The USF Genomics Program offers three of training workshops, the RNA-seq Illumina Sequencing Laboratory Workshop, the RNA-seq Data Analysis Computational Workshop, and the Microbiome Analysis Training. The RNA-seq workshops are offered three times a year in February, May, and September. The first Microbiome Analysis Training will be offered in November 2019. These workshops are open to all in the USF community. The RNA-seq Illumnia Sequencing Laboratory Workshop is required for access to use the USF Genomics Equipment Core (more information here).
This training course includes presentations on technology theory and practical experience with entire laboratory sample prep workflow. Experienced trainers will lead you through using TapeStation and Miseq equipment. By the end of this training, you will be able to: complete Illumina library preparation process and set up a sequencing run using Illumina Miseq Control Software.
Min Zhang, MD - USF Genomics Equipment Core
Illumina Field Applicantions Specialist
This 5-day in-depth course will instruct participants on the theoretical and practical concepts related to RNA sequencing analysis, enabling them to perform these analyses independently.
The first workshop module is an introduction to data analysis using Linux, assuring that all participants are able to follow the practical parts. The second module discusses advantages and disadvantages of current sequencing technologies and their implications on data analysis. The third module introduces preprocessing raw reads, read mapping, visualization of mapped reads and one proceeds with first hands-on analyses (QC, mapping, visualization). The fourth module introduces ‘R’ a programming language to perform statistical computing and visualization, and ‘Bioconductor’ an R based toolshed for analyzing genomic data. The last module addresses quantification of gene expression and predict differentially expressed genes, followed by a whole day hands-on RNA-Seq analysis pipeline: TUXEDO protocol. More information on the RNAseq Data-Analysis Workshop can be found at on the USF Omics Hub GitHub website.
Day 1 (Monday Morning): Illumina Sequencing Introduction & Computer and Software Check (open to non-registered participants)
Day 2 (Friday): Illumina Sequencing Refresher & Linux for Bioinformatics
Day 3 (Monday): Introduction to RNA sequencing & RNA-seq Alignment and Visualization
Day 4 (Tuesday): Introduction to R and Bioconductor & Expression and Differential Expression
Day 5 (Wednesday): RNA-seq - TUXEDO Pipeline Hands-on Workshop Structure
Jenna Oberstaller, PhD
Charley Wang, PhD
Justin Gibbons, PhD
If you have questions regarding the USF Genomics Workshops, email firstname.lastname@example.org.
Due to enrollment caps, there is a waitlist for the USF Genomics Workshops. You will be contacted after registering about the next available workshop.
This 3-Day in-depth course will instruct participants on theoretical and practical concepts related to microbiome sequencing analysis, enabling them to perform these analyses independently.
Day 1: Introduction to Microbiome Data Analysis and Introduction to R and Data-Plotting (One-day crash course)
Day 2: Metataxonomic analysis Experimental Design and Metataxonomic analysis OTU/ASV Generation
Day 3: Metataxonomic analysis Visualization
More information on the Microbiome Analysis Workshop can be found on the USF Omics Hub GitHub website.
This one-day workshop is designed for researchers and clinicians who wish to incorporate microbiome analysis into their research, and it assumes little or no related prior knowledge.
Admittance to the course is limited and requires registration.
Click here for Online Registration
Questions regarding the USF Genomics Training can be sent to email@example.com
Jenna Oberstaller, PhD
Anujit Sarkar, PhD
Charley Wang, PhD
Justin Gibbons, PhD
Thomas Keller, PhD
Swamy Rakesh Adapa, MS
Microorganisms can be found across all environments on Earth and are critical to Earth’s systems and cycles. Microbiome research encompasses many areas including: air, soil and aquatic microbial ecology, bioremediation, microbiome of the built environment, geomicrobiology, agriculture, and the human microbiome.
The human microbiome is all of of microbes including bacteria, fungi, protozoa and viruses that live on and inside the human body. The microbiome contains more genes than the human genome. The gut microbiome is an important component of human health, aiding digestion and regulating the immune system.
The environmental microbiome has a significant impact on human health. The microbial composition of the built environment has been linked to the development of disease. Understanding how factors like humidity, pH, chemical exposures, temperature, filtration, surface materials, and air flow can impact microbes and microbial communities are critical to engineering an environment optimal for human health .
The human microbiome is essential for maintaining health. Getting a better understanding of the microbial makeup of a healthy person is as important as identifying microbiome phenomenon that enable disease. There are diverse, complex communities of microbes in our bodies inhabiting many organs ranging from the gastrointestinal tract and the mouth to our skin and reproductive organs that are interacting with cells in the body helping them to absorb nutrients and modulating various immunological functions. Studying the microbiome can help us find new ways to keep the microbiome healthy, repair it and leverage it to prevent and treat disease.
There are currently two methods to perform power calculations for 16S marker sequencing studies:
● PERMANOVA: A permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances .
● Powerful in hypothesis testing
● Accurate in quantifying effect size
● Multiple factors can be addressed at once
● Also ideal for investigating interaction terms
● Requires precise sampling design with multiple replications
● Relies on assumptions of normality and common variances (in that distribution of means across samples is normal)
● Dirichlet multinomial: Parametric model based on Dirichlet multinomial distribution .
