USF Genomics

USF Genomics Program Training

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).


  • RNA-seq Illumina Sequencing Laboratory Workshop

    Goal

    This 5-day intensive hands-on training to enables participants to confidently prepare high-quality sample libraries for sequencing.

    Description

    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.

     

    Participant Requirements

    • To maximize effectiveness, this training is limited to 6 participants at a time.  
    • A basic knowledge of biochemistry and molecular biology and familiarity with laboratory equipment usage are pre-requisites.  
    • Participants will need to bring your own micropipettes (10µl, 200µl and 1000µl) 
    • Participants may use this to test their own experimental samples, each      participant can prep two samples.sequence analysis or command line experience are not required. 

     

    Workshop Structure:

    Day 1 Morning (Monday): Illumina sequencing technology introduction (open to non-registered participants)
    Day 1 Afternoon (Monday): Illumina sequencing technology overview and RNA sample prep
    Day 3 (Tuesday): Library Prep (1) - mRNA purification, 1st, 2nd strand cDNA synthesis
    Day 4 (Wednesday): Libarary Prep (2) - A-Tailing, ligation, PCR amplification, Beads purification
    Day 5 (Thursday): qPCR quantification, Normalize & pool, Set up Miseq run, Base-Space

     

    Training Instructors:

    Min Zhang, MD - USF Genomics Equipment Core

    Illumina Field Applicantions Specialist

     

  • RNA-seq Data Analysis Computational Workshop

    Goal

    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.

     

    Description

    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.

     

    Participant Requirements

    • Admittance to the course is limited to 10 participants.
    • A basic knowledge of biochemistry and molecular biology, a basic familiarity with computer usage and a need for RNA sequence analysis for current ongoing research are recommended pre-requisites.
    • Participants will be required to complete an online module prior to workshop.
    • Participants will need to have their own laptop to access the USF research Computing  infrastructure for hands-on sessions.
    • Prior sequence analysis or command line experience are not required. 

     

    Workshop Structure

    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

     

    Training Instructors

    Jenna Oberstaller, PhD

    Charley Wang, PhD

    Justin Gibbons, PhD

  • RNA-seq Workshop Registration

    Click here for Online Registration 

    If you have questions regarding the USF Genomics Workshops, email genomics@health.usf.edu. 

    Due to enrollment caps, there is a waitlist for the USF Genomics Workshops. You will be contacted after registering about the next available workshop.

  • Microbiome Analysis Training Workshop

    Hands-on Microbiome Training

    Goal

    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. 

     

    Topics Covered

    • Microbiome: A rapidly-evolving field driving emerging analytic technologies: Overview of marker-sequencing (16S, 18S, & ITS)
    • Best-practices in microbiome experimental design from the bench to the computer: How many samples do I need? Which technology is best to answer my biological question?
    • Brief introduction to R for microbiome data-analysis
    • Hands-on 16S rRNA data-analysis using DADA2 (R Package)
    • Visualizing data with the R Microbiome-package

     

    Workshop Structure

    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

     

    Participant Requirements

    • Admittance to the course is limited and requires advanced registration.
    • A basic knowledge of biochemistry and molecular biology, a basic familiarity with computer usage, and a need for microbiome sequence analysis for current ongoing research projects are recommended prerequisites.
    • Prior sequence analysis or command line experience are not required.
    • Participants should bring laptops with a minimum of the following configuration: Mac - OS X 10.12 (64-bit) or Windows - 7 (64-bit)

     

  • Microbiome Analysis Training

    Methods and Experimental Design: Considerations for Microbiome Studies

    Goal

    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. 

     

    Topics

    • Microbiome: A rapidly-evolving field driving emerging analytic technologies. 
    • Overview of marker-sequencing (16S, 18S, ITS & ITN) and metagenomic-sequencing technologies and principles of data-analysis. 
    • Best-practices in microbiome experimental design from the bench to the computerL How many samples do I need? Which technology is best to answer my biological question?

     

    Participants

    • Admittance to the course is not limited, but advanced registration is appreciated.
    • Prerequisites are a need for microbiome sequence analysis for current ongoing research. 
  • Microbiome Analysis Training Registration

    The second Microbiome Analysis Training will be held in Summer 2020.

