MOTIVATION: Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data. RESULTS: We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2000-5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150-350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions. AVAILABILITY AND IMPLEMENTATION: www.dllab.org/software/VirusPredictor.html.

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

BACKGROUND: Local similarity analysis (LSA) of time series data has been extensively used to investigate the dynamics of biological systems in a wide range of environments. Recently, a theoretical method was proposed to approximately calculate the statistical significance of local similarity (LS) scores. However, the method assumes that the time series data are independent identically distributed, which can be violated in many problems. RESULTS: In this paper, we develop a novel approach to accurately approximate statistical significance of LSA for dependent time series data using nonparametric kernel estimated long-run variance. We also investigate an alternative method for LSA statistical significance approximation by computing the local similarity score of the residuals based on a predefined statistical model. We show by simulations that both methods have controllable type I errors for dependent time series, while other approaches for statistical significance can be grossly oversized. We apply both methods to human and marine microbial datasets, where most of possible significant associations are captured and false positives are efficiently controlled. CONCLUSIONS: Our methods provide fast and effective approaches for evaluating statistical significance of dependent time series data with controllable type I error. They can be applied to a variety of time series data to reveal inherent relationships among the different factors.

BACKGROUND: Beta diversity, which involves the assessment of differences between communities, is an important problem in ecological studies. Many statistical methods have been developed to quantify beta diversity, and among them, UniFrac and weighted-UniFrac (W-UniFrac) are widely used. The W-UniFrac is a weighted sum of branch lengths in a phylogenetic tree of the sequences from the communities. However, W-UniFrac does not consider the variation of the weights under random sampling resulting in less power detecting the differences between communities. RESULTS: We develop a new statistic termed variance adjusted weighted UniFrac (VAW-UniFrac) to compare two communities based on the phylogenetic relationships of the individuals. The VAW-UniFrac is used to test if the two communities are different. To test the power of VAW-UniFrac, we first ran a series of simulations which revealed that it always outperforms W-UniFrac, as well as UniFrac when the individuals are not uniformly distributed. Next, all three methods were applied to analyze three large 16S rRNA sequence collections, including human skin bacteria, mouse gut microbial communities, microbial communities from hypersaline soil and sediments, and a tropical forest census data. Both simulations and applications to real data show that VAW-UniFrac can satisfactorily measure differences between communities, considering not only the species composition but also abundance information. CONCLUSIONS: VAW-UniFrac can recover biological insights that cannot be revealed by other beta diversity measures, and it provides a novel alternative for comparing communities.

BACKGROUND: Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. RESULTS: Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. CONCLUSION: Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

MOTIVATION: An important area of research in the postgenomics era is to relate high-dimensional genetic or genomic data to various clinical phenotypes of patients. Due to large variability in time to certain clinical events among patients, studying possibly censored survival phenotypes can be more informative than treating the phenotypes as categorical variables. Due to high dimensionality and censoring, building a predictive model for time to event is more difficult than the classification/linear regression problem. We propose to develop a boosting procedure using smoothing splines for estimating the general proportional hazards models. Such a procedure can potentially be used for identifying non-linear effects of genes on the risk of developing an event. RESULTS: Our empirical simulation studies showed that the procedure can indeed recover the true functional forms of the covariates and can identify important variables that are related to the risk of an event. Results from predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed method can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. In addition, there is clear evidence of non-linear effects of some genes on survival time.

MOTIVATION: The expressions of many genes associated with certain periodic biological and cell cycle processes such as circadian rhythm regulation are known to be rhythmic. Identification of the genes whose time course expressions are synchronized to certain periodic biological process may help to elucidate the molecular basis of many diseases, and these gene products may in turn represent drug targets relevant to those diseases. RESULTS: We propose in this paper a statistical framework based on a shape-invariant model together with a false discovery rate (FDR) procedure for identifying periodically expressed genes based on microarray time-course gene expression data and a set of known periodically expressed guide genes. We applied the proposed methods to the alpha-factor, cdc15 and cdc28 synchronized yeast cell cycle data sets and identified a total of 1010 cell-cycle-regulated genes at a FDR of 0.5% in at least one of the three data sets analyzed, including 89 (86%) of 104 known periodic transcripts. We also identified 344 and 201 circadian rhythmic genes in vivo in mouse heart and liver tissues with FDR of 10 and 2.5%, respectively. Our results also indicate that the shape-invariant model fits the data well and provides estimate of the common shape function and the relative phases for these periodically regulated genes.

MOTIVATION: Time-course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. To account for time dependency of the gene expression measurements over time and the noisy nature of the microarray data, the mixed-effects model using B-splines was introduced. This paper further explores such mixed-effects model in analyzing the time-course gene expression data and in performing clustering of genes in a mixture model framework. RESULTS: After fitting the mixture model in the framework of the mixed-effects model using an EM algorithm, we obtained the smooth mean gene expression curve for each cluster. For each gene, we obtained the best linear unbiased smooth estimate of its gene expression trajectory over time, combining data from that gene and other genes in the same cluster. Simulated data indicate that the methods can effectively cluster noisy curves into clusters differing in either the shapes of the curves or the times to the peaks of the curves. We further demonstrate the proposed method by clustering the yeast genes based on their cell cycle gene expression data and the human genes based on the temporal transcriptional response of fibroblasts to serum. Clear periodic patterns and varying times to peaks are observed for different clusters of the cell-cycle regulated genes. Results of the analysis of the human fibroblasts data show seven distinct transcriptional response profiles with biological relevance. AVAILABILITY: Matlab programs are available on request from the authors.