The utility of maximum standardized uptake values (SUVmax) of primary tumors has been evaluated to predict sentinel node (SN) and non-SN metastasis in clinically node-negative (cN0) patients.18F-FDG PET/CT was performed on 414 cN0 patients. The following parameters were evaluated: SUVmax at 60 min (SUVmax1), SUVmax at 120 min (SUVmax2), percent change between SUVmax1 and SUVmax2 (ΔSUVmax%), SN metastasis foci maximum size (SN meta size), and ratio of metastatic SNs to total SNs or SN ratio (SNR). It was assessed whether these were risk factors for SN metastasis. The relationship between these parameters and the status of SN and/or non-SN metastasis was retrospectively explored to predict non-SN metastasis.T factor (pT), nuclear grade, lymphatic invasion (Ly), and Ki-67 labeling index. On multivariate analysis, pT and Ly were independent predictive factors for SN metastasis. In SN meta-positive cases, SN meta size, SNR, and ΔSUVmax% were predictors for non-SN metastasis on univariate analyses, and the former two were independent predictors on multivariate analysis. The combination of SUVmax2 and ΔSUVmax% was an independent predictor of non-SN metastasis (P = 0.0312) and was associated with prediction of non-SN metastasis negative status with high probability (92.3%).All SUV parameters significantly correlated with pathological In patients with cN0 breast cancer, SUV parameters of the primary tumor were correlated with pathological features. The combination of SUVmax2 and ΔSUVmax% may be useful for predicting non-SN metastasis.The online version of this article (10.1245/s10434-020-08269-0) contains supplementary material, which is available to authorized users. From September 2005 to December 2017, 18F-FDG-PET/CT was performed for 820 consecutive preoperative patients who received histological diagnosis of primary breast carcinoma. Of these, 406 patients were excluded from the study because of (1) preoperative medication therapy (n = 123), (2) ductal carcinoma in situ (DCIS) (n = 23), (3) distant metastasis (n = 5), (4) SN not being identified by SNB (n = 20), (5) ALND without SNB (n = 180), (6) difficulty measuring SUVmax (n = 111), (7) acquisition of one time point with 18F-FDG PET/CT (n = 7), and/or (8) diabetes mellitus (n = 47). These eight factors frequently overlapped. There were four cases of SN metastasis negative and non-SN metastasis positive, but these four had received preoperative medication therapy and were excluded from the study. For the 123 patients who received preoperative medication therapy, the medication was only aromatase inhibitors (AI) in 13 patients (10.6%), AI followed by tamoxifen in 1 patient (0.8%), only chemotherapy in 84 patients (68.3%), chemotherapy followed by AI in 9 patients (7.3%), chemotherapy combined antihuman epidermal growth factor receptor 2 (HER2) therapy in 14 patients (11.4%), and chemotherapy combined antiHER2 therapy followed by AI in 2 patients (1.6%). Ultimately, 414 cN0 patients were eligible for the study.This study was approved by the institutional review board of the National Defense Medical College. Informed consents were obtained from all patients with regard to Additionally, 56 cN0 patients with SN macrometastasis and ALND were eligible Fig. . ALNDs w18F-FDG PET/CT. When the axillary node status was equivocal in a patient, fine needle aspiration cytology was performed, and the case was judged cN0 if cytological examination was negative. In all these cases, the histological diagnosis of breast cancer was made by core needle biopsy before surgery. After these examinations, 18F-FDG PET/CT was performed prior to surgery, and the interval between core needle biopsy and surgery was 42 days on average.Altogether, the 414 patients had no clinical evidence of ALN metastasis by physical examination and image findings, e.g., mammography, ultrasound examination, and 18F-FDG PET/CT scans  at the Tokorozawa PET Diagnostic Imaging Clinic . Patients fasted for at least 6 h before the examination. The first scan was performed 1 h after intravenous administration of 3.7 Mbq/kg 18F-FDG. The first scan was of whole-body images from head to thigh, and the second scan was chest only within 50–60 min after first examination.All patients received 18F-FDG uptake. The SUV was calculated using decay-corrected tissue activity divided by the injected dose per patient body as represented by this formulaAfter image reconstruction, regions of interest (ROI) were placed in one area of the primary breast lesion showing the highest The SUVmax1 and SUVmax2 were obtained at dual time points: the SUVmax at the early (60 min) and delayed (120 min) phase, respectively. The ΔSUVmax% was calculated using the formulaFrom September 2005 to March 2008, SNs were in principle identified using radioactive tin colloid alone . After April 2008, SNs were in principle identified using both radioactive tin colloid and blue dye in all cases . In the former era, positive rates of metastasis were 17.1% (14/82) on the patient basis and 9.8% (16/164 nodes) on the SN basis. In the latter era, positive rates of metastasis were 21.1% (70/332) on the patient basis and 15.2% (89/586 nodes) on the SN basis. For intraoperative frozen section diagnosis, each SN was sliced into 2-mm-thick pieces, cut into 5–10-µm-thick sections, fixed with formalin for a short time, and stained with hematoxylin and eosin. Tumor macrometastasis, micrometastasis, and isolated tumor cells were defined in accordance with the Union for International Cancer Control (UICC) eighth edition. For the tumor deposit size, the diameter of the largest metastatic deposit in the frozen or paraffin-embedded permanent section  was measured. SNR was defined as numerical ratio of metastasis-positive SNs (macro- and micrometastasis) to all resected SNs.T (pT) and N (pN) factors and stage were determined by the clinical and pathological recording of breast cancer by UICC eighth edition.Two observers (H.T. and Y.Y.) performed pathological diagnosis. Pathological tumor size was defined as the largest diameter of a tumor including both invasive and non-invasive components, and pathological invasive tumor size was defined as the largest diameter of the invasive component of a tumor. NG was given according to the General Rules for Clinical and Pathological Recording of Breast Cancer, seventeenth edition.http://www.mskcc.org/nomogram).The MSKCC nomograms were available on the MSKCC web site . The correlations between SUVmax parameters  and clinicopathological factors were evaluated using the nonparametric Wilcoxon test and the Kruskal–Wallis test. Receiver operating characteristic (ROC) curves were drawn to find the optimal cutoff value of SUVmax parameters for the prediction of SN. ROC curves were also drawn to find the optimal cutoff values of SUVmax parameters, the number of SN metastasis, SNR, and SN meta size for the prediction of non-SN metastasis. The Youden index [= sensitivity − (1 − specificity) of each cutoff value] was calculated, and the highest value was taken as the optimal cutoff point. All statistical analyses were two-sided with significance defined as a P value of < 0.05.Statistical analyses were performed using JMPP < 0.0001, R2 = 0.968). However, there were weak correlations between SUVmax1 and ΔSUVmax% , and between SUVmax2 and ΔSUVmax% .From the 414 patients, age, cT, pathological tumor size, pathological invasive tumor size, pT, hormonal receptor status, HER2 status, Ki-67 LI, subtype, NG, Ly, histological type, pN, pStage, and SUV parameters  were acquired , 1 in 67 (16.2%), 2 in 14 (3.4%), 3 in 2 (0.5%), and 4 in 1 (0.2%). The number of SNs removed by SNB was 1 in 198 (47.8%), 2 in 137 (33.1%), 3 in 51 (12.3%), 4 in 21 (5.1%), and 5 or more in 7 (1.7%). The SNR was 0 in 330 (79.7%), 0.13 in 1 (0.3%), 0.25 in 5 (1.2%), 0.33 in 9 (2.2%), 0.5 in 19 (4.6%), 0.67 in 8 (1.9%), and 1 in 42 (10.1%). Axillary lymph node status was classified as no metastasis in 325 (78.5%), isolated tumor cells in 5 (1.2%), micrometastasis in 21 (5.1%), and macrometastasis in 63 individuals (15.2%).P = 0.0103), the mean pathological tumor size (P = 0.0017), the mean pathological invasive tumor size (P < 0.0001), pT (P < 0.0001), ER (P = 0.0070), PgR (P = 0.0031), and Ly (P < 0.0001). With regard to HER2, Ki-67 LI, and NG, there were no significant differences between the two groups.The number of patients with SN metastasis, including macrometastasis and micrometastasis, was 84 20.3%) Fig. . All pat0.3% Fig.The optimal cutoff values of SUVmax1, SUVmax2, and ΔSUVmax% for the prediction of SN metastasis were 3.4 , 3.0 , and 2.5 , respectively.By univariate and multivariate logistic analyses in comparing pre- and postoperative factors, the odds ratios for SN metastasis were found to be significantly higher in the cT3 group than in the cT1/2 group, higher in the pT3 group than in the pT1/2 group, higher in the ER-positive group than in the ER-negative group, and higher in Ly-positive group than in Ly-negative group , and group D was non-SN metastasis negative .Among 63 patients with SN macrometastasis, the 56 patients who received ALND were eligible Fig. . These pThe optimal cutoff values of number of SN metastases, SNR, and SN meta size for the prediction of non-SN metastasis were 2.0 , 0.67 , and 6.0 mm , respectively. Similarly, the optimal cutoff values of SUVmax1, SUVmax2, and ΔSUVmax% of the primary site for the prediction of non-SN metastasis were 7.6 , 3.0 , and 20.0 , respectively.P = 0.0106), mean SN meta size , SN meta size , SNR , and ΔSUVmax% . Although there was no significant difference in SUVmax1 and SUVmax2 between these two groups, they tended to be higher in group C than in group D.The clinicopathological factors of these groups (group C and D) are presented in Table P = 0.0586).On univariate analyses, SN meta size, SNR, and ΔSUVmax% were statistically significant factors for the prediction of non-SN metastasis Table . On multP = 0.0470, 0.0312, respectively) (Table There were 13 patients with low SUVmax2 (< 3.0) and low ΔSUVmax% (< 20.0). Of these, 12 were patients without non-SN metastasis (92.3%) Table . The senP < 0.0001) and between group C and D (P = 0.0296). The distributions of the probabilities of SN metastasis and non-SN metastasis are presented in Supplementary Fig. 1.According to MSKCC nomograms, the median probabilities of SN metastasis were 58.0% (11.0–98.0%) and 34.0% (0.0–93.0%) in groups A and B, respectively, and the median probabilities of non-SN metastasis were 28.0% (11.0–68.0%) and 20.0% (9.0–77.0%) in groups C and D, respectively. There were significant differences between group A and B  by using a combination of SUVmax2 and ΔSUVmax% values.Several clinicopathological factors have been described as predictors of SN metastasis in breast cancer.In the present study, pT, ER, and Ly were independent predictors of SN metastasis, and tumor invasion size was significantly different between non-SN-metastasis-positive and non-SN-metastasis-negative groups. ER-positive cN0 cases showed significantly higher odds ratio of SN metastasis than ER-negative cN0 cases. Although this result appeared paradoxical, it is in agreement with findings of other large-scale studies.1518F-FDG PET/CT was performed using the DTP method, and it was confirmed that the combination of SUVmax2 and ΔSUVmax% was useful to predict non-SN metastasis using preoperative features. This combination was also superior in sensitivity (94.7%) and NPV (92.3%) to SUVmax1, SUVmax2, and ΔSUVmax% alone, and to the combination of SUVmax1 and ΔSUVmax%. These results might support the idea that the combination of SUVmax and ΔSUVmax% represents a more biological characteristic of the tumor.Given the findings of ACOSOG Z0011 and AMAROS trials, in cN0 and SN-positive patients, axillary radiotherapy could be chosen instead of ALND if further axillary treatment is needed.PET/CT scans are not performed routinely, and the SUVmax parameters are not introduced clinically for many patients. If SUVmax parameters are shown to be excellent for prediction of SN and/or non-SN metastasis and their utility is widely accepted, this method may be included as one preoperative diagnostic tool in the future.18F-FDG PET/CT is higher than the total cost of whole-body examinations, including MRI, bone scintigraphy, and abdominal ultrasonography, but 18F-FDG PET/CT appears to have superiority in that the diagnosis of both local and systemic status of a disease is possible in only 3 h. Therefore, the PET/CT was considered to be more convenient to the patients than the combination of other whole-body examinations.The cost of Limitations of this study include its retrospective nature and that it was conducted in a single facility with a relatively small number of patients. Another prospective multicenter trial is needed to confirm the effectiveness of SUVmax and ΔSUVmax% in the prediction of SN and non-SN metastasis.In conclusion, SUVmax of the primary tumor was a predictive factor of SN and/or non-SN metastasis in patients with cN0 breast cancer. Furthermore, it was possible to estimate non-SN metastasis negativity with a high probability by combining SUVmax2 and ΔSUVmax%. From these results arises the possibility of minimizing unnecessary ALND.P < 0.0001) and between group C and D (P = 0.0296) (TIFF 547 kb)Distribution of probabilities of (A) SN metastasis and (B) non-SN metastasis, according to the MSKCC nomograms. (A) Median probabilities of SN metastasis 58.0% (11.0–98.0%) and 34.0% (0.0–93.0%) in groups A and B, respectively. (B) Median probabilities of non-SN metastasis 28.0% (11.0–68.0%) and 20.0% (9.0–77.0%) in groups C and D, respectively. Significant differences between group A and B (Below is the link to the electronic supplementary material.


