Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm.

BACKGROUND: RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem. We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments. RESULTS: As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. CONCLUSIONS: Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

EventPointer: an effective identification of alternative splicing events using junction arrays.

BACKGROUND: Alternative splicing (AS) is a major source of variability in the transcriptome of eukaryotes. There is an increasing interest in its role in different pathologies. Before sequencing technology appeared, AS was measured with specific arrays. However, these arrays did not perform well in the detection of AS events and provided very large false discovery rates (FDR). Recently the Human Transcriptome Array 2.0 (HTA 2.0) has been deployed. It includes junction probes. However, the interpretation software provided by its vendor (TAC 3.0) does not fully exploit its potential (does not study jointly the exons and junctions involved in a splicing event) and can only be applied to case-control studies. New statistical algorithms and software must be developed in order to exploit the HTA 2.0 array for event detection. RESULTS: We have developed EventPointer, an R package (built under the aroma.affymetrix framework) to search and analyze Alternative Splicing events using HTA 2.0 arrays. This software uses a linear model that broadens its application from plain case-control studies to complex experimental designs. Given the CEL files and the design and contrast matrices, the software retrieves a list of all the detected events indicating: 1) the type of event (exon cassette, alternative 3', etc.), 2) its fold change and its statistical significance, and 3) the potential protein domains affected by the AS events and the statistical significance of the possible enrichment. Our tests have shown that EventPointer has an extremely low FDR value (only 1 false positive within the tested top-200 events). This software is publicly available and it has been uploaded to GitHub. CONCLUSIONS: This software empowers the HTA 2.0 arrays for AS event detection as an alternative to RNA-seq: simplifying considerably the required analysis, speeding it up and reducing the required computational power.

Joint analysis of miRNA and mRNA expression data.

miRNAs are small RNA molecules ('22 nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.

Improving miRNA-mRNA interaction predictions.

BACKGROUND: MicroRNAs are short RNA molecules that post-transcriptionally regulate gene expression. Today, microRNA target prediction remains challenging since very few have been experimentally validated and sequence-based predictions have large numbers of false positives. Furthermore, due to the different measuring rules used in each database of predicted interactions, the selection of the most reliable ones requires extensive knowledge about each algorithm. RESULTS: Here we propose two methods to measure the confidence of predicted interactions based on experimentally validated information. The output of the methods is a combined database where new scores and statistical confidences are re-assigned to each predicted interaction. The new scores allow the robust combination of several databases without the effect of low-performing algorithms dragging down good-performing ones. The combined databases obtained using both algorithms described in this paper outperform each of the existing predictive algorithms that were considered for the combination. CONCLUSIONS: Our approaches are a useful way to integrate predicted interactions from different databases. They reduce the selection of interactions to a unique database based on an intuitive score and allow comparing databases between them.

Quantification of miRNA-mRNA interactions.

miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/.