Fall 2020: Investigating Microbial Communities
10
Pipelines and computational resources.
BIT 477/577 Fall 2020 Students
Module Learning Objectives (MOs)
- MO 4.1. Describe the importance of processing reads before use.
- MO 4.2. Examine different approaches used for processing 16S reads.
- MO 4.3. Evaluate the output from a Nephele pipeline.
- MO 4.4. Justify your conclusions based on data.
- MO 4.5. Propose a future experiment based on your results.
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/