A member of the newly discovered RNA family, circular RNA (circRNA) is considered as the intermediate product of by-product splicing or abnormal RNA splicing. With the development of RNA sequencing, circRNA has recently drawn research interest. CircRNA exhibits stability, species conservatism, and tissue cell specificity. It acts as a miRNA sponge in the circRNA-microRNA (miRNA-mRNA axis, which can regulate gene transcription and protein translation. Studies have confirmed that circRNA is ubiquitous in eukaryotic cells, which play an important role in the regulation of human gene expression and participate in the occurrence and development of various human diseases. CircRNA may be closely related to the occurrence and development of female reproductive system diseases. By analyzing the biological functions and mechanism of circRNA, we find that circRNA has certain development prospects as biomarkers of the female reproductive system diseases. The production and degradation of circRNA, biological functions, and their association with the occurrence of diseases of female reproductive system are reviewed in this article. Non-coding RNA comprises 95% of the total amount of RNA . Most noIn this article, we summarized the origin, synthesis, and degradation of circRNAs in known mammalian cells. We also studied the function of circRNAs and discussed the expression and mechanism of circRNAs in female reproductive system diseases.Nature in 2013. Subsequent research results revealed the considerable potential of circRNAs in biological development [CircRNAs were first identified in 1976 in an electron microscopy-based study of RNA viruses (such as the hepatitis D virus [HDV]) . HDV waselopment ,w,hich melopment –19. Circelopment , or bothelopment . Howeverelopment . Among telopment . Unlike elopment , 25.Accumulating evidence shows that circRNA is generated during mRNA processing before splicing . The exoThe degradation pathway of circRNAs remains inconclusive. A recent study found that in mammalian cells, circRNAs can be cleared by the release of extracellular vesicles or microbubbles . HoweverRBPs participate in cell processes  . Stable The interaction of circRNAs (mainly the Sry circRNA and CIRS-7) as miRNA sponges with miRNAs has increasingly attracted research interest. As an endogenous RNA , circRNAAlmost all circRNAs distributed in the cytoplasm are produced by exons , 32. CiREscherichia coli, transfection of circRNAs carrying the green fluorescent protein open reading frame can activate the expression of the green fluorescent protein. Only viral circRNAs have thus far been found to encode proteins in eukaryotic cells [The majority of circRNAs produced by reverse splicing are mainly located in the cytoplasm , 15, andic cells . The HBVic cells . The traic cells . Legniniic cells . Moreoveic cells .A large portion of the circRNAs we have identified is thus far derived from exons. Therefore, the formation of this circRNA may affect the selective splicing of related precursor mRNA (pre-mRNA), which may lead to changes in gene expression Fig. e. AlthouCircRNA expression in preimplantation embryos has thus far been reported in mice and humans. A total of 2891 circRNAs and 913 new transcripts have been found in mouse preimplantation embryos. Most circRNAs are distinct from the preimplantation stage, and a large proportion of has shown a dynamic expression pattern during this developmental process . CircRNAGranulosa cells (GCs) play an important role in oocyte maturation and early embryo development , 76. SomEndometriosis (EMT) is a challenging disease characterized by dysmenorrhea and infertility. However, its etiology has yet to be clarified, and no effective markers or treatments exist. It affects the reproductive outcomes for women of childbearing age and exhibits the malignant characteristics of adhesion, invasion, and angiogenesis , 82. Xu Approximately 15% of women in their reproductive years worldwide are affected by infertility , 89. NumRSA is a commonly occurring disease in women worldwide. Qian et al. chose 35 pairs of patients with RSA and healthy controls. The abnormal expression of circRNA in 3 villi tissues was analyzed using a circRNA chip. A total of 594 abnormally expressed circRNAs were detected, 335 of which were upregulated. The downregulation of 259 circRNAs in the microarray showed numerous circRNA miR-133a binding sites . Wang etAs a member of the newly discovered RNA family, CircRNA has shown its distinct advantages and has drawn research interest in RNA research because of its stability, high specificity, and conservative sequence. However, research on circRNA has yet to be developed. Various types of circRNA have been identified; however, few circRNA biological functions have been determined. Most mechanisms are still in the research stage. The biological functions of RNA remain largely unknown. The potential functions of circRNA-as a miRNA sponge, as RNA binding protein sponge, and rolling circle translation-have added a regulatory network to cell function. The coding ability and stability of circRNAs, which can be used in biotechnology requiring peptide production with the steady development of RNA technology, the circRNA field is anticipated to considerably develop in the next few years. The location, transportation, and degradation in the living cells of circRNAs, as well as the complete circRNA interactive body are expected to be elucidated. The association of circRNA with ovarian endometriosis, repeated reproductive failure and recurrent abortion, has been revealed gradually. However, the whole remains in the initial stages of research. Only several circRNA families participate in the development of diseases related to the female reproductive system. More in-depth studies need to be conducted to confirm the role of circRNA in the pathogenesis of female reproductive diseases and present innovations in the diagnosis and treatment of diseases related to the female reproductive system. CircRNAs can be secreted into the extracellular environment and detected in blood, urine, tissues, and secretions by non-invasive means. This finding indicates that a specific circRNA may be used as a biomarker and a disease prognostic indicator. Researchers can present a more detailed and in-depth study of the known function of circRNAs, as well as provide new evidence and direction for clinical diagnosis, treatment, and prognosis prediction by discovering the molecular mechanisms of circRNAs.


