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Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types.

Objective To test whether the probability of having a live birth (LB) with the first IVF cycle (C1) can be predicted and personalized for patients in diverse environments. Design Retrospective validation of multicenter prediction model. Setting Three university-affiliated outpatient IVF clinics located in different countries. Patient(s) Using primary models aggregated from >13,000 C1s, we applied the boosted tree method to train a preIVF-diversity model (PreIVF-D) with 1,061 C1s from 2008 to 2009, and validated predicted LB probabilities with an independent dataset comprising 1,058 C1s from 2008 to 2009. Intervention(s) None. Main Outcome Measure(s) Predictive power, reclassification, receiver operator characteristic analysis, calibration, dynamic range. Result(s) Overall, with PreIVF-D, 86% of cases had significantly different LB probabilities compared with age control, and more than one-half had higher LB probabilities. Specifically, 42% of patients could have been identified by PreIVF-D to have a personalized predicted success rate >45%, whereas an age-control model could not differentiate them from others. Furthermore, PreIVF-D showed improved predictive power, with 36% improved log-likelihood (or 9.0-fold by log-scale; >1,000-fold linear scale), and prediction errors for subgroups ranged from 0.9% to 3.7%. Conclusion(s) Validated prediction of personalized LB probabilities from diverse multiple sources identify excellent prognoses in more than one-half of patients.

Xenopus is one of the major model systems for the study of vertebrate embryogenesis and basic cell biological processes. There are multiple advantages to the use of Xenopus as an experimental system, such as the availability of large abundant eggs that are easily manipulated, ready accessibility to any developmental stage, and conservation of cellular pathways between Xenopus and mammals. In the past 50 years, landmark studies on Xenopus have been critical toward our understanding of nuclear reprogramming (Gurdon et al. 1958), embryonic patterning (Harland and Gerhart 1997; De Robertis 2006), membrane channels and receptors (Kusano et al. 1977), and cell cycle control (Murray and Kirschner 1989; Murray et al. 1989; Glotzer et al. 1991). Genomics resources for Xenopus research have emerged in the past 10–15 years. During the early days of the genomics era, several cDNA sequencing efforts, such as EST (expressed sequence tag) projects, have allowed the construction of full length cDNA clones and identification of Xenopus open reading frames (ORFs) (Gilchrist et al. 2004; Morin et al. 2006; Fierro et al. 2007). Microarrays have also been used to investigate the expression levels of annotated genes and gave some insights into transcriptome changes over development as well as expression differences between two closely related frog species, Xenopus laevis and Xenopus tropicalis (Yanai et al. 2011). In addition, forward and reverse genetic screens have uncovered mutations that affect a myriad of organogenesis and differentiation processes in Xenopus (Goda et al. 2006), while a genetic map based on simple sequence length polymorphism (SSLP) markers, which can be used to clone genes identified by mutation, has recently been generated (Wells et al. 2011). Notably, while early developmental and molecular studies have been performed on Xenopus laevis, its closely related cousin Xenopus tropicalis has proven to be more widely used for genetic and genomic research. This is mainly because Xenopus laevis has a more complex pseudotetraploid genome, while Xenopus tropicalis has a smaller and more amenable diploid genome. Hence, the initial genome sequencing effort has been directed mostly at Xenopus tropicalis, whose genome has recently been published (Hellsten et al. 2010). Strikingly, the frog genome is highly syntenic with the human genome, with regions of synteny frequently spanning more than a hundred genes. Nevertheless, although it is largely assembled into multiple scaffolds, the Xenopus tropicalis genome is yet to be sequenced at the same depth and annotated at the same level of details and accuracy as the genomes of human and mouse. Importantly, annotations of protein-coding and noncoding genes are strikingly incomplete, including the widely used RefSeq and Ensembl annotations. The advent of high-throughput sequencing technologies has had an enormous impact on genomics. In particular, such technologies have revolutionized studies of the transcriptome in many species from yeast to humans and have revealed tremendous amounts of complexities and gaps in our understanding of any transcriptome (Wang et al. 2009). Not only does RNA sequencing (RNA-seq) provide a more accurate measurement of expression levels, it provides single nucleotide resolution and has the ability to reveal novel splice junctions, unannotated transcripts, and allele-specific expression. Here, we present the first comprehensive study of the transcriptome of Xenopus tropicalis using RNA-seq over development from a two-cell fertilized embryo to a feeding tadpole. We report evidence for transcription of more than a hundred genes prior to the midblastula transition, when the embryonic genome is generally believed to be transcriptionally silent. We also discovered thousands of novel splicing events, including exon skipping in annotated genes, as well as thousands of unannotated, potentially noncoding transcripts. Hence, our data serve as a valuable resource for developmental biologists and the general genomics community. Furthermore, to extend the reach of our work, we have created an interactive website (http://hci.stanford.edu/∼jcchuang/frog-genes/latest/) that allows users to not only browse the heatmaps in this manuscript but to also query the expression profile of any RefSeq or Ensembl annotated gene with ease.

