>CCNL1|NM_020307|3'UTR
   1 CTTTCTCTTCCTTTGAGCCTGCATCAGTTCTTGGTTTTGCCTATCTACAGTGTGATGTATGGACTCAATCAAAAACATTA
  81 AACGCAAACTGATTAGGATTTGATTTCTTGAAACCCTCTAGGTCTCTAGAACACTGAGGACAGTTTCTTTTGAAAAGAAC
 161 TATGTTAATTTTTTTGCACATTAAAATGCCCTAGCAGTATCTAATTAAAAACCATGGTCAGGTTCAATTGTACTTTATTA
 241 TAGTTGTGTATTGTTTATTGCTATAAGAACTGGAGCGTGAATTCTGTAAAAATGTATCTTATTTTTATACAGATAAAATT
 321 GCAGACACTGTTCTATTTAAGTGGTTATTTGTTTAAATGATGGTGAATACTTTCTTAACACTGGTTTGTCTGCATGTGTA
 401 AAGATTTTTACAAGGAAATAAAATACAAATCTTGTTTTTTCTAAACTGCTTCAAATACCTTATTTAAATAAATTATTAAA
 481 AAGGAAAATTTTAATAGCAAAATGGCTGTACTGGTTTTCTGACAAACTTATTCTTTCCTCCATTAGATGGTAGTTTGAGA
 561 TTATTAAAATGCAATTCTTAACTGCATTGTGAGCTATTGTAAGTCTTCACTGTATATATGGAAAAAGTAAATGGGAGTCG
 641 CCCAACAATGTGACATTAAATGTTATATTTTAGCACTAAACCAAAAAAAAAAAAAAAAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' auGUAGGUU-UUCUAGUGUAAc 5' |: |||| || :| ||||| Target 5' gtCGCCCAACAATGTGACATTa 3'	637 - 658	131.00	-7.40
2	miRNA 3' augUAGGUUUU----CUAGUGUAAc 5' ||: |||| |:||| :|| Target 5' ctaATTAAAAACCATGGTCAGGTTc 3'	201 - 225	112.00	-8.90
3	miRNA 3' auGUAGGU-UUUCU---AGUGUaac 5' ||| :: ||||| |:||| Target 5' tgCATGTGTAAAGATTTTTACAagg 3'	391 - 415	103.00	-7.10

