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However, there is not a computational proposal that integrates information from both CLIP-seq and RNAcompete data, simultaneously.
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66 In this section, we briefly describe the above-mentioned computational proposals and present specific advantages.
In addition, this information is quite valuable to design, test and validate new computational proposals.
We note that during the second period, computational proposals are essential to handle and process CLIP-seq data.
However, there are no reviews focusing on computational proposals designed for CLIP-seq data, even though this is a rapidly evolving field.
For these reasons, we present a review on the current status of computational proposals used and designed for CLIP-seq data.
Computational proposals, designed for RBPs, face two main challenges: (1) to provide insights into the RBP RNA interaction structure and (2) to enable and facilitate CLIP-seq data processing.
Huerta notes, however, that unsolicited computational neuroscience proposals are becoming routine.
In computational neuroscience, many proposals may be assigned to specialized study sections, outside of the regular standing committees.
Due to the computational costs of our proposal, parallel implementations are required, and several approaches for shared memory systems such as multicores are analysed in this paper.
For assessing the validity of the proposal, a computational comparison is performed between the plain use of a state-of-the-art optimization solver and the proposals made in this work.
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