>ATAD5|NM_024857|3'UTR
   1 TGTTCCATACTAACAATGCTTTGTATAGATTATCATGTGGTCCTTAAGATACATTTTTATATTATGTGGATCTTCATGGA
  81 AAAGTATATTTCTCGATGTACATTTTAAACAAACAATTTGTATATTTTTTTATTGGCGGGTAAATATTTAAAATATTTGA
 161 GTTACAAATTTTATATATGATTGTAATTTTTTTTCTGAATTTTTTGTATTATCTGATTTAGCTTTGTTGGAGTATTTTTT
 241 GTATGTGAGTGAACTGTTTCTGGAAGGTAGAGTTCATTAAGATGAACTCCCTATTTCAAGTGTTTATATTATATATTAGC
 321 TTAATATTCAGATACATTATCTTGGCTGCTAACATTAGTGTCACTAAAGTTGGTATACAATCTCCCACTGCTAAATTTGA
 401 CTGGCTTTACAAAAACAAAAACATTATCTGGTGAATTATATTTTTAACCTAAAAGTTAAGGATCCTCATATTGTACAGTT
 481 TTTTTTGTGTGCTTTTTTTTTTTTTTTTTGAGACGGAATCTTGCTCTGTCACCCAGGCTGGAGTGCAGTGGCCTGGTATC
 561 GGCTCAGTGCAACTTTTGCCTCCCGGGTTCAAGCGATTATCCTGCCTCAGCCTCCTGAATAGCTGGGATTACAGGCATGT
 641 GCCACCTTGCCCAGCTAATTTTTGTATTTTTAGAAGAGACAGGGTTTTACCATGTTGGTTAGGCTGGTCTCTTAACTCCT
 721 GACCTCAAGTGATCCATCTGCCTCGGCCTCCCAAAGTGCTGGGATCACAGGCGTGAGCCACCTCACCTGGCCTATATTGT
 801 ACAGTTTTGAACAGTATAGATGCATACCTGTTTACAAATGTGTATGAAGATAGATATTTTTACCTCTTATTTGTTCAATT
 881 TACTTTTTCTTGTATTAATTAGTATATTGATCTAATTAAAGGTTAAAGCTAAAGGCTTTATGAAATGTTTAAAAAAAAAA
 961 AAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' cgaggacAGGUUGACCGAg 5' |:::||||||| Target 5' tgctaaaTTTGACTGGCTt 3'	389 - 407	148.00	-13.90
2	miRNA 3' cgAGGACAGGUUGACCGag 5' | :| |:: |:|||| Target 5' taTATTTTTTTATTGGCgg 3'	121 - 139	113.00	-5.20
3	miRNA 3' cgAGGACAGGUUGACCgag 5' ||||| :|:|||| Target 5' ccTCCTGAATAGCTGGgat 3'	611 - 629	109.00	-19.32

