The RECOMB/ISCB Conference on Regulatory and Systems Genomics, with DREAM Challenges and Cytoscape Workshops is running this week in San Diego.
A bunch of us from Sage Bionetworks are here to connect with the DREAM community. In introductory remarks, Stephen Friend framed the challenges as piloting new modes of collaboration and engagement addressing multidimensional problems based on the idea that open innovation will trump closed silos.
Lincoln Stein: The Future of Genomic Databases
I first heard Lincoln Stein speak at an O'Reilly conference in 2002, on building a bioinformatics nation. The same themes of openness and integration reappeared in Stein's talk on The Future of Genomic Databases.
Stein asks, "Open Data + open source = reproducible science?" Not exactly. Stein presents some emerging solutions to the remaining obstacles: big data sets, complex workflows, unportable code and data access restrictions.
Cloud computing, specifically colocation of data and compute, enables handling big data. Containers (ie Docker) address the problem of code portability. The Global Alliance is working towards providing APIs both to encapsulate technical complexity and to provide a control point at which to enforce restrictions.
In case we're wondering what to do with all the machine cycles made available by Amazon and Google, bioinformatics workflows are growing in complexity. Workflow managers like Seqware and Galaxy provide a formalized description of multistep processes and manage tools and their dependencies.
Legal restrictions hinder data integration. But, donors want their samples to contribute to research. Licensure for data access combined with uniform consent could reduce the friction resulting in a streamlined data access process. On the other hand, technical solutions involve homomorphic encryption and agent based federated queries.
As a parting thought, Stein notes that digital infrastructure enables experiments in incentive structures and economic models, citing micropayments, ratings, and challenges.
Andrea Califano spoke on the genotype to phenotype linkage in cancer. Thinking of the cell as an integrator of signals, Califo's group traces from gene or protein expression signatures of cell states (normal, neoplastic, metastatic) back through the network to the master regulators responsible for that signature. One related paper is Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks.
Paul Boutros Somatic Mutation Calling Challenge
Paul Boutros presented the Somatic Mutation Calling Challenge (SMC-DNA). He announced the intention for the SMC challenges to become a living benchmark, an objective standard against which future methods will be tested.
Paul also crowned the Broad Institute's MuTect (single nucleotide) and novoBreak (structural variants) by Ken Chen's lab at MD Anderson the winners of the synthetic tumor phase of SMC-DNA. The plan is to announce winners on real tumor data in February after experimental validation.
The SMC challenge is a bit unique for DREAM in its level of specialization. In the other challenge, a couple of methods were highlighted: Gaussian process regression and dictionary learning for sparse representation.
But, increasingly, the main differentiator is application of biological domain knowledge, especially with respect to selecting and processing features. Li Liu of Arizona State's Biodesign Institute, for example, won part of the Accute Myoloid Leukemia challenge by weighting proteins based on their evolutionary conservation.
Another theme is that genetic features seem to have poor signal compared to more downstream features, gene expression or clinical variables. Peddinti Gopalacharyulu, a top performer in the Gene Essentiality Challenge, commented that perhaps the way to use genetics is to extract the component of gene expression that is not explained by genetic features.
Two of the Dream 9.5 challenges are follow-ups to the Somatic Mutation Calling challenge from the 8.5 round. The SMC empire expands into RNA and tumor heterogeneity. In the olfaction challenge, the goal is to predict, from molecular features, odor as described by human subjects. The Prostate cancer challenge asks participants to classify patients according to survival using data sourced from the comparator arms of clinical trials.
For the DREAM 10 round, there's an imaging challenge in the works and a sequel to the ALS challenge challenge from DREAM 7.
On to RECOMB
That's just the DREAM part of the meeting, or, really, the subset that fit into my brain. As an added bonus, there were several representatives from Cytoscape-related projects and some conversation about the Global Alliance for Genomics and Health.