MicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies focus on mature miRNAs, which leaves many unknowns about primary miRNAs (pri-miRNAs). To fill the gap, we attempted to model the ...expression of pri-miRNAs in 1829 primary cell types, cell lines, and tissues in this study. We demonstrated that the expression of pri-miRNAs can be modeled well by the expression of specific sets of mRNAs, which we termed their associated mRNAs. These associated mRNAs differ from their corresponding target mRNAs and are enriched with specific functions. Most associated mRNAs of a miRNA are shared across conditions, while on average, about one-fifth of the associated mRNAs are condition-specific. Our study shed new light on understanding miRNA biogenesis and general gene transcriptional regulation.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One ...of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs ( http://hulab.ucf.edu/research/projects/DmiRT/ ). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
The authors of the BASiNET tool claim that the survey paper ‘A systematic evaluation of computational tools for lncRNA identification’ incorrectly evaluates the BASiNET tool. Here, we point ...out that the survey paper correctly evaluates the BASiNET tool and why the evaluation should not be carried out as BASiNET authors suggest.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The computational identification of long non-coding RNAs (lncRNAs) is important to study lncRNAs and their functions. Despite the existence of many computation tools for lncRNA identification, to our ...knowledge, there is no systematic evaluation of these tools on common datasets and no consensus regarding their performance and the importance of the features used. To fill this gap, in this study, we assessed the performance of 17 tools on several common datasets. We also investigated the importance of the features used by the tools. We found that the deep learning-based tools have the best performance in terms of identifying lncRNAs, and the peptide features do not contribute much to the tool accuracy. Moreover, when the transcripts in a cell type were considered, the performance of all tools significantly dropped, and the deep learning-based tools were no longer as good as other tools. Our study will serve as an excellent starting point for selecting tools and features for lncRNA identification.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Cell–cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands ...and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Gene transcription start site (TSS) identification is important to understanding transcriptional gene regulation. Cap Analysis Gene Expression (CAGE) experiments have recently become common practice ...for direct measurement of TSSs. Currently, CAGE data available in public databases created unprecedented opportunities to study gene transcriptional initiation mechanisms under various cellular conditions. However, due to potential transcriptional noises inherent in CAGE data, in-silico methods are required to identify bonafide TSSs from noises further. Here we present a computational approach dlCAGE, an end-to-end deep neural network to identify TSSs from CAGE data. dlCAGE incorporate de-novo DNA regulatory motif features discovered by DeepBind model architecture, as well as existing sequence and structural features. Testing results of dlCAGE in several cell lines in comparison with current state-of-the-art approaches showed its superior performance and promise in TSS identification from CAGE experiments.
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