Proteomics Lab: streamlining computational proteomics for life scientists

Consultations with Australian researchers have established that computational proteomics is going
through a major transition, where growth in experimental complexity and data set sizes, coupled
with the challenges of navigating, understanding and redeploying software from a diverse and
scattered ecosystem are now significant blockers to research programs. There is thus a demonstrable
need to 1) accelerate the discovery and onboarding of proteomics researchers to both fit-for-purpose
software and scalable computational resources, while 2) democratising the ability to access and
reuse these same resources. Key to this is to also remove the need for researchers to worry about
managing the underpinning research infrastructure that makes this possible.

Methods
To address the challenges facing computational proteomics in Australia, a working group consisting
of members of the Australian proteomics community, the Australian BioCommons and Galaxy
Australia has created a shared space on Galaxy Australia called Proteomics Lab
(https://proteomics.usegalaxy.org.au/). This gives researchers a proteomics specific lens into the
diverse capabilities accessible via Galaxy Australia, including numerous resources created by the
Galaxy-P platform, curated to include tools, reference data and training of relevance to proteomics.
The Proteomics Lab webpage is built into Galaxy Australia’s Media site, which uses a Python
framework to render the webpage from a structured data schema. This simplifies the curation and
update of content that makes up the functionality of the page.

Novel Aspect
The provision of a national shared computational platform that is being tailored to the needs of
Australian proteomics researchers.

Preliminary Data or Plenary Speaker Abstract
The Proteomics Lab (https://proteomics.usegalaxy.org.au/) provides a proteomics-centric tools list,
and a central panel curated in consultation with community members. The central panel highlights
critical software (including workflows), in-built reference data, and help information (e.g. Galaxy
Training Network tutorials), all supported by an on-going collaborative effort to optimise the installed
proteomics software using real world data. For example, collaboration with community researchers
has allowed Galaxy maintainers to tailor MaxQuant to access more computational resources and
accommodate analysis of more typical sample set sizes (n = 120 is the current maximum possible),
and in the past six months MaxQuant on Galaxy Australia has supported hundreds of jobs, submitted
by researchers from 13 different Australian research institutions. In addition, users can currently
access more than 90 additional proteomics software tools, supported by 32 tutorials created by the
global Galaxy Training Network. We invite the life science research community to use Proteomics
Lab, and to help us to grow the number of optimised tools, workflows and tutorials available.
Collectively, we can ensure that the service continues to be fit-for-purpose and can support best
practice implementation of proteomics in Australia.