Motivation: Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers. Results: We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data. Availability: Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto Contact: whwong@stanford.edu

Background: Linkage and association analysis based on haplotype transmission disequilibrium can be more informative than single marker analysis. Several works have been proposed in recent years to extend the transmission disequilibrium test (TDT) to haplotypes. Among them, a powerful approach called the evolutionary tree TDT (ET-TDT) incorporates information about the evolutionary relationship among haplotypes using the cladogram of the locus. Methods: In this work we extend this approach by taking into consideration the sparsity of causal mutations in the evolutionary history. We first introduce the notion of a Bradley-Terry (BT) graph representation of a haplotype locus. The most important property of the BT graph is that sparsity of the edge set of the graph corresponds to small number of causal mutations in the evolution of the haplotypes. We then propose a method to test the null hypothesis of no linkage and association against sparse alternatives under which a small number of edges on the BT graph have non-nil effects. Results and Conclusion: We compare the performance of our approach to that of the ET-TDT through a power study, and show that incorporating sparsity of causal mutations can significantly improve the power of a haplotype-based TDT.

SummaryA balance between Six2-dependent self-renewal and canonical Wnt signaling-directed commitment regulates mammalian nephrogenesis. Intersectional studies using chromatin immunoprecipitation and transcriptional profiling identified direct target genes shared by each pathway within nephron progenitors. Wnt4 and Fgf8 are essential for progenitor commitment; cis-regulatory modules flanking each gene are cobound by Six2 and β-catenin and are dependent on conserved Lef/Tcf binding sites for activity. In vitro and in vivo analyses suggest that Six2 and Lef/Tcf factors form a regulatory complex that promotes progenitor maintenance while entry of β-catenin into this complex promotes nephrogenesis. Alternative transcriptional responses associated with Six2 and β-catenin cobinding events occur through non-Lef/Tcf DNA binding mechanisms, highlighting the regulatory complexity downstream of Wnt signaling in the developing mammalian kidney.

We report NMR spin echo measurements of the average spin density wave condensate displacement at 4.2 K in (TMTSF${)}_{2}$${\mathrm{PF}}_{6}$ when it is driven by an electric field below the depinning threshold over time scales of 10--25 \ensuremath{\mu}s. The displacement, which corresponds to internal deformations, varies linearly as a function of the electric field and has a restoring force constant per electronic charge k=(1.3\ifmmode\pm\else\textpm\fi{}0.3)\ifmmode\times\else\texttimes\fi{}${10}^{\mathrm{\ensuremath{-}}9}$ N/m. At the depinning threshold the average condensate displacement is 0.46\ifmmode\pm\else\textpm\fi{}0.06 \AA{}. The corresponding low frequency dielectric constant is (3.4\ifmmode\pm\else\textpm\fi{}0.7)\ifmmode\times\else\texttimes\fi{}${10}^{9}$, which is close to that obtained from electrical transport measurements.

For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors. Their method of constructing the complete data sets is closely related to the Gibbs sampler. Both required iterations, and, similar to the EM algorithm, convergence can be slow. We introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights. In many applications this new procedure works very well without the need for iterations. Sensitivity analysis, influence analysis, and updating with new data can be performed cheaply. Bayesian prediction and model selection can also be incorporated. Examples taken from a wide range of applications are used for illustration.

