microRNA Sequencing Data Research Articles

Empirical insights into the stochasticity of small RNA sequencing.

The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.

MiR-509-3p is clinically significant and strongly attenuates cellular migration and multi-cellular spheroids in ovarian cancer

Ovarian cancer presents as an aggressive, advanced stage cancer with widespread metastases that depend primarily on multicellular spheroids in the peritoneal fluid. To identify new druggable pathways related to metastatic progression and spheroid formation, we integrated microRNA and mRNA sequencing data from 293 tumors from The Cancer Genome Atlas (TCGA) ovarian cancer cohort. We identified miR-509-3p as a clinically significant microRNA that is more abundant in patients with favorable survival in both the TCGA cohort (P = 2.3E–3), and, by in situ hybridization (ISH), in an independent cohort of 157 tumors (P < 1.0E–3). We found that miR-509-3p attenuated migration and disrupted multi-cellular spheroids in HEYA8, OVCAR8, SKOV3, OVCAR3, OVCAR4 and OVCAR5 cell lines. Consistent with disrupted spheroid formation, in TCGA data miR-509-3p's most strongly anti-correlated predicted targets were enriched in components of the extracellular matrix (ECM). We validated the Hippo pathway effector YAP1 as a direct miR-509-3p target. We showed that siRNA to YAP1 replicated 90% of miR-509-3p-mediated migration attenuation in OVCAR8, which contained high levels of YAP1 protein, but not in the other cell lines, in which levels of this protein were moderate to low. Our data suggest that the miR-509-3p/YAP1 axis may be a new druggable target in cancers with high YAP1, and we propose that therapeutically targeting the miR-509-3p/YAP1/ECM axis may disrupt early steps in multi-cellular spheroid formation, and so inhibit metastasis in epithelial ovarian cancer and potentially in other cancers.

Small RNA Sequencing Uncovers New miRNAs and moRNAs Differentially Expressed in Normal and Primary Myelofibrosis CD34+ Cells

Myeloproliferative neoplasms (MPN) are chronic myeloid cancers thought to arise at the level of CD34+ hematopoietic stem/progenitor cells. They include essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF). All can progress to acute leukemia, but PMF carries the worst prognosis. Increasing evidences indicate that deregulation of microRNAs (miRNAs) might plays an important role in hematologic malignancies, including MPN. To attain deeper knowledge of short RNAs (sRNAs) expression pattern in CD34+ cells and of their possible role in mediating post-transcriptional regulation in PMF, we sequenced with Illumina HiSeq2000 technology CD34+ cells from healthy subjects and PMF patients. We detected the expression of 784 known miRNAs, with a prevalence of miRNA up-regulation in PMF samples, and discovered 34 new miRNAs and 99 new miRNA-offset RNAs (moRNAs), in CD34+ cells. Thirty-seven small RNAs were differentially expressed in PMF patients compared with healthy subjects, according to microRNA sequencing data. Five miRNAs (miR-10b-5p, miR-19b-3p, miR-29a-3p, miR-379-5p, and miR-543) were deregulated also in PMF granulocytes. Moreover, 3’-moR-128-2 resulted consistently downregulated in PMF according to RNA-seq and qRT-PCR data both in CD34+ cells and granulocytes. Target predictions of these validated small RNAs de-regulated in PMF and functional enrichment analyses highlighted many interesting pathways involved in tumor development and progression, such as signaling by FGFR and DAP12 and Oncogene Induced Senescence. As a whole, data obtained in this study deepened the knowledge of miRNAs and moRNAs altered expression in PMF CD34+ cells and allowed to identify and validate a specific small RNA profile that distinguishes PMF granulocytes from those of normal subjects. We thus provided new information regarding the possible role of miRNAs and, specifically, of new moRNAs in this disease.

Large-scale profiling of microRNAs for The Cancer Genome Atlas.

The comprehensive multiplatform genomics data generated by The Cancer Genome Atlas (TCGA) Research Network is an enabling resource for cancer research. It includes an unprecedented amount of microRNA sequence data: ∼11 000 libraries across 33 cancer types. Combined with initiatives like the National Cancer Institute Genomics Cloud Pilots, such data resources will make intensive analysis of large-scale cancer genomics data widely accessible. To support such initiatives, and to enable comparison of TCGA microRNA data to data from other projects, we describe the process that we developed and used to generate the microRNA sequence data, from library construction through to submission of data to repositories. In the context of this process, we describe the computational pipeline that we used to characterize microRNA expression across large patient cohorts.

