MRNA Precursor

TarBase5.0 contains data extracted from a total of 203 scientific papers resulting in 1333 entries describing a regulatory interaction between a miRNA and a target 3′ UTR (summarized in Table 1).
Table 1.

A list of all TarBase5.0 entries

Organism	Number of papers	Number of entries	Microarray data	pSILAC data
Homo sapiens	110	285	328	474
Mus musculus	28	105	13	–
D. melanogaster	23	77	–	–
C. elegans	18	14	–	–
Plants	21	30	–	–
Danio rerio	1	1	–	–
Rat	2	2	–	–
Total	203	514	341	474

The TarBase5.0 data set contains miRNA targets that tested either positive (induces target gene repression) or negative (no influence on target gene expression). For each experiment with a positive outcome the target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data is extracted. Additionally, for each miRNA and protein-coding gene, the database contains links to several other relevant and useful databases such as Ensembl (10 (link)), Hugo (11 (link)), UCSC genome browser (12 (link)) and SwissProt (13 (link)).
There are a number of direct and indirect experimental procedures that have been developed to test a possible miRNA–mRNA interaction. The entries in Tarbase5.0 are classified into four categories: TRUE or FALSE in the cases where an assay provides direct experimental evidence, or MICROARRAY and/or pSILAC in the cases that present only indirect evidence from high-throughput techniques to measure miRNA-mediated global transcriptomic or proteomic changes. All of these approaches make use of technology for miRNA knock down or overexpression. To overexpress a miRNA, expression constructs can be engineered using the mature miRNA, the precursor (hairpin) miRNA, or the pri-miRNA sequence for transfection into in vitro or in vivo transformed cells. Also, silencing of a specific miRNA can be accomplished by introducing chemically modified oligonucleotides that are perfectly complimentary to the mature miRNA (antagomirs) (14 (link)). These methods for modifying miRNA expression allow for several types of follow-up techniques to quantify and interpret differences in target gene expression. Below we provide a more detailed description of each of the four categories:
TRUE or FALSE: The most commonly used method for providing direct experimental evidence is the reporter gene assay. In its simplest form, an expression vector containing a reporter gene [i.e. Luciferase or Green Fluorescent Protein (GFP)] is first modified by cloning the predicted target 3′UTR downstream of the reporter gene, and then transfected into a cell line of interest in the absence and presence of the cognate miRNA. Despite the general utility of this approach to assay for 3′UTR-mediated effects on reporter protein expression, it is not informative for the precise location of the miRNA response element (MRE) or number of miRNA target sites in the 3′UTR. Integration of the reporter gene assay with site directed mutagenesis of the predicted MRE (and, further, restoring the complementarity of the miRNA–MRE interaction by mutating the mature miRNA sequence) yields a much more specific and direct result. To measure effects on reporter mRNA levels, the most commonly applied technique is quantitative RT-PCR (qRT-PCR). Measuring effects on both protein and mRNA levels can help provide information about the mode of miRNA-mediated silencing: mRNA translational repression or immediate RISC-mediated mRNA cleavage and degradation. A miRNA–MRE interaction is reported as TRUE or FALSE based on the results of the reporter gene assay.
MICROARRAY and/or pSILAC: These high-throughput approaches measure global changes in the transcriptome (15 (link)) or proteome (8 (link),16 (link)) given the presence or absence of a miRNA. Despite their power for large-scale analysis, these techniques only provide indirect evidence about a miRNA's targets since it is not possible to distinguish between primary direct targets and secondary indirect targets. Other high-throughput methods like degradome sequencing (17 (link),18 (link)) are also immensely useful but only in the scenarios where a miRNA induces RISC-mediated mRNA cleavage.
In order to facilitate user interaction, the query function is divided into several functionally related subgroups. The initial screen of the TarBase5.0 user interface allows users to query based on miRNA, gene and organism. For more advanced queries, the user can utilize the extended query options. In this case, the search menus are arranged into four functionally related groups.
The first group contains the fields with information about the miRNA–target interaction: the validity of the interaction (field ‘Support Type’, either true or false), the function of the interaction which can be either translational repression or mRNA cleavage (field ‘DataType’), the sufficiency of a single target site to exert the specific function (field ‘S_S_S’) and the number of miRNA response elements present in the specific UTR (field ‘MRE’).
The second group contains the fields that refer to the experimental methods that led to the reported result. The field ‘Direct Support’ refers to experimental procedures that provide direct evidence regarding the miRNA–target interaction (i.e. reporter gene assays) while ‘Indirect Support’ refers to experimental procedures that provide more global, system-wide miRNA-mediated effects (i.e. microarrays).
The third group corresponds to biological properties of the miRNA or target gene: biological functions (field ‘Protein Type’), specific expression profiles (field ‘miRNA Expression’) or the physiological processes in which this interaction is involved (field ‘Event or Pathology’). The fourth and final group contains some general query features such as the scientific paper (searchable by Author or PMID).
The results are presented in a similar format as the query fields. By default, the results screen (Figure 2) shows only the repression type, the miRNA identifier, the target gene identified by the HGNC symbol (if it is a human gene), the common gene name, the Refseq isoform id (particularly relevant in cases of gene variants or SNP haplotypes), the affected biological processes and the paper containing the information presented. Users can opt to view more detailed information by clicking on the ‘+’ box so that the expanded results view is opened (Figure 2). The additional information is divided into three categories: miRNA information, gene information and experimental conditions.
Figure 2.

