Sequencing methods have become commonplace. Many laboratories generate tens of samples in a day and hundreds of libraries worth of data every month. Sequence data, however, as opposed to previous
Sequencing methods have become commonplace. Many laboratories generate tens of samples in a day and hundreds of libraries worth of data every month. Sequence data, however, as opposed to previous technologies, significant computational power and knowledge required for a scientific discovery. The current bottleneck for many laboratories is the processing and analysis of data in a timely manner. As sequencing becomes both cost effective and inexpensive, experiments increase in complexity. A single sequencing run usually includes many different conditions and replicates designed to answer a specific question. To process all samples in a study as a unit, automated data processing pipelines are necessary by leveraging the power of parallel nature of current computer environments called high performance computing (HPC) systems for a faster outcome. To address data processing and analysis, we implemented a highly parallel, platform, called DolphinNext specifically designed to process sequencing data generated towards answering a specific question. Once the data is processed, the result is usually a count table that specifies the estimated number of reads that originate from genomic loci. Differential analysis to determine which loci have different cellular activity in different conditions is based on this count tables and it requires a common, iterative, cycle of data assessment, data preparation and differential analysis. We developed DEBrowser as an R bioconductor project, to interactively visualize every step of the differential analysis of count data without requiring any programming expertise. It is based on a shiny infrastructure, which offers an interactive, web based graphical user interface for R packages. We leveraged the reactive programming of shiny to visualize the data at all stages of the analysis.
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