5 Matching Annotations
  1. Sep 2018
  2. Oct 2017
    1. tenfold cross-validation technique

      How do AUROCs look for our proteins of interest compared to the AUROCs for the iCLIP'ed proteins in Additional File 2?

    2. The following describes a typical biological application of computational target detection. A published CLIP-seq experiment for a protein of interest is available for kidney cells, but the targets of that protein are required for liver cells. The original CLIP-seq targets may have missed many correct targets due to differential expression in the two tissues and the costs for a second CLIP-seq experiment in liver cells may not be within the budget or the experiment is otherwise not possible. We provide a solution that uses an accurate protein-binding model from the kidney CLIP-seq data, which can be used to identify potential targets in the entire transcriptome. Transcripts targeted in liver cells can be identified with improved specificity when target prediction is combined with tissue-specific transcript expression data.

      use case

    3. Peak detection leads to high-fidelity binding sites; however, it again increases the number of false negatives. Therefore, to complete the RBP interactome, computational discovery of missing binding sites is essential.

      iCLIP data are not comprehensive

    4. GraphProt: modeling binding preferences of RNA-binding proteins