ID	Duplex structure	Position	Score	MFE
1	miRNA 3' agAUUGU---GGUGU----C---ACACCAAAc 5' ||| : |:||| | |||||||| Target 5' aaTAAGGTGTCTACAAGGTGTTTTGTGGTTTc 3'	217 - 248	146.00	-10.70
2	miRNA 3' agauuguggugucacACCAAac 5' ||||| Target 5' gagttctttcatctaTGGTTga 3'	51 - 72	100.00	-8.50
3	miRNA 3' agaUUGUGGUGUC---AC-ACCAAAc 5' |||||: | | || |||| | Target 5' gaaAACACTTCTGTCTTGATGGTCTa 3'	174 - 199	99.00	-7.12

Mutant ID	Mutant Position	Mutant Source
1180230	116	ClinVar
COSN31574510	12	COSMIC
COSN24314369	19	COSMIC
COSN31491175	29	COSMIC
COSN31537381	40	COSMIC
COSN26981878	46	COSMIC
COSN30179510	47	COSMIC
COSN30124105	56	COSMIC
COSN30479499	82	COSMIC
COSN31541741	111	COSMIC
COSN31605314	159	COSMIC
COSN31573282	184	COSMIC
COSN31599504	239	COSMIC
COSN31526216	283	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs765945741	3	dbSNP
rs1285767312	6	dbSNP
rs79174420	7	dbSNP
rs773713629	8	dbSNP
rs1288870202	13	dbSNP
rs767778057	15	dbSNP
rs762305484	16	dbSNP
rs774570582	21	dbSNP
rs768920029	23	dbSNP
rs1391671064	27	dbSNP
rs758707466	39	dbSNP
rs373917637	40	dbSNP
rs71371449	42	dbSNP
rs1411962749	43	dbSNP
rs753614465	44	dbSNP
rs1473827438	46	dbSNP
rs748913729	50	dbSNP
rs1425344080	56	dbSNP
rs1162964084	60	dbSNP
rs372649886	63	dbSNP
rs12231	65	dbSNP
rs980400521	79	dbSNP
rs1392109458	80	dbSNP
rs117588718	94	dbSNP
rs1024865370	105	dbSNP
rs983042114	108	dbSNP
rs951271907	110	dbSNP
rs1379499483	111	dbSNP
rs1244834931	113	dbSNP
rs1288902236	114	dbSNP
rs1132367	116	dbSNP
rs1217238172	121	dbSNP
rs995624321	124	dbSNP
rs141815199	126	dbSNP
rs1283543277	127	dbSNP
rs111849607	128	dbSNP
rs1445040152	129	dbSNP
rs899915551	131	dbSNP
rs1379890252	134	dbSNP
rs1018290787	135	dbSNP
rs1237872335	138	dbSNP
rs112791986	147	dbSNP
rs570136616	149	dbSNP
rs538043316	159	dbSNP
rs1010885801	164	dbSNP
rs1198278047	166	dbSNP
rs536665430	171	dbSNP
rs763406735	177	dbSNP
rs1055021753	180	dbSNP
rs201389981	181	dbSNP
rs1451391555	189	dbSNP
rs1203141511	194	dbSNP
rs547362688	199	dbSNP
rs765574818	200	dbSNP
rs1348404730	211	dbSNP
rs1044717357	223	dbSNP
rs1278996827	227	dbSNP
rs949034912	229	dbSNP
rs1460707939	239	dbSNP
rs1203269211	240	dbSNP
rs570541533	248	dbSNP
rs1459128805	250	dbSNP
rs192578662	255	dbSNP
rs1363335118	262	dbSNP
rs1273680838	264	dbSNP
rs1469014722	268	dbSNP
rs951369637	273	dbSNP
rs1233376354	295	dbSNP
rs1430341819	298	dbSNP
rs919810074	303	dbSNP
rs973864103	308	dbSNP
rs1469659527	309	dbSNP
rs112000562	311	dbSNP
rs1334258116	321	dbSNP
rs964133419	325	dbSNP
rs1018341367	328	dbSNP
rs1391641797	334	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.

- mBio, 2013

UNLABELLED: The question of how HIV-1 interfaces with cellular microRNA (miRNA) biogenesis and effector mechanisms has been highly controversial. Here, we first used deep sequencing of small RNAs present in two different infected cell lines (TZM-bl and C8166) and two types of primary human cells (CD4(+) peripheral blood mononuclear cells [PBMCs] and macrophages) to unequivocally demonstrate that HIV-1 does not encode any viral miRNAs. Perhaps surprisingly, we also observed that infection of T cells by HIV-1 has only a modest effect on the expression of cellular miRNAs at early times after infection. Comprehensive analysis of miRNA binding to the HIV-1 genome using the photoactivatable ribonucleoside-induced cross-linking and immunoprecipitation (PAR-CLIP) technique revealed several binding sites for cellular miRNAs, a subset of which were shown to be capable of mediating miRNA-mediated repression of gene expression. However, the main finding from this analysis is that HIV-1 transcripts are largely refractory to miRNA binding, most probably due to extensive viral RNA secondary structure. Together, these data demonstrate that HIV-1 neither encodes viral miRNAs nor strongly influences cellular miRNA expression, at least early after infection, and imply that HIV-1 transcripts have evolved to avoid inhibition by preexisting cellular miRNAs by adopting extensive RNA secondary structures that occlude most potential miRNA binding sites. IMPORTANCE: MicroRNAs (miRNAs) are a ubiquitous class of small regulatory RNAs that serve as posttranscriptional regulators of gene expression. Previous work has suggested that HIV-1 might subvert the function of the cellular miRNA machinery by expressing viral miRNAs or by dramatically altering the level of cellular miRNA expression. Using very sensitive approaches, we now demonstrate that neither of these ideas is in fact correct. Moreover, HIV-1 transcripts appear to largely avoid regulation by cellular miRNAs by adopting an extensive RNA secondary structure that occludes the ability of cellular miRNAs to interact with viral mRNAs. Together, these data suggest that HIV-1, rather than seeking to control miRNA function in infected cells, has instead evolved a mechanism to become largely invisible to cellular miRNA effector mechanisms.

- Neoplasia (New York, N.Y.), 2016

MicroRNA (miRNA) deregulation in prostate cancer (PCa) contributes to PCa initiation and metastatic progression. To comprehensively define the cancer-associated changes in miRNA targeting and function in commonly studied models of PCa, we performed photoactivatable ribonucleoside-enhanced cross-linking immunoprecipitation of the Argonaute protein in a panel of PCa cell lines modeling different stages of PCa progression. Using this comprehensive catalogue of miRNA targets, we analyzed miRNA targeting on known drivers of PCa and examined tissue-specific and stage-specific pathway targeting by miRNAs. We found that androgen receptor is the most frequently targeted PCa oncogene and that miR-148a targets the largest number of known PCa drivers. Globally, tissue-specific and stage-specific changes in miRNA targeting are driven by homeostatic response to active oncogenic pathways. Our findings indicate that, even in advanced PCa, the miRNA pool adapts to regulate continuing alterations in the cancer genome to balance oncogenic molecular changes. These findings are important because they are the first to globally characterize miRNA changes in PCa and demonstrate how the miRNA target spectrum responds to staged tumorigenesis.