RNA editing is a posttranscriptional nucleotide modification in humans. Of the various types of RNA editing, the adenosine to inosine substitution is the most widespread in higher eukaryotes, which is mediated by the ADAR family enzymes. Inosine is recognized by the biological machinery as guanosine; therefore, editing could have substantial functional effects throughout the genome. RNA editing could contribute to cancer either by exclusive editing of tumor suppressor/promoting genes or by introducing transcriptomic diversity to promote cancer progression. Here, we provided a comprehensive overview of the RNA editing sites in gastric adenocarcinoma and highlighted some of their possible contributions to gastric cancer. RNA-seq data corresponding to 8 gastric adenocarcinoma and their paired nontumor counterparts were retrieved from the GEO database. After preprocessing and variant calling steps, a stringent filtering pipeline was employed to distinguish potential RNA editing sites from SNPs. The identified potential editing sites were annotated and compared with those in the DARNED database. Totally, 12362 high-confidence adenosine to inosine RNA editing sites were detected across all samples. Of these, 12105 and 257 were known and novel editing events, respectively. These editing sites were unevenly distributed across genomic regions, and nearly half of them were located in 3′UTR. Our results revealed that 4868 editing sites were common in both normal and cancer tissues. From the remaining sites, 3985 and 3509 were exclusive to normal and cancer tissues, respectively. Further analysis revealed a significant number of differentially edited events among these sites, which were located in protein coding genes and microRNAs. Given the distinct pattern of RNA editing in gastric adenocarcinoma and adjacent normal tissue, edited sites have the potential to serve as the diagnostic biomarkers and therapeutic targets in gastric cancer. RNA editing is a common and essential posttranscriptional alteration of RNA sequences, affecting millions of bases, expanding the transcriptome diversity and the functions of RNA transcripts . AlthougRNA editing diversifies the transcriptome when editing located in coding mRNA sequences; at the same time, editing in the noncoding sequence could have a fundamental consequence. 3′UTRs usually comprise key elements and are involved in numerous regulatory processes. Editing in these elements can modulate the regulation of mRNA expression . Indeed,RNA editing is critical for growth and development in mice and humans. Hence, knockout mice for either ADAR or ADARB1 genes die early in development. Interestingly, the exonic substitution of an underedited transcript of the AMPA receptor gene with the edited version could rescue ADARB1-knockout mice , 9.ADAR gene mutations are associated with several human diseases. Aicardi-Goutieres syndrome   and dyscADAR proteins bind to a specific dsRNA structure formed either intramolecularly or intermolecularly; thus, ADAR particularly edits A to I on RNAs that adopt this double-strand structure . There aThe advent of next-generation sequencing (NGS) has greatly improved the genome-wide identification of RNA editing sites through RNA sequencing (RNA-seq) technologies, and so far several million high-confidence editing sites have been recognized in the human genome . IdentifRNA editing has been studied in gastric cancers ; neverthRaw paired-end RNA-seq samples related to eight primary gastric adenocarcinoma and their paired nontumor counterparts were retrieved from the publicly available Gene Expression Omnibus (GEO) database (accession number GSE85465). Nontumor counterparts refer to samples harvested from the stomach, from sites distant from the tumor and exhibiting no visible evidence of tumor or intestinal metaplasia/dysplasia upon surgical assessment. The original data and sample details are described by Ooi et al. . RNA-seqhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was employed to control the raw read quality [First, FastQC v0.11.5 , PCR-induced duplicate reads mapped to the same location were excluded, except those with the highest mapping quality score. To improve the quality of reads and the alignment of the indel flanking regions, the remaining reads were locally realigned around putative indels and the base quality values were recalibrated by using the GATK tool v3.5 (https://www.broadinstitute.org/gatk).Using the MarkDuplicates tool from the Picard package  were called with stand call_onf and stand_emit_conf values of 30 and mbq of 25 . Next, tAdditionally, several quality-aware filtering steps were employed to increase the accuracy of identifying true RNA editing sites. First, the sites with more than one nonreference type and homozygous sites for the alternative allele were filtered. Second, the sites with fewer than three RDD supporting reads were discarded, and the sites with at least 10-read coverage were kept for further analysis. The RDD sites with an extreme or a rare degree of variation (the threshold for the editing ratio was between 10% and 90%) were removed under the assumption that 100% editing efficiency is unrealistic. Third, RDDs located in regions with bidirectional transcription (transcription that occurs on both the positive and negative strands) were also filtered. Fourth, the GMATo software was used for the detection of simple sequence repeat (SSR) patterns. RDDs located in SSR regions were considered biased with an offset of ±3 bases , and thoIn order to predict the conservation of the editing sites, neighborhood nucleotides, 10 bp upstream and 10 bp downstream of the edited sites, were extracted. Then, the WebLogo software was employed to generate a consensus sequence logo and investigate the sequence context flanking the identified potential editing sites .ftp://ftp.ncbi.nih.gov/repository/UniGene/) to investigate the presence of the editing events identified by our pipeline in these sequences. Using BLAST, 50 bp upstream and downstream flanking regions of editing sites were extracted and queried against the human EST sequences. Alignments with e-values < 10−5 were considered significant and counted. Since most of A to I RNA editing occurs in Alu repeats [http://genome.ucsc.edu/) and their distribution patterns across the genome were compared with the patterns of identified A to I editing sites.To validate detected RNA editing sites, we applied the publicly available human expressed sequence tags (ESTs) . Nucleic acid quality and quantity were verified by using 1% agarose gels and using the NanoDrop spectrophotometer . RNA samples were reverse transcribed into cDNA using the Easy™ cDNA Synthesis Kit . Amplification was performed by using an XP thermal cycler . Cycling conditions were as follows: 94°C for 5 min, 10 cycles of 98°C for 10 s, 67°C for 10 s with a decrement of 1°C every cycle, and 72°C for 35 s, and then 23 cycles of 98°C for 10 s, 57°C for 10 s, and 72°C for 35 s. The primers were designed to amplify products of an appropriate size for gDNA and cDNA sequencing. The identified editing sites were considered confirmed if the cDNA sequence was heterozygous while the corresponding DNA sequencing was homozygous. The ImageJ software was used for quantification of sequencing data .In order to predict microRNAs whose binding is affected by RNA editing, we downloaded the predicted microRNA binding data of highly conserved miRNA families from the miRcode database . The intP value was <0.05.Statistical significance for differences between cancer and normal tissue editing ratios was assessed by the Wilcoxon rank test. Spearman's correlation coefficient was used to determine the relationship between chromosome length, number of Alu elements, number of protein coding genes, and number of editing sites. Differences were considered significant when the adjusted High-throughput RNA-seq technology has facilitated the discovery of transcriptome-wide RNA editing events across individuals and tissues at unprecedented throughput and resolution. However, the main obstacle in identifying bona fide RNA editing sites using RNA-seq data is the distinction of RNA editing sites from rare SNPs and technical artifacts caused by sequencing or read mapping error. To accurately detect the RNA editing sites at the transcriptome-wide level in gastric cancer, we developed a computational approach by using a precise strategy see . This stADAR enzyme targets dsRNA of any sequence, but it has a sequence preference in the vicinity of the editing sites. Consistent with the known attributes of ADAR substrates, our results showed that the nucleotide immediately upstream (relative -1 location) of the edited site had a strong preference for G depletion and T enrichment, while the nucleotide immediately downstream (relative +1 location) of the editing site showed significantly depleted T and favored G see .The location of identified editing sites was compared with the position of Alu elements across the genome. Interestingly, distribution of A to G editing sites and Alu elements was very similar across the genome. This is more obvious when we look closely at chromosomes 1, 9, 16, and 19, where ends of these chromosomes are rich in Alu repeats but the middle of chromosomes are relatively vacant see . Next, tThe location of editing sites was annotated according to the Ensembl database. As shown in RNA editing sites often appear in clusters, due to simultaneous editing of multiple adenosines by ADAR proteins. In this respect, we investigated whether the identified editing sites were in clusters or not. We found that 34% of genes were edited in more than five sites. Furthermore, the frequency of editing sites (number of edited sites located in the gene) was calculated to evaluate clustering of editing sites. Our results showed an editing rate of 5.1, indicating on average five editing sites per gene. Interestingly, editing rates were different when genomic regions were considered separately. The editing rate in 3′UTRs was predominant, 7.1 editing sites per gene, and exons with 1.4 editing sites per gene exhibited the least editing rate. The editing rate in upstream and downstream regions, which included a large number of editing sites, was 3.5 and 3.8 sites per gene, respectively. The frequency of the number of editing sites across genomic regions is shown in The average RNA editing level across all sites was 30.72, which means that approximately 31% of each gene transcript was edited in a given site. In the present study, editing level for most of the identified editing sites ranged from 15 to 25. The frequency distribution of RNA editing levels is shown in r = 0.47, P = 0.02). As shown in r = 0.6, P < 0.002). In addition, the correlation of editing with both the number of Alu elements and the number of protein coding genes were calculated. Surprisingly, we found that correlation of editing with the number of protein coding genes was stronger than the number of Alu elements, where Spearman's correlation coefficient was 0.91 and 0.85, respectively , including 12 novel editing sites. Interestingly, MUC4, an epithelial glycoprotein coding gene, was edited in two positions (3:195780295 and 3:195780902), which caused p.L3762P and p.S2560P, respectively . MicroRNThe identification of RNA editing sites deeply depends on sequencing technology and bioinformatics approaches. We developed a pipeline for identifying RNA editing events in primary gastric cancer and normal tissues by screening RNA differences from the reference genome followed by successive and rigorous filtering criteria. Some of the previous studies have applied coupled RNA and DNA sequences to identify editing events , 54; howOur analyses found that the RNA editing sites were highly associated with both the number of protein coding genes and Alu element distribution in the genome. The frequency of editing sites was correlated with the size of chromosomes. These results are in good agreement with Chigaev et al.'s study who reported that correlation of editing frequency with protein coding genes was stronger than lincRNA density . HoweverTo date, no specific sequence has been found that characterizes editing sites of any of the ADAR enzymes. However, in the neighborhood of edited adenosine, there are preferred and opposed preferences. Consistent with previous studies, there was an overrepresentation of guanosine in the downstream position of the editing site, while guanosine was depleted in the upstream neighboring position , 55. SinWe wonder whether RNA editing could function as an additional mechanism contributing to tumorigenesis by generating specific RNA editing sites that were unique to cancer samples. In search of the answer to this question, we found that 28.4% and 32.2% of the identified editing sites were specific to cancer and normal tissues, respectively. These tissue-specific editing sites could contribute to cancer initiation and progression, if they are located in an important gene. Some of cancer-specific editing sites and their role in the pathogenesis of cancer have been identified in previous studies. RNA editing of the transcription factor PROX1, a candidate tumor suppressor, leads to several missense substitutions including E328G, R334G, and H536R and loses tumor suppressive functions. These editing events have been seen in a number of esophageal, pancreatic, and colon cancer samples, but not in cDNA libraries of many normal tissues .We also found a remarkable number of common editing events between cancer and normal tissues, in which their editing levels were significantly different. Deregulated editing level in cancer and normal common editing sites could be an important contributor in tumorigenesis. Chen et al. reported that RNA editing level of AZIN1 was increased by at least 10% in hepatocellular carcinoma compared to the adjacent normal liver. The edited isoform compared with wild-type AZIN1 has increased affinity to antizyme, which leads to neutralization of antizyme-mediated degradation of ornithine decarboxylase and cyclin D1 and promotes cell proliferation . In thisOur results showed that the vast majority of editing sites in gastric cancer were located in 3′UTR and up/downstream regions as well as in coding regions. According to their genomic location, these RNA editing events could lead to various functional impacts and apply their effects through several dominant mechanisms. First and most important, RNA editing events in the exonic region can cause amino acid change and imitate cancer-associated missense mutations. Our pipeline identified 81 editing events with nonsynonymous effect, including 12 novel editing events. Notably, we found four missense RNA mutations in the mucin family . Normal gastric epithelial cells transcribe MUCs, which have several functions including protection against mechanical and infectious lesions, lubrication, and acid resistance . SeveralSecond, RNA editing could affect microRNA target recognition and subsequently affect the expression profile of the genes. Dysregulation of microRNA target recognition has been linked to cancers , 64. In Third, the editing of microRNA sequences could alter their binding affinity or target recognition properties. Since microRNAs play a role in nearly all cellular pathways and pathological processes, including cancer initiation and progression, fluctuations of their targeting are an important contributor to cancer . Our anaWe consider a candidate gene with several editing sites and experimentally validated some of these sites. However, it should be noted that validating a small number of editing sites in 42 clinical samples is a limitation of the study which warrants further investigation and our validation strategy was biased towards a specific gene.Findings of the current study uncovered a relatively large number of RNA editing sites, which were unevenly distributed across the genome. Editing level of these sites and editing rate of different genes had diverse distribution. We also found a significant number of exclusively edited genes in cancer and normal tissues, which are likely to contribute to cancer initiation and progression.Gastric cancer initiation and progression are driven by the cumulative effects of genetic and epigenetic alterations; RNA editing, a widespread posttranscriptional mechanism, could be part of these alterations. Depending on genomic location and level of editing, this phenomenon could lead to missense mutations, affecting microRNA biosynthesis and targeting, changing splicing patterns, and modifying microRNA target sites. The editome of gastric cancer vastly differs from that of adjacent tissue in terms of both type and number of editing sites. Given the distinct pattern of RNA editing between gastric cancer and normal tissues, edited sites have the potential to serve as biomarkers and therapeutic targets in gastric cancer.