Retinoic Acid Induces Embryonic Stem Cell Differentiation by Altering Both Encoding RNA and microRNA Expression.

Retinoic acid (RA) is a vitamin A metabolite that is essential for early embryonic development and promotes stem cell neural lineage specification; however, little is known regarding the impact of RA on mRNA transcription and microRNA levels on embryonic stem cell differentiation. Here, we present mRNA microarray and microRNA high-output sequencing to clarify how RA regulates gene expression. Using mRNA microarray analysis, we showed that RA repressed pluripotency-associated genes while activating ectoderm markers in mouse embryonic stem cells (mESCs). Moreover, RA modulated the DNA methylation of mESCs by altering the expression of epigenetic-associated genes such as Dnmt3b and Dnmt3l. Furthermore, H3K4me2, a pluripotent histone modification, was repressed by RA stimulation. From microRNA sequence data, we identified two downregulated microRNAs, namely, miR-200b and miR-200c, which regulated the pluripotency of stem cells. We found that miR-200b or miR-200c deficiency suppressed the expression of pluripotent genes, including Oct4 and Nanog, and activated the expression of the ectodermal marker gene Nestin. These results demonstrate that retinoid induces mESCs to differentiate by regulating miR-200b/200c. Our findings provide the landscapes of mRNA and microRNA gene networks and indicate the crucial role of miR-200b/200c in the RA-induced differentiation of mESCs.

Global MicroRNA Analysis in Primary Hepatocyte Cultures.

MicroRNAs are small non-coding molecules that regulate gene expression and in return affect diverse biological functions, including those involved in toxicity and development of disease. Recent evidence suggests that microRNAs play an important role in liver pathologies, like viral hepatitis, alcoholic liver, hepatocellular carcinoma, or drug-induced liver injury. Furthermore, numerous studies demonstrated the high potential of microRNAs as promising non-invasive biomarkers of liver disease or as relevant targets for therapeutic treatment. This chapter describes a method for global microRNA analysis of primary hepatocytes by high-throughput sequencing. The method comprises the isolation of high-quality total RNA, analysis of microRNA sequencing data, and the validation of the findings by reverse transcriptase quantitative polymerase chain reaction analysis.

MiRseqViewer: multi-panel visualization of sequence, structure and expression for analysis of microRNA sequencing data.

Deep sequencing of small RNAs has become a routine process in recent years, but no dedicated viewer is as yet available to explore the sequence features simultaneously along with secondary structure and gene expression of microRNA (miRNA). We present a highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multipanel windows. This helps users to easily examine the relationships between the structure of precursor and the sequences and abundance of final products and thereby will facilitate the studies on miRNA biogenesis and regulation. The project manager handles multiple samples of multiple groups. The read alignment is imported in BAM file format. Implemented features comprise sorting, zooming, highlighting, editing, filtering, saving, exporting, etc. Currently, miRseqViewer supports 84 organisms whose annotation is available at miRBase. miRseqViewer, implemented in Java, is available at https://github.com/insoo078/mirseqviewer or at http://msv.kobic.re.kr. sanghyuk@ewha.ac.kr.

ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs.

BackgroundComplex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features.ResultsWe show on microRNA sequencing data from a clinical cancer study how our software ShrinkBayes tackles the aforementioned challenges. In addition, we illustrate its comparatively good performance on multi-parameter inference for groups using a data-based simulation. Finally, in the small sample size setting, we demonstrate its high power and improved FDR estimation by use of Gaussian mixture priors that include a point mass.ConclusionShrinkBayes is a versatile software package for the analysis of count-based sequencing data, which is particularly useful for studies with small sample sizes or complex designs.

