Background: Alternative splicing (AS) is a posttranscriptional process that produces differ-ent transcripts from the same gene and is important to produce diverse protein products in response to environmental stimuli. AS occurs at specific sites on the mRNA sequence, some of which have been de-fined. Multiple bioinformatics tools have been developed to detect AS from experimental data.Objectives: The goal of this review is to help researchers use specific tools to aid their research and to develop new AS detection tools based on these previously established tools.Method: We selected 15 AS detection tools that were recently published; we classified and delineated them on several aspects. Also, a performance comparison of these tools with the same starting input was conducted.Result: We reviewed the following categorized features of the tools: Publication information, working principles, generic and distinct workflows, running platform, input data requirement, sequencing depth dependency, reads mapped to multiple locations, isoform annotation basis, precise detected AS types, and performance benchmarks.Conclusion: Through comparisons of these tools, we provide a panorama of the advantages and short-comings of each tool and their scopes of application.

BackgroundMicroRNAs (miRNA) are short nucleotides that interact with their target genes through 3′ untranslated regions (UTRs). The Cancer Genome Atlas (TCGA) harbors an increasing amount of cancer genome data for both tumor and normal samples. However, there are few visualization tools focusing on concurrently displaying important relationships and attributes between miRNAs and mRNAs of both cancer tumor and normal samples. Moreover, a deep investigation of miRNA-mRNA target and biological relationships across multiple cancer types by integrating web-based analysis has not been thoroughly conducted.ResultsWe developed an interactive visualization tool called MMiRNA-Viewer that can concurrently present the co-relationships of expression between miRNA-mRNA pairs of both tumor and normal samples into a single graph. The input file of MMiRNA-Viewer contains the expression information including fold changes between normal and tumor samples for mRNAs and miRNAs, the correlation between mRNA and miRNA, and the predicted target relationship by a number of databases. Users can also load their own input data into MMiRNA-Viewer and visualize and compare detailed information about cancer-related gene expression changes, and also changes in the expression of transcription-regulating miRNAs.To validate the MMiRNA-Viewer, eight types of TCGA cancer datasets with both normal and control samples were selected in this study and three filter steps were applied subsequently. We performed Gene Ontology (GO) analysis for genes available in final selected 238 pairs and also for genes in the top 5 % (95 percentile) for each of eight cancer types to report a significant number of genes involved in various biological functions and pathways. We also calculated various centrality measurement matrices for the largest connected component(s) in each of eight cancers and reported top genes and miRNAs with high centrality measurements.ConclusionsWith its user-friendly interface, dynamic visualization and advanced queries, we also believe MMiRNA-Viewer offers an intuitive approach for visualizing and elucidating co-relationships between miRNAs and mRNAs of both tumor and normal samples. We suggest that miRNA and mRNA pairs with opposite fold changes of their expression and with inverted correlation values between tumor and normal samples might be most relevant for explaining the decoupling of mRNAs and their targeting miRNAs in tumor samples for certain cancer types.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1219-y) contains supplementary material, which is available to authorized users.

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BackgroundSmall noncoding regulatory RNAs (sRNAs) are post-transcriptional regulators, regulating mRNAs, proteins, and DNA in bacteria. One class of sRNAs, trans-acting sRNAs, are the most abundant sRNAs transcribed from the intergenic regions (IGRs) of the bacterial genome. In Streptococcus pyogenes, a common and potentially deadly pathogen, many sRNAs have been identified, but only a few have been studied. The goal of this study is to identify trans-acting sRNAs that can be substrates of RNase III. The endoribonuclease RNase III cleaves double stranded RNAs, which can be formed during the interaction between an sRNA and target mRNAs.ResultsFor this study, we created an RNase III null mutant of Streptococcus pyogenes and its RNA sequencing (RNA-Seq) data were analyzed and compared to that of the wild-type. First, we developed a custom script that can detect intergenic regions of the S. pyogenes genome. A differential expression analysis with Cufflinks and Stringtie was then performed to identify the intergenic regions whose expression was influenced by the RNase III gene deletion.ConclusionThis analysis yielded 12 differentially expressed regions with >|2| fold change and p ≤ 0.05. Using Artemis and Bamview genome viewers, these regions were visually verified leaving 6 putative sRNAs. This study not only expanded our knowledge on novel sRNAs but would also give us new insight into sRNA degradation.

BackgroundIt is generally thought that most canonical or non-canonical splicing events involving U2- and U12 spliceosomes occur within nuclear pre-mRNAs. However, the question of whether at least some U12-type splicing occurs in the cytoplasm is still unclear. In recent years next-generation sequencing technologies have revolutionized the field. The “Read-Split-Walk” (RSW) and “Read-Split-Run” (RSR) methods were developed to identify genome-wide non-canonical spliced regions including special events occurring in cytoplasm. As the significant amount of genome/transcriptome data such as, Encyclopedia of DNA Elements (ENCODE) project, have been generated, we have advanced a newer more memory-efficient version of the algorithm, “Read-Split-Fly” (RSF), which can detect non-canonical spliced regions with higher sensitivity and improved speed. The RSF algorithm also outputs the spliced sequences for further downstream biological function analysis.ResultsWe used open access ENCODE project RNA-Seq data to search spliced intron sequences against the U12-type spliced intron sequence database to examine whether some events could occur as potential signatures of U12-type splicing. The check was performed by searching spliced sequences against 5’ss and 3’ss sequences from the well-known orthologous U12-type spliceosomal intron database U12DB. Preliminary results of searching 70 ENCODE samples indicated that the presence of 5’ss with U12-type signature is more frequent than U2-type and prevalent in non-canonical junctions reported by RSF. The selected spliced sequences have also been further studied using miRBase to elucidate their functionality. Preliminary results from 70 samples of ENCODE datasets show that several miRNAs are prevalent in studied ENCODE samples. Two of these are associated with many diseases as suggested in the literature. Specifically, hsa-miR-1273 and hsa-miR-548 are associated with many diseases and cancers.ConclusionsOur RSF pipeline is able to detect many possible junctions (especially those with a high RPKM) with very high overall accuracy and relative high accuracy for novel junctions. We have incorporated useful parameter features into the pipeline such as, handling variable-length read data, and searching spliced sequences for splicing signatures and miRNA events. We suggest RSF, a tool for identifying novel splicing events, is applicable to study a range of diseases across biological systems under different experimental conditions.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1801-y) contains supplementary material, which is available to authorized users.