Mutant ID	Mutant Position	Mutant Source
COSN15902783	7	COSMIC
COSN31566943	20	COSMIC
COSN30112510	29	COSMIC
COSN16610001	43	COSMIC
COSN32053457	49	COSMIC
COSN30514291	66	COSMIC
COSN16608711	73	COSMIC
COSN31559088	93	COSMIC
COSN30135929	100	COSMIC
COSN30193135	115	COSMIC
COSN31561587	139	COSMIC
COSN16610916	342	COSMIC
COSN22826332	359	COSMIC
COSN29978098	510	COSMIC
COSN15744400	756	COSMIC
COSN5178726	922	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs1248212142	3	dbSNP
rs763336294	8	dbSNP
rs1387240182	11	dbSNP
rs768957053	12	dbSNP
rs112211452	13	dbSNP
rs772902855	17	dbSNP
rs560274061	19	dbSNP
rs991756723	26	dbSNP
rs775060675	28	dbSNP
rs377485351	33	dbSNP
rs765984091	36	dbSNP
rs1419631860	44	dbSNP
rs963495322	69	dbSNP
rs1174299441	76	dbSNP
rs1477015144	81	dbSNP
rs1376538601	85	dbSNP
rs1178725164	87	dbSNP
rs189060490	95	dbSNP
rs549418951	96	dbSNP
rs995199514	98	dbSNP
rs1202147732	107	dbSNP
rs1279809102	121	dbSNP
rs1242984824	125	dbSNP
rs1221398036	138	dbSNP
rs141919642	138	dbSNP
rs534901072	139	dbSNP
rs1371352934	141	dbSNP
rs1010638763	142	dbSNP
rs912841521	145	dbSNP
rs1329610275	151	dbSNP
rs1475141962	155	dbSNP
rs1020348557	160	dbSNP
rs1168605671	165	dbSNP
rs547210815	166	dbSNP
rs1425653832	172	dbSNP
rs1335973549	174	dbSNP
rs571633693	182	dbSNP
rs1468218824	184	dbSNP
rs1335833828	187	dbSNP
rs1412623781	187	dbSNP
rs942851572	189	dbSNP
rs1441042373	195	dbSNP
rs1378073279	196	dbSNP
rs1177567593	197	dbSNP
rs1007173275	199	dbSNP
rs1040313938	201	dbSNP
rs1019444564	202	dbSNP
rs1212699322	206	dbSNP
rs1441179291	207	dbSNP
rs1280324822	209	dbSNP
rs1397356339	210	dbSNP
rs1356012229	211	dbSNP
rs1307207649	218	dbSNP
rs965262415	220	dbSNP
rs1325311978	225	dbSNP
rs377609474	236	dbSNP
rs1010177718	242	dbSNP
rs1271481607	243	dbSNP
rs893211536	245	dbSNP
rs1339769714	257	dbSNP
rs145771333	265	dbSNP
rs1320608729	276	dbSNP
rs1421464277	280	dbSNP
rs1358026933	300	dbSNP
rs1157533152	301	dbSNP
rs1405532368	302	dbSNP
rs1222298683	304	dbSNP
rs754610497	307	dbSNP
rs1183931615	320	dbSNP
rs1324737884	322	dbSNP
rs574765678	325	dbSNP
rs556908972	331	dbSNP
rs568926703	332	dbSNP
rs958822246	333	dbSNP
rs1446685668	334	dbSNP
rs1215222288	340	dbSNP
rs1322526533	344	dbSNP
rs536101881	347	dbSNP
rs1292377418	352	dbSNP
rs993369970	356	dbSNP
rs554570056	367	dbSNP
rs1035587397	375	dbSNP
rs1401724778	379	dbSNP
rs781057940	380	dbSNP
rs1297015489	381	dbSNP
rs573140055	383	dbSNP
rs181454640	388	dbSNP
rs558753318	398	dbSNP
rs1012747269	403	dbSNP
rs1404406922	417	dbSNP
rs1467419423	417	dbSNP
rs1156637024	420	dbSNP
rs1036593891	428	dbSNP
rs918239103	429	dbSNP
rs931007382	434	dbSNP
rs1479058567	439	dbSNP
rs1261710066	441	dbSNP
rs79708739	442	dbSNP
rs1382495085	443	dbSNP
rs578138109	451	dbSNP
rs544349630	452	dbSNP
rs889493538	453	dbSNP
rs963122856	475	dbSNP
rs545954595	477	dbSNP
rs1220290330	478	dbSNP
rs1391131142	479	dbSNP
rs36112780	479	dbSNP
rs1189098925	486	dbSNP
rs529713453	486	dbSNP
rs1332429505	487	dbSNP
rs987120115	487	dbSNP
rs78748189	488	dbSNP
rs201724926	490	dbSNP
rs1467577720	492	dbSNP
rs377727706	492	dbSNP
rs1427309167	493	dbSNP
rs1434261933	493	dbSNP
rs1475293552	493	dbSNP
rs71274248	493	dbSNP
rs1189569285	494	dbSNP
rs1313334395	510	dbSNP
rs1411308667	511	dbSNP
rs1259395622	512	dbSNP
rs1350221972	513	dbSNP
rs1205944753	515	dbSNP
rs1288180530	516	dbSNP
rs912810743	519	dbSNP
rs1232984887	524	dbSNP
rs1489886604	524	dbSNP
rs1377185820	525	dbSNP
rs1311077806	527	dbSNP
rs1444145212	541	dbSNP
rs1394704206	542	dbSNP
rs964679605	544	dbSNP
rs1269475475	549	dbSNP
rs1041253303	558	dbSNP
rs975572900	559	dbSNP
rs901030724	561	dbSNP
rs1010144933	562	dbSNP
rs1179325503	567	dbSNP
rs1453931120	570	dbSNP
rs1381135987	572	dbSNP
rs1180678171	573	dbSNP
rs1438068976	578	dbSNP
rs1249504783	585	dbSNP
rs1484179326	585	dbSNP
rs1363014899	586	dbSNP
rs1209516642	587	dbSNP
rs541855468	596	dbSNP
rs756593935	597	dbSNP
rs968705263	605	dbSNP
rs778113384	606	dbSNP
rs1306710304	609	dbSNP
rs199647562	610	dbSNP
rs7216902	613	dbSNP
rs186855300	617	dbSNP
rs1389042791	618	dbSNP
rs1305306827	620	dbSNP
rs1335606067	621	dbSNP
rs9906530	633	dbSNP
rs1311791591	634	dbSNP
rs1357596334	639	dbSNP
rs1221778718	640	dbSNP
rs1292606124	641	dbSNP
rs745956060	642	dbSNP
rs148033670	644	dbSNP
rs1451831885	650	dbSNP
rs1194070197	655	dbSNP
rs1187956914	665	dbSNP
rs571214174	667	dbSNP
rs141756724	677	dbSNP
rs1253839181	679	dbSNP
rs1187059386	681	dbSNP
rs190449701	684	dbSNP
rs972280341	686	dbSNP
rs761577471	699	dbSNP
rs1419501737	709	dbSNP
rs1180085646	712	dbSNP
rs1382957730	717	dbSNP
rs1443649503	723	dbSNP
rs1163598495	729	dbSNP
rs1351186161	735	dbSNP
rs899010070	736	dbSNP
rs1459888124	739	dbSNP
rs765038695	745	dbSNP
rs1245968593	746	dbSNP
rs920884103	751	dbSNP
rs1396188770	753	dbSNP
rs1414658712	757	dbSNP
rs1025999380	760	dbSNP
rs35396270	761	dbSNP
rs1447931038	763	dbSNP
rs574749073	767	dbSNP
rs568866410	773	dbSNP
rs536249461	774	dbSNP
rs984157857	776	dbSNP
rs1313523564	778	dbSNP
rs1017231629	782	dbSNP
rs1265297952	783	dbSNP
rs910909536	792	dbSNP
rs976089090	793	dbSNP
rs540248981	795	dbSNP
rs1211194290	798	dbSNP
rs914717090	800	dbSNP
rs182223162	818	dbSNP
rs969274313	823	dbSNP
rs977626501	824	dbSNP
rs924682920	828	dbSNP
rs1280719155	832	dbSNP
rs185964242	835	dbSNP
rs1325865920	839	dbSNP
rs566641095	842	dbSNP
rs915918342	848	dbSNP
rs945903907	852	dbSNP
rs1041578113	855	dbSNP
rs1466009733	865	dbSNP
rs1038007577	870	dbSNP
rs904406538	871	dbSNP
rs1475045110	874	dbSNP
rs1200396630	877	dbSNP
rs1452159327	879	dbSNP
rs1415304846	894	dbSNP
rs71372244	894	dbSNP
rs1213451901	900	dbSNP
rs534124616	906	dbSNP
rs560150743	907	dbSNP
rs1194547221	921	dbSNP
rs1047586126	923	dbSNP
rs890168295	924	dbSNP
rs1466613731	932	dbSNP
rs1235566732	936	dbSNP
rs1034738929	942	dbSNP
rs1369046006	943	dbSNP
rs1303025381	949	dbSNP
rs1008602370	951	dbSNP
rs1017200140	951	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.

