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tranSMART 19.0 was a major project to update tranSMART to build the 16.3 release code with an updated version of GRAILS (2.5.4), to incorporate a code cleanup of the earlier 16.2 release by Paul Avillach’s i2b2-tranSMART team at Harvard, to update the database schema to match the i2b2 schema for all the tables they have in common, to update the javascript stylesheets and images to use the asset pipeline plugin, and to review and improve the data loading procedures. Testing for this release is extensive because the major code revisions require everything to be validated.

Target developments

Grails

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5.0 and Java 11

The upgrade to grails 2.5.4 takes brought tranSMART up to using Java8, but this is still no longer commercially supported by Oracle. To upgrade to Java 11 we need to migrate to the very latest (released in July 2019) Grails 4Grails 5.0.

Grails 4.0 supports supported java 11 for the first time. It requires the use of the asset pipeline (already ported in tranSMART 19) and also requires the groovy and java source code to be reorganised. This is the same as the work required to port an earlier tranSMART branch (17.1) to grails 3. Code is moved to new locations, the BuildConfig files are rewritten, and a gradle build script is needed. The result is a far simpler and faster build environment for developers.

Database servers

Postgres 11, 12, 13, 14 and beyond

TranSMART has supported Postgres releases from 9.2 5 up to 10 and 11 14 but has not exploited any of their new features.

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At the Paris 2017 i2b2/tranSMART European users meeting a group gave a talk describing their integration of flow cytometry data into tranSMART. This could be imported into tranSMART 2022.

Other users have imported flow cytometry as clinical data. We need to review the alternatives, describe what can be dome using clinical data, and implement a new data type with additional benefits.

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(Some of these are also being considered for early adopion in tranSMART 19.x)

The TraIT Cell-Line Use Case project added the capability to load entire studies with platform annotations and high-dimensional data in a single step. This could be extended to the current set of 200+ curated studies providing a simpler solution that does not require checking for multiple platforms and multuple high-dimensional datatypes as used in a few of these (mainly GEO) studies.

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