Here we introduce a new data set of RNA elements in the human genome that are recognized by RNA-binding proteins (RBPs), generated as part of the Encyclopedia of DNA Elements (ENCODE) project phase III. This class of regulatory elements functions only when transcribed into RNA, as they serve as the binding sites for RBPs that control post-transcriptional processes such as splicing, cleavage and polyadenylation, and the editing, localization, stability and translation of mRNAs. We describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. Integrative analyses using five assays identify RBP binding sites on RNA and chromatin in vivo, the in vitro binding preferences of RBPs, the function of RBP binding sites and the subcellular localization of RBPs, producing 1,223 replicated data sets for 356 RBPs. We describe the spectrum of RBP binding throughout the transcriptome and the connections between these interactions and various aspects of RNA biology, including RNA stability, splicing regulation and RNA localization. These data expand the catalogue of functional elements encoded in the human genome by the addition of a large set of elements that function at the RNA level by interacting with RBPs.Many proteins regulate the expression of genes by binding to specific regions encoded in the genome A combination of five assays is used to produce a catalogue of RNA elements to which RNA-binding proteins bind in human cells. They interact with RNA to form ribonucleoprotein complexes, which govern the maturation and fate of their target RNA substrates and regulate numerous aspects of gene expression, including pre-mRNA splicing, cleavage and polyadenylation, RNA stability, RNA localization, RNA editing, and translation. Many RBPs participate in more than one of these processes, such as regulation of both alternative splicing and poly(A) site usage by NOVA2. These roles are essential for normal human physiology, as defects in RBP function are associated with genetic and somatic disorders, such as neurodegeneration, autoimmunity and cancer3. The regulatory roles of RBPs are also affected by the subcellular localization of RBPs and their RNA substrates, as post-transcriptional steps are often carried out in both membrane- and phase-separated sub-cellular compartments.RBPs are a diverse class of proteins that are involved in regulating gene expression4. However, recent mass spectrometry-based methods have identified hundreds of proteins bound to RNA in human and mouse cells8, suggesting that the human genome may contain 1,542 or more RBP-encoding genes1. This large repertoire of RBPs is likely to underlie the tremendous complexity of post-transcriptional regulation, motivating efforts to systematically investigate the binding properties, RNA targets, and functional roles of these proteins.Traditionally, RBPs were identified by the affinity purification of single proteins• We report, to our knowledge, the largest effort to date to systematically map and study the functions of 356 human RBPs using integrative approaches consisting of 5 assays that focus on different aspects of RBP activity.• We examine in vivo binding activity using enhanced CLIP (eCLIP) assays. Two hundred and twenty-three eCLIP data sets for 150 RBPs provide a set of candidate functional elements directly bound by each RBP and show a variety of in vivo RNA target classes.• We infer the functions of the RNA elements identified by eCLIP through analyses of 472 knockdown followed by RNA sequencing (KD–RNA-seq) profiles of 263 RBPs, identifying RNA expression and splicing regulatory patterns.• We decipher the in vitro binding specificities of 78 RBPs using RNA Bind-N-Seq assays and identify connections between in vitro and in vivo binding. We find that eCLIP peaks containing in vitro motifs are more strongly associated with regulation.• We map the subcellular localization of 274 RBPs using immunofluorescence, indicating widespread organelle-specific regulation of RNA processing.• We profile the DNA association patterns of 39 RBPs by chromatin IP and sequencing (ChIP–seq), suggesting that there is broad interconnectivity between chromatin association and RNA processing.The elucidation of RBP–RNA regulatory networks requires the integration of multiple data types, each viewing the RBP through a different lens. In vivo binding assays such as crosslinking and immunoprecipitation (IP) followed by sequencing (CLIP-seq) provide a set of candidate functional elements that are directly bound by each RBP. Assessments of in vitro binding affinity uncover the mechanisms that drive these interactions and improve the identification of functional associations. Functional assays that identify targets whose expression or alternative splicing responds to RBP perturbation can strengthen evidence of function. For example, the observation by CLIP-seq of protein binding within introns flanking exons whose splicing is sensitive to RBP levels provides support for the RBP as a splicing factor and for the binding sites as splicing regulatory elements. In vivo interactions of RBPs with chromatin can also be assayed to provide insight into the roles of some RBPs as transcriptional regulators and to provide evidence for co-transcriptional deposition of RBPs on target RNA substrates. Thus, integration of these data types can identify both factor-specific regulatory modules and the roles of RBPs in broader cellular regulatory networks.To work towards developing a comprehensive understanding of the binding and function of the human RBP repertoire, we used five assays to produce 1,223 replicated data sets for 356 RBPs that participate in diverse aspects of RNA biology and encompass diverse sequence and structural characteristics 10. We generated high-quality eCLIP profiles for 120 RBPs in K562 cells and 103 in HepG2 cells  or CRISPR to deplete individual RBPs followed by RNA sequencing (RNA-seq). We depleted 235 RBPs in K562 cells and 237 RBPs in HepG2 cells  (k = 5) that could be clustered into a single motif. The remaining RBPs had more complex patterns of binding, best described by two motifs (32 of 78), or even three or more motifs (9 RBPs). These data also indicate that many RBPs are sensitive to the sequence and RNA structural context in which motifs are embedded.We used RNA Bind-N-Seq (RBNS)9 to conduct systematic immunofluorescence imaging of 274 RBPs in HepG2 cells and 268 RBPs in HeLa cells, in conjunction with 12 markers for specific organelles and subcellular structures (http://rnabiology.ircm.qc.ca/RBPImage/).To illuminate the functional properties of RBPs in intracellular space, we used our validated antibodiesructures . These d14, we performed ChIP–seq to generate a resource of DNA elements associated with 37 RBPs  using at least two different assays and 129 (37%) using at least three different assays, providing opportunities for integrated analysis using multiple data sets as shown for regulation of BP1 Fig. . The incng frame .https://www.encodeproject.org). An additional 50 data sets were not included in further analyses as they did not meet these stringent standards but contained a reproducible signal (Gene Expression Omnibus (GSE107768); Extended Data Fig. We performed 488 eCLIP experiments, each including biological duplicate IPs and a paired size-matched input . We further required that significant peaks be reproducibly identified across both biological replicates using an irreproducible discovery rate (IDR) approach  peak in the other cell type  if the overall target RNA expression was within a factor of five Fig. . Extendiype Fig. . By contype Fig. . Thus, tR value) of each 5mer in RBNS-bound sequences and compared these to corresponding enrichments in eCLIP peaks (ReCLIP). We focused on 5mers because they were most robust12 and because most proteins analysed by RBNS contained RNA recognition motif (RRM) or hnRNP K homology (KH) domains, which bind about 3–5 bases of RNA19. Significantly enriched 5mers in vitro and in vivo were mostly in agreement, with 15 of the 23 RBPs having significant overlap , the top five RBNS 5mers explained fewer than half of eCLIP peaks. Some of these RBPs have affinities to RNA structural features or to extended RNA sequence elements that are not well represented by 5mersaks Fig. , but a sP < 0.05, Extended Data Fig. P < 0.02) difference between RBNS+ and RBNS– peaks (in the opposite direction) in the downstream intron region and no significant difference elsewhere  motifs but not G-rich (eCLIP-only) motifs were enriched adjacent to PCBP2-regulated exons , we observed a wide variety of RNA maps for cassette exons , alternative 3′ splice site events and alternative 5′ splice site events  can regulate exon inclusion or exclusion or alternative 5′ or 3′ splice site usage. To consider how RBP enrichment is associated with splicing regulation, we identified all significant alternative splicing events by comparing RNA-seq data from cells in which RBPs were knocked down with data from paired non-target control cells   of the RBPs profiled in HepG2 cells and 33 (64%) in K562 cells showed a reproducible ChIP–seq peak, with at least 200  peaks . We also observed increased eCLIP signal at unspliced transcripts for nuclear RBPs versus spliced transcripts for cytoplasmic RBPs 33  strand , the light (L) strand , or both strands , and mitochondrial localization by immunofluorescence was generally associated with significantly increased eCLIP enrichment on mitochondrial RNAs Fig. . Next, wTo our knowledge, this study represents the largest effort to date to systematically study the functions of human RBPs using integrative approaches. The resulting catalogue of functional RNA elements substantially expands the repertoire of known regulatory components encoded in the human genome. Although DNA binding proteins mostly influence gene expression levels, the functions of RBPs encompass a broader range of activities that expand transcriptome and proteome complexity, extending outside the nucleus and into the cytoplasm and organelles and contributing to multiple paths by which RNA substrates are altered . We have demonstrated the effectiveness of combining in vivo maps of RNA-binding sites of 150 RBPs identified using eCLIP with orthogonal approaches, such as in vitro evaluation of RNA affinity for the same RBPs, chromatin association by ChIP–seq, and functional assessment of transcriptome changes by RBP depletion and RNA-seq. At the molecular level, we have confirmed that the in vivo and in vitro preferences of RBPs are highly correlated, and show that eCLIP peaks containing motifs that reflect intrinsic RNA affinity are more predictive of regulation than eCLIP peaks alone. We have confirmed, using unbiased genome-wide analyses, that SR and hnRNP proteins have broadly antagonistic effects on alternative splicing, and we have found evidence that the upstream 5′ splice sites of cassette exons have a larger role in splicing regulation than is generally appreciated. We have also implicated an RNA structure bound by an RBP in the processing of mitochondrial transcripts, and elucidated new RNA splicing maps for many RBPs. Furthermore, our data provide, to our knowledge, the first systematic investigation of chromatin-associated gene regulation and RNA processing at the level of RBP–nucleic acid interactions. At the cellular level, immunofluorescence analysis using our extensive repository of RBP-specific antibodies places these molecular interactions within particular subcellular contexts. We have confirmed that many RBPs are localized to nuclear speckles, mitochondria and other compartments, and have identified many new proteins that reside at these sites, emphasizing the necessity of localization data for interpreting RBP–RNA regulatory networks.34, RNA