predicting protein secondary structure: disease diagnosis, subtype, and treatment etc.) and discover underlying biological principles not easily identified through using other algorithms. The learning outcomes in this workshop include an introduction to deep learning and the various types of deep learning networks (ANN, CNN), how neural networks work, what they can be used for, as well as how to build a basic CNN and ANN in Python and R to assess biomedical data.

Organizer:

  • Danielle Maeser, This email address is being protected from spambots. You need JavaScript enabled to view it.

URL: https://maese005.wixsite.com/mysite 

Max capacity: 60

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Machine Learning on Microbiome Data: Theory and Practice



The workshop will focus on the theory and application of machine learning to microbiome datasets, including bacterial and viral communities. The following topics will be addressed:

  • Introduction to supervised machine learning, and how to implement a machine learning workflow using R and Python.
  • Applying machine learning algorithms (such as Random Forest) on microbiome datasets for disease risk prediction in humans.
  • Introduction to bacteria-phage interactions, and how it contributes to bacterial pathogenicity.
  • Applying machine learning methods to predict bacterial pathogenicity induced by phages.

Organizers:

  • Tatiana Lenskaia, This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Sambhawa Priya, This email address is being protected from spambots. You need JavaScript enabled to view it.

Participation can be in-person or online, a participant will need a computer with Internet access and pre-installed R and Python.

URL: https://glbio-mlmb.github.io/

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Mass Spectormetry (MS)-based multi-omics analysis using the Galaxy-P bioinformatics platform: A case study in COVID19 data analysis

The Galaxy for proteomics (Galaxy-P) team at the University of Minnesota will lead a hands-on workshop in the use of MS-based multi-omics informatics tools, using the investigation of COVID19 proteomics data as a case study. We will train participants in well-validated tools and workflows available for multi-omic data analysis within the publicly accessible Galaxy platform. We will also leverage ongoing developments of the Galaxy Training Network (GTN) which rovides online, hands-on training in complex bioinformatics tools for novice and advanced users.

Our workshop would be targeted to an audience of mixed expertise, from wet-bench scientists to data scientists and bioinformaticians. The workshop will be presented in two modules: 1) In module 1, participants will be introduced to the Galaxy platform, GTN resources, and basic usage of Galaxy for bioinformatics analysis. They will also be introduced to publicly available data and resources for COVID-19 bioinformatics analysis. 2) In the second module, researchers will be instructed in hands-on training resources and introduction to multi-omics resources focusing on tools for identification of expressed COVID19 proteins, human host proteins, and potentially co-infecting microbial components which involves integration of metagenomic and proteomic data. Tools for visualizing and interpreting results will also be discussed. Attendees will be required to bring their laptop computer so that they can access public Galaxy resources for the training.

Organizer:

  • Tim Griffin, This email address is being protected from spambots. You need JavaScript enabled to view it.

URL: https://covid19.galaxyproject.org/proteomics/

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An Introduction to Bayesian Networks and their Microbiome Applications

This workshop will focus on the application of Bayesian Networks (BNs) to count data originating from next-generation sequencing experiments.  The workshop will cover an introduction to Bayesian statistics and probabilistic models, comparative discussion of BNs packages in R, and generating and testing hypotheses using BNs. Participants will be trained in applications of BNs to metabolomic and microbiome data.

Organizer:

  • Breanna Shi

URL: https://breannashi.github.io/BayesiannetworkforMicrobiomeData/


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