    Admittance to the course is limited and requires registration.

    Click here for Online Registration

    Questions regarding the USF Genomics Training can be sent to genomics@usf.edu

     

    Microbiome Data Analysis Training Instructors:

    Jenna Oberstaller, PhD

    Anujit Sarkar, PhD

    Charley Wang, PhD

    Justin Gibbons, PhD

    Thomas Keller, PhD

    Swamy Rakesh Adapa, MS

Microbiome Guidelines

The purpose of these guidelines is to provide guidance to researchers that seek to implement microbiome research in their programs. The guidelines assist in the experimental design and execution of a microbiome experiment including the subsequent data analysis.

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 [1].

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.

Experimental Design

Microbiome studies can address a broad range of scientific questions using various experimental designs. Before designing an experiment review the literature for previous experiments that had similar experimental types and expected outcomes.
  • Cross-sectional study: cross sectional studies are used to find differences in microbiome composition between different populations, such as healthy individuals and those with a disease, or comparing people from different geographic regions.cross sectional studies are useful for finding differences in microbial communities between different human populations, such as healthy individuals and those with diseases, or individuals living in different geographic regions.  
  • Longitudinal study: Longitudinal studies take measurements from the same individuals over time. Longitudinal studies that collect baseline samples before disease onset, can help establish causality. It is important to collect data on all possible confounding variables to control for their effects. Otherwise, if not controlled for, confounding variables can obscure patterns in microbiome data. For examples studies that reported changes in the microbiome of people with diabetes were confounded by the effects of the drug metformin [2].
  • Interventional study: Interventional studies involve measuring the effect of a treatment or other intervention on the microbiomes of the subjects., Common interventions include the use of drugs or a probiotic. A double-blind randomized control study is an example of an interventional study that allows the identification of how a treatment effects the microbiome and disease state.   
  • Environmental study: Environmental studies are concerned with the analysis of environmental microbes that are native to various environments. Those environments can range from lakes and permafrost to deserts and other extreme sites such as sulfur springs or fire gas craters. An environmental study of a particular group of microbes that thrive in the environment can give insights into their interaction with both the biotic and abiotic components of their environment. 

There are currently two methods to perform power calculations for 16S marker sequencing studies:

● PERMANOVA:[1]  A permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances [3].

○ Advantages:

● Powerful in hypothesis testing

● Accurate in quantifying effect size

● Multiple factors can be addressed at once

● Also ideal for investigating interaction terms

○   Disadvantages:

● 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 [4].

○ Advantages:

● Ideal when communities are diverse with different sizes and the matrix is sparse

● Identifies envirotypes or enterotypes, groups of communities with a similar composition

○ Disadvantages:

● 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]

Careful consideration of inclusion and exclusion criteria is critical to decrease the effects of confounding covariates on microbiome experiments. The most common confounders are medication and diet [7]. See Earth Microbiome Guidelines for a list of subject metadata to collect.
  • Cage effects: The microbiomes of co-housed animal models tend to become more similar. In studies using rodents and zebrafish the strongest association is often which animals are co-housed together [8, 9]. Due to this experiments must be replicated across cages.  
  • Parental effects: Early life exposure greatly influences the development of an individual's microbiome and immune system [10]. Due to this littermates should be randomized between cages and experimental conditions.  
  • Environmental factors: Diet, litter, vendor, shipment facility of origin and early life exposures have all been shown to influence the microbiome and should be considered when planning an experiment [11, 12].

