A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins

Kishore, Shivendra; Jaśkiewicz, Łukasz; Burger, Lukas; Hausser, Jean; Khorshid, Mohsen; Zavolan, Mihaela

doi:10.1038/nmeth.1608

Cited by 446 publications

(488 citation statements)

References 34 publications

Supporting

Mentioning

445

Contrasting

Unclassified

Order By: Relevance

“…miRTarBase, and 7 PAR-CLIP datasets [39,40] to obtain a set of miRNA targets, and then exclude them from RefSeq genes to get the 'biologically negative targets'. We then randomly pair the real positive miRNAs with these biologically negative targets to generate a biologically negative dataset.…”

Section: Discussionmentioning

confidence: 99%

“…To answer this question, we obtain 19641 UTR targets detected by PAR-CLIP [39,40], among which 1757 UTR targets are not predicted by TargetScanHuman version 6.2. We randomly select 300 of these targets as inputs to mirMark's UTR-level classifier, in combination with miRNAs in miRBase.…”

Section: Utr-level Comparison With Existing Methodsmentioning

confidence: 99%

“…We used the PAR-CLIP data from previous studies [39,40] to compare the performance between mirMark and TargetScan. We obtained one set of data from the supplementary material of Hafler et al [39], and the other datasets from Kishore et al [40], which have the following accession IDs and samples:…”

Section: Data Availabilitymentioning

confidence: 99%

“…We obtained one set of data from the supplementary material of Hafler et al [39], and the other datasets from Kishore et al [40], which have the following accession IDs and samples:…”

Section: Data Availabilitymentioning

confidence: 99%

See 3 more Smart Citations

mirMark: a site-level and UTR-level classifier for miRNA target prediction

et al. 2014

View full text Add to dashboard Cite

MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site-and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Utr-level Comparison With Existing Methodsmentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 99%

“…We obtained one set of data from the supplementary material of Hafler et al [39], and the other datasets from Kishore et al [40], which have the following accession IDs and samples:…”

Section: Data Availabilitymentioning

confidence: 99%

See 2 more Smart Citations

mirMark: a site-level and UTR-level classifier for miRNA target prediction

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Reads mapped to the same genome region with identical barcodes are regarded as the duplicates from a single RNA molecule and thus are counted as one. This strategy has been reported to greatly improve the quantification performance of CLIP [18,60,89,140]. Ion Torrent and Ion Proton sequencers require much less cDNA compared to Illumina sequencers.…”

Section: Quantitative Analysismentioning

confidence: 99%

CLIP: viewing the RNA world from an RNA-protein interactome perspective

Zhang

Xie²,

Xu³

et al. 2015

Sci. China Life Sci.

View full text Add to dashboard Cite

The pervasive transcription of the genome creates many types of non-coding RNAs (ncRNAs). However, we know very little regarding the functions and the regulatory mechanisms of these ncRNAs. Exploring the interactions of RNA and RNA binding proteins (RBPs) is vital because it can allow us to truly understand how these ncRNAs behave in vivo. High-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP or CLIP-seq) and its variants have been successfully used as systemic techniques to study RBP binding sites. In this review, we will explain the major differences between the CLIP techniques, summarize successful applications of these techniques, discuss limitations of CLIP, present some suggested solutions and project their promising future roles in studying the RNA world. CLIP, CLASH, RPBs, ncRNAs, functional RNomics Citation:Zhang

show abstract

Prognostic value ofBRAF/MIR‐17 signature and B‐Raf protein expression in patients with colorectal cancer: A pilot study

Ibrahiem

Fawzy

AlSel

et al. 2020

Clinical Laboratory Analysis

View full text Add to dashboard Cite

Background: Despite the recent improvement in colorectal cancer (CRC) treatment, it still has a poor prognosis with a low survival rate. Genetic and epigenetic mechanisms have proved to play a substantial role in CRC tumorigenesis and progression. According to Gene Ontology and TargetScan analyses, the B-Raf proto-oncogene (BRAF) gene is one of the microRNA-17 (miR-17) targets. We aimed to explore the prognostic value of B-Raf protein and BRAF/microRNA-17 (MIR-17) gene expression signature in CRC archived samples. Methods: B-Raf protein expression was identified by immunohistochemistry, while gene expression studies were quantified by real-time qPCR in 53 paired archived CRC specimens. Results: The BRAF showed higher expressions in CRC specimens relative to noncancer tissues (p = 0.006). MIR17 expression was inversely and significantly correlated with both B-Raf protein (r = −0.79, p < 0.001) and gene expression (r = −0.35, p = 0.010) and showed a significant direct correlation with a high rate of relapse (p = 0.020). BRAF/miR-17 expression in CRC was associated inversely with tumor size, high grade of colonic carcinoma, lymph node metastasis, and carcinoma subtype. Spearman correlation and Kaplan-Meier survival curve analyses revealed that disease-free survival and overall survival were inversely and significantly correlated with positive B-Raf protein expression (r = −0.31 and −0.35, p = 0.023 and 0.011, respectively) and directly correlated with log BRAF/MIR17 ratio (r = 0.50 and 0.41, p < 0.001 and = 0.003, respectively). Cox hazard regression analysis revealed the BRAF/MIR17 ratio could predict both types of patients' survival, among other variables. Conclusion: BRAF/MIR17 ratio could have prognostic utility in patients with CRC. Further larger-scale studies are warranted to confirm this utility.

show abstract

A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins

Cited by 446 publications

References 34 publications

mirMark: a site-level and UTR-level classifier for miRNA target prediction

mirMark: a site-level and UTR-level classifier for miRNA target prediction

CLIP: viewing the RNA world from an RNA-protein interactome perspective

Prognostic value ofBRAF/MIR‐17 signature and B‐Raf protein expression in patients with colorectal cancer: A pilot study

Contact Info

Product

Resources

About