Background: MicroRNAs (miRNAs) are identified as crucial gene regulators in response to myocardial infarction (MI). However, the overall relationships between miRNAs and the gene targets which contribute to the cellular phenotypes in MI are not fully elucidated. To make a better understanding towards functional roles of miRNAs in MI, useful information was mined through bioinformatic techniques. Method: MI-related miRNAs were retrieved from publications, and PicTar, TargetScanS, and miRanda programs were used to predict their gene targets. Gene ontology (GO) and pathway analyses of gene targets were applied to uncover functional roles of miRNAs. The miRNA-gene networks were illustrated by Pajek tool. Finally, validation experiments were performed towards two important miRNAs in the networks. Result: Up to 119 MI-related miRNAs were retrieved from publications. GO and pathway analyses for their predicted gene targets demonstrated that these dysregulated miRNAs were enriched in cardiovascular-related phenotypes. Through illustrating miRNA-gene networks, overall relationships between miRNAs and gene targets were detected especially in processes of apoptosis and angiogenesis. Moreover, experimental data supported bioinformatic predictions that miR-106b served as an anti-apoptotic modulator through inhibition of p21 expression and miR-15b displayed anti-angiogenesis activity. Conclusion: The miRNAs played essential roles in pathological processes of MI. Further, miR-106b and miR-15b maybe mediated as robust regulators in apoptosis or angiogenesis following MI, respectively.

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The cell type identification is among the most important tasks in single-cell RNA-sequencing (scRNA-seq) analysis. Many in silico methods have been developed and can be roughly categorized as either supervised or unsupervised. In this study, we investigated the performances of 8 supervised and 10 unsupervised cell type identification methods using 14 public scRNA-seq datasets of different tissues, sequencing protocols and species. We investigated the impacts of a number of factors, including total amount of cells, number of cell types, sequencing depth, batch effects, reference bias, cell population imbalance, unknown/novel cell type, and computational efficiency and scalability. Instead of merely comparing individual methods, we focused on factors’ impacts on the general category of supervised and unsupervised methods. We found that in most scenarios, the supervised methods outperformed the unsupervised methods, except for the identification of unknown cell types. This is particularly true when the supervised methods use a reference dataset with high informational sufficiency, low complexity and high similarity to the query dataset. However, such outperformance could be undermined by some undesired dataset properties investigated in this study, which lead to uninformative and biased reference datasets. In these scenarios, unsupervised methods could be comparable to supervised methods. Our study not only explained the cell typing methods’ behaviors under different experimental settings but also provided a general guideline for the choice of method according to the scientific goal and dataset properties. Finally, our evaluation workflow is implemented as a modularized R pipeline that allows future evaluation of new methods. Availability: All the source codes are available at https://github.com/xsun28/scRNAIdent.

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BackgroundThe single molecule, real time (SMRT) sequencing technology of Pacific Biosciences enables the acquisition of transcripts from end to end due to its ability to produce extraordinarily long reads (>10 kb). This new method of transcriptome sequencing has been applied to several projects on humans and model organisms. However, the raw data from SMRT sequencing are of relatively low quality, with a random error rate of approximately 15 %, for which error correction using next-generation sequencing (NGS) short reads is typically necessary. Few tools have been designed that apply a hybrid sequencing approach that combines NGS and SMRT data, and the most popular existing tool for error correction, LSC, has computing resource requirements that are too intensive for most laboratory and research groups. These shortcomings severely limit the application of SMRT long reads for transcriptome analysis.ResultsHere, we report an improved tool (LSCplus) for error correction with the LSC program as a reference. LSCplus overcomes the disadvantage of LSC’s time consumption and improves quality. Only 1/3–1/4 of the time and 1/20–1/25 of the error correction time is required using LSCplus compared with that required for using LSC.ConclusionsLSCplus is freely available at http://www.herbbol.org:8001/lscplus/. Sample calculations are provided illustrating the precision and efficiency of this method regarding error correction and isoform detection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1316-y) contains supplementary material, which is available to authorized users.

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