While the number of human miRNA candidates continuously increases, only a few of them are completely characterized and experimentally validated. Toward determining the total number of true miRNAs, we employed a combined in silico high- and experimental low-throughput validation strategy. We collected 28 866 human small RNA sequencing data sets containing 363.7 billion sequencing reads and excluded falsely annotated and low quality data. Our high-throughput analysis identified 65% of 24 127 mature miRNA candidates as likely false-positives. Using northern blotting, we experimentally validated miRBase entries and novel miRNA candidates. By exogenous overexpression of 108 precursors that encode 205 mature miRNAs, we confirmed 68.5% of the miRBase entries with the confirmation rate going up to 94.4% for the high-confidence entries and 18.3% of the novel miRNA candidates. Analyzing endogenous miRNAs, we verified the expression of 8 miRNAs in 12 different human cell lines. In total, we extrapolated 2300 true human mature miRNAs, 1115 of which are currently annotated in miRBase V22. The experimentally validated miRNAs will contribute to revising targetomes hypothesized by utilizing falsely annotated miRNAs.

BackgroundWe present the first sequencing data using the combinatorial probe-anchor synthesis (cPAS)-based BGISEQ-500 sequencer. Applying cPAS, we investigated the repertoire of human small non-coding RNAs and compared it to other techniques.ResultsStarting with repeated measurements of different specimens including solid tissues (brain and heart) and blood, we generated a median of 30.1 million reads per sample. 24.1 million mapped to the human genome and 23.3 million to the miRBase. Among six technical replicates of brain samples, we observed a median correlation of 0.98. Comparing BGISEQ-500 to HiSeq, we calculated a correlation of 0.75. The comparability to microarrays was similar for both BGISEQ-500 and HiSeq with the first one showing a correlation of 0.58 and the latter one correlation of 0.6. As for a potential bias in the detected expression distribution in blood cells, 98.6% of HiSeq reads versus 93.1% of BGISEQ-500 reads match to the 10 miRNAs with highest read count. After using miRDeep2 and employing stringent selection criteria for predicting new miRNAs, we detected 74 high-likely candidates in the cPAS sequencing reads prevalent in solid tissues and 36 candidates prevalent in blood.ConclusionsWhile there is apparently no ideal platform for all challenges of miRNome analyses, cPAS shows high technical reproducibility and supplements the hitherto available platforms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0287-1) contains supplementary material, which is available to authorized users.

Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at .

We present GeneTrail 3, a major extension of our web service GeneTrail that offers rich functionality for the identification, analysis, and visualization of deregulated biological processes. Our web service provides a comprehensive collection of biological processes and signaling pathways for 12 model organisms that can be analyzed with a powerful framework for enrichment and network analysis of transcriptomic, miRNomic, proteomic, and genomic data sets. Moreover, GeneTrail offers novel workflows for the analysis of epigenetic marks, time series experiments, and single cell data. We demonstrate the capabilities of our web service in two case-studies, which highlight that GeneTrail is well equipped for uncovering complex molecular mechanisms. GeneTrail is freely accessible at: http://genetrail.bioinf.uni-sb.de.

MicroRNAs (miRNA) posttranscriptionally regulate gene expression and are important in tumorigenesis. Previous deep sequencing identified the miRNA profile of prostate carcinoma versus nonmalignant prostate tissue. Here, we generated miRNA expression profiles of prostate carcinoma by deep sequencing, with increasing tumor stage relative to corresponding nonmalignant and healthy prostate tissue, and detected clearly changed miRNA expression patterns. The miRNA profiles of the healthy and nonmalignant tissues were consistent with our previous findings, indicating a high fidelity of the method employed. In the tumors, quantitative real-time PCR (qRT-PCR) analysis of 40 paired samples of prostate carcinoma versus normal tissue revealed significant upregulation of miR-20a, miR-148a, miR-200b, and miR-375 and downregulation of miR-143 and miR-145. Hereby, miR-375 increased from normal to organ-confined tumors (pT2 pN0), slightly decreased in tumors with extracapsular growth (pT3 pN0), but was then expressed again at higher levels in lymph node metastasizing (pN1) tumors. The sequencing data for miR-375 were confirmed by Northern blotting and qRT-PCR. The regulation for other selected miRNAs could, however, not be confirmed by qRT-PCR in individual tumor stages. MiR-200b, in addition to miR-200c and miR-375 reduced the expression of SEC23A. Interestingly, miR-375, found by sequencing in pT2 upregulated by us and others in tumor versus normal tissue, and miR-15a, found by sequencing in pT2 and pT3 and in the metastasizing tumors, target the phosphatases PHLPP1 and PHLPP2, respectively. PHLPP1 and PHLPP2 dephosphorylate members of the AKT family of signal transducers, thereby inhibiting cell growth. Coexpression of miR-15a and miR-375 resulted in downregulation of PHLPP1/2 and strongly increased prostate carcinoma cell growth.Implications: These genomic data reveal relevant miRNAs in prostate cancer that may have biomarker and therapeutic potential. Mol Cancer Res; 12(2); 250-63. Ó2013 AACR. IntroductionProstate carcinoma is the second most frequently diagnosed malignancy and a leading cause of cancer-related death in men worldwide (1). The underlying mechanisms resulting in invasive growth after dissemination of the primary tumor from the initial site in the prostate are not completely understood. MicroRNAs (miRNAs) are now recognized as contributing factors to the induction, growth, and metastasis