Abstract
ISMARA ( ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client.
Highlights
Motif activity response analysis (MARA) is a general method that models genome-wide expression or chromatin state data in terms of computationally predicted regulatory sites for transcription factors (TFs) and microRNAs to infer the key regulators, their targets, and regulatory interactions between regulators, that are operating in a given system (Arnold et al, 2013; Balwierz et al, 2014; Suzuki et al, 2009)
ISMARA is a highly popular tool, the current sizes of raw next-generation sequencing datasets are so large, that their upload to the web server can require many hours, and this has become a major bottleneck for many users
We have developed a stand-alone client application that completely automates the process of pre-processing the user’s raw data on her/his own computer, and transmits the much smaller resulting processed files to the ISMARA server for analysis
Summary
Motif activity response analysis (MARA) is a general method that models genome-wide expression or chromatin state data in terms of computationally predicted regulatory sites for transcription factors (TFs) and microRNAs to infer the key regulators, their targets, and regulatory interactions between regulators, that are operating in a given system (Arnold et al, 2013; Balwierz et al, 2014; Suzuki et al, 2009). ISMARA is a highly popular tool, the current sizes of raw next-generation sequencing datasets are so large (up to hundreds of GBs), that their upload to the web server can require many hours, and this has become a major bottleneck for many users. To address this problem, we have developed a stand-alone client application (called the ISMARA client) that completely automates the process of pre-processing the user’s raw data on her/his own computer, and transmits the much smaller resulting processed files to the ISMARA server for analysis. Since the processed files are many orders of magnitude smaller than the original raw files, the upload is short, even with slow Internet connection speeds
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