The work performed in the first 18 months of the project can be broken down into three major components: 1. Development of statistical and machine learning methods to utilise multi-omics data, 2. Outreach and dissemination activities, and 3. Exploitation of results. Here, we describe the different work and results under these three components.
1. We have performed literature reviews to understand the types of machine learning and statistical methods used in multi-omics and time series multi-omics data analyses, giving us a platform on which to build our new methods. Further, we have been exploring the use of machine learning methods and structural models to integrate data from multi-omics datasets. In addition, machine learning methods are being developed for time series data, and in particular, we have initialized a R package for such methods, which integrate data type standards to ease the application of these methods. Alongside time series methods, mechanistic models have been developed to understand the interactions in the microbiome community. Finally, using genomic and phenotypic data from HoloFood chickens, we are developing methods to incorporate the evolutionary history of the genetic variation to better identify genotype-phenotype associations.
2. A stakeholder synergy meeting to map our stakeholders was held, including other EU projects, industrial stakeholders, academic researchers, and policy bodies. We organized a webinar, in conjunction with other EU projects and a center of excellence, with speakers from around the world sharing their insights into the technical challenges and the advantages of multi-omics approaches. Several partners have offered courses and workshops across the world to train the next generation of researchers in methods for multi-omics data analyses. The training material from these workshops and courses have been made freely available. We circulate a monthly newsletter updating the partners on the progress of the project. To engage a wider audience on our project, we have produced 2 videos highlighting the work planned in the project, with one video specifically targeted at high school students. Finally, as part of our outreach efforts, FindingPheno has establlished a persence on 3 SoMe platforms, viz., Twitter, LinkedIn and YouTube. Twitter and LinkedIn are the primary channels for outreach to the wider community.
3. We are in the early stages of this task, and one of the primary avenues for exploitation of results until now, has been the use of the MGnify pipeline and a project specific landing page for FindingPheno. In the project landing page, several publicly available datasets from tomatoes, honey bees and soyabean have been processed, and are now being used to both refine and validate our models. This platform will also be used for other datasets, once they have been incorporated into this pipeline, will be used to generate host-microbiome interaction results from the three focus organisms, chicken, salmon and maize.