Comprehensive molecular characterization of urothelial bladder carcinoma

Urothelial carcinoma of the bladder is a common malignancy that causes approximately 150,000 deaths per year worldwide. So far, no molecularly targeted agents have been approved for treatment of the disease. As part of The Cancer Genome Atlas project, we report here an integrated analysis of 131 urothelial carcinomas to provide a comprehensive landscape of molecular alterations. There were statistically significant recurrent mutations in 32 genes, including multiple genes involved in cell-cycle regulation, chromatin regulation, and kinase signalling pathways, as well as 9 genes not previously reported as significantly mutated in any cancer. RNA sequencing revealed four expression subtypes, two of which (papillary-like and basal/squamous-like) were also evident in microRNA sequencing and protein data. Whole-genome and RNA sequencing identified recurrent in-frame activating FGFR3–TACC3 fusions and expression or integration of several viruses (including HPV16) that are associated with gene inactivation. Our analyses identified potential therapeutic targets in 69% of the tumours, including 42% with targets in the phosphatidylinositol-3-OH kinase/AKT/mTOR pathway and 45% with targets (including ERBB2) in the RTK/MAPK pathway. Chromatin regulatory genes were more frequently mutated in urothelial carcinoma than in any other common cancer studied so far, indicating the future possibility of targeted therapy for chromatin abnormalities.Supplementary informationThe online version of this article (doi:10.1038/nature12965) contains supplementary material, which is available to authorized users.

Integrated analysis of microRNA and mRNA expression and association with HIF binding reveals the complexity of microRNA expression regulation under hypoxia.

BackgroundIn mammalians, HIF is a master regulator of hypoxia gene expression through direct binding to DNA, while its role in microRNA expression regulation, critical in the hypoxia response, is not elucidated genome wide. Our aim is to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression.MethodsMCF-7 cells were incubated at 1% Oxygen for 16, 32 and 48 h. SiRNA against HIF-1α and HIF-2α were performed as previously published. MicroRNA and mRNA expression were assessed using microRNA microarrays, small RNA sequencing, gene expression microarrays and Real time PCR. The Kraken pipeline was applied for microRNA-seq analysis along with Bioconductor packages. Microarray data was analysed using Limma (Bioconductor), ChIP-seq data were analysed using Gene Set Enrichment Analysis and multiple testing correction applied in all analyses.ResultsHypoxia time course microRNA sequencing data analysis identified 41 microRNAs significantly up- and 28 down-regulated, including hsa-miR-4521, hsa-miR-145-3p and hsa-miR-222-5p reported in conjunction with hypoxia for the first time. Integration of HIF-1α and HIF-2α ChIP-seq data with expression data showed overall association between binding sites and microRNA up-regulation, with hsa-miR-210-3p and microRNAs of miR-27a/23a/24-2 and miR-30b/30d clusters as predominant examples. Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer. Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts involved in microRNA processing were found regulated by hypoxia, of which DICER (down-regulated) and AGO4 (up-regulated) were HIF dependent. DICER expression was found inversely correlated to hypoxia in breast cancer.ConclusionsIntegrated analysis of microRNA, mRNA and ChIP-seq data in a model cell line supports the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level, with the presence of HIF binding sites at microRNA genomic loci associated with up-regulation. The identification of hypoxia and HIF regulated microRNAs relevant for breast cancer is important for our understanding of disease development and design of therapeutic interventions.

CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data.

BackgroundmiRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues.ResultsWe developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline’s superior performances, flexibility, and practical use in research and biomarker discovery.ConclusionsCAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-423) contains supplementary material, which is available to authorized users.

MiRBase: annotating high confidence microRNAs using deep sequencing data

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

MLL-Rearranged Acute Myeloid Leukemias Drive Expression Of Mir-9, a Critical Oncogene In Leukemogenesis