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:  

  • Use the same reagents during sample preparation (molecular biology grade water,  PCR reagents). This minimizes the confounding effects of introducing foreign microbial DNA
  • Sequencing controls can help identifying contaminants
  • The wider laboratory environment can also introduce contaminations [13]     
  • Try to \maximize the starting sample biomass
  • Take steps to minimize the risk of contamination during sample collection     
  • Consider treating PCR and extraction kit reagents to reduce contaminant DNA
  • Technical controls should be processed concurrently with your environmental samples
  • Randomization of your samples during preparation  can avoid creating false patterns
  • Using different kits/reagents for replicates helps identify contaminations  
  • The use of mock communities (reference samples with a known composition) can be useful for standardizing analyses and identification of contaminants [14, 15]
  • Negative controls should be used to monitor contamination as it arises.
  • Longitudinal studies should include multiple baseline samples to assess intrinsic variability  
  • Use blanks during sampling, DNA extraction, PCR, and sequencing to detect contamination  
  • Metadata, appropriate controls, including extraction and reagent blanks should be carefully curated  
  • Apply a robust study design that isolates and interrogates variables of interest
  • For marker gene sequencing, in order to minimize Chimera formation it  has been proposed to minimize the amount of template DNA in the PCR, minimize the number of rounds of PCR, minimize the amount of shearing in the template DNA and to use DNA polymerases that have proofreading ability

For more further recommendations on technical variations please review:

  1. Salter, Cox et al. 2014
  2. Knight, R., Vrbanac, A., Taylor, B.C. et al. Best practices for analysing microbiomes. Nat Rev Microbiol 16, 410–422 (2018). https://doi.org/10.1038/s41579-018-0029-9
  3. 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  
High throughput microbiome approaches
Omics  Microbial material Outcome Advantages Disadvantages
 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

Recommended protocols and standards

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.

 

Shipping protocol

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.

About the Authors and References

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.

Jan Dahrendorff is a graduate student in the Genomics Program in the College of Public Health. He is interested in the genetic and epigenetic predictors of stress related psychopathology. Further, he is interested in identifying mechanisms involved in the change of genomic biology through lived experiences that might shape psychiatric risk and resilience. Jan is currently working as a graduate research assistant in Dr. Monica Uddin's lab and in the Omics Hub.
Samira Jahangiri is a Masters student of the USF Genomics Program at the University of South Florida's College of Public Health. Samira currently works as a graduate assistant for the Omics Hub and as a research assistant in Dr. Rays Jiang's computational biology lab. Samira is currently working on ChIP-seq and RNA-seq Bioinformatics analysis in High-Performance Computing (HPC)/Linux environments to develop pipelines for studying high-throughput sequencing machines' outputs, and methodologies used in metagenomics classifications. 
  1. Engineering, N.A.o., E. National Academies of Sciences, and Medicine, Microbiomes of the Built Environment: A Research Agenda for Indoor Microbiology, Human Health, and Buildings. 2017, Washington, DC: The National Academies Press. 317.
  2. Mardinoglu, A., J. Boren, and U. Smith, Confounding Effects of Metformin on the Human Gut Microbiome in Type 2 Diabetes. Cell Metabolism, 2016. 23(1): p. 10-12.
  3. Kelly, B.J., et al., Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics, 2015. 31(15): p. 2461-8.
  4. La Rosa, P.S., et al., Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLOS One, 2012. 7(12): p. e52078.
  5. Holmes, I.; K. Harris, and C. Quince, Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS One, 2012. 7(2): p. e30126.
  6. Wang, T. and H. Zhao, A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics, 2017. 73(3): p. 792-801.
  7. Knight, R., et al., Best practice for analysing microbiomes. Nat Rev Microbiol, 2018. 16(7): p. 410-422.
  8. Ridaura, V.K., et al., Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science, 2013. 341(6150): p. 1241214.
  9. Stagaman, K., et al., The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish. The ISME Journal, 2017. 11(7): p. 1630-1639.
  10. Snijders, A.M., et al., Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome. Nat Microbiol, 2016. 2: p. 16221.
  11. Ley, R.E., et al., Obesity alters gut microbial ecology. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(31): p. 11070.
  12. Friswell, M.K., et al., Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice. PLOS ONE, 2010. 5(1): p. e8584.
  13. Salter, S.J., et al., Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biology, 2014. 12(1): p. 87.
  14. Jumpstart Consortium Human Microbiome Project Data Generation Working, G., Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research. PLOS ONE, 2012. 7(6): p. e39315. 
  15. Chase, J., et al., Geography and Location Are the Primary Drivers of Office Microbiome Composition. mSystems, 2016. 1(2).