ArticleBioMed research international2015
Assessing Computational Steps for CLIP-Seq Data Analysis.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Signal or noise? RNA-binding proteins and the challenges of binding site assignments.Nucleic acids research · 2026Review
- Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs.Genome biology · 2023Article
- Clip for studying protein-RNA interactions that regulate virus replication.Methods (San Diego, Calif.) · 2020Review
- CLIP-related methodologies and their application to retrovirology.Retrovirology · 2018Review
- The PUF binding landscape in metazoan germ cells.RNA (New York, N.Y.) · 2016Article
- 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
5 authors at 3 institutions in 1 country.
Funding
Abstract
RNA-binding protein (RBP) is a key player in regulating gene expression at the posttranscriptional level. CLIP-Seq, with the ability to provide a genome-wide map of protein-RNA interactions, has been increasingly used to decipher RBP-mediated posttranscriptional regulation. Generating highly reliable binding sites from CLIP-Seq requires not only stringent library preparation but also considerable computational efforts. Here we presented a first systematic evaluation of major computational steps for identifying RBP binding sites from CLIP-Seq data, including preprocessing, the choice of control samples, peak normalization, and motif discovery. We found that avoiding PCR amplification artifacts, normalizing to input RNA or mRNAseq, and defining the background model from control samples can reduce the bias introduced by RNA abundance and improve the quality of detected binding sites. Our findings can serve as a general guideline for CLIP experiments design and the comprehensive analysis of CLIP-Seq data.
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Identifiers
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.