A critical area of cancer biology is the study of the deregulation of noncoding RNAs called microRNAs (miRNAs). Acute leukemia represents one of the most deadly cancers in the United States. One subset of leukemia with a poor to intermediate clinical outcome are chromosomal translocations involving Mixed Lineage Leukemia (MLL). As MLL-translocations are sufficient to drive leukemogenesis, and few additional mutations are observed in patients, it is imperative to understand the biology driving leukemogenesis. Previously, we and others have shown that several miRNAs are deregulated in MLL-rearranged Acute Myeloid Leukemia (AML). To identify miRNAs that are driving leukemogenesis we performed messenger RNA and miRNA expression profiling on primary patient samples and identified microRNA-9 (miR-9) as specifically overexpressed in MLL-rearranged AML. We further confirmed this observation using publically available microRNA sequencing data from the Cancer Genome Atlas (TCGA) and several AML cell lines. After showing that MLL directly binds and regulates miR-9 we show that depletion of MLL-fusion expression leads to the loss of miR-9 expression. Using publically available Illumina 450K methylation data from TCGA, we show that there is no significant difference in modified cytosine between miR-9 high and miR-9-low patients, suggesting that expression of miR-9 is likely not be regulated by DNA methylation machinery in AML patients. We show that miR-9 in the presence of MLL-AF9 (a common MLL-fusion) promotes colony growth over multiple passages while blocking miR-9 using a miR-9 sponge remarkably inhibits MLL-fusion-mediated cell transformation. Furthermore, we show that miR-9 increases proliferation and reduces apoptosis of human MLL-rearranged leukemic cells in vitro using MTT and Caspase 3/7 assays. We then show that co-transfection of miR-9 with MLL-AF9 in a bone marrow transplantation assay results in a higher leukemia burden in vivo compared to MLL-AF9 alone and promotes an immature cellular phenotype. Using microarray data we found several putative miR-9 targets by identifying genes that had an inverse correlation to miR-9. Next, we verified several genes were being inhibited by miR-9 such as Ras homology gene family member H (RHOH) and Ring1- and YY1-binding protein (RYBP). To understand the role of miR-9 in context with other miRNAs we did an association analysis of the top 300 differentially expressed miRNAs in the TCGA dataset. We found interestingly, that two of the miR-9 genes, miR-9-1 and miR-9-2, are highly correlated with each other across all the patients although they are located on distinct chromosomes. We also found that several other miRs were either negatively (e.g., miR-130a and miR-221) or positively (e.g., miR-191 and miR-642) associated with miR-9 expression, suggesting that these miRs might be operating either cooperatively or antagonistically in a complex circuitry. To support this hypothesis we found in univariate analysis that miR-9 itself was not a good predictor of patient survival but was a better predictor when combined with other miRs including the miR-181 family. Together this suggests that miR-9 is an important and critical regulator of MLL-rearranged AML and is a very good candidate for potential therapeutic targeting. Disclosures:No relevant conflicts of interest to declare.

MiRNA-Seq normalization comparisons need improvement

BACKGROUND Currently there is no method of best practice for the normalization of microRNA sequencing data (miRNA-Seq). Therefore, we read with interest a recent article in RNA by Garmire and Subramaniam that set out to compare various normalization strategies specifically for this application (Garmire and Subramaniam 2012). They compared methods currentlyinusefornormalizationofmessengerRNAsequencing (mRNA-Seq) data, such as total-depth normalization (“raw”) and Trimmed Mean of M-values (“TMM”). Additionally, they compared many methods not used previously with sequencing data, such as global scaling, and borrowed fromstrategies appliedtomicroarraystudies,such asquantile normalization (QN). The article attracted our attention for many reasons, but notably for the claimed poor performance and “abnormal results” of our TMM method (Robinson and Oshlack2010).Afterinvestigating,wediscoveredthatTMM’s claimedpoorperformancewastheresultofanerrorthatshiftedlog-ratiosinthewrongdirection.Furthermore,wefeltthat various practical issues were not satisfyingly discussed; we comment briefly on these here and provide reproducible reanalyses to support our claims (see Supplemental Material).

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

The optimal management of breast cancer (BC) presents challenges due to the heterogeneous molecular classification of the disease. We performed survival analysis on a cohort of 466 patients with primary invasive ductal carcinoma (IDC), the most frequent type of BC, by integrating mRNA, microRNA (miRNA), and DNA methylation next-generation sequencing data from The Cancer Genome Atlas (TCGA). Expression data from eight other BC cohorts were used for validation. The prognostic value of the resulting miRNA/mRNA signature was compared with that of other prognostic BC signatures. Thirty mRNAs and seven miRNAs were associated with overall survival across different clinical and molecular subclasses of a 466-patient IDC cohort from TCGA. The prognostic RNAs included PIK3CA, one of the two most frequently mutated genes in IDC, and miRNAs such as hsa-miR-328, hsa-miR-484, and hsa-miR-874. The area under the curve of the receiver-operator characteristic for the IDC risk predictor in the TCGA cohort was 0.74 at 60 mo of overall survival (P < 0.001). Most relevant for clinical application, the integrated signature had the highest prognostic value in early stage I and II tumors (receiver-operator characteristic area under the curve = 0.77, P value < 0.001). The genes in the RNA risk predictor had an independent prognostic value compared with the clinical covariates, as shown by multivariate analysis. The integrated RNA signature was successfully validated on eight BC cohorts, comprising a total of 2,399 patients, and it had superior performance for risk stratification with respect to other RNA predictors, including the mRNAs used in MammaPrint and Oncotype DX assays.

