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Merge pull request #768 from MoeRichert-USDA/update-21bioinfo
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update 21-bioinformatics
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MoeRichert-USDA authored Nov 20, 2024
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In this hands-on workshop, participants will learn how to predict the functional roles of proteins by analyzing their sequence data using state-of-the-art bioinformatics tools powered by AI. The focus will be on understanding how AI-based methods are applied to predict protein characteristics and other downstream uses for gene annotations. <!--excerpt-->

Two such examples will be predicting signal peptides (indicators of protein secretion) and subcellular localization (where the protein operates in the cell). Participants will use sample datasets to explore how computational models can interpret protein sequences and provide insights into their biological roles. By the end of the session, attendees will have the knowledge and skills to functionally annotate proteins in any gene annotation.

## Tutorial Setup Instructions

Steps to prepare for the tutorial session:

1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide](https://scinet.usda.gov/guides/access/web-based-login).

1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas' login node.

1. **Request resources on a compute node** by running the following command:

{: .copy-code }
```
salloc --reservation=forum-gpu -A scinet_workshop1 -t 03:30:00 -n 1 --mem 8G
```

1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.

{: .copy-code }
```
mkdir -p /90daydata/shared/$USER
cd /90daydata/shared/$USER
cp -r /project/ai_forum/functional_annotation .
```

1. **Stop the interactive job** on the compute node by running the command exit.


## Tutorials

* [Identifying secreted proteins and predicting their subcellular localization](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM)
* [DeepGoPlus - Using AI to associate GO terms with novel proteins](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/DeepGoPlus_AI_Functional_Prediction_of_Proteins)
* [A ProtTrans Pipeline to Differentiate Transmembrane Domains from Other Proteins](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/Protein_Classification_with_ProtTrans)

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