SUMMARY We study the covariance structure of a Markov chain generated by the Gibbs sampler, with emphasis on data augmentation. When applied to a Bayesian missing data problem, the Gibbs sampler produces two natural approximations for the posterior distribution of the parameter vector: the empirical distribution based on the sampled values of the parameter vector, and a mixture of complete data posteriors. We prove that Rao-Blackwellization causes a one-lag delay for the autocovariances among dependent samples obtained from data augmentation, and consequently, the mixture approximation produces estimates with smaller variances than the empirical approximation. The covariance structure results are used to compare different augmentation schemes. It is shown that collapsing and grouping random components in a Gibbs sampler with two or three components usually result in more efficient sampling schemes.

SUMMARY Retroviral overexpression of reprogramming factors (Oct4, Sox2, Klf4, c-Myc) generates induced pluripotent stem cells (iPSCs). However, the integration of foreign DNA could induce genomic dysregulation. Cell-permeant proteins (CPPs) could overcome this limitation. To date, this approach has proved exceedingly inefficient. We discovered a striking difference in the pattern of gene expression induced by viral versus CPP-based delivery of the reprogramming factors, suggesting that a signaling pathway required for efficient nuclear reprogramming was activated by the retroviral, but not CPP approach. In gain- and loss-of-function studies, we find that the toll-like receptor 3 (TLR3) pathway enables efficient induction of pluripotency by viral or mmRNA approaches. Stimulation of TLR3 causes rapid and global changes in the expression of epigenetic modifiers to enhance chromatin remodeling and nuclear reprogramming. Activation of inflammatory pathways are required for efficient nuclear reprogramming in the induction of pluripotency.

In applications of hierarchical models (HMs), a potential weakness of empirical Bayes estimation approaches is that they do not to take into account uncertainty in the estimation of the variance components (see, e.g., Dempster, 1987 ). One possible solution entails employing a fully Bayesian approach, which involves specifying a prior probability distribution for the variance components and then integrating over the variance components as well as other unknowns in the HM to obtain a marginal posterior distribution of interest (see, e.g., Draper, 1995 ; Rubin, 1981 ). Though the required integrations are often exceedingly complex, Markov-chain Monte Carlo techniques (e.g., the Gibbs sampler) provide a viable means of obtaining marginal posteriors of interest in many complex settings. In this article, we fully generalize the Gibbs sampling algorithms presented in Seltzer (1993) to a broad range of settings in which vectors of random regression parameters in the HM (e.g., school means and slopes) are assumed multivariate normally or multivariate t distributed across groups. Through analyses of the data from an innovative mathematics curriculum, we examine when and why it becomes important to employ a fully Bayesian approach and discuss the need to study the sensitivity of results to alternative prior distributional assumptions for the variance components and for the random regression parameters.

Chromatin immunoprecipitation coupled with ultra-high-throug put parallel DNA sequencing (ChIP-seq) is an effective technology for the investigation of genome-wide protein-DNA interactions. Examples of applications include the studies of RNA polymerases transcription, transcriptional regulation, and histone modifications. The technology provides accurate and high-resolution mapping of the protein-DNA binding loci that are important in the understanding of many processes in development and diseases. Since the introduction of ChIP-seq experiments in 2007, many statistical and computational methods have been developed to support the analysis of the massive datasets from these experiments. However, because of the complex, multistaged analysis workflow, it is still difficult for an experimental investigator to conduct the analysis of his or her own ChIP-seq data. In this chapter, we review the basic design of ChIP-seq experiments and provide an in-depth tutorial on how to prepare, to preprocess, and to analyze ChIP-seq datasets. The tutorial is based on a revised version of our software package CisGenome, which was designed to encompass most standard tasks in ChIP-seq data analysis. Relevant statistical and computational issues will be highlighted, discussed, and illustrated by means of real data examples.

