ArticleBioinformatics (Oxford, England)2019
ShaKer: RNA SHAPE prediction using graph kernel.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses.Viruses · 2022Review
- Native RNA or cDNA Sequencing for Transcriptomic Analysis: A Case Study onFrontiers in bioengineering and biotechnology · 2022Article
- Predicting RNA SHAPE scores with deep learning.RNA biology · 2020Article
- The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summarySHAPE experiments are used to probe the structure of RNA molecules. We present ShaKer to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained on experimental SHAPE information. While other available methods require a manually curated reference structure, ShaKer predicts reactivity data based on sequence input only and by sampling the ensemble of possible structures. Thus, ShaKer is well placed to enable experiment-driven, transcriptome-wide SHAPE data prediction to enable the study of RNA structuredness and to improve RNA structure and RNA-RNA interaction prediction. For performance evaluation, we use accuracy and accessibility comparing to experimental SHAPE data and competing methods. We can show that Shaker outperforms its competitors and is able to predict high quality SHAPE annotations even when no reference structure is provided. AVAILABILITY AND IMPLEMENTATION: ShaKer is freely available at https://github.com/BackofenLab/ShaKer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.