>FZD9|NM_003508|3'UTR
   1 CCACACAGGCCTGGCGCGGGGTGGCTGCTGCCCCCTCCTTGCCCTCCACGCCCTGCCCCCTGCATCCCCTAGAGACAGCT
  81 GACTAGCAGCTGCCCAGCTGTCAAGGTCAGGCAAGTGAGCACCGGGGACTGAGGATCAGGGCGGGACCCCGTGAGGCTCA
 161 TTAGGGGAGATGGGGGTCTCCCCTAATGCGGGGGCTGGACCAGGCTGAGTCCCCACAGGGTCCTAGTGGAGGATGTGGAG
 241 GGGCGGGGCAGAGGGGTCCAGCCGGAGTTTATTTAATGATGTAATTTATTGTTGCGTTCCTCTGGAAGCTGTGACTGGAA
 321 TAAACCCCCGCGTGGCA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' aguuagaGUCGGAUGAAUCGUu 5' ||||: || ||||| Target 5' ctagagaCAGCTGAC-TAGCAg 3'	69 - 89	111.00	-13.50
2	miRNA 3' agUUAGAGUCGGAUGAAUCGUu 5' || |||| | | | |||| Target 5' tcAAGGTCAGGCAAGTGAGCAc 3'	101 - 122	104.00	-10.30
3	miRNA 3' agUUAGAGUCGGAUGAAUcguu 5' |: | ||::||:||| Target 5' ccAGCCGGAGTTTATTTAatga 3'	258 - 279	88.00	-10.60

Mutant ID	Mutant Position	Mutant Source
COSN32068375	3	COSMIC
COSN26976933	7	COSMIC
COSN4894068	17	COSMIC
COSN28863348	29	COSMIC
COSN30574646	51	COSMIC
COSN14830799	66	COSMIC
COSN30540675	87	COSMIC
COSN30504732	106	COSMIC
COSN30536501	111	COSMIC
COSN32052991	125	COSMIC
COSN30124773	139	COSMIC
COSN30103260	143	COSMIC
COSN26639413	177	COSMIC
COSN8038914	190	COSMIC
COSN31513096	191	COSMIC
COSN30170664	194	COSMIC
COSN31482248	219	COSMIC
COSN31549530	279	COSMIC
COSN31481986	306	COSMIC
rs1178947	66	GWAS

Mutant ID	Mutant Position	Mutant Source
rs782427952	1	dbSNP
rs1282290338	4	dbSNP
rs782010349	12	dbSNP
rs1237788129	16	dbSNP
rs782121372	17	dbSNP
rs201753892	18	dbSNP
rs537327658	19	dbSNP
rs782075383	24	dbSNP
rs375855111	35	dbSNP
rs982203598	38	dbSNP
rs781911666	41	dbSNP
rs782146330	43	dbSNP
rs368686112	51	dbSNP
rs927638540	53	dbSNP
rs1178947	66	dbSNP
rs1345136236	67	dbSNP
rs1159869832	69	dbSNP
rs1054916937	74	dbSNP
rs574154028	91	dbSNP
rs1187494663	98	dbSNP
rs1475663291	108	dbSNP
rs1259288548	124	dbSNP
rs947545672	125	dbSNP
rs1487899434	134	dbSNP
rs1261208835	135	dbSNP
rs1217925116	136	dbSNP
rs1319388801	138	dbSNP
rs782211885	143	dbSNP
rs570421960	152	dbSNP
rs1233446685	154	dbSNP
rs1356986432	157	dbSNP
rs903449103	160	dbSNP
rs1398571273	167	dbSNP
rs1384747823	172	dbSNP
rs1289955963	181	dbSNP
rs1455869787	182	dbSNP
rs1178946	183	dbSNP
rs1019557484	184	dbSNP
rs552938836	186	dbSNP
rs568135369	189	dbSNP
rs1191828485	190	dbSNP
rs1262235859	191	dbSNP
rs113683726	193	dbSNP
rs1487466781	194	dbSNP
rs1028366061	195	dbSNP
rs952541980	205	dbSNP
rs984373902	206	dbSNP
rs1271923836	213	dbSNP
rs1213789883	214	dbSNP
rs1336436775	215	dbSNP
rs1255722903	226	dbSNP
rs1217689403	228	dbSNP
rs1326206495	229	dbSNP
rs782632335	233	dbSNP
rs1384365384	240	dbSNP
rs563308060	246	dbSNP
rs1366149260	255	dbSNP
rs372383503	259	dbSNP
rs961572082	263	dbSNP
rs1441691448	264	dbSNP
rs151034742	265	dbSNP
rs1168780347	267	dbSNP
rs1461731573	296	dbSNP
rs575772278	297	dbSNP
rs1172287536	300	dbSNP
rs927607632	301	dbSNP
rs1252327198	304	dbSNP
rs1194225266	308	dbSNP
rs937700444	313	dbSNP
rs1235901133	317	dbSNP
rs782266329	325	dbSNP
rs914983937	330	dbSNP
rs1310853485	331	dbSNP
rs1303286182	332	dbSNP
rs1373279515	336	dbSNP
rs1329400011	338	dbSNP