- Genome biology, 2013

BACKGROUND: In recent years, a variety of small RNAs derived from other RNAs with well-known functions such as tRNAs and snoRNAs, have been identified. The functional relevance of these RNAs is largely unknown. To gain insight into the complexity of snoRNA processing and the functional relevance of snoRNA-derived small RNAs, we sequence long and short RNAs, small RNAs that co-precipitate with the Argonaute 2 protein and RNA fragments obtained in photoreactive nucleotide-enhanced crosslinking and immunoprecipitation (PAR-CLIP) of core snoRNA-associated proteins. RESULTS: Analysis of these data sets reveals that many loci in the human genome reproducibly give rise to C/D box-like snoRNAs, whose expression and evolutionary conservation are typically less pronounced relative to the snoRNAs that are currently cataloged. We further find that virtually all C/D box snoRNAs are specifically processed inside the regions of terminal complementarity, retaining in the mature form only 4-5 nucleotides upstream of the C box and 2-5 nucleotides downstream of the D box. Sequencing of the total and Argonaute 2-associated populations of small RNAs reveals that despite their cellular abundance, C/D box-derived small RNAs are not efficiently incorporated into the Ago2 protein. CONCLUSIONS: We conclude that the human genome encodes a large number of snoRNAs that are processed along the canonical pathway and expressed at relatively low levels. Generation of snoRNA-derived processing products with alternative, particularly miRNA-like, functions appears to be uncommon.

- 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.

- Genome research, 2016

DNA damage activates TP53-regulated surveillance mechanisms that are crucial in suppressing tumorigenesis. TP53 orchestrates these responses directly by transcriptionally modulating genes, including microRNAs (miRNAs), and by regulating miRNA biogenesis through interacting with the DROSHA complex. However, whether the association between miRNAs and AGO2 is regulated following DNA damage is not yet known. Here, we show that, following DNA damage, TP53 interacts with AGO2 to induce or reduce AGO2's association of a subset of miRNAs, including multiple let-7 family members. Furthermore, we show that specific mutations in TP53 decrease rather than increase the association of let-7 family miRNAs, reducing their activity without preventing TP53 from interacting with AGO2. This is consistent with the oncogenic properties of these mutants. Using AGO2 RIP-seq and PAR-CLIP-seq, we show that the DNA damage-induced increase in binding of let-7 family members to the RISC complex is functional. We unambiguously determine the global miRNA-mRNA interaction networks involved in the DNA damage response, validating them through the identification of miRNA-target chimeras formed by endogenous ligation reactions. We find that the target complementary region of the let-7 seed tends to have highly fixed positions and more variable ones. Additionally, we observe that miRNAs, whose cellular abundance or differential association with AGO2 is regulated by TP53, are involved in an intricate network of regulatory feedback and feedforward circuits. TP53-mediated regulation of AGO2-miRNA interaction represents a new mechanism of miRNA regulation in carcinogenesis.

- 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.