Example of a result screen for a TarBase query. The context-specific links to other resources are indicated by the blue arrows.

The ‘miRNA information’ category contains the properties of the specific miRNA such as the miRNA's sequence [extracted from miRBase (19 (link))], the number and sequences of the MREs, their locations within the gene's 3′UTR, and the affected tissues (extracted from the paper). The ‘Gene information’ category gathers mostly biological properties of the target gene like the protein type, Ensembl and SwissProt IDs and chromosome location, providing direct links to Ensembl, SwissProt and the UCSC browser respectively. Moreover, expression profiles and tumor involvement are also provided for human genes (information extracted from the Ensembl eGenetics database). Finally, the ‘Experimental conditions’ category provides the nature of the direct or indirect evidence for the miRNA–target gene interaction. The cell lines used to carry out the specific experiment are also presented in order to render the experimental conditions more complete and reproducible.

piPipes comprises five pipelines designed to analyze small RNA-seq, RNA-seq, degradome- and CAGE-seq, ChIP-seq or genome-seq data. The small RNA-seq pipeline reports the abundance, length distribution, nucleotide composition and 5′-to-5′ distance (‘Ping-Pong’ signature) of piRNAs assigned to genomic annotations, including individual transposon families and piRNA clusters, the initial sources of piRNA precursor transcripts. The RNA-seq pipeline reports the normalized abundance of transcripts from both genes and transposons. The degradome-seq pipeline offers methods to identify piRNA-directed cleavage products. This pipeline can also be used to analyze any long RNA sequencing method designed to define RNA 5′ ends, e.g. CAGE-seq. The ChIP-seq pipeline uses the widely used peak-calling algorithm MACS2 (Zhang et al., 2008 (link)), focusing on piRNA clusters and transposons. The genome-seq pipeline detects novel transposition events as well as structural variation. Supplementary Figure S1 illustrates the general piPipes workflow, using the small RNA-seq pipeline as an example. First, all reads aligned to ribosomal RNA (rRNA) sequences are removed. The remaining reads are then mapped to microRNA (miRNA) hairpin sequences to quantify the abundance and 5′- and 3′-end heterogeneity of mature miRNAs. Reads that do not match rRNAs or miRNAs are then mapped to the reference genome. piPipes next assigns reads to different genomic features (e.g., transposons, piRNA clusters and genes) by their coordinates. To achieve maximal speed, piPipes parallelizes this step on multiple threads using ParaFly software from the Trinity package (Grabherr et al., 2011 (link)). For the reads assigned to each genomic feature, piPipes draws publication-quality graphs of length distribution and nucleotide composition, as well as the distance between the 5′ ends of two small RNAs from opposite strands of the same locus, a standard method for detecting piRNA ‘Ping-Pong’ amplification or siRNA phasing (Fig. 1A and Supplementary Fig. S1B). Furthermore, piPipes generates a table that summarizes the number of unique and multiple mappers counted as species (distinct sequences) or reads. The RNA-seq pipeline also starts with rRNA removal. The