MOTIVATION: As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios. RESULTS: To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA-miRNA network. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php.

In recent decades, unmanned aerial vehicles (UAVs) have been widely adopted such as serving as flying base stations. Compared to traditional solutions, the deployment of UAV s is fast and low-cost. However, due to UAVs' limited energy capacity, efficiently utilizing UAV s' energy is a matter of concern. One solution is to use rechargeable UAV s, but the challenge of charging schedule comes in the wake of it. In this paper, this problem is modeled as a non-cooperative game where a UAV can choose a strategy in order to maximize its payoff. Also, we have proved that the proposed game is an exact potential game (EPG) which ensures a Nash equilibrium (NE) with best-response dynamics. Numerical results show that the proposed algorithm has a larger coverage ratio and is more flexible than other algorithms in most environments; hence, a better performance.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

BACKGROUND: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. RESULTS: To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/ . CONCLUSIONS: RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

MOTIVATION: Exome capture kits have capture efficiencies that range from 40 to 60%. A significant amount of off-target reads are from the mitochondrial genome. These unintentionally sequenced mitochondrial reads provide unique opportunities to study the mitochondria genome. RESULTS: MitoSeek is an open-source software tool that can reliably and easily extract mitochondrial genome information from exome and whole genome sequencing data. MitoSeek evaluates mitochondrial genome alignment quality, estimates relative mitochondrial copy numbers and detects heteroplasmy, somatic mutation and structural variants of the mitochondrial genome. MitoSeek can be set up to run in parallel or serial on large exome sequencing datasets. AVAILABILITY: https://github.com/riverlee/MitoSeek

BACKGROUND: Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size. RESULTS: We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data. CONCLUSIONS: The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.