To meet the increasing demand in the field, numerous long noncoding RNA (lncRNA) databases are available. Given many lncRNAs are specifically expressed in certain cell types and/or time-dependent manners, most lncRNA databases fall short of providing such profiles. We developed a strategy using logic programming to handle the complex organization of organs, their tissues and cell types as well as gender and developmental time points. To showcase this strategy, we introduce 'RenalDB' (http://renaldb.uni-frankfurt.de), a database providing expression profiles of RNAs in major organs focusing on kidney tissues and cells. RenalDB uses logic programming to describe complex anatomy, sample metadata and logical relationships defining expression, enrichment or specificity. We validated the content of RenalDB with biological experiments and functionally characterized two long intergenic noncoding RNAs: LOC440173 is important for cell growth or cell survival, whereas PAXIP1-AS1 is a regulator of cell death. We anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.

RNA editing of adenosine residues to inosine ('A-to-I editing') is the most common RNA modification event detectible with RNA sequencing (RNA-seq). While not directly detectable, inosine is read by next-generation sequencers as guanine. Therefore, mapping RNA-seq reads to their corresponding reference genome can detect potential editing events by identifying 'A-to-G' conversions. However, one must exercise caution when searching for editing sites, as A-to-G conversions also arise from sequencing errors as well as mutations. To address these complexities, several algorithms and software products have been developed to accurately identify editing events. Here, we survey currently available methods to analyze RNA editing events and introduce a new easy-to-use bioinformatics tool 'RNAEditor' for the detection of RNA editing events. During the development of RNAEditor, we noticed editing often happened in clusters, which we named 'editing islands'. We developed a clustering algorithm to find editing islands and included it in RNAEditor. RNAEditor is freely available at http://rnaeditor.uni-frankfurt.de. We anticipate that RNAEditor will provide biologists with an easy-to-use tool for studying RNA editing events and the newly defined editing islands.

Intensive research in past two decades has uncovered the presence and importance of noncoding RNAs (ncRNAs), which includes microRNAs (miRs) and long ncRNAs (lncRNAs). These two classes of ncRNAs interact to a certain extent, as some lncRNAs bind to miRs to sequester them. Such lncRNAs are collectively called 'competing endogenous RNAs' or 'miRNA sponges'. In this study, we screened for lncRNAs that may act as miRNA sponges using the publicly available data sets and databases. To uncover the roles of miRNA sponges, loss-of-function experiments were conducted, which revealed the biological roles as miRNA sponges. LINC00324 is important for the cell survival by binding to miR-615-5p leading to the de-repression of its target BTG2. LOC400043 controls several biological functions via sequestering miR-28-3p and miR-96-5p, thereby changing the expressions of transcriptional regulators. Finally, we also screened for circular RNAs (circRNAs) that may function as miRNA sponges. The results were negative at least for the selected circRNAs in this study. In conclusion, miRNA sponges can be identified by applying a series of bioinformatics techniques and validated with biological experiments.

Maintaining the consistency of genomic annotations is an increasingly complex task because of the iterative and dynamic nature of assembly and annotation, growing numbers of biological databases and insufficient integration of annotations across databases. As information exchange among databases is poor, a 'novel' sequence from one reference annotation could be annotated in another. Furthermore, relationships to nearby or overlapping annotated transcripts are even more complicated when using different genome assemblies. To better understand these problems, we surveyed current and previous versions of genomic assemblies and annotations across a number of public databases containing long noncoding RNA. We identified numerous discrepancies of transcripts regarding their genomic locations, transcript lengths and identifiers. Further investigation showed that the positional differences between reference annotations of essentially the same transcript could lead to differences in its measured expression at the RNA level. To aid in resolving these problems, we present the algorithm 'Universal Genomic Accession Hash (UGAHash)' and created an open source web tool to encourage the usage of the UGAHash algorithm. The UGAHash web tool (http://ugahash.uni-frankfurt.de) can be accessed freely without registration. The web tool allows researchers to generate Universal Genomic Accessions for genomic features or to explore annotations deposited in the public databases of the past and present versions. We anticipate that the UGAHash web tool will be a valuable tool to check for the existence of transcripts before judging the newly discovered transcripts as novel.