● Ideal when communities are diverse with different sizes and the matrix is sparse
● Identifies envirotypes or enterotypes, groups of communities with a similar composition
● All variables must share a common variance parameter
● All variables are mutually independent, up to the constraint that they must sum up to one
● Does not take into account that microbial taxa are related evolutionarily [5, 6]
Although it is difficult to account for all bias in your microbiome experiment adhering to the following recommendations can help you mitigate technical variation and subsequently increase your chances of generating robust results:
For more further recommendations on technical variations please review:
|Marker genes (16S, ITS)||16S rDNA||Microbiota composition||Fast and cheap||Low resolution, PCR bias|
|Metagenomics||Total DNA||Composition and function potential||High resolution, functional potential characterization||Higher cost, computationally challenging|
|Metatranscriptomics||mRNA/cDNA||Gene expression differences||Reveal gene expression||mRNA instability, lack of standard database|
|Metaproteomics||Proteins||Protein expression profiling||Differences in proteins synthesized by microbiota||Technologically challenging|
|Metabolomics||Metabolites||Metabolic profiling||Reveal metabolite differences||Lack of reference database, many unknown metabolites|
Marker gene analysis: Microbial phylogenies of a sample are determined by using primers that target specific regions of a gene of interest. This method provides only indirect evidence of the genetic content of the samples microbiome. Marker gene sequencing methods are susceptible to PCR biases such as limited primer coverage, which can result in failure to amplify some taxa or differential amplification of templates, altering the relative abundance of species distorting results.
A cost effective method for obtaining a low resolution view of a microbial community.
More comprehensive review under: The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data Marc A. Sze, Patrick D. Schloss mSphere May 2019, 4 (3) e00163-19; DOI: 10.1128/mSphere.00163-19
Metagenomics: Comprehensively samples all genes in all organisms present in a given complex sample. All DNA is captured including viral and eukaryotic DNA. Library construction, assembly and reference databases for annotation can all be potential sources of bias. Relatively expensive preparation, sequencing and analysis of the samples but yields detailed genomic information at high taxonomic resolution.
More comprehensive review under: Quince, C., Walker, A. W. & Simpson, J. T. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).
Metatranscriptomics: Large-scale metatranscriptomics approaches measure the active transcription of the microbiome. RNA-Sequencing is used to profile transcription giving insights into gene expression and the functional domains of the microbiome. This method is biased towards detecting transcripts from organisms with higher transcription rates.
Method for sequencing microbial RNA to provide better insights into the functional activity of a microbial community.
More comprehensive review under: Bashiardes, S., Zilberman- Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights. 10, 19–25 (2016).
Metaproteomics: Metaproteomics is a method for the identification and quantification of proteins from microbial communities. This allows more direct measurement of microbiome function.
More comprehensive review under: Kleiner, M.
Metaproteomics: Much More than Measuring Gene Expression in Microbial Communities.mSystems. 4 (3) (2019). Metabolomics: This method is the systematic study of metabolites within cells, biofluids, tissues or organisms. Small-molecule metabolite profiles can give insights into cellular processes.
More comprehensive review under: Riekeberg, E., & Powers, R. (2017). New frontiers in metabolomics: from measurement to insight. F1000Research, 6, 1148. doi:10.12688/f1000research.11495.1
Metabolomics: This method is the systematic study of metabolites within cells, biofluids, tissues or organisms. Small-molecule metabolite profiles can give insights into cellular processes.
More comprehensive review under: Riekeberg, E., & Powers, R. (2017). New frontiers in metabolomics: from measurement to insight. F1000Research, 6, 1148. doi: 10.12688/f1000research.11495.1
The Earth Microbiome Project (EMP) is a massive collaborative effort to characterize the microbial life of Earth. DNA sequencing and mass spectrometry technologies are applied to understand patterns in microbial ecology across the biomes and habitats of Earth. The EMP currently has the following protocols and guidelines:
Minimum Information about any (x) Sequence (MIxS)
Specifies what information needs to be collected for genomes, metagenomes and marker genes sequences. Provides templates for how to store this metadata.
EMP Ontology (EMPO)
The EMP Ontology (EMPO) uses existing ontologies to assign samples to habitats in hierarchical framework that captures the major axes of microbial community diversity.
DNA extraction protocol
Currently protocol: This protocol is used for proper DNA extraction for streamlined metagenomics of diverse environmental samples
Low-biomass protocol: Protocol designed for low-biomass samples. It incorporates positive and negative controls as well as bioinformatic methods to identify differences in microbial communities with as few as 50 to 500 cells
Primer ordering and resuspension
Here you can find information on how to order primers for your polymerase chain reaction as well as additional information on best practice in handling and aliquoting your primers.
16S Illumina amplicon protocol
This protocol is designed for amplification of prokaryotes (bacteria and archaea) using paired-end 16S community sequencing on the Illumina platform. Primers 515F-806R target the V4 region of the 16S SSU rRNA
18S Illumina amplicon protocol
This protocol is designed to amplify eukaryotes broadly with a focus on microbial eukaryotic lineages. It is similar to the 16S protocol but uses different primers, PCR conditions and different sequencing primers.
ITS Illumina Amplicon protocol
This protocol is designed to amplify fungal microbial eukaryotic lineages. Paired-end community sequencing is used on the Illumina platform with primers ITS1f-ITS2.
This protocol is for sending DNA, amplicons, and primers. Proper shipping is important to avoid damaging the samples.
The current costs for sequencing services can be found at the USF Genomics website.
Justin Gibbons has a PhD from the University of South Florida Morsani College of Medicine. His dissertation research focused on using genomics approaches to understand drug resistance in the malaria parasite Plasmodium falciparum. Justin currently works as a postdoctoral scholar for the USF Genomics Program as a computational biology consultant in the Omics Hub. He is currently working on several projects ranging from drug resistance in malaria to how the microbiome effects the health of preterm infants.