remaining reads are then mapped to the genome using STAR (Dobin et al., 2013 (link)). piPipes quantifies transcript abundance from genomic alignment by both Cufflinks (Trapnell et al., 2010 (link)) and HTSeq-count (Anders et al., 2014 (link)). In addition, direct mapping of the reads to the transcriptome is performed using Bowtie2 followed by eXpress quantification (Roberts and Pachter, 2013 (link)). Degradome-seq and CAGE-seq share the same pipeline because both methods aim to characterize the 5′ ends of RNAs. This pipeline discards reads that can only be mapped to the genome via soft clipping of their 5′ ends (i.e. the prefixes of these reads do not map to the genome). The alignment procedure is otherwise similar to that used for RNA-seq data. The nucleotide composition for each genomic feature is calculated as in the small RNA pipeline (Supplementary Fig. S3B). The ChIP-seq pipeline aligns the ChIP and input libraries to the genome using Bowtie2. piPipes calls peaks using MACS2 (Zhang et al., 2008 (link)), which supports both narrow (such as transcription factors) and broad (such as histone 3 trimethyl lysine 9, H3K9me3) peaks. Transcription start site, transcription end site and metagene analyses of different genomic features are implemented by bwtool (Pohl and Beato, 2014 (link)). The genome-seq pipeline applies different algorithms, including BreakDancer (Chen et al., 2009 (link)), RetroSeq (Hormozdiari et al., 2010 (link); Keane et al., 2013 (link)) and TEMP (Zhuang et al., 2014 (link)), to discover transposon insertion, deletion and other structural variation events (Supplementary Fig. S5). piPipes uses a Circos plot (Zhang et al., 2013 (link)) to represent the variant loci discovered by each algorithm across different chromosomes (Fig. 1D).
Fig. 1.

Gallery of piPipes Figures (A) Barplot representing length distribution of Drosophila w¹ ovary small RNAs assigned to sense (blue) and antisense (red) strands of transposons. (B) Scatterplot comparing w¹ to aub^HN2/QC42 Drosophila ovary RNA-seq reads assigned to mRNA (NM; red), non-coding RNA (NR; green) and transposons (blue). (C) Metagene plot of H3K9me3 ChIP-seq of piRNA clusters from flies in which piwi mRNA was depleted by double-stranded RNA-triggered RNA driven by a triple Gal4 driver (SRX215630). (D) Circos plot representing the locations of, from the periphery to the center, cytological position, piRNA clusters, SV discovered by TEMP (tiles), retroSeq (tiles) and VariationHunter (links) using genomic sequencing of 2–4-day-old ovaries

The small RNA-seq, RNA-seq and ChIP-seq pipelines can each be run in two modes, allowing analysis of a single sample or a pair of samples. The dual-sample mode uses the output from the single-sample mode and performs pair-wise comparison as illustrated by balloonplots and scatterplots (Supplementary Fig. S1C and D). The comparison can be performed on miRNA, piRNA or mRNA. Figure 1B illustrates a scatterplot showing the mRNA abundance in an RNA-seq dataset analyzed by the RNA-seq pipeline in the dual-sample mode. The dual-sample mode of the RNA-seq pipeline also uses Cuffdiff (Trapnell et al., 2013 (link)) to perform differential analysis on genic transcripts. In the dual-sample mode, the ChIP-seq pipeline uses MACS2 to identify differentially enriched loci (Supplementary Fig. S4).