MOTIVATION: Years of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how 'non-clock' pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function. RESULTS: We developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples is correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues. AVAILABILITY AND IMPLEMENTATION: LTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Robust oscillation of clock genes is a core feature of the circadian system. Relative amplitude (rAMP) measures the robustness of clock gene oscillations but only works for longitudinal samples. We lack a method for estimating robust oscillations from human samples without labeled time. We show that the normalized coefficient of variation (nCV) of 10 clock genes is linearly correlated with their normalized rAMP, independent of time labels. We found that the mean nCV of clock genes are consistently decreased in tumors compared to nontumors, suggesting a new therapeutic target in cancer treatment by enhancing clock robustness. nCV can provide a simple measure of the clock robustness in population-level datasets. AVAILABILITY AND IMPLEMENTATION: The nCV package (https://github.com/gangwug/nCV) and web application (https://github.com/gangwug/nCVapp) are available on the GitHub repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting periodicity in large scale data remains a challenge. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here, we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to conveniently evaluate periodicity in time-series data. MetaCycle has two functions, meta2d and meta3d, designed to analyze two-dimensional and three-dimensional time-series datasets, respectively. Meta2d implements N-version programming concepts using a suite of algorithms and integrating their results. AVAILABILITY AND IMPLEMENTATION: MetaCycle package is available on the CRAN repository (https://cran.r-project.org/web/packages/MetaCycle/index.html) and GitHub (https://github.com/gangwug/MetaCycle). CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy. RESULTS: We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development. AVAILABILITY AND IMPLEMENTATION: Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana. RESULTS: From these results, we discovered that each algorithm had different strengths. Based on our findings, we make recommendations for selecting and applying these methods depending on the nature of the data and the periodic patterns of interest. Additionally, these results can also be used to inform the design of large-scale biological rhythm experiments so that the resulting data can be used with these algorithms to detect periodic signals more effectively.

MOTIVATION: A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously. RESULTS: We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability. AVAILABILITY: The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine (http://cbil.upenn.edu/RUM). CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.

UNLABELLED: Oscillations in mRNA and protein of circadian clock components can be continuously monitored in vitro using synchronized cell lines. These rhythms can be highly variable due to culture conditions and are non-stationary due to baseline trends, damping and drift in period length. We present a technique for characterizing the modal frequencies of oscillation using continuous wavelet decomposition to non-parametrically model changes in amplitude and period while removing baseline effects and noise. AVAILABILITY: The method has been implemented as the package waveclock for the free statistical software program R and is available for download from http://cran.r-project.org/