The purpose of this Core is to provide bioinformatic analysis of transcriptomic, DNA-protein interactions and Chromatin Conformation Capture projects. Next-generation sequencers have transformed genomic research, yet the analysis of these data remains a bottleneck. Our Core will provide the essential data analysis service to render these data accessible and interpretable to the members of this Program Project. The PI and staff of this Core have extensive experience in the analysis of genomic data, as well as familiarity with the underlying biology of the associated projects. Despite the fact that microarray and sequencing cores exist at UCLA, none of these provide data analysis as a service. Therefore the typical biology group that does not have internal computational expertise is often left with data and no ability to interpret it. The Core we are proposing here will remove this impediment so that all the groups within this Program Project will be able to not only collect sequencing data from their samples, but also obtain processed and analyzed data that can be directly interpreted by researchers without computational expertise. This functionality should render genomics research far more accessible to all members of this Program Project. We will also work with computationally experienced researchers in each lab of this Program to refine analysis tools.
The Aims of this Core are: 1. Analysis of RNA-seq data: we will provide quantification and variant detection analyses of RNA-seq data. 2. Analysis of ChlP-seq data: we will provide the location of peaks, average peak distributions and motifs. 3. Analysis of 4C data: we will provide the locations of domains that interact with """"""""bait"""""""" loci, and analyze their properties with other genomic data. 4. Data display on the UCSC genome browser: in all cases, this core will also load genome-wide data onto our installation of the UCSC genome browser so that users can see at single base resolution the data generated from each sample. We will also upload data and analysis tools to the Wiki site for exchange. 5. Data quality metrics: we will generate quality metrics for sequence data to provide an estimate of the quality of the sample.
|Xie, Yuan; Lowry, William E (2018) Manipulation of neural progenitor fate through the oxygen sensing pathway. Methods 133:44-53|
|Brumbaugh, Justin; Di Stefano, Bruno; Wang, Xiuye et al. (2018) Nudt21 Controls Cell Fate by Connecting Alternative Polyadenylation to Chromatin Signaling. Cell 172:629-631|
|Brumbaugh, Justin; Di Stefano, Bruno; Wang, Xiuye et al. (2018) Nudt21 Controls Cell Fate by Connecting Alternative Polyadenylation to Chromatin Signaling. Cell 172:106-120.e21|
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