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CQLS Software Tips

This is a place to highlight tips and software demos with the goal of preserving expertise and knowledge from within the CQLS and the departments and labs that we serve.

Looking for R tips? Python? Command line questions? Help with the CQLS infrastructure? You’ve come to the right place!

Note

This content was previously hosted at tips.cqls.oregonstate.edu and software.cqls.oregonstate.edu/tips

Contributing to the tips page

Do you think you have an idea that you'd like to highlight on the page? Do you want to convert your BUG talk into a post that can be a guide for other folks to follow for their own research? Let us know! We'd be happy to have guest authors for tips posts, especially as you become experts at specific software tools and pipelines.

  • 1 min read

Using alphafold on the Wildwood infrastructure

Introduction

Alphafold is a tool that researchers can use to predict 3D protein structures based on the primary amino acid sequence. Alphafold has an advantage over traditional 3D modeling tools as it can leverage GPUs for some of the computational steps, which significantly increases the amount of data that can be analyzed in a reasonable amount of time.

The Alphafold database has many structures pre-computed, and we suggest researchers use the pre-computed models when possible.

  • 3 min read

Using MATLAB on CQLS HPC

Introduction to MATLAB on the CQLS infrastructure

Users that are looking to work with MathWorks MATLAB software are able to access it at the command line. First off, MATLAB will not run on the front end machines shell-hpc.cqls.oregonstate.edu and files-hpc.cqls.oregonstate.edu. Users are able to run the software stack on the processing machines using the -nodisplay option when accessing MATLAB via the command line.

  • 6 min read

16S sequencing protocol

Purpose & Assumptions

The purpose of this guide is to describe the primary differences in analyzing sequences generated using the Earth Microbiome Project (EMP) or standard Illumina protocols. In other words, this post will describe the practical aspects and applications with respect to analyzing the data, rather than the theory behind the process, study design, etc.