30 July - 1 August 2014, at the Dana-Farber Cancer Institute, Boston, USA
by Laurent Gatto, SSI Fellow and Senior Research Associate at the Computational Proteomics Unit, University of Cambridge
This conference highlights current developments within and beyond Bioconductor. Morning scientific talks and afternoon practicals provide conference participants with insights and tools required for the analysis and comprehension of high-throughput genomic data.
Reports of the event and an outline of Bioconductor have already been contributed as SSI blog posts.
The conference started with a developer day aimed at promoting interaction between the Bioconductor core team and the developer community. Each member of the core team presented their main work and developers were given the opportunity to give brief presentation about some of their work. I presented the maker project. All participants also had a discussion about community needs.
The afternoon was devoted to tutorials offered by developers. I taught the unit testing course and package development.
The main conference days had formal talks scheduled in the morning and workshops in the afternoon. The morning lectures were a great opportunity to hear some cutting edge research.
On the first afternoon, I delivered a workshop about R/Bioconductor packages for proteomics data analysis. I was quite pleased with the attendance. There were about 15 people with a healthy mix of seasoned proteomics bioinformaticians (including developers from some packages I was talking about) and people that were interested in learning about proteomics and existing software within the Bioconductor ecosystem.
On the last afternoon, I attended the Integrated pathway analysis of multiple omics datasets by Aedin Culhane and Parallel Computing with Bioconductor in the Amazon Cloud by Valerie Obenchain. The first workshop was close to my research interests and a good update on Aedin's work that I had already read about. The second workshop was of particular interest, as I currently need to run some heavy computations on large clusters myself.
The latter topic is still something my collaborator and myself are investigating, comparing amazon services, Window's Azure infrastructure, our local HPC clusters and the EBI clusters I have access to. This is without doubt a hot topic in the field of computational biology.
I also met and discussed with several people working on data integration. These discussions were followed up with a hangout (and more to follow) with the multi-assay working group.