editing35, modifications such as pseudouridylation and m6A methylation, and translation efficiency. As we and others continue to embark on comprehensive characterization of functional RNA elements for remaining RBPs and across various cell-types and conditions, functional validation of these elements at large-scale using CRISPR–Cas9 genome-editing36, RNA modulation, and other technologies will become increasingly essential to study the functional roles these elements have in cellular and organismal phenotypes.We have surveyed the in vivo binding patterns of 150 RBPs, comprising roughly 10% of human proteins that have been predicted to interact directly with RNA. We expect that the data reported here will provide a useful framework upon which to build analyses of other aspects of RNA regulation, such as microRNA processingCell lines were purchased from ATCC and were not formally authenticated, but confirmation of expected gene expression patterns were performed for RNA-seq and eCLIP experiments. Cell lines were routinely tested for mycoplasma contamination .9. Annotation of RBP function was performed by integration of published literature, with manual inspection of references for less well-established annotations. Annotation of RNA-binding domain presence was determined by UniProt Domain Descriptions, and a database of cell-essential genes was obtained from published high-throughput CRISPR screening efforts37.RBPs were chosen from a previously described list of 1,072 known RBPs, proteins containing RNA-binding domains, and proteins characterized as being associated with polyadenylated RNA, based on the availability of high-quality antibodies9. A ‘biosample’ of HepG2 or K562 cells was defined as a batch of cells starting from a single unfrozen stock, passaged for less than 30 days under standard ENCODE reference conditions, and validated for high viability and non-confluence at the time of crosslinking. All cells within a biosample were pooled and UV crosslinked on ice at 400 mJoules/cm2 with 254 nm radiation. The biosample was then split into 20-million-cell aliquots for eCLIP experiments.Antibodies for eCLIP were pre-screened using a set of defined metrics10, which is provided as associated documentation with each eCLIP experiment on the ENCODE portal (https://www.encodeproject.org/documents/fa2a3246-6039-46ba-b960-17fe06e7876a/@@download/attachment/CLIP_SOP_v1.0.pdf). In brief, 20 million crosslinked cells were lysed and sonicated, followed by treatment with RNase I (Thermo Fisher) to fragment RNA. Antibodies were pre-coupled to species-specific (anti-rabbit IgG or anti-mouse IgG) Dynabeads (Thermo Fisher), added to lysate, and incubated overnight at 4 °C. Prior to IP washes, 2% of sample was removed to serve as the paired input sample. For IP samples, high- and low-salt washes were performed, after which RNA was dephosphorylated with FastAP (Thermo Fisher) and T4 PNK (NEB) at low pH, and a 3′ RNA adaptor was ligated with T4 RNA ligase (NEB). Ten per cent of IP and input samples were run on an analytical PAGE Bis-Tris protein gel, transferred to PVDF membrane, blocked in 5% dry milk in TBST, incubated with the same primary antibody used for IP , washed, incubated with secondary HRP-conjugated species-specific TrueBlot antibody (Rockland), and visualized with standard enhanced chemiluminescence imaging to validate successful IP. Ninety per cent of IP and input samples were run on an analytical PAGE Bis-Tris protein gel and transferred to nitrocellulose membranes, after which the region from the protein size to 75 kDa above protein size was excised from the membrane, treated with proteinase K (NEB) to release RNA, and concentrated by column purification (Zymo). Input samples were then dephosphorylated with FastAP (Thermo Fisher) and T4 PNK (NEB) at low pH, and a 3′ RNA adaptor was ligated with T4 RNA ligase (NEB) to synchronize with IP samples. Reverse transcription was then performed with AffinityScript (Agilent), followed by ExoSAP-IT (Affymetrix) treatment to remove unincorporated primer. RNA was then degraded by alkaline hydrolysis, and a 3′ DNA adaptor was ligated with T4 RNA ligase (NEB). qPCR was then used to determine the required amplification, followed by PCR with Q5 (NEB) and gel electrophoresis to size-select the final library. Libraries were sequenced on the HiSeq 2000, 2500, or 4000 platform (Illumina). Each ENCODE eCLIP experiment consisted of IP from two independent biosamples, along with one paired size-matched input (sampled from one of the two IP lysates before IP washes).eCLIP experiments were performed as previously described in a detailed standard operating procedureeCLIP experiments for the ENCODE project were performed using two biological replicates, paired with a size-matched input control subsampled from one of the two replicate samples . Fifty-one out of 270 (19%) and 37 out of 218 (17%) experiments gave failed IP-western blot results in K562 or HepG2 cells, respectively, indicating either potential sensitivity to enzymatic steps and additional buffer exchanges performed during the eCLIP procedure, or a lack of expression in HepG2 cells  using 1.84-fold amplification per cycle on the basis of analysis of the eCLIP data resource  that generated adaptor-only sequencing libraries in either replicate were abandoned. Next, an extrapolated PCR cycles required (eCT) metric was used to quantify library yieldresource . Thirty-resource .10 and is provided (including description of steps as well as commands run) as a ‘Pipeline protocol’ attached to each eCLIP data set available on the ENCODE website at https://www.encodeproject.org/documents/3b1b2762-269a-4978-902e-0e1f91615782/@@download/attachment/eCLIP_analysisSOP_v2.0.pdf  and –log10(P) between the two biological replicates). Finally, 57 ‘blacklist’ regions were identified that were common artefacts across multiple data sets and lacked normal peak shapes , the peak annotation was chosen in the following priority order: tRNA, miRNA, miRNA-proximal (within 500 nt), CDS, 3′UTR, 5′UTR, 5′ splice site (within 100 nt of exon), 3′ splice site (within 100 nt of exon), proximal intron (within 500 nt of splice site region), distal intron (further than 500 nt from the splice site region), followed by noncoding exonic. If the peak overlapped multiple gene annotations, the final annotation was chosen as follows: tRNA, miRNA, CDS, 3′UTR, 5′UTR, miRNA-proximal, noncoding exonic, 5′ splice site, 3′ splice site, proximal intron, distal intron. To identify RBP clusters, the fraction of peaks annotated to each class out of the total number of peaks was calculated, and hierarchical clustering was performed in MATLAB (2018a) using correlation distance and average linkage. Clusters were obtained by cutting the tree at six clusters 39, 501 60mer sequences containing simple repeats of all 1 to 6-nt kmers, and the rRNA precursor transcript NR_046235.1 obtained from GenBank. Each transcript was assigned to one of 185 families of multi-copy elements . Within each family, transcripts were given a priority value, with primary transcripts prioritized over pseudogenes.A separate pipeline was developed to quantify enrichment for retrotransposable and other multi-copy elements. A database of multicopy elements was generated, including 5,606 transcripts obtained from GENCODE v19 covering 34 abundant non-coding RNAs including rRNA, snRNA, and vault RNAs as well as their pseudogenes, 606 tRNA transcripts obtained from GtRNAdb Post-adaptor trimming paired-end sequencing reads were mapped to this repetitive element database using bowtie2 (v. 2.2.6) with options ‘-q–sensitive -a -p 3–no-mixed –reorder’ to output all mappings. Read mappings were then processed according to the following rules. First, for each read pair only mappings with the lowest mismatch score (fewest mismatches and insertions or deletions) were kept. Next, for equally scoring mappings within a repeat family described above, the mapping to the transcript with the highest priority was identified as the ‘primary’ match. Only read pairs that mapped to a single repeat family were considered, whereas read pairs that mapped with equal scores to multiple repeat families were discarded from quantification at this stage. Mapping to the reverse strand of a transcript was considered distinct from forward strand mapping, such that each family paired with a separate antisense family composed of the same transcripts with the same priority order .χ2 test where appropriate.Next, repeat mappings were integrated with unique genomic mappings identified from the standard eCLIP processing pipeline (described above) as follows. If a read pair mapped both uniquely to the genome and to a repetitive element, the mapping scores were compared; if the unique genome mapping was more than two mismatches per read  better than to the repeat element, the unique genomic mapping was used; otherwise, it was discarded and only the repeat mapping was kept. Next, PCR duplicate removal was performed (similar to the standard eCLIP processing pipeline) by comparing all read pairs based on their mapping start and stop position (either within the genome or within the mapped primary repeat) and unique molecular identifier sequence, removing all but one read pair for read pairs that shared these three values. Finally, the number of post PCR-duplicate removal read pairs mapping to each multi-copy family was counted in both IP and paired input sample and normalized for sequencing depth (counting post-PCR duplicate read pairs from both unique genomic mapping and repeat mapping). In addition, to better quantify signal to RepBase elements, RepeatMasker-identified repetitive elements in the hg19 genome were obtained from the UCSC Genome Browser. Element counts for RepBase elements were determined as the sum of repeat family-mapped read pairs plus uniquely genome-mapped read pairs that overlapped RepeatMasked RepBase elements. After removing repeat-mapping elements, the remaining reads were grouped and quantified on the basis of transcript region annotations . Significance was determined by Fisher’s exact test, or Pearson’s pi × log2(pi/qi), where pi and qi are the fraction of total reads in IP and input, respectively, that map to element i. A merged relative information for both replicates was calculated by defining pi as the average fraction of total reads between the two biological replicates. To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t-SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10. To identify clusters, clustering was performed in using the DBSCAN (v1.0) MATLAB package, with options epsilon = 3 and MinPts = 2.To summarize overall eCLIP signal, a relative information content metric was applied. The relative information content of each element in each replicate was calculated as χ2 test if all observed and expected values were above 5). Only regions with at least 10 reads in one of IP or input, and where at least 10 reads would be expected in the comparison data set given the total number of usable reads, were considered, and significant regions were defined as those with fold enrichment ≥4 and P ≤ 0.00001.For analyses that used binding considered at the level of regions , read density was counted for the indicated region for both IP and paired input, and significance was determined by Fisher’s exact test  or CRISPR-mediated gene disruption. RNAi was performed by transducing cells with lentiviruses expressing shRNAs (TRC collection) targeting an RBP followed by puromycin selection for 5 days. CRISPR-mediated gene disruption was performed by transfecting cells with a plasmid expressing Cas9 and a guide RNA (gRNA) targeting an RBP, followed by puromycin selection for 5 days. In each case, knockdowns were performed in biological