1. IGA Workflow
Alessandro Gervaso Vittorio Zamboni

2. Introduction
IGA Workflow is a web-based tool created
using the Django Framework.
It is intended as a management tool for the IGA
wet-lab.
We use it to track the lifecycle of biological
samples, from vial to file.

3. Overview
● Laboratory management, from sample to
flowcell
● Bioinformatic analyses
● Pipelines management
● Technology
● Other applications and future developments

4. Biology for dummies

5. Technology - Basics
Database server
● Postgresql
● Redis
Workers
● Celery
Web server
● nginx + uWSGI

6. Overview - Lab
● SAMPLE: The basic unit in the lab
● LIBRARY: a treated sample with an attached
chemical TAG.
● POOL: a set of libraries, ready to be placed
on a flowcell's lane.

7. Overview - Lab

8. Overview - Lab
The main challenge was to replace notebooks
with a tool that allows to:
● insert samples, libraries, pools, and edit
them;
● create lanes and runs and the configuration
files for the physical sequencer;
● collect the sequencer results and map them
in a easy way
Almost done!

9. Overview - Lab
We started using the basic Django admin
BUT
● the page loading was slow
● due to the admin's nature we lacked
flexibility
● we were forcing the lab people procedures
● management was cumbersome

12. Overview - Analyses
After the physical sequencing the raw data
(basecalls) must be converted in FASTQ files.
The FASTQ files are FASTA files with some
embedded quality stats.
They are the starting point for almost every
genomic analysis.

13. Overview - Analyses
To optimize time and resources we use a
cluster of Celery workers.
● we track the software packages used
● we track their parameters
● we create a set of useful stats

15. Technology
Additions:
● Informational celery tasks (list directories
content, copy files between devices with
dbus plugin and UDisks instead of ugly
hacks, ...)

16. Overview - Pipelines
FASTQ files alignments and assemblies
Each analysis use different software in
sequence or in parallel.
Using hundreds of samples, the analyses can't
be handled manually.

17. Pipelines
The results of each pipelines (like previous
analyses) must be tracked.
Since CLI based software is not user-friendly,
we develop a graphical pipeline builder.
Users are able to choose and combine different
softwares to perform their own analyses.

18. Pipeline
● Workers have different queues in order to
satisfy different tasks
● Worker's tasks talk each other with Redis to
avoid inconsistencies and to improve
performances

19. Pipelines
Different queues (stored in Redis dbs):
○ available
○ queued
○ active
○ completed
○ error

20. Pipelines
Video demonstration of a working pipeline with
use of different kind of steps.
(1 video: 1 minute)

21. Under development
● simple interface that allow customers to:
○ insert their samples directly
○ watch the results of their pipeline in a genome
browser - also made with Django (see below)!
● barcodes
● genome browser (like GMOD GBrowse, but
with the greatness of Python instead of the
confusion of Perl)

22. Genome browser
An application that allows browsing a genome's
annotations (like genes, or where reads are
aligned).
Actually, the best web genome browser is
GMOD Gbrowse.

23. Genome browser
The challenge is to develop a genome browser
that has a set of basic features and could
accept plugins for particular type of data - like
GMOD Gbrowse.
In addition, it must be quick and easy to
manage - NOT like GMOD Gbrowse.

24. Genome browser
Video demonstration of the genome browser.
(2 videos: 1 minute + 1 minute)

25. Acknowledgments
● The wet-lab Teams that developed:
ladies at IGA ● Django
● JQuery
● nginx
● WEBdeBS ● uWSGI
● Celery
● Redis
● pip and virtualenv
● PostgreSQL
● All the open source projects involved