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Examines a model-based analysis of oligonucleotide DNA expression. Measurement of mRNA transcript abundance; Reduction of the adverse effects of probe-specific biases; Description of the automatic handling of image contamination; Impact of presence percentage on the level of probe usage; Treatment of probe effects in expression array analysis.

The article presents information on a study which reviewed computational molecular biology and the opportunities it presents to statistical researchers. Molecular biology is one of the most important scientific frontiers in the second half of the twentieth century. During this period, the basic principles of how genetic information is encoded in the DNA and how this information is used to direct the function of a cell were worked out at the molecular level, and methods were developed to clone, to sequence, and to amplify DNA. As a result, a large amount of biological sequence information has been generated and deposited into publicly accessible data bases. The phenomenal growth of DNA sequence data underpinned by a fundamental shift in the way such data are produced. Many components of basic cellular processes are highly conserved, although their uses and regulation have diverged greatly among extant organisms. Sequence alignment is the basic tool that allows to detect these conserved components.

This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring in using the rule, the multiple-try method can also be exploited to achieve the effect of a griddy Gibbs sampler without having to bear with griddy approximations, and the effect of a hit-and-run algorithm without having to figure out the required conditional distribution in a random direction. [ABSTRACT FROM AUTHOR]

Density estimation is a building block for many other statistical methods, such as classification, nonparametric testing, and data compression. In this paper, we focus on a nonparametric approach to multivariate density estimation, and study its asymptotic properties under both frequentist and Bayesian settings. The estimated density function is obtained by considering a sequence of approximating spaces to the space of densities. These spaces consist of piecewise constant density functions supported by binary partitions with increasing complexity. To obtain an estimate, the partition is learned by maximizing either the likelihood of the corresponding histogram on that partition, or the marginal posterior probability of the partition under a suitable prior. We analyze the convergence rate of the maximum likelihood estimator and the posterior concentration rate of the Bayesian estimator, and conclude that for a relatively rich class of density functions the rate does not directly depend on the dimension. We also show that the Bayesian method can adapt to the unknown smoothness of the density function. The method is applied to several specific function classes and explicit rates are obtained. These include spatially sparse functions, functions of bounded variation, and Hölder continuous functions. We also introduce an ensemble approach, obtained by aggregating multiple density estimates fit under carefully designed perturbations, and show that for density functions lying in a Hölder space (H1,β, β ≤ 1), the ensemble method can achieve minimax convergence rate up to a logarithmic term, while the corresponding rate of the density estimator based on a single partition is suboptimal for this function class. [ABSTRACT FROM AUTHOR]

Tanner and Wong (in press) have introduced the data augmentation algorithm for the analysis of parametric missing data problems. In this article, this paradigm is used to develop an algorithm for the nonparametric estimation of the hazard function from grouped and censored lifetime data. This algorithm makes use of the notions of cross-validation and multiple imputation to prescribe the appropriate degree of smoothing for the nonparametric hazard estimate. A procedure for estimating the variance of the estimator is also proposed. The nonparametric hazard estimate and corresponding variance formula are shown to perform well in a simulation study. The algorithm is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

The idea of data augmentation arises naturally in missing value problems, as exemplified by the standard ways of filling in missing cells in balanced two-way tables. Thus data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. This device is used to great advantage by the EM algorithm (Dempster, Laird, and Rubin 1977) in solving maximum likelihood problems. In situations when the likelihood cannot be approximated closely by the normal likelihood, maximum likelihood estimates and the associated standard errors cannot be relied upon to make valid inferential statements. From the Bayesian point of view, one must now calculate the posterior distribution of parameters of interest. If data augmentation can be used in the calculation of the maximum likelihood estimate, then in the same cases one ought to be able to use it in the computation of the posterior distribution. It is the purpose of this article to explain how this can be done. The basic idea is quite simple. The observed data y is augmented by the quantity z, which is referred to as the latent data. It is assumed that if y and z are both known, then the problem is straightforward to analyze, that is, the augmented data posterior p(O I Y, z) can be calculated. But the posterior density that we want is p(O I Y), which may be difficult to calculate directly. If, however, one can generate multiple values of z from the predictive distribution p(z l Y) (i.e., multiple imputations of z), then p(O I Y) can be approximately obtained as the average of p(theta | Y, z) over the imputed z's. However, p(z [ y) depends, in turn, on p(O [ y). Hence if p(theta | y) was known, it could be used to calculate p(z | Y). This mutual dependency between p(O I Y) and p(z I Y) leads to an iterative algorithm to calculate p(O [ y).... [ABSTRACT FROM AUTHOR]

