Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/.

In clinical trials, information about certain time points may be of interest in making decisions about treatment effectiveness. Rather than comparing entire survival curves, researchers can focus on the comparison at fixed time points that may have a clinical utility for patients. For two independent samples of right-censored data, Klein et al. (2007) compared survival probabilities at a fixed time point by studying a number of tests based on some transformations of the Kaplan-Meier estimators of the survival function. However, to compare the survival probabilities at a fixed time point for paired right-censored data or clustered right-censored data, their approach would need to be modified. In this paper, we extend the statistics to accommodate the possible within-paired correlation and within-clustered correlation, respectively. We use simulation studies to present comparative results. Finally, we illustrate the implementation of these methods using two real data sets.

Orchid gynostemium, the fused organ of the androecium and gynoecium, and ovule development are unique developmental processes. Two DROOPING LEAF/CRABS CLAW (DL/CRC) genes were identified from the Phalaenopsis orchid genome and functionally characterized, including PeDL1 and PeDL2. Phylogenetic analysis indicated that the most recent common ancestor of orchids contained the duplicated DL/CRC-like genes. The temporal and spatial expression analysis indicated PeDL genes are specifically expressed in the gynostemium and at the early stages of ovule development. Both PeDLs could partially complement an Arabidopsis crc-1 mutant. Virus-induced gene silencing (VIGS) of PeDL1 and PeDL2 affects the number of protuberant ovule initials differentiated from the placenta. Transient overexpression of PeDL1 in Phalaenopsis orchids caused abnormal development of ovule and stigmatic cavity of gynostemium. PeDL1, instead of PeDL2, could form a heterodimer with PeCIN8. Paralogous retention and subsequent divergence of the gene sequence of PeDL1 and PeDL2 in P. equestris might result in the differentiation of function and protein behaviors. These results reveal the ancestral duplicated DL/CRC-like genes in orchids play important roles in orchid reproductive organ innovation.

Mutation density patterns reveal unique biological properties of specific genomic regions and shed light on the mechanisms of carcinogenesis. While previous studies reported insightful mutation density patterns associated with certain genomic regions such as transcription start sites and DNA replication origins, a tool that can systematically investigate mutational spatial patterns is still lacking. Thus, we developed MutDens, a bioinformatics tool for comprehensive analysis of mutation density patterns around genomic features, i.e., genomic positions, in humans and other model species. By scanning the bidirectional vicinity regions of given positions, MutDens systematically characterizes the mutation density for single-base substitution mutational classes after adjusting for total mutation burden and local nucleotide proportion. Analysis results using MutDens not only verified the previously reported transcriptional strand bias around transcription start sites and replicative strand bias around DNA replication origins, but also identified novel mutation density patterns around other genomics features, such as enhancers and retrotransposon insertion polymorphism sites. To our knowledge, MutDens is the first tool that systematically calculates, examines, and compares mutation density patterns, thus providing a valuable avenue for investigating the mutational landscapes associated with important genomic features.

Traditional wheezes detection methods are based on the frequency and durations of acoustic signal or the location of peaks from successive spectra. In these methods, the discriminative threshold used to identify peaks usually is fixed empirically. Therefore, accuracy of detected wheeze is affected by environment noise and artificial factors. The objective of this study is to classify normal and abnormal (wheezing) respiratory sounds using Cepstral analysis in Gaussian Mixture Models. The sound signal is divided in overlapped segments, which are characterized by a reduced dimension feature vectors using Mel-Frequency Cepstral Coefficients. In this study the ;speaker' is wheeze. During the test phase, an unknown sound is compared to all the GMM models and the classification decision is based on the Maximum Likelihood criterion. In these processes, identification is based on threshold value. If the threshold is bigger than zero, the sound is normal. Otherwise, the sound is wheeze. From experimental results, when the Gaussian mix number is 16, the accuracy of identification of wheeze is up to 90%.