The Lage team posted its preprint of an algorithm to identify cancer dependencies directly from tumor genome data with validation using targeted CRISPR screens. Great work by @hornheiko @CFagre @anikagupta18 and a wonderful collaboration with @broadinstitute @jamesTneal: https://www.biorxiv.org/content/10.1101/2020.08.27.270520v1
Kasper joins Sally John, director of translational biology at Biogen, for a conversation about Translational Genetics in Neuroscience, hosted by the Harvard Brain Initiative and the Arlotta Lab.
Frederik Lassen moves to Oxford, UK, to start a PhD program at the Wellcome Centre for Human Genetics. He will continue collaborating with the Lage Lab and we wish him the best of luck in his new scientific endeavors!
Joshua Ching joins the Lage group as part of MIT’s Undergraduate Research Opportunities Program (UROP). He will bring a much appreciated data analysis component to our efforts and will collaborate with senior members in the group. Welcome to the Lage team, Joshua!
Yu-Han and colleagues combined untargeted metabolomics with genetics to identify metabolites that may be causes or effects of obesity. They further grouped these metabolites into pathways to highlight how distinct biological mechanisms may be involved in obesity. Their findings demonstrate the strong potential of using untargeted metabolomics and genetically informed causal inference (Mendelian randomization) to uncover causal biological connections between metabolites and various human diseases, especially as larger datasets with both genotype and metabolite profiling data become available in the future. Congratulations on the great work, Yu-Han!
Kasper joins the Danish national discussion about transparency & openness of the COVID19 models & data in an important effort led by the Danish Prime Minister, Mette Frederiksen, to better communicate COVID19-related information to the public.
Our study co-led by @gretapinta, @flassen_, @yuhanhsu and @mjapkim, ‘Genoppi: an open-source software for robust and standardized integration of proteomic and genetic data’, is now posted as a pre-print on bioRxiv. Genoppi allows the seamless integration of proteomic data with genetic information from a multitude of public or custom gene lists to maximize the interpretation of protein interaction datasets.
The cancer network algorithm paper titled “Prediction of cancer driver genes through network-based moment propagation of mutation scores” has been accepted for presentation at ISMB 2020 and for inclusion in the conference proceedings. This work is in collaboration with Karsten Borgwardt and led by Heiko Horn and Anja Gumpinger.
Kasper presented the Stanley Center Brain Interaction Network project to the Open Targets Community. Great job Kasper!