The article comments on the article "Smooth Pycnophylactic Interpolation for Geographical Regions," by Waldo R. Tobler that appeared in the 1979 issue of the "Journal of the American Statistical Association." Authors would like to begin by thanking Tobler for a very interesting contribution to the important problem of obtaining smooth surfaces with the volume-matching property. Although Tobler's main interest is in obtaining smooth surfaces and not in solving optimization problems, authors think it is useful and important to state precisely the optimization problem being solved, to establish the existence and uniqueness of the solution, and to establish that the numerical algorithms involved do converge to a unique solution. The authors' position is that two experimenters employing the same definition of smoothness should obtain comparable contour maps. This requirement could be very important, for example, in analyzing the geographic distribution of various types of cancer incidence and matching the resultant contour maps with contour maps of, say, air-pollution density. If the disease data and air-pollution data were sculpted by different methods, then the two maps are not necessarily comparable. If an algorithm does not converge to a unique solution, then different experimenters can obtain different maps for the same data.

Summary: We present an open source, platform independent tool, called CisGenome Browser, which can work together with any other data analysis program to serve as a flexible component for genomic data visualization. It can also work by itself as a standalone genome browser. By working as a light-weight web server, CisGenome Browser is a convenient tool for data sharing between labs. It has features that are specifically designed for ultra high-throughput sequencing data visualization. [ABSTRACT FROM PUBLISHER]

The article comments on the paper by Murray Aitkin, Nan Laird and Brian Francis which presents an analyses of survival of patients in the Stanford Heart Transplantation Program, published in the June 1983 issue of the "Journal of the American Statistical Association." Aitkin, Laird, and Francis make the important observation that one should not expect the effect of the transplant to fit the proportional hazards model. Their approach is to model pre-transplant and post-transplant survival separately. The authors, as well as several of the discussants, point out the need for further diagnostics to accompany their parametric and semiparametric analyses. For the post-transplant data the authors consider a number of models, most of which share the proportional hazards assumption. The survival times are then partitioned into several groups such that the scores within each group are approximately the same. If the proportional hazards assumption holds, then, at least approximately, the observations in each group should be realizations of the same failure process from which a nonparametric estimate of the hazard function can be obtained. Under the proportional hazards model we expect the estimates to be proportional. Furthermore, the estimates should conform to the parametric form if the assumed parametric model is valid.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of dis- eases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high- resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue decon- volution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical util- ities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring. [ABSTRACT FROM AUTHOR]

Summary: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods. Contact: whwong@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Microarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts. Results: To study cross-hybridization, we map Affymetrix exon array probes to a set of annotated mRNA transcripts, allowing a small number of mismatches or insertion/deletions between the two sequences. Based on a systematic study of the degree to which probes with a given match type to a transcript are affected by cross-hybridization, we developed a strategy to correct for cross-hybridization biases of gene-level expression estimates. Comparison with Solexa ultra high-throughput sequencing data demonstrates that correction for cross-hybridization leads to a significant improve-ment of gene expression estimates. Availability: We provide mappings between human and mouse exon array probes and off-target transcripts and provide software extending the GeneBASE program for generating gene-level expression estimates including the cross-hybridization correction http://biogibbs.stanford.edu/~kkapur/GeneBase/. Contact: whwong@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: SeqMap is a tool for mapping large amount of short sequences to the genome. It is designed for finding all the places in a reference genome where each sequence may come from. This task is essential to the analysis of data from ultra high-throughput sequencing machines. With a carefully designed index-filtering algorithm and an efficient implementation, SeqMap can map tens of millions of short sequences to a genome of several billions of nucleotides. Multiple substitutions and insertions/deletions of the nucleotide bases in the sequences can be tolerated and therefore detected. SeqMap supports FASTA input format and various output formats, and provides command line options for tuning almost every aspect of the mapping process. A typical mapping can be done in a few hours on a desktop PC. Parallel use of SeqMap on a cluster is also very straightforward. Contact: whwong@stanford.edu [ABSTRACT FROM AUTHOR]

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar, copy archived at swh:1:rev:cf27438d3f8245c34c357ec5f077528e6befe829. [ABSTRACT FROM AUTHOR]

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess herita- bility enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect rele- vant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes’ relevant tissues and shared heritability for biological and therapeutic insights. [ABSTRACT FROM AUTHOR]

Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network has a nested multilayer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify noncoding variant pairs associated with pathogenic genes in diseases beyond genome-wide association studies analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting diseaseassociated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants. [ABSTRACT FROM AUTHOR]

Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks. [ABSTRACT FROM AUTHOR]

A person's genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person's phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference contextspecific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others. [ABSTRACT FROM AUTHOR]

In both Turner syndrome (TS) and Klinefelter syndrome (KS) copy number aberrations of the X chromosome lead to various developmental symptoms. We report a comparative analysis of TS vs. KS regarding differences at the genomic network level measured in primary samples by analyzing gene expression, DNA methylation, and chromatin conformation. X-chromosome inactivation (XCI) silences transcription from one X chromosome in female mammals, on which most genes are inactive, and some genes escape from XCI. In TS, almost all differentially expressed escape genes are downregulated but most differentially expressed inactive genes are upregulated. In KS, differentially expressed escape genes are upregulated while the majority of inactive genes appear unchanged. Interestingly, 94 differentially expressed genes (DEGs) overlapped between TS and female and KS and male comparisons; and these almost uniformly display expression changes into opposite directions. DEGs on the X chromosome and the autosomes are coexpressed in both syndromes, indicating that there are molecular ripple effects of the changes in X chromosome dosage. Six potential candidate genes (RPS4X, SEPT6, NKRF, CX0rf57, NAA10, and FLNA) for KS are identified on Xq, as well as candidate central genes on Xp for TS. Only promoters of inactive genes are differentially methylated in both syndromes while escape gene promoters remain unchanged. The intrachromosomal contact map of the X chromosome in TS exhibits the structure of an active X chromosome. The discovery of shared DEGs indicates the existence of common molecular mechanisms for gene regulation in TS and KS that transmit the gene dosage changes to the transcriptome. [ABSTRACT FROM AUTHOR]

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this "coupled clustering" problem as an optimization problem and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single-cell RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC-seq) data. [ABSTRACT FROM AUTHOR]

The article discusses principles and guidelines associated with General Education Maps and Markers (GEMs), a project of the Association of American Colleges and Universities. It mentions that Chinese University of Hong Kong (CUHK) adopted a strategic plan that emphasized student-centered learning and learning outcomes. It states that general education program is offered university-wide and in individual colleges, complementing the formal curricula by delivering wholeperson education and care.

Genetic efficiency in higher organisms depends on mechanisms to create multiple functions from single genes. To investigate this question for an enzyme family, we chose aminoacyl tRNA synthetases (AARSs).They are exceptional in their progressive and accretive proliferation of noncatalytic domains as the Tree of Life is ascended. Here we report discovery of a large number of natural catalytic nulls (CNs) for each human AARS. Splicing events retain noncatalytic domains while ablating the catalytic domain to create CNs with diverse functions. Each synthetase is converted into several new signaling proteins with biological activities "orthogonal" to that of the catalytic parent. We suggest that splice variants with nonenzymatic functions may be more general, as evidenced by recent findings of other catalytically inactive splice-variant enzymes. [ABSTRACT FROM AUTHOR]

The Xenopus embryo has provided key insights into fate specification, the cell cycle, and other fundamental developmental and cellular processes, yet a comprehensive understanding of its transcriptome is lacking. Here, we used paired end RNA sequencing (RNA-seq) to explore the transcriptome of Xenopus tropicalis in 23 distinct developmental stages. We determined expression levels of all genes annotated in RefSeq and Ensembl and showed for the first time on a genome- wide scale that, despite a general state of transcriptional silence in the earliest stages of development, approximately 150 genes are transcribed prior to the midblastula transition. In addition, our splicing analysis uncovered more than 10,000 novel splice junctions at each stage and revealed that many known genes have additional unannotated isoforms. Furthermore, we used Cufflinks to reconstruct transcripts from our RNA-seq data and found that ~13.5% of the final contigs are derived from novel transcribed regions, both within introns and in intergenic regions. We then developed a filtering pipeline to separate protein-coding transcripts from noncoding RNAs and identified a confident set of 6686 noncoding transcripts in 3859 genomic loci. Since the current reference genome, XenTro3, consists of hundreds of scaffolds instead of full chromosomes, we also performed de novo reconstruction of the transcriptome using Trinity and uncovered hundreds of transcripts that are missing from the genome. Collectively, our data will not only aid in completing the assembly of the Xenopus tropicalis genome but will also serve as a valuable resource for gene discovery and for unraveling the fundamental mechanisms of vertebrate embryogenesis. [ABSTRACT FROM AUTHOR]