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A presentation from the NIH about how to get the most out of your Nephele results:

https://www.slideshare.net/bcbbslides/nephele-20-introduction

I thought the exploring outputs section was particularly helpful.

One interesting side note I found when looking into Nephele is that it uses the R package dada2, which was created by NCSU’s very own Dr. Ben Callahan! Here’s the paper. https://www-nature-com.prox.lib.ncsu.edu/articles/nmeth.3869

Here is a nice tutorial page for customizing your data visualizations from Nephele. Nice data is great, but being able to communicate it clearly is everything! https://nephele.niaid.nih.gov/user_guide_tutorials/

I thought that this site from the University of Utah that I found by browsing the Nephele site is pretty interesting because it defines the concepts of microbiota and microbiomes and applies them in the context of human health.

https://learn.genetics.utah.edu/content/microbiome/

Here is a short video explaining what quality control of raw reads is and how we can perform QC using FastQC: https://youtu.be/lUk5Ju3vCDM

This paper (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2593568/), while it is a few years old now (2008), does a good job providing a detailed overview of the common steps in a metagenomic pipeline. It focuses on the theory behind each step, why it is important, and what choices need to be made.

Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) is said to be one of the largest repositories for metagenomic databases.  It’s a free open access source that automatically compares data against the repositories to provide phylogenic output information.  The program also has the ability to conduct a functional analysis of the metagenomic samples. To register for an account: https://www.mg-rast.org/

Publication: https://pubmed.ncbi.nlm.nih.gov/26791506/

The Data Equity Framework seems to be a relatively new idea, this is the first I had heard of it. It reveals many disparities that are so easy to not be cognizant of as a developing scientist. Truthfully, if more light is shed on these areas and change is made, I think then the way we do science will be revolutionized and rush in a new era of discovery. With that said, I wanted to find if there are any universities taking steps to build data equity. I found that the University of Michigan is building an institute and has held a workshop this year to take steps towards building equity in data science. This is an area that all research institutions should begin to consider and develop training programs. https://midas.umich.edu/fides/