duplicate along with a pair of control knockdowns using a scrambled shRNA or gRNA. Protein was extracted from half of each sample and used to confirm knockdown of the target RBP by western blotting. RNA was extracted from half of each sample and used to perform qRT–PCR to confirm knockdown of the targeted RBP transcript. We strived to obtain a knockdown efficiency of the target protein and/or RNA of at least 50% compared to the scrambled control, and for the knockdown efficiency to be within 10% between replicates. We used the extracted RNA to prepare RNA-seq libraries with the Illumina Tru-seq stranded mRNA library preparation kit. Paired-end 100-bp reads were generated from the RNA-seq libraries to an average depth of 63 million reads per replicate, and a minimum of 20 million reads per replicate, on an Illumina HiSeq 2500.40 with Bowtie2 version 2.1.041, and STAR version 2.4.042. All analyses described in this manuscript used the GRCh37/GENCODE v19 alignments, but the GRCh38/GENCODE v24 alignments are also available at the ENCODE portal. In all cases, alignments were performed against the male reference genome sequence for HepG2 cells or the female reference genome for K562 cells and simultaneously to the ERCC spike-in sequences. The command line parameters for the TopHat alignments were: -a 8 -m 0–min-intron-length 20–max-intron-length 1000000–read-edit-dist 4–read-mismatches 4 -g 20–no-novel-juncs–no-discordant–no-mixed. In some rare cases, TopHat 2.0.8 misassigned some reads to both strands or did not assign reads to either strand. To correct these errors, we used a custom script, tophat_bam_xsA_tag_fix.pl, to properly assign the SAM flag values. Gene expression levels were quantified using RSEM (v1.2.23)43 and Cufflinks (v2.0.2)44. Only samples with a Pearson correlation coefficient on FPKM values of 0.9 or greater between replicates were used for further analysis. Samples with a correlation below 0.9 were repeated. We used the custom script (makewigglefromBAM-NH.py) to convert the single .bam alignment files into plus or minus strand and unique and multi-mapped .bam files, and then converted the intermediate .bam files into bigwig files. A single, final .bam file was generated for each RNA-seq sample by merging the .bam files that contained the aligned read with the one that contained the unmapped reads. The merged .bam and bigwig files were submitted to the ENCODE Data Coordination Center (https://www.encodeproject.org/). In total, 237 HepG2 knockdown experiments (223 shRNA and 14 CRISPR) and 235 K562 knockdown experiments (217 shRNA and 18 CRISPR) were used for further analysis.Reads were aligned to both GRCh37 using the GENCODE v19 annotations and GRCh38 using the GENCODE v24 annotations using both TopHat version 2.0.845 was used with the –gcBias option to normalize for local GC content and quantify transcript abundance. Transcripts were then merged to genes using tximport (v1.14.2)46, after which CQN (v1.32.0)47 was used to normalize for gene-level GC content and length biases. DESeq2 (v1.26.0)48 was then used to quantify differential expression, with differentially expressed (DE) genes defined as those with a P value < 0.05 and adjusted P (Padj) < 0.05.Salmon (v1.1.0)2(fold-change)| ≥ 2, moderate when 1< |log2(fold-change)| < 2, and weak when |log2(fold-change)| ≤ 1. For the purposes of simplifying the analysis, we considered significant differential expression to be strong if |log49. The knockdown replicate bam files and their control replicate bam files with the Gencode v19 annotation file were analysed using rMATS, to report five types of the differential AS events: SE (skipped exon), MXE , A3SS , A5SS  and RI (retained intron). Events with |inclusion level difference| > 0.05, P < 0.05 and FDR < 0.05 were identified as significantly differentially expressed AS events.Differential alternative splicing (AS) events were analysed using rMATS (v 3.2.1.beta)50 was used to detect differentially processed tandem 3′ UTR events  site usage). Four pairwise comparisons between the two knockdown samples and two controls were run using compare-miso: KD-rep1 versus CN-rep1, KD-rep1 versus CN-rep2, KD-rep2 versus CN-rep1 and KD-rep2 versus CN-rep2. Significant tandem 3′ UTR events were identified if abs(Bayes factor) ≥5 and P < 0.05 on both the more_reads versus fewer_reads comparison and the fewer_reads versus more_reads comparison.MISO Ψ| ≥ 30%, moderate when 15% ≤ |ΔΨ| < 30%, and weak if 5% < |ΔΨ| < 15%.For the purposes of simplifying the analysis, we considered significant differential alternative splicing levels to be strong if |Δ51. To correct for batch effects, for each batch of experiments performed on a given day, the same scrambled shRNA or gRNA was used as a non-specific control alongside a batch of experimental shRNAs or gRNAs that targeted a set of RBPs. This provided a consistent, non-specific control experiment in every batch that could be used to normalize data downstream. In addition to biological controls, if a given batch of biological samples was too large to make all the RNA-seq libraries in parallel, libraries were made from the non-specific control RNA samples in each subset of libraries made from a given biological batch. Analyses that compared eCLIP peaks with gene expression or alternative splicing changes in RNA-seq upon RBP knockdown used changes identified relative to these within-batch paired controls. However, to enable further integrated analyses, additional batch correction was performed  flanked by constant adaptor sequences were synthesized and incubated with an SBP-tagged recombinant RBP  at several concentrations . RNA–protein complexes were isolated with streptavidin-conjugated affinity resin and eluted RNA was prepared for deep sequencing, resulting in 10–20 million reads per RBP pulldown concentration with a similar number of input reads sequenced per in vitro transcription reaction.kmer enrichments  were calculated as the frequency of each kmer in the pulldown library reads divided by its frequency in the input library; enrichments from the pulldown library with the highest individual kmer R value were used for each RBP. The mean and s.d. of R values were calculated across all kmers for a given k to calculate the RBNS Z-score for each kmer. RBNS k = 5: the most enriched 5mer was given a weight equal to its excess enrichment over the input library (= R – 1), and all occurrences of that 5mer were masked in both the pulldown and input libraries to eliminate subsequent counting of lower-affinity ‘shadow’ 5mers . All enrichments were then recalculated on the masked read sets to obtain the most enriched 5mer and its corresponding weight, with this process continuing until the enrichment Z-score  was less than 3. All 5mers determined from this procedure were aligned to minimize mismatches to the most enriched 5mer, with a new motif initiated if the number of mismatches plus offsets exceeded two. The frequencies of each nucleotide in the position weight matrix, as well as the overall percentage of each motif, were determined from the weights of the individual aligned 5mers that went into that motif; empty unaligned positions before or after each aligned 5mer were assigned pseudocounts of 25% of each nucleotide, and outermost positions of the motif logo were trimmed if they had >75% unaligned positions. To improve the robustness of the motif logos, the pulldown and input read sets were each divided in half and the above procedure was performed independently on each half; only 5mers identified in corresponding motif logos from both halves were included in the alignments to make the final motif logo. In Fig. RBNS motif logos were made using the following iterative procedure for l-lysine-coated 96-well clear bottom plates , at a concentration of 2,000 cells per well in DMEM + 10% FBS. After 72 h in standard growth conditions (37 °C and 5% CO2), cells were fixed with 3.7% formaldehyde, permeabilized in PBS + 0.5% Triton X-100 and blocked in PBS + 0.2% Tween-20 + 2% BSA (PBTB), all conducted for 20 min at room temperature. Primary antibodies directed against specific RBPs  and marker proteins were subsequently applied to the cells at a final concentration of 2 μg/ml in PBTB and incubated overnight at 4 °C. The cells were next washed three times for 10 min each in PBST and incubated with secondary antibodies  for 90 min at room temperature. After three PBTB washes, the cells were counterstained with DAPI for 5 min, washed three times in PBS and stored in PBS at 4 °C. Subcellular marker antibodies and dilutions used were as follows: rat anti-α-tubulin, MCA78G, 1:200 ; mouse anti-CD63, ab8219, 1:200 (Abcam); mouse anti-coilin, GTX11822, 1:100 (GeneTex); mouse anti-DCP1a, sc100706, 1:200 (Santa Cruz Biotechnology); mouse anti-fibrillarin, ab4566, 1:200 dilution (Abcam); mouse anti-GM130, #610822, 1:200 (Becton Dickinson); mouse anti-KDEL, ENZSPA827D, 1:200 (Enzo Life Sciences); mouse anti-phosphotyrosine, #9411S, 1:200 (NEB); mouse anti-PML, sc-966, 1:50 (Santa Cruz Biotechnology); mouse anti-SC35, GTX11826, 1:200 (GeneTex). For staining with Mitotracker , cells were incubated with 100 nM dye in tissue culture medium for 45 min at 37 °C before fixation. For staining with phalloidin , cells were incubated with 50 μg/ml of phalloidin for 20 min before DAPI staining.HepG2 cells were seeded in poly-Imaging was conducted on an ImageXpress Micro high content screening system (Molecular Devices). For each RBP–marker combination, 10–20 high-resolution images were acquired in the DAPI, FITC and Cy5 channels, using a 40× objective. Automated laser-based auto-focusing and auto-exposure functions were used for sample imaging, with exposure times ranging from 250 to 3,000 ms, 100 to 500 ms and 50 to 100 ms for RBP, marker and DAPI channels, respectively. Raw unprocessed greyscale images from individual channels were acquired as high-resolution TIF files of 726 kb each. An in-house MATLAB script was developed to batch normalize image intensity values and add blue, green or red colours to the respective channels, which were subsequently merged as colour JPEG files. The final images were uploaded on a server accessible through the RBP Image Database website. A MySQL relational database (version 5.1.73) was implemented, along with a MyISAM storage engine, to store the images, data annotations and characteristics. A controlled vocabulary of descriptors was devised to document RBP subcellular localization features.Image analysis to quantify nuclear:cytoplasmic staining ratios, or to assess the degree of RBP targeting to punctate subcellular structures , was conducted using ‘Granularity’, ‘Colocalization’ and ‘Multi Wavelength Cell Scoring’ analysis modules from the MetaXpress v3.1 software (Molecular Devices), according to the manufacturer’s recommendations. For localization categories including microtubules, actin, cell cortex, ER, focal adhesions, mitochondria and mitotic apparatus, manual localization grading was conducted by ranking candidate RBPS as strongly or weakly co-localized with respective protein markers. The Circos plot of localization co-occurrance . In brief, before coupling with RBP antibodies, magnetic beads were equilibrated by washing with ChIP dilution buffer and blocked with glycogen, BSA and tRNA in ChIP dilution buffer. Between ten million and twenty million HepG2 and K562 cells were crosslinked in 1% formaldehyde diluted in 1× PBS for 20 min and then quenched by adding glycine. Cell nuclei were extracted by resuspending the cell pellet with cell lysis buffer with occasional inversion. Nucleus pellets resuspended in nuclear lysis buffer were sonicated with a Branson Sonifier cell disruptor. Ninety-five per cent of nuclear lysate was diluted to a final concentration of 