Abstract
The LINCS L1000 data repository contains almost two million gene expression profiles for thousands of small molecules and drugs. However, due to the complexity and the size of the data repository and a lack of an interoperable interface, the creation of pharmacologically meaningful workflows utilizing these data is severely hampered. In order to overcome this limitation, we developed the L1000 Viewer, a search engine and graphical web interface for the LINCS data repository. The web interface serves as an interactive platform allowing the user to select different forms of perturbation profiles, e.g., for specific cell lines, drugs, dosages, time points and combinations thereof. At its core, our method has a database we created from inferring and utilizing the intricate dependency graph structure among the data files. The L1000 Viewer is accessible via http://L1000viewer.bio-complexity.com/.
Highlights
We are living in the era of big data that sparked the establishment of the field data science (Smith, 2006; Ma’ayan et al, 2014; Jin et al, 2015; Emmert-Streib and Dehmer, 2019)
In order to facilitate the access and subset of raw data from the Library of Integrated Network-based Cellular Signatures (LINCS) data repository we developed the L1000 Viewer
The LINCS data is a vast collection of gene expression profiles that includes many experimental samples covering more than seventy human cell lines
Summary
We are living in the era of big data that sparked the establishment of the field data science (Smith, 2006; Ma’ayan et al, 2014; Jin et al, 2015; Emmert-Streib and Dehmer, 2019). The recent growth of high-throughput biomedical and pharmacogenomic data (Edgar et al, 2002; Barrett et al, 2013; Woo et al, 2015; Musa et al, 2017) presents opportunities and at the same time challenges for their analysis. Novel approaches for finding, selecting and downloading specific subdata from large data repositories are required. This is a problem for obtaining raw data (Musa et al, 2018)
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