We wish to establish a Research Computing Core (RCC) to complement both the imaging core and to provide an underlying platform to support a seamless continuation of computation from the advanced technology and science provided from our partnering Pis, Drs. Hensch, Dulac and Lichtman. The Research Computing Core will pay special interest to the analysis and genomic/connectomic studies of imprinting within pan/albumin (PV)-positive GABAergic interneurons. Advanced tools for automated image segmentation of neurons, synapses, and genome wide informatics analysis will be developed in close collaboration within the imaging core led by Dr. Zhuang. These tools will also enhance and support the informatics analysis of genome studies from Dr. Dulac's team. In addition, significant information technology infrastructure will be provided to support multi-terabyte data sets resulting from Dr. Lichtman's laboratory studies.
Through collaboration with Drs. Dulac, Lichtman, Hensch and our partner Dr Zhuang in the imaging core, the Research Computing Core (RCC) will help provide advanced computation, large scale genomic data analysis and support for our combined and continued studies into brain function shaped by the genetics and environment during critical periods of neuronal circuit development. This is a one of a kind endeavor pulling together existing partnerships into a cohesive core, further extending the ability for the Research Computing organization to not only support the individual faculty, but more importantly to actively extend the reach of the RCC to be able to reflect the true collaborative nature of a Conte Center. By also welcoming in students and researchers into the core RCC will be able to provide more than just traditional super computing support. Albeit we have started to understand (from the pioneering work of our Pis) that imprinting is strikingly regulated across brain regions over the lifespan of the individual with maternally inherited genes in brain development, there is much still left to discover. Any single faculty led endeavor would not be capable of handling the scale and complexity of such studies - by supporting and underpinning this primary research with an advanced underlying core platform such as we further describe RCC we will be able to dramatically extend and enhance our science. RCC will be able to provide both continuity and systematization of our continued analysis of complex genomic and connectomic datasets. The core will enable sustainability and longevity of data and analysis between each of the areas of research thrust and will also provide platforms to facilitate computational collaboration at scale. Existing analysis experience within the RCC (Drs. Cuff, Zhang and Karger) will be brought to bear upon the challenging data and genomic studies that will be driven in particular from the data intensive and computationally sophisticated experimental methods derived within the Dulac and Lichtman groups. By utilizing the existing experience within Research Computing (in particular with Dr. Dutta's significant experience with managing data from the Large Hadron Collider, and galactic models, and full sky surveys), RCC will be ready to accept, interpret and analyze large-scale transcriptional data. RCC will also provide required and necessary infrastructure through both virtual and physical machines and storage to support the Center web site (WWW) - the website will be supplemented by education and outreach materials developed by our administrative core. By drawing upon the existing high speed campus and global networks (40+GB/s) already available to RCC through the Harvard University network, RCC will also be able to provide collaborative opportunities at a global scale through both rapidly sharing and by annotating data and methods.
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|Berger, Daniel R; Seung, H Sebastian; Lichtman, Jeff W (2018) VAST (Volume Annotation and Segmentation Tool): Efficient Manual and Semi-Automatic Labeling of Large 3D Image Stacks. Front Neural Circuits 12:88|
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|Morgan, Josh L; Lichtman, Jeff W (2017) Digital tissue and what it may reveal about the brain. BMC Biol 15:101|
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