Data Analysis Core

点击翻译按钮获取中文摘要

PROJECT SUMMARY The Data Analysis Core (DAC) of the Midwest Murine Tissue Mapping Center (MM-TMC) of Senescent Cells (SnCs) will build upon extensive computational resources to meet all the Center’s informatics and data analytics needs. The DAC MPIs are Jinhua Wang, an expert in genome informatics and bioinformatics modeling with a long track record in successfully building and leading informatics cores for center grants at the University of Minnesota (UMN), and Alexander Misharin, a senior researcher specializing in single cell data and integrative genomics at Northwestern University. The DAC also includes experts in cross-species comparative genomics, transcriptomic analysis, gene pathway modeling, genetic biomarker selection, proteomics/metabolomics data analysis and tool development, deep neural network modeling of cellular imaging, and statistical planning, quality control measures, and statistical hypothesis testing. The overall goal of the DAC is to perform multi-scale and multi-modality analysis of the collected data (for SnC identification, novel SnC biomarker discovery, SnC spatial pattern discovery, and SnC cellular states dynamics modeling) and prepare it for the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a murine SnC 4D Atlas. The DAC MPIs will work closely with Yale and UMN human TMC DAC centers to inform and help advance the ongoing analysis carried out in human tissues. Notably, DAC MPI Wang has had productive collaborations with Yale and UMN human DAC directors for more than a decade. Select murine tissues (liver, lung, skeletal muscle, and adipose) over a range of ages, strains, and perturbations will be analyzed with both bulk and single cell profiling and spatial analysis by the MM-TMC Biological Analysis Core (BAC). The DAC will be responsible for data ingestion from the BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. We will 1) build or use the best practice tools for data storage, search, retrieval, analysis, and multi-omics data joint embedding; 2) create a comprehensive murine SnC biomarker set, including both known and novel biomarkers; and 3) establish a cross-comparison procedure to bridge murine and human SnC analyses. In collaboration with the SenNet Consortium, the DAC will establish benchmarks, contribute to standard operating procedures and standards development, and prepare and share datasets with the CODCC to enable a murine SnC 4D atlas. The DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data analytics, data storage and management capabilities at MM-TMC institutions. The DAC will also work closely with the other TMCs and the CODCC to develop and implement customized SenNet-wide standards fine-tuned to the needs of the consortium including: 1) data quality metrics, ontologies, and data elements; 2) integration of imaging and omics data analytical tools for visualization, segmentation, and annotation; 3) SOPs; 4) Common Data Elements; and 5) a network public data sharing policy.