1. A Few Questions Before We Begin
Transcriptomics with Non-model Organisms
A Few Questions Before We Begin
Will anyone be following along as I go through steps?
General steps:
Organization
Read assessment
Assembly
DEG analysis
Annotation
Does anyone have a different workflow they’re trying to figure out?
How many are interested in genes? How many isoforms?

2. Transcriptomics with Non-model Organisms
Focus Forum Webinar
Transcriptomics with Non-model Organisms
Blake Joyce, Science Informatician, The University of Arizona
March 24, 2016

3. A quick note:
I’m a biologist. My background isn’t bioinformatics or computation
Specifically:
Ecology
Plant secondary metabolism engineering/biotech
Some biochemistry

4. “Code-curious Biologist”
Carotenoid bioengineering
Wayne Parrott Lab
UGA
Engineering Biosensors
Phytoremediation
Neal Stewart Lab
UT (Knoxville, TN)

5. Fungible, Drop-In Ready Advanced Biofuels
Or Biogasoline…
Fungible, Drop-In Ready Advanced Biofuels
Neal Stewart Lab
UT (Knoxville, TN)

6. Key Concepts We will be using two major platforms
Discovery Environment (graphical user interface environment)
Atmosphere (cloud computing)
This will serve as an introduction into de novo transcriptomics
Skills that we will learn (gently)
A typical bioinformatic analysis with Trinity/Trinotate tools
(Some) best practices that I use when doing analyses
Command line tools (take notes! I swear it’s only scary at first)
How to move a lot of data (GBs or TBs) pretty quickly (hours or days)
*Initial Results will vary depending on your internet connection and firewall
Local (your computer) vs remote compute (Atmosphere/Cloud, DE)

7. Very Important!
Running these tools in CyVerse frees up time for you to do bioinformatics
No tool installation required. No software updates maintenance
Minimal/No downtown, computer maintenance
Help and knowledgebase is readily at hand (e.g. wiki)
Tradeoff: You’ve got to spend time understanding the tools
Tradeoff: You’ve got to spend time understanding the science of bioinformatics
Tradeoff: You may have to spend time experimenting and assessing results
But
I think as a scientist I’d rather spend my time doing these last three things
There isn’t an easy button. Bioinformatics is a science.
Ease frequently means: assumptions, black boxes, paywalls, lack of reproducibility
Alright, I’m done with the soapbox

8. Well Then, Let’s Get Down to It
You Are Here (Currently)
Atmosphere
LOCAL COMPUTER
Tucson, AZ, USA, Earth
Fast Data Transfer
Trinity assembler
Trinotate annotation
Discovery Environment
Fast Data Transfer
Slow step
(relatively speaking)
Many Apps
Data Storage
Put Data into DE

9. Sometimes Things Happen (Locally)
You Are Here (Currently)
Still going over here!
LOCAL COMPUTER
Tucson, AZ, USA, Earth
Atmosphere
Fast Data Transfer
Trinity assembler
Trinotate annotation
Analysis
Discovery Environment
Fast Data Transfer
Many Apps
Data Storage

10. Local is Relative. CyVerse Stays Put.
You Are Here (Currently)
Still going over here!
LOCAL COMPUTER
???, Earth
Atmosphere
SSH Terminal Log In
(Internet Required)
Trinity assembler
Trinotate annotation
Analysis
Discovery Environment
Website Log In
(Internet Required)
Fast Data Transfer
Many Apps
Data Storage

11. Let’s go to the analysis

12. Wrapping Up (For Now)
We’re constantly upgrading/updating/modifying/improving
You can help us set up different tools
Or modify existing ones for their exact needs (there’s a lot of possibilities)
We work with communities so they can establish new tools
This means just one person does the change and everyone benefits
Impress your friend, scare your enemies
Bioinformatic tool development looks good on your CV
Updated Trinity assembler on the DE (GUI all the way to annotation)
Updated downstream assessment of annotation
Focusing on “What biology can we learn from these annotations?”
How can we relate these annotations to metabolic pathways?

13. CyVerse is supported by the National Science Foundation under Grants No. DBI and DBI