Variations in RNA sequence lead to differences in RNA structure called riboSNitches, if important structural elements are disrupted. Recent ultra-high throughput techniques, such as SHAPE-MaP and PARS, enable the collection of structural information on RNAs at a genome-wide scale. With the ability to gather large amounts of structural information, it is important to accurately identify those structural changes that can potentially result in a phenotypic outcome. We have developed an automated approach to identify and classify structure change in chemical mapping data. This method utilizes random forest classification on a set of seven characterizing features. The default mutate and map SHAPE data sets (or another user specified data set) can be used to build a classifier. The classifier is then used to identify structure change in other SHAPE traces. Enabling scientists to identify structure change may help guide experiments that examine RNA structure and its role in biological processes.
|Author||Chanin Tolson [aut, cre]|
|Date of publication||2016-02-12 19:52:20|
|Maintainer||Laederach Lab <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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