- Cell, 2010

RNA transcripts are subject to posttranscriptional gene regulation involving hundreds of RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs) expressed in a cell-type dependent fashion. We developed a cell-based crosslinking approach to determine at high resolution and transcriptome-wide the binding sites of cellular RBPs and miRNPs. The crosslinked sites are revealed by thymidine to cytidine transitions in the cDNAs prepared from immunopurified RNPs of 4-thiouridine-treated cells. We determined the binding sites and regulatory consequences for several intensely studied RBPs and miRNPs, including PUM2, QKI, IGF2BP1-3, AGO/EIF2C1-4 and TNRC6A-C. Our study revealed that these factors bind thousands of sites containing defined sequence motifs and have distinct preferences for exonic versus intronic or coding versus untranslated transcript regions. The precise mapping of binding sites across the transcriptome will be critical to the interpretation of the rapidly emerging data on genetic variation between individuals and how these variations contribute to complex genetic diseases.

- Nature methods, 2011

Cross-linking and immunoprecipitation (CLIP) is increasingly used to map transcriptome-wide binding sites of RNA-binding proteins. We developed a method for CLIP data analysis, and applied it to compare CLIP with photoactivatable ribonucleoside-enhanced CLIP (PAR-CLIP) and to uncover how differences in cross-linking and ribonuclease digestion affect the identified sites. We found only small differences in accuracies of these methods in identifying binding sites of HuR, which binds low-complexity sequences, and Argonaute 2, which has a complex binding specificity. We found that cross-link-induced mutations led to single-nucleotide resolution for both PAR-CLIP and CLIP. Our results confirm the expectation from original CLIP publications that RNA-binding proteins do not protect their binding sites sufficiently under the denaturing conditions used during the CLIP procedure, and we show that extensive digestion with sequence-specific RNases strongly biases the recovered binding sites. This bias can be substantially reduced by milder nuclease digestion conditions.

- Nature, 2013

Circular RNAs (circRNAs) in animals are an enigmatic class of RNA with unknown function. To explore circRNAs systematically, we sequenced and computationally analysed human, mouse and nematode RNA. We detected thousands of well-expressed, stable circRNAs, often showing tissue/developmental-stage-specific expression. Sequence analysis indicated important regulatory functions for circRNAs. We found that a human circRNA, antisense to the cerebellar degeneration-related protein 1 transcript (CDR1as), is densely bound by microRNA (miRNA) effector complexes and harbours 63 conserved binding sites for the ancient miRNA miR-7. Further analyses indicated that CDR1as functions to bind miR-7 in neuronal tissues. Human CDR1as expression in zebrafish impaired midbrain development, similar to knocking down miR-7, suggesting that CDR1as is a miRNA antagonist with a miRNA-binding capacity ten times higher than any other known transcript. Together, our data provide evidence that circRNAs form a large class of post-transcriptional regulators. Numerous circRNAs form by head-to-tail splicing of exons, suggesting previously unrecognized regulatory potential of coding sequences.