1% triton X-100, 0.1% sodium deoxycholate and 1× proteinase inhibitor cocktail and was subjected to IP with antibody-coupled beads; the other 5% of nuclear lysate was used as input chromatin. Stringent washes were performed before elution. Input and immunoprecipitated chromatin DNAs were recovered by decrosslinking, RNase A digestion, proteinase K treatment, phenol/chloroform extraction and precipitation with ethanol. Library construction was performed using the ChIP–seq Sample Prep Kit (Illumina). DNA libraries between 200 and 400 bp were gel-purified, quantified with Qubit and sequenced on the Illumina HiSeq 2000/2500. All RBP ChIP–seq experiments were performed in duplicate. Antibodies used in RBP ChIP–seq experiments were validated by IP and shRNA or CRISPR knockdown according to ENCODE RBP antibody characterization guidelines.Chromatin IP was implemented according to the ChIP Protocol optimized for RNA-binding proteins (https://www.encodeproject.org/chip-seq/transcription_factor/). After removal of low-quality and PCR duplicate reads, peaks were identified with SPP and reproducible peaks across biological replicates were identified with the IDR pipeline to yield two sets  of peaks at an IDR threshold of 0.0552.RBP ChIP–seq data sets used in this study were processed by the ENCODE Data Coordinating Center with the same uniform processing pipelines described previously for transcription factor ChIP–seq  > 0.05 from rMATS analysis (described above). The number of unique events was defined as the number of non-overlapping events obtained upon combining all experiments for a given sampling. A differentially spliced event was considered bound if for any RBP in which the event was differentially included upon KD, there was an eCLIP peak for the same RBP in the same cell type between the start of the upstream flanking exon and the end of the downstream flanking exon for skipped exons and mutually exclusive exons, the start of the upstream flanking exon and end of the common exon region for A3SS, the start of the common exon and end of the common exon region for A5SS, and the start of the upstream and stop of the downstream exons for retained introns.Differentially spliced events were defined as those with To perform saturation of transcript regions, the highest-expressed transcript for each gene was first identified using transcript-level quantification from the same rRNA-depleted RNA-seq experiments described above. The following regions were then identified: the entire unspliced transcript (pre-mRNA), all exons (exon), 5′ UTR, CDS, 3′UTR, all introns (intron), 100-nt intronic regions flanking the 5′ and 3′ splice sites (splice site), proximal intronic regions extending from 100 nt to 500 nt from the 5′ and 3′ splice site , and distal intronic regions extending from 500 nt and beyond from the 5′ and 3′ splice sites. Saturation calculations were then performed as described above for all genes  or 223rd (if it was profiled in only one cell type) data sets for other RBPs), or a profiled RBP done in second cell type .To compare the fold-increase between profiling new RBPs in additional cell lines , weakly (1.2 < fold-difference ≤ 2), moderately (2 < fold-difference ≤ 5) or strongly (fold-difference > 5) differential (each of which required expression TPM ≥ 1 in both K562 and HepG2 cells), cell type-specific genes (TPM < 0.1 in one cell type and TPM ≥ 1 in the other), or other . Peaks were then categorized on the basis of the expression change of their associated gene  were generated. EMBOSS compseq55 was used on these two peak sets and the Z-scores of the difference between real and background 5mer and 6mer frequencies were calculated.eCLIP 5mer and 6mer Z-score threshold for 5mers included in eCLIP logos was 2.8, as this threshold produced eCLIP logos containing the most similar number of 5mers to that of the Z ≥ 3 5mer RBNS logos. Each eCLIP motif logo was filtered to include only 5mers that occurred in both of the corresponding eCLIP replicate logos. eCLIP motif logos were made separately for all eCLIP peaks, only 3′UTR peaks, only CDS peaks, and only intronic peaks, with the eCLIP logo of those 4 (or 8 if CLIP was performed in both cell types) with the highest similarity score to the RBNS logo shown in Fig. P < 0.05. The top ‘eCLIP-only’ logo in each region was the highest eCLIP logo, if any, comprised of 5mers that had no overlap with any RBNS Z ≥ 3 5mers .To produce eCLIP logos in a similar manner for comparison with RBNS logos, an analogous procedure was carried out on the eCLIP peak sequences : the two halves of the RBNS pulldown read set were replaced with the two eCLIP replicate peak sequence sets , and the input RBNS sequences were replaced by random regions within the same gene as each peak that preserved peak length and transcript region . The enrichment All eCLIP/RBNS comparisons were for the same RBP with the following exceptions in which the eCLIP RBP was compared to a closely related RBNS protein: KHDRBS2 eCLIP versus KHDRBS1 RBNS; PABPN1 eCLIP versus PABPN1L RBNS; PTBP1 eCLIP versus PTBP3 RBNS; PUM2 eCLIP versus PUM1 RBNS; and RBM15 eCLIP versus RBM15B RBNS.Ψ between 0.05 and 0.95 in at least one of the controls or KDs were considered for each RBP. Each eCLIP peak (extended 50 nt 5′ of the peak start) was first checked for whether it overlapped the SE, and if not then for whether it overlapped the upstream or downstream flanking 250 nt. To compare the magnitude of splicing changes upon KD for eCLIP+ versus eCLIP– skipped exons while minimizing the confounding factors of different wild-type host gene expression level and skipped exon Ψ values among these two sets of skipped exons, we created a matched set of eCLIP– skipped exons by selecting for each eCLIP+ skipped exon a skipped exon in the same decile of wild-type gene expression and wild-type Ψ for each corresponding skipped exon with an eCLIP peak. A cumulative distribution function of the ΔΨ changes upon KD was compared for the eCLIP+ versus eCLIP– skipped exons in each of the six skipped exon (SE) direction–eCLIP region combinations , with significance P < 0.05 for a one-sided Wilcoxon rank-sum test that |ΔΨ|SE, peak > |ΔΨ|SE, no peak. If the eCLIP+ versus eCLIP– comparison was significant, the eCLIP peaks were divided into those that did and did not contain the top RBNS 5mer. The ΔΨ values for all RBPs in each of the six skipped exon direction–eCLIP regions were combined for comparison in Fig. kmer, the top 5mer from the aforementioned ‘eCLIP-only’logo was used from the first region with an eCLIP-only logo among: all peaks; CDS peaks; intron peaks; and 3′UTR peaks (the more highly enriched 5mer if eCLIP was performed in both cell types). The resulting ‘eCLIP-only’ 5mers for Extended Data Fig. To assess the splicing regulatory effects of RBNS+ and RBNS– eCLIP peaks for Fig. P ≤ 0.00001 and log2(fold enrichment) ≥ 4, as described above) were overlapped with the set of genes with significantly altered expression in KD–RNA-seq . Enrichment was calculated separately for knockdown-increased and knockdown-decreased genes, with significance determined by Fisher’s exact test . Comparisons with either knockdown-increased or knockdown-decreased genes from KD–RNA-seq were performed only if more than 10 genes showed significant changes. To avoid biases due to RNA abundance, for each comparison of a region type with each eCLIP data set, a background set of genes was created by identifying all genes for which the region type  had at least 10 reads in one of IP or input; at least 10 reads would be expected in the opposite (IP or input) data set given the total number of usable reads. For cumulative distribution plots, genes were separated on the basis of their eCLIP fold enrichment in IP versus input for the indicated transcript region.To increase sensitivity for gene expression analysis, significant binding was determined at the level of transcript regions  instead of using peaks. To identify significant enrichment between binding and expression changes, genes with significantly enriched eCLIP signal at regions  read densities overlapped with alternatively spliced (AS) regions from rMATS JunctionCountsOnly files from the same cell type using the RBP-Maps methodology24 . AnalyseR) between a vector that contained both included-upon knockdown and excluded-upon knockdown RBP-responsive event eCLIP enrichment for each RBP. If an RBP had fewer than the minimum required number of events  for either knockdown-included or knockdown-excluded events, the correlation was calculated only using the other event type.Correlation between splicing maps was defined as the Pearson correlation  skipped exons identified in knockdown of RBP To calculate the number of RBPs bound per exon, the set of spliceosomal RBPs was taken from manual annotation of RBP functions (described above and listed in Supplementary Data http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromSynth), histone modifications by ChIP–seq from ENCODE or the Broad Institute (http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeBroadHistone) and eCLIP–seq data from ENCODE (https://www.encodeproject.org) were downloaded and compared with RBP ChIP–seq data.For integrative analyses, DNaseI HS data  were considered. ChIP–seq signals were calculated for each eCLIP peak along with surrounding regions that are ten times the length of eCLIP peak on each side. Wilcoxon rank-sum tests were then performed to see whether ChIP–seq signals were enriched at the middle regions relative to the flanking regions.Padj < 0.05 by DESeq2), and one-tailed Fisher’s exact tests were then performed to test the dependence of RBP binding and differential expression. Odds ratio was defined as (a/b)/(c/d), where a is the number of genes with RBP ChIP–seq peaks and differential expression (or splicing) upon RBP knockdown, b is the number of genes with RBP ChIP–seq peaks but no differential expression, c is the number of genes without ChIP–seq peaks but with differential expression, and d is the number of genes without ChIP–seq peaks or differential expression. The above procedure was performed 100 times to give the distribution of the odds ratio, and a significant dependence was defined as when the null hypothesis was rejected at level of 0.05 at least 95 times. The correlation between RBP association and genes with regulated alternative splicing events  was investigated similarly.To see whether those differentially expressed genes after RBP knockdown were enriched in RBP binding at chromatin level, equal numbers of genes with similar expression level either with or without binding to the TSS region were randomly sampled, the number of differentially expressed genes after knockdown of the RBP were counted 56 with default parameters. Shown is the MFE secondary structure prediction.Localization annotations and calculation of nuclear versus cytoplasmic ratio were generated from immunofluorescence imaging as described above. ‘Nuclear RBPs’ were defined as those with a nuclear:cytoplasmic ratio ≥ 2, and ‘cytoplasmic RBPs’ were defined as those with a nuclear:cytoplasmic ratio ≤ 0.5. Spliced reads were defined as reads that mapped across an annotated GENCODE v19 splice junction (extending at least ten bases into each exon) and unspliced reads were defined as reads that overlapped an exon–intron junction (extending at least ten bases into both the exon and intron regions). Significance between groups was determined by Wilcoxon rank-sum test. Prediction of RNA secondary structure was performed using the RNAfold webserver  for all datasets generated in this study.Supplementary Data 2 RBP gene expression in ENCODE cell lines and tissues. Contains RNA expression (in transcripts per million reads) for all RBPs profiled in this study for