Evaluation of normalization methods in mammalian microRNA-Seq data

Simple total tag count normalization is inadequate for microRNA sequencing data generated from the next generation sequencing technology. However, so far systematic evaluation of normalization methods on microRNA sequencing data is lacking. We comprehensively evaluate seven commonly used normalization methods including global normalization, Lowess normalization, Trimmed Mean Method (TMM), quantile normalization, scaling normalization, variance stabilization, and invariant method. We assess these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Additionally, we evaluate the methods with results from quantitative PCR validation. Our results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst. The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.

MYC Protein Inhibits Transcription of the MicroRNA Cluster MC-let-7a-1∼let-7d via Noncanonical E-box

The human microRNA cluster MC-let-7a-1∼let-7d, with three members let-7a-1, let-7f-1, and let-7d, is an important cluster of the let-7 family. These microRNAs play critical roles in regulating development and carcinogenesis. Therefore, precise control of MC-let-7a-1∼let-7d level is critical for cellular functions. In this study, we first showed that the expression of these three members was significantly reduced in human hepatocellular carcinoma HepG2 cells as compared with the immortalized human liver L02 cells. We demonstrated that the MC-let-7a-1∼let-7d cluster was encoded by a single polycistronic transcript driven by a 10-kb upstream promoter, with two MYC-binding sites. Importantly, MYC inhibited MC-let-7a-1∼let-7d promoter activity via binding to the noncanonical E-box 3 downstream of the transcription start sites, whereas it enhanced promoter activity by binding to the canonical E-box 2 upstream of the transcription start sites. We found that although the binding affinity of MYC to E-box 2 was stronger than E-box 3, the binding quantum of MYC to E-box 3 was significantly higher in cancerous HepG2 cells as compared with the noncancerous L02 cells. In addition, forced expression of let-7 could reverse the MYC-mediated cell proliferation. These findings suggested that in L02 cells with a low level of MYC, MYC binds mainly to E-box 2 to enhance MC-let-7a-1∼let-7d expression. However, in HepG2 cells with an elevated MYC, the extra MYC could bind to E-box 3 to suppress the transcription of MC-let-7a-1∼let-7d and thus enable HepG2 cells to maintain a high level of MYC and a low level of let-7 microRNAs simultaneously.

MESAdb: microRNA Expression and Sequence Analysis Database

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

MiRBase: tools for microRNA genomics

miRBase is the central online repository for microRNA (miRNA) nomenclature, sequence data, annotation and target prediction. The current release (10.0) contains 5071 miRNA loci from 58 species, expressing 5922 distinct mature miRNA sequences: a growth of over 2000 sequences in the past 2 years. miRBase provides a range of data to facilitate studies of miRNA genomics: all miRNAs are mapped to their genomic coordinates. Clusters of miRNA sequences in the genome are highlighted, and can be defined and retrieved with any inter-miRNA distance. The overlap of miRNA sequences with annotated transcripts, both protein- and non-coding, are described. Finally, graphical views of the locations of a wide range of genomic features in model organisms allow for the first time the prediction of the likely boundaries of many miRNA primary transcripts. miRBase is available at http://microrna.sanger.ac.uk/.

MiRBase: microRNA sequences, targets and gene nomenclature

The miRBase database aims to provide integrated interfaces to comprehensive microRNA sequence data, annotation and predicted gene targets. miRBase takes over functionality from the microRNA Registry and fulfils three main roles: the miRBase Registry acts as an independent arbiter of microRNA gene nomenclature, assigning names prior to publication of novel miRNA sequences. miRBase Sequences is the primary online repository for miRNA sequence data and annotation. miRBase Targets is a comprehensive new database of predicted miRNA target genes. miRBase is available at .