Owing greatly to the advancement of next-generation sequencing (NGS), the amount of NGS data is increasing rapidly. Although there are many NGS applications, one of the most commonly used techniques 'RNA sequencing (RNA-seq)' is rapidly replacing microarray-based techniques in laboratories around the world. As more and more of such techniques are standardized, allowing technicians to perform these experiments with minimal hands-on time and reduced experimental/operator-dependent biases, the bottleneck of such techniques is clearly visible; that is, data analysis. Further complicating the matter, increasing evidence suggests most of the genome is transcribed into RNA; however, the majority of these RNAs are not translated into proteins. These RNAs that do not become proteins are called 'noncoding RNAs (ncRNAs)'. Although some time has passed since the discovery of ncRNAs, their annotations remain poor, making analysis of RNA-seq data challenging. Here, we examine the current limitations of RNA-seq analysis using case studies focused on the detection of novel transcripts and examination of their characteristics. Finally, we validate the presence of novel transcripts using biological experiments, showing novel transcripts can be accurately identified when a series of filters is applied. In conclusion, novel transcripts that are identified from RNA-seq must be examined carefully before proceeding to biological experiments.

MOTIVATION: Increasing evidences suggest that most of the genome is transcribed into RNAs, but many of them are not translated into proteins. All those RNAs that do not become proteins are called 'non-coding RNAs (ncRNAs)', which outnumbers protein-coding genes. Interestingly, these ncRNAs are shown to be more tissue specifically expressed than protein-coding genes. Given that tissue-specific expressions of transcripts suggest their importance in the expressed tissue, researchers are conducting biological experiments to elucidate the function of such ncRNAs. Owing greatly to the advancement of next-generation techniques, especially RNA-seq, the amount of high-throughput data are increasing rapidly. However, due to the complexity of the data as well as its high volume, it is not easy to re-analyze such data to extract tissue-specific expressions of ncRNAs from published datasets. RESULTS: Here, we introduce a new knowledge database called 'C-It-Loci', which allows a user to screen for tissue-specific transcripts across three organisms: human, mouse and zebrafish. C-It-Loci is intuitive and easy to use to identify not only protein-coding genes but also ncRNAs from various tissues. C-It-Loci defines homology through sequence and positional conservation to allow for the extraction of species-conserved loci. C-It-Loci can be used as a starting point for further biological experiments. AVAILABILITY AND IMPLEMENTATION: C-It-Loci is freely available online without registration at http://c-it-loci.uni-frankfurt.de. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Due to the development of high-throughput technologies such as microarrays, it has become possible to determine genome-wide expression changes in a single experiment. Although much attention has been paid to identify differentially expressed genes, the functions of tens of thousands of genes in different species still remain unknown. RESULTS: C-It is a knowledge database that has its focus on 'uncharacterized genes'. C-It contains expression profiles of various tissues from human, mouse, rat, chicken and zebrafish. By applying our previously introduced algorithm DGSA (Database-Dependent Gene Selection and Analysis), it is possible to screen for uncharacterized, tissue-enriched genes in the species mentioned above. C-It is designed to include further expression studies, which might provide more comprehensive coverage of gene expression patterns and tissue-enriched splicing isoforms. We propose that C-It will be an excellent starting point to study uncharacterized genes. AVAILABILITY: C-It is freely available online without registration at http://C-It.mpi-bn.mpg.dehttp://C-It.mpi-bn.mpg.de.

SUMMARY: The Exon Array Analyzer (EAA) is a web server, which provides a user-friendly interface to identify alternative splicing events analyzed with Affymetrix Exon Arrays. The EAA implements the Splice Index algorithm to identify differential expressed exons. The use of various filters allows reduction of the number of false positive hits. Results are presented with detailed annotation information and graphics to identify splice events and to facilitate biological validations. To demonstrate the versatility of the EAA, we analyzed exon arrays of 11 different murine tissues using sample data provided by Affymetrix (http://www.affymetrix.com). Data from the heart were compared with other tissues to identify exons that undergo heart-specific alternatively splicing, resulting in the identification of 885 differentially expressed probe sets in 649 genes. AVAILABILITY: The web interface is available at http://EAA.mpi-bn.mpg.de/. Detailed documentation is available on the EAA web site (http://EAA.mpi-bn.mpg.de/supp.php) including screen shots, example analyzes and step by step instructions. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.