Mutant ID	Mutant Position	Mutant Source
COSN30529633	5	COSMIC
COSN30173575	25	COSMIC
COSN30176383	54	COSMIC
COSN30522761	62	COSMIC
COSN31582289	84	COSMIC
COSN1923627	89	COSMIC
COSN30535080	117	COSMIC
COSN31571576	284	COSMIC
COSN31482162	298	COSMIC
COSN1923626	308	COSMIC
COSN22811282	332	COSMIC
COSN7827950	352	COSMIC
COSN28685266	359	COSMIC
COSN28810574	463	COSMIC
COSN20678939	655	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs771366552	4	dbSNP
rs749596489	5	dbSNP
rs1316868258	7	dbSNP
rs777461968	15	dbSNP
rs769435206	16	dbSNP
rs780626373	17	dbSNP
rs1407625070	24	dbSNP
rs758957176	30	dbSNP
rs929152364	36	dbSNP
rs200033201	38	dbSNP
rs898992606	39	dbSNP
rs371314438	43	dbSNP
rs1326894234	45	dbSNP
rs751470106	51	dbSNP
rs1293707132	59	dbSNP
rs1383645633	59	dbSNP
rs969292368	61	dbSNP
rs943062654	67	dbSNP
rs1021349455	68	dbSNP
rs777356907	70	dbSNP
rs118144837	72	dbSNP
rs1180449978	76	dbSNP
rs184355678	83	dbSNP
rs552574994	84	dbSNP
rs540619875	88	dbSNP
rs1185097234	93	dbSNP
rs1197661328	97	dbSNP
rs1259256331	115	dbSNP
rs997430476	128	dbSNP
rs1199654424	134	dbSNP
rs577155151	135	dbSNP
rs897780201	139	dbSNP
rs922869110	142	dbSNP
rs978354943	152	dbSNP
rs1455181925	162	dbSNP
rs1354275971	166	dbSNP
rs1264915965	167	dbSNP
rs868658425	169	dbSNP
rs1245848719	172	dbSNP
rs1343785283	176	dbSNP
rs1222924476	180	dbSNP
rs1269351421	186	dbSNP
rs527978595	190	dbSNP
rs1214242035	196	dbSNP
rs1022415933	200	dbSNP
rs1260544337	205	dbSNP
rs771183041	220	dbSNP
rs887625783	222	dbSNP
rs989525701	228	dbSNP
rs11546246	230	dbSNP
rs530109989	242	dbSNP
rs570175527	243	dbSNP
rs1270517898	245	dbSNP
rs1365202019	248	dbSNP
rs1033651993	263	dbSNP
rs776189195	268	dbSNP
rs906514102	273	dbSNP
rs1026686193	276	dbSNP
rs993428332	277	dbSNP
rs1330757699	290	dbSNP
rs1384986888	296	dbSNP
rs749468652	299	dbSNP
rs1425052573	310	dbSNP
rs1305205349	319	dbSNP
rs1363876750	322	dbSNP
rs1404432913	326	dbSNP
rs1057101738	332	dbSNP
rs938931130	335	dbSNP
rs1037507404	344	dbSNP
rs1306913801	346	dbSNP
rs555109503	347	dbSNP
rs1225112386	351	dbSNP
rs888844750	356	dbSNP
rs1489696933	361	dbSNP
rs1472445324	365	dbSNP
rs1051493029	373	dbSNP
rs933010763	374	dbSNP
rs1481319310	375	dbSNP
rs769464364	377	dbSNP
rs914751931	377	dbSNP
rs985879025	388	dbSNP
rs181713493	390	dbSNP
rs1383123109	391	dbSNP
rs1424321447	393	dbSNP
rs757914718	397	dbSNP
rs557823932	398	dbSNP
rs1356422707	423	dbSNP
rs1440029244	426	dbSNP
rs953288523	429	dbSNP
rs560658492	430	dbSNP
rs1248664705	442	dbSNP
rs1450359777	449	dbSNP
rs1359141399	458	dbSNP
rs1227441684	459	dbSNP
rs1273260775	469	dbSNP
rs1308751118	474	dbSNP
rs1201749094	476	dbSNP
rs978788664	478	dbSNP
rs571353608	483	dbSNP
rs975712350	483	dbSNP
rs961709436	484	dbSNP
rs547860306	494	dbSNP
rs1014827040	501	dbSNP
rs1469998509	508	dbSNP
rs1168720442	524	dbSNP
rs1421846300	525	dbSNP
rs1333442973	527	dbSNP
rs1266431887	529	dbSNP
rs151034858	531	dbSNP
rs1329670768	533	dbSNP
rs915416995	541	dbSNP
rs1382586927	542	dbSNP
rs1431845325	551	dbSNP
rs989577948	556	dbSNP
rs1270420441	564	dbSNP
rs959465587	572	dbSNP
rs1023914425	574	dbSNP
rs1231069788	581	dbSNP
rs926730833	583	dbSNP
rs1330433208	587	dbSNP
rs1013002590	593	dbSNP
rs1323655917	594	dbSNP
rs1441646884	599	dbSNP
rs1193573508	601	dbSNP
rs982202918	608	dbSNP
rs971178522	617	dbSNP
rs894600046	622	dbSNP
rs1186125221	633	dbSNP
rs541336832	639	dbSNP
rs142755874	640	dbSNP
rs530808516	645	dbSNP
rs903202651	646	dbSNP
rs963262964	648	dbSNP
rs563355040	654	dbSNP
rs369384061	657	dbSNP
rs1320640755	660	dbSNP
rs190798460	661	dbSNP
rs779444887	667	dbSNP
rs1277179542	689	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.

- Genome biology, 2014

BACKGROUND: Various microRNAs (miRNAs) are up- or downregulated in tumors. However, the repression of cognate miRNA targets responsible for the phenotypic effects of this dysregulation in patients remains largely unexplored. To define miRNA targets and associated pathways, together with their relationship to outcome in breast cancer, we integrated patient-paired miRNA-mRNA expression data with a set of validated miRNA targets and pathway inference. RESULTS: To generate a biochemically-validated set of miRNA-binding sites, we performed argonaute-2 photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (AGO2-PAR-CLIP) in MCF7 cells. We then defined putative miRNA-target interactions using a computational model, which ranked and selected additional TargetScan-predicted interactions based on features of our AGO2-PAR-CLIP binding-site data. We subselected modeled interactions according to the abundance of their constituent miRNA and mRNA transcripts in tumors, and we took advantage of the variability of miRNA expression within molecular subtypes to detect miRNA repression. Interestingly, our data suggest that miRNA families control subtype-specific pathways; for example, miR-17, miR-19a, miR-25, and miR-200b show high miRNA regulatory activity in the triple-negative, basal-like subtype, whereas miR-22 and miR-24 do so in the HER2 subtype. An independent dataset validated our findings for miR-17 and miR-25, and showed a correlation between the expression levels of miR-182 targets and overall patient survival. Pathway analysis associated miR-17, miR-19a, and miR-200b with leukocyte transendothelial migration. CONCLUSIONS: We combined PAR-CLIP data with patient expression data to predict regulatory miRNAs, revealing potential therapeutic targets and prognostic markers in breast cancer.