Welcome to the Bioinformatics Workshop!

Transcription

1 Welcome to the Bioinformatics Workshop! UCD Genome Center Bioinformatics Core August 29, 2016

2 First Notes Non-UCD Folks can log into the lab PCs with Userid: temp10 and password: BioInfo Documentation is at: bioinformatics.ucdavis.edu/training/documentation/ Wireless access via Eduroam (eduroam.org) or ucd-guest

3 The Schedule - Logistics Computer room is open 8:45-5:30 Lectures and exercises between 9 and 5 No food/drink at the computer terminals (Drink-safe zone by the door) Lunch most days at Segundo Dining Commons (food court-style, see this week's menu here) Remember to take breaks!

4 The Schedule Monday: Mostly introductory materials Beginning to analyze data Tuesday, 9:30am: Sequencing Technologies lecture at the Genome Center auditorium (meet there or walk from Sci Lab) Tuesday pm/wednesday: Exome sequencing and Variant Finding Start to work at your own pace (with help nearby) Thursday/Friday am: RNA-Seq Friday afternoon: Command-line demo, Q&A, Evaluations

5 The Bioinformatics Core Nik Joshi Monica Britton Joe Fass Blythe Durbin-Johnson Jie (Jessie) Li Matt Settles Mike Lewis Adam Schaal Richard Feltstykket

6 The Genome Center The tall building across from the football stadium We re in Room 1300 (under the clock)

7 What We Do Project Work grant planning, methods, letters of support data analysis, consulting (experimental design) manuscript methods Training workshops (Galaxy, command line) Systems Administration HPC spec, setup cluster, instrument, lab server maintenance build custom tools Web Applications, DB build, connect custom applications

8 What We Do - Species Snapshot

9 What We Do - Project Snapshot RNA-Seq (differential expression, annotation) GO-, pathway enrichment Transcriptome assembly and annotation Exome sequencing (variant discovery) Genome sequencing (assembly, variant discovery) mirna-seq (differential expression) ChIP-Seq (peak finding, motifs, differential binding) bacterial/fungal community profiling, metagenomics Pathogen discovery and more

10 What Does Big Data Really Mean? As the cost of sequencing decreases, it s much easier to generate huge amounts of data. Rapid technology change means software has to keep up with changing data characteristics. Both features encourage centralized approach to data storage and analysis. So, where can the data be stored and accessed, with relative ease, from my laptop?

11 Analysis Considerations For each analysis you will need to evaluate: Which tools are applicable to your data How to best combine those tools What parameters are important How to assess the quality of data What is the useful information in the output and many more things

12 Bioinformatics Platforms Linux command line cutting edge, flexible highest learning curve (~months to years) Galaxy (usegalaxy.org) deployed on many public servers, lots of history open source and relatively easy to develop for CyVerse (cyverse.org) previously called iplant, younger than Galaxy not open source yet, but includes open source tools Commercial products (Geneious, CLC, etc.) black boxes (no public review for bugs) they develop new tools for you or, they don t

13 Comparison of Bioinformatics Interfaces Some of these are easier to transition between than others. Galaxy Workshop

14 Linux Command Line It can be challenging and frustrating to learn concepts, tools, workflows, and command line at the same time. So, this week, we ll be using Galaxy. But, don t be afraid to try command line. (Find a Software Carpentry workshop near you.) Galaxy Workshop

15 Galaxy - A Well-Supported Resource Users Support (biostar.usegalaxy.org) Training On-line (wiki.galaxyproject.org/learn) Galaxy Training Network (wiki.galaxyproject.org/teach/gtn) Galaxy Workshop

16 Galaxy is a Collection of Tools Advantages User-friendly interface with drop-down menus Workflows facilitate processing data All Galaxy tools are available for command line Not a Black Box Disadvantages All parameters may not be available for some tools Installation of new software and writing new tools usually requires use of the command line. Check out this recent article about using Galaxy Galaxy Workshop

17 Galaxy is a Workflow Manager Some examples from Galaxy Main (requires identical tool / wrapper versions) All workflows must be adjusted for: weird data new technologies new tools The workflow / pipeline can sometimes make a big difference to the outcome! Workflows can be published to document your methods and allow reproducibility Galaxy Workshop

18 Open Source & Reproducibility Galaxy and command-line tools are open source Open source does not always mean free Open source tools don t guarantee reproducibility! The pipeline, commands, environment (hardware, OS), data must also be open. public data archives - NCBI (various), GigaDB, etc. code / commands in public, stable repositories (GitHub, SourceForge, Google Code, etc.) or workflow management solutions (Galaxy, CyVerse) Galaxy Workshop

19 Open Source & Reproducibility The most critical---and commonly missing---link in making biological research reproducible is publishing code. Ideally, all papers should include detailed methods, including software with version numbers and parameters, along with custom scripts, submitted as Supplemental Materials.

20 Where Does the Computing Power Reside? Public Galaxy (shared with everyone!) Often limited compute power, scheduling Not readily customizable (request changes from administrators) Private Galaxy (shared within lab, or personal) On your own server Best for heavy compute user Fully customizable (with experience) In the Cloud Best for lighter use On single servers, you get what you pay for Some turnkey(-ish) solutions: Galaxy s CloudMan data transfer may be an issue Galaxy Workshop

21 Cloud Computing The Cloud is just a computer in another location. Often a computer in a warehouse somewhere. Cloud computing - either via command line, or stock or customized GUI (e.g. Galaxy) - may be a cost effective solution for a small-ish lab with light computing requirements. "If within an entire year one only needs to run their computers for less than 30% of time then cloud computing may be worth it." ~ Istvan Albert (

22 Amazon Web Services (AWS) This week we ll be using computers in an Amazon warehouse. Important AWS Reminders Stop your instances overnight to save $$ Terminate (instances) and delete (volumes, AMIs, snapshots) after the course to save $$$$ Don t leave the AWS taxi waiting with the meter running!

23 Important Data Analysis Considerations Bioinformatics is experimental! It is as scientific as the wetlab experiments Read this recent post: Bioinformatics is just like bench science and should be treated as such Tools and applications are constantly evolving Best Practices are extremely hard to come by do your own validation / bench-marking But, computation doesn t consume sample, reagents so: Don t be afraid to take chances or to make mistakes!

24 Realistic Expectations For This Workshop Will you be able to analyze some of your data after this week? Yes Will you be able to analyze all of your data after this week? Maybe Some workshop participants have done a complete analysis with publishable results using Galaxy. However, complex experimental designs may require additional analysis. Will you be a more knowlegable consumer, and be better able to discuss experimental designs, sequencing, and data analysis with experts? Definitely!

25 A Few Words of Encouragement Have realistic expectations What is your area of expertise? Did you learn it in a week? Bioinformatics can be frustrating You ll make mistakes, and get error messages Do a sanity check of any unusual results Don t be afraid to ask questions! (of humans and Google) Get to know your neighbors, here and at home Take frequent breaks!