BACKGROUND: MicroRNAs (miRNAs) play a critical role in down-regulating gene 
expression. By coupling with Argonaute family proteins, miRNAs bind to target 
sites on mRNAs and employ translational repression. A large amount of 
miRNA-target interactions (MTIs) have been identified by the crosslinking and 
immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP 
(PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high 
efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion 
in miRNA-RNA-protein crosslinking regions. This artificial error obviously 
reduces the mappability of reads. However, a specific tool to analyze CLIP and 
PAR-CLIP data that takes T to C conversion into account is still in need.
RESULTS: We herein propose the first CLIP and PAR-CLIP sequencing analysis 
platform specifically for miRNA target analysis, namely miRTarCLIP. From 
scratch, it automatically removes adaptor sequences from raw reads, filters low 
quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, 
identifies high confidence miRNA target sites, and provides annotations from 
external databases. With multi-threading techniques and our novel C to T 
reversion procedure, miRTarCLIP greatly reduces the running time comparing to 
conventional approaches. In addition, miRTarCLIP serves with a web-based 
interface to provide better user experiences in browsing and searching targets 
of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, 
we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing 
datasets. miRTarCLIP not only shows comparable results to that of other existing 
tools in a much faster speed, but also reveals interesting features among these 
putative target sites. Specifically, we used miRTarCLIP to disclose that T to C 
conversion within position 1-7 and that within position 8-14 of miRNA target 
sites are significantly different (p value = 0.02), and even more significant 
when focusing on sites targeted by top 102 highly expressed miRNAs only (p value 
= 0.01). These results comply with previous findings and further suggest that 
combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA 
target prediction.
CONCLUSION: To sum up, we devised a systematic approach for mining miRNA-target 
sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow 
with a graphical web-based browser, which provides a user friendly interface and 
detailed annotations of MTIs. We also showed through real-life examples that 
miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can 
be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.