BackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills.ResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0492-3) contains supplementary material, which is available to authorized users.

The ISTD is an example of a new class of simulation models capable of realistically representing complex drug transport and drug-drug interaction phenomena.

Identification of diffuse signals from the chromatin immunoprecipitation and high-throughput massively parallel sequencing (ChIP-Seq) technology poses significant computational challenges, and there are few methods currently available. We present a novel global clustering approach to enrich diffuse CHIP-Seq signals of RNA polymerase II and histone 3 lysine 4 trimethylation (H3K4Me3) and apply it to identify putative long intergenic non-coding RNAs (lincRNAs) in macrophage cells. Our global clustering method compares favorably to the local clustering method SICER that was also designed to identify diffuse CHIP-Seq signals. The validity of the algorithm is confirmed at several levels. First, 8 out of a total of 11 selected putative lincRNA regions in primary macrophages respond to lipopolysaccharides (LPS) treatment as predicted by our computational method. Second, the genes nearest to lincRNAs are enriched with biological functions related to metabolic processes under resting conditions but with developmental and immune-related functions under LPS treatment. Third, the putative lincRNAs have conserved promoters, modestly conserved exons, and expected secondary structures by prediction. Last, they are enriched with motifs of transcription factors such as PU.1 and AP.1, previously shown to be important lineage determining factors in macrophages, and 83% of them overlap with distal enhancers markers. In summary, GCLS based on RNA polymerase II and H3K4Me3 CHIP-Seq method can effectively detect putative lincRNAs that exhibit expected characteristics, as exemplified by macrophages in the study.

MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site-and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.

Historically, graphene-based transistor fabrication has been time-consuming due to the high demand for carefully controlled Raman spectroscopy, physical vapor deposition, and lift-off processes. For the first time in a three-terminal graphene field-effect transistor embodiment, we introduce a rapid fabrication technique that implements non-toxic eutectic liquid-metal Galinstan interconnects and an electrolytic gate dielectric comprised of honey. The goal is to minimize cost and turnaround time between fabrication runs; thereby, allowing researchers to focus on the characterization of graphene phenomena that drives innovation rather than a lengthy device fabrication process that hinders it. We demonstrate characteristic Dirac peaks for a single-gate graphene field-effect transistor embodiment that exhibits hole and electron mobilities of 213 ± 15 and 166 ± 5 cm 2/V·s respectively. We discuss how our methods can be used for the rapid determination of graphene quality and can complement Raman Spectroscopy techniques. Lastly, we explore a PN junction embodiment which further validates that our fabrication techniques can rapidly adapt to alternative device architectures and greatly broaden the research applicability.

MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0500-5) contains supplementary material, which is available to authorized users.

Advancements in flexible circuit interconnects are critical for widespread adoption of flexible electronics. Non-toxic liquid-metals offer a viable solution for flexible electrodes due to deformability and low bulk resistivity. However, fabrication processes utilizing liquid-metals suffer from high complexity, low throughput, and significant production cost. Our team utilized an inexpensive spray-on stencil technique to deposit liquid-metal Galinstan electrodes in top-gated graphene field-effect transistors (GFETs). The electrode stencils were patterned using an automated vinyl cutter and positioned directly onto chemical vapor deposition (CVD) graphene transferred to polyethylene terephthalate (PET) substrates. Our spray-on method exhibited a throughput of 28 transistors in under five minutes on the same graphene sample, with a 96% yield for all devices down to a channel length of 50 μm. The fabricated transistors possess hole and electron mobilities of 663.5 cm2/(V·s) and 689.9 cm2/(V·s), respectively, and support a simple and effective method of developing high-yield flexible electronics.

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