K562 cells, HepG2 cells, and 40 human tissues profiled by the GTEx consortium.Supplementary Data 3 Summary information for eCLIP experiments. Contains summary information for all eCLIP experiments, including antibodies  as well as general sequencing library information .Supplementary Data 4 Summary information for RNA-seq experiments. Contains summary information for RBP knockdown/RNA-seq experiments, including number of differentially expressed genes and alternative splicing events.Supplementary Data 5 Summary information for RBNS experiments. Contains summary information for RNA Bind-N-Seq (RBNS) experiments, including reaction conditions (temperature and read length) as well as primary analysis results (enriched consensus motifs).Supplementary Data 6 Summary information for ChIP-seq experiments. Contains summary information of ChIP-seq experiments, including number of usable reads and significantly enriched peaks, as well as experimental quality assessment metrics .Supplementary Data 7 Automated and manual quality assessment of eCLIP datasets. Contains results from automated eCLIP quality assessment pipeline, including read number, total relative information, and IDR rescue and self-consistency ratio metrics. Tabs include 223 released datasets, 76 rejected datasets, and 50 datasets with reproducible signal of questionable reliability made available on the Gene Expression Omnibus.Supplementary Data 8 Summary information for questionable quality eCLIP experiments. Contains summary information for 50 eCLIP experiments with reproducible signal of questionable reliability, including antibodies  as well as general sequencing library information .Supplementary Data 9 Summary information for eCLIP experiments failing quality assessment. Contains summary information for 76 eCLIP experiments which failed manual quality assessment, including antibodies  as well as general sequencing library information .Supplementary Data 10 eCLIP blacklist regions. 56 regions which showed consistent artifact signal across many eCLIP experiments and were excluded from analyses.Supplementary Data 11 Overlap between eCLIP and ChIP-seq peaks. Contains statistics of overlap between eCLIP and ChIP-seq peaks.Supplementary Data 12 eCLIP adapters used. Contains sequences of in-line barcoded RNA adapters used for eCLIP experiments.Supplementary Data 13


Although proteins have represented the molecular target of choice in the development of new drug candidates, the pharmaceutical importance of ribonucleic acids has gradually been growing. The increasing availability of structural information has brought to light the existence of peculiar three-dimensional RNA arrangements, which can, contrary to initial expectations, be recognized and selectively modulated through small chemical entities or peptides. The application of classical computational methodologies, such as molecular docking, for the rational development of RNA-binding candidates is, however, complicated by the peculiarities characterizing these macromolecules, such as the marked conformational flexibility, the singular charges distribution, and the relevant role of solvent molecules. In this work, we have thus validated and extended the applicability domain of SuMD, an all-atoms molecular dynamics protocol that allows to accelerate the sampling of molecular recognition events on a nanosecond timescale, to ribonucleotide targets of pharmaceutical interest. In particular, we have proven the methodological ability by reproducing the binding mode of viral or prokaryotic ribonucleic complexes, as well as that of artificially engineered aptamers, with an impressive degree of accuracy. Ribonucleic acid (RNA) is a polymer whose biological importance has increased progressively over the last 50 years. Despite the central dogma of molecular biology considering this nucleic acid simply as a functional messenger between DNA genetic information storage and protein biosynthesis, RNA has recently been reappraised as an ancestral molecule of primary importance in the abiogenesis process. At the origin of life, RNA probably encompassed both an informational role, which progressively evolved toward involving the more stable and easily replicable DNA polymer, and a catalytic function, which was gradually flanked by more versatile proteins  that can reach up to a few thousands nucleotides   was used to perform most of the general molecular modeling operations, such as RNA and ligand preparation. All these operations have been performed on an 8 CPU (Intel® Xeon® CPU E5-1620 3.50 GHz) Linux workstation. Molecular dynamics (MD) simulations were performed with an ACEMD engine  of NVT followed by 500,000 steps (1 ns) of NPT simulations were carried out, both using 2 fs as time step and applying harmonic positional constraints on RNA and ligand heavy atoms by a force constant of 1 kcal mol−1 Å−2, gradually reducing with a scaling factor of 0.1. During this step, the temperature was maintained at 310 K by a Langevin thermostat with low dumping of 1 ps−1 and the pressure at 1 atm by a Berendsen barostat  unbiased MD trajectories are collected, and these monitor, during the entire simulation, the distance between the ligand center of mass with respect to the ribonucleic acid binding site; then, those distance points are fitted into a linear function. Only productive MD steps in which the computed slope is negative are maintained, thus indicating a ligand approach toward the RNA binding site. Otherwise, the simulation is restarted by randomly assigning the atomic velocities from the previous set of coordinates. The supervision algorithm controlled the sampling until the distance between the ligand and the ribonucleic binding site dropped below 5 Å, at which point it was disabled, and a short classical MD simulation was performed, allowing the system to relax. For each case study, up to a maximum of 10 SuMD binding simulations were collected, of which only the best was thoroughly analyzed and discussed in the manuscript. A detailed report on SuMD protocol performance can be found in the All the SuMD trajectories collected were analyzed by an in-house tool written in tcl and python languages, as described in the original publication  and is also able to inhibit the virus replication in a comparable range of concentration  encoding for 13 proteins  is a retrovirus belonging to the Lentivirus family, and it is responsible for acquired immunodeficiency syndrome (AIDS). RNA–protein interactions play a fundamental role in controlling the HIV replication cycle and, consequently, virulence profile . Riboswitches, which are structured elements typically found in the 5′ untranslated regions (UTR) of mRNAs, represent an interesting example of the latter case . Then, the ligand conformation remained stable until the end of the simulation. From an interactive point of view, as reported in S-adenosyl-(L)-methionine (SAM) is a fundamental cofactor that serves as the primary methyl group donor in a large set of biochemical reactions. In bacteria, SAM homeostasis is so important to the point that at least six classes of RNA riboswitch regulatory elements have since now been characterized , or 7-aminomethyl-7-deazaguanine, is a metabolic intermediate in the synthetic pathway that, starting from guanosine-5′-triphosphate (GTP) nucleotide, originates the hypermodified guanine derivate queuosine (Q). Q has been detected both in eubacteria and eukaryotic organisms where it occupies the anticodon wobble position of tRNAs specific for the amino acid asparagine, aspartate, histidine, and tyrosine  or Thermoanaerobacter tengcongenesis (Tt), the expression of genes responsible for Q biosynthesis is negatively modulated by the intermediate PreQ1 intracellular concentration. PreQ1, binding to a small aptameric RNA motif composed of 34 nucleotides determines the folding of the PreQ1 riboswitch in an “H-type” pseudoknot structure in which more than half of the nucleobases engage in triplet or quartet interactions , and this allowed us to speculate about the quite impressive binding affinity characterizing this endogenous precursor (Kd = 2 nM) , toward the solved crystallographic conformation. This behavior has also been captured by the interaction energy graph  and the ability to induce premature transcriptional termination  and has reached the impressive value of 0.5 Å  from the jellyfish in vitro to bind 3,5-difluoro-4-hydroxybenzylidene imidazolinone-2-oxime (DFHO), a fluorophore analogous of red fluorescent protein (RFP) , stacking between two G-quadruplex planes stabilized by the presence of K+ ions  (Corn is one recently developed RNA aptamer engineered  0.34 Å) . Moreove 0.34 Å) . From thOver the last decades, among all the biological macromolecules, proteins have represented the target of choice for the development of new drug candidates. Nucleic acids, on the other hand, have so far represented a less attractive target due to the difficulty in guaranteeing a selective recognition mechanism. The recent discovery of peculiar and physiologically stable three-dimensional conformation characterizing RNAs oligomers has, however, paved the way for the investigation of SMIRNA. The increasing availability of structural data for a wide range of relevant therapeutic ribonucleic targets has promoted the application of well-validated SBDD computational approaches, such as molecular docking, also in this field. However, the remarkable flexibility and the peculiar electrostatic potential, which distinguish nucleic acids from proteins, have readily highlighted the limitation of many of these methodologies. MD simulations would allow us to overcome some of the aforementioned problems; however, the computational cost required to capture rare events such as ligand binding has so far limited their routine utilization.In this work, we have investigated the applicability domain of SuMD in the field of pharmaceutically relevant RNA polymers. The performances of the protocol were measured as the geometrical accuracy, expressed in terms of RMSD, with which an experimentally solved complex is predicted by the SuMD simulation. Case studies in this research were chosen in such a way as to span very different ribonucleic secondary, tertiary, and even quaternary structures, starting from small duplex stem-loops up to pseudoknot or aptameric homodimers, which contain G-quadruplex motifs. Furthermore, the recognition of different ligands was investigated, both small organic molecules and folded α-helical peptides.min lower than 5 Å, are in line with those previously described when applying SuMD methodology to peptide–protein recognition. Moving toward more complex binding sites, such as the one that characterizes pseudoknot riboswitch structures or G-quadruple-shaped clefts, the geometric accuracy of the method progressively improves, with the best results obtained in the artificial aptameric structure (RMSDmin 0.34 Å). These findings are in agreement with a recent perspective work that assessed how the complexity of an RNA binding site, measured in terms of information content, could represent a valuable discriminant to individuate druggable oligonucleotides  since the recognition between two extremely flexible entities, i.e., the small peptide and the RNA duplex, represents a very challenging case. However, the results obtained, with an RMSDFurthermore, the high conformational flexibility that has characterized all the investigated ribonucleic structures (RMSD computed on RNA backbone are reported on The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.MB carried out the experiment. MB wrote the manuscript with support from MS. MS and SM supervised the project. MB and SM conceived the original idea.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.