>TUBB2A|NM_001069|3'UTR
   1 AAACTTCTCAGATCAATCGTGCATCCTTAGTGAACTTCTGTTGTCCTCAAGCATGGTCTTTCTACTTGTAAACTATGGTG
  81 CTCAGTTTTGCCTCTGTTAGAAATTCACACTGTTGATGTAATGATGTGGAACTCCTCTAAAAATTACAGTATTGTCTGTG
 161 AAGGTATCTATACTAATAAAAAAGCATGTGTAGAAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' uucCUCGUUCCGC-CGUAGaga 5' || ||| || ||||| Target 5' tcaGATCAA-TCGTGCATCctt 3'	8 - 28	113.00	-9.50
2	miRNA 3' uuccucguuccgCCGUAGAga 5' ||:|||| Target 5' attgtctgtgaaGGTATCTat 3'	151 - 171	109.00	-7.70
3	miRNA 3' uuCCUC--GUUCCGCCGUAGAga 5' || | ||: : || ||| Target 5' atGGTGCTCAGTTTTGCCTCTgt 3'	75 - 97	93.00	-5.50

Mutant ID	Mutant Position	Mutant Source
COSN19707453	7	COSMIC
COSN31599363	25	COSMIC
COSN26642981	30	COSMIC
COSN20051078	35	COSMIC
COSN30156040	45	COSMIC
COSN30477550	46	COSMIC
COSN30189948	48	COSMIC
COSN5087217	59	COSMIC
COSN30517455	81	COSMIC
COSN30482986	92	COSMIC
COSN30157219	94	COSMIC
COSN31516465	94	COSMIC
COSN30532241	129	COSMIC
COSN31610034	142	COSMIC
COSN31602309	145	COSMIC
COSN31586849	156	COSMIC
COSN30126712	163	COSMIC
COSN31539629	171	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs777062028	2	dbSNP
rs1252088773	4	dbSNP
rs764492313	5	dbSNP
rs759149588	6	dbSNP
rs766895377	9	dbSNP
rs1397798395	11	dbSNP
rs773699055	12	dbSNP
rs770665341	16	dbSNP
rs762463562	18	dbSNP
rs760987108	19	dbSNP
rs956741889	20	dbSNP
rs773170649	21	dbSNP
rs1432943135	22	dbSNP
rs1309515144	30	dbSNP
rs769656196	35	dbSNP
rs1324165071	36	dbSNP
rs1412040819	37	dbSNP
rs72843326	38	dbSNP
rs781346126	39	dbSNP
rs1277042627	40	dbSNP
rs768447032	40	dbSNP
rs1262495570	43	dbSNP
rs1168505608	45	dbSNP
rs375083268	49	dbSNP
rs562932953	55	dbSNP
rs1003032383	56	dbSNP
rs1273018772	59	dbSNP
rs969477381	61	dbSNP
rs1168000696	64	dbSNP
rs1044651891	65	dbSNP
rs1477387363	68	dbSNP
rs1429230661	85	dbSNP
rs145215507	90	dbSNP
rs1481646522	94	dbSNP
rs1240156252	95	dbSNP
rs1475003382	96	dbSNP
rs898507927	101	dbSNP
rs1420258461	104	dbSNP
rs1038351981	106	dbSNP
rs3205007	107	dbSNP
rs3205008	109	dbSNP
rs1162928094	110	dbSNP
rs1270046583	114	dbSNP
rs1394023142	116	dbSNP
rs749283603	123	dbSNP
rs1436295732	125	dbSNP
rs1430597906	127	dbSNP
rs540952596	127	dbSNP
rs1196476950	138	dbSNP
rs777633833	145	dbSNP
rs933837370	151	dbSNP
rs866944719	154	dbSNP
rs921055081	155	dbSNP
rs772818777	162	dbSNP
rs1281832024	163	dbSNP
rs1446236775	165	dbSNP
rs1281342050	167	dbSNP
rs975362341	174	dbSNP
rs1484689874	179	dbSNP
rs1345973720	180	dbSNP
rs1417269875	190	dbSNP
rs533112511	197	dbSNP
rs747853799	198	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.

- 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.