ReviewBioinformatics and biology insights2014
Computational Methods for CLIP-seq Data Processing.
Review in Bioinformatics and biology insights, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Article
- Clip for studying protein-RNA interactions that regulate virus replication.Methods (San Diego, Calif.) · 2020Review
- omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data.Genome biology · 2018Article
- CLIP-related methodologies and their application to retrovirology.Retrovirology · 2018Review
- A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.Nucleic acids research · 2017Article
- Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles.RNA (New York, N.Y.) · 2017Article
- Highly accessible AU-rich regions in 3' untranslated regions are hotspots for binding of regulatory factors.PLoS computational biology · 2017Article
- Techniques and Approaches to Genetic Analyses in Nephrological Disorders.Journal of pediatric genetics · 2016Review
- miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database.Nucleic acids research · 2016Article
- Specificity and nonspecificity in RNA-protein interactions.Nature reviews. Molecular cell biology · 2015Review
- Design and bioinformatics analysis of genome-wide CLIP experiments.Nucleic acids research · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.
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What OpenQuestion holds
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 OpenQuestion graph.