It was known from Metropolis et al. [J. Chem. Phys. 21 (1953) 1087--1092] that one can sample from a distribution by performing Monte Carlo simulation from a Markov chain whose equilibrium distribution is equal to the target distribution. However, it took several decades before the statistical community embraced Markov chain Monte Carlo (MCMC) as a general computational tool in Bayesian inference. The usual reasons that are advanced to explain why statisticians were slow to catch on to the method include lack of computing power and unfamiliarity with the early dynamic Monte Carlo papers in the statistical physics literature. We argue that there was a deeper reason, namely, that the structure of problems in the statistical mechanics and those in the standard statistical literature are different. To make the methods usable in standard Bayesian problems, one had to exploit the power that comes from the introduction of judiciously chosen auxiliary variables and collective moves. This paper examines the development in the critical period 1980--1990, when the ideas of Markov chain simulation from the statistical physics literature and the latent variable formulation in maximum likelihood computation (i.e., EM algorithm) came together to spark the widespread application of MCMC methods in Bayesian computation., Published in at http://dx.doi.org/10.1214/10-STS341 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Chromatin immunoprecipitation (ChIP) coupled with genome tiling array hybridization (ChIP-chip) and ChIP followed by massively parallel sequencing (ChIP-seq) are high-throughput approaches to profiling genome-wide protein-DNA interactions. Both technologies are increasingly used to study transcription-factor binding sites and chromatin modifications. CisGenome is an integrated software system for analyzing ChIP-chip and ChIP-seq data. This unit describes basic functions of CisGenome and how to use them to find genomic regions with protein-DNA interactions, visualize binding signals, associate binding regions with nearby genes, search for novel transcription-factor binding motifs, and map existing DNA sequence motifs to user-supplied genomic regions to define their exact locations.

We show that high quality microarray gene expression profiles can be obtained following FACS sorting of cells using combinations of transcription factors. We use this transcription factor FACS (tfFACS) methodology to perform a genomic analysis of hESC-derived endodermal lineages marked by combinations of SOX17, GATA4, and CXCR4, and find that triple positive cells have a much stronger definitive endoderm signature than other combinations of these markers. Additionally, SOX17(+) GATA4(+) cells can be obtained at a much earlier stage of differentiation, prior to expression of CXCR4(+) cells, providing an important new tool to isolate this earlier definitive endoderm subtype. Overall, tfFACS represents an advancement in FACS technology which broadly crosses multiple disciplines, most notably in regenerative medicine to redefine cellular populations.

Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest. This paper introduces a statistical model for estimating isoform abundance from RNA-Seq data and is flexible enough to accommodate both single end and paired end RNA-Seq data and sampling bias along the length of the transcript. Based on the derivation of minimal sufficient statistics for the model, a computationally feasible implementation of the maximum likelihood estimator of the model is provided. Further, it is shown that using paired end RNA-Seq provides more accurate isoform abundance estimates than single end sequencing at fixed sequencing depth. Simulation studies are also given., Published in at http://dx.doi.org/10.1214/10-STS343 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Chromatin immunoprecipitation coupled with ultra-high-throug put parallel DNA sequencing (ChIP-seq) is an effective technology for the investigation of genome-wide protein-DNA interactions. Examples of applications include the studies of RNA polymerases transcription, transcriptional regulation, and histone modifications. The technology provides accurate and high-resolution mapping of the protein-DNA binding loci that are important in the understanding of many processes in development and diseases. Since the introduction of ChIP-seq experiments in 2007, many statistical and computational methods have been developed to support the analysis of the massive datasets from these experiments. However, because of the complex, multistaged analysis workflow, it is still difficult for an experimental investigator to conduct the analysis of his or her own ChIP-seq data. In this chapter, we review the basic design of ChIP-seq experiments and provide an in-depth tutorial on how to prepare, to preprocess, and to analyze ChIP-seq datasets. The tutorial is based on a revised version of our software package CisGenome, which was designed to encompass most standard tasks in ChIP-seq data analysis. Relevant statistical and computational issues will be highlighted, discussed, and illustrated by means of real data examples.

We report low field proton NMR measurements in the spin density wave (SDW) state of (TMTSF${)}_{2}$${\mathrm{PF}}_{6}$ that show a narrowing of the linewidth when the SDW is depinned by an electric field. The NMR absorption amplitude affirms that sample heating is negligible. A shortening of the spin-phase memory time corresponding to the line narrowing is also observed. Low field measurements of the spin-lattice relaxation rate show a fluctuation enhancement just below the spin density wave transition and a rapid drop below 3 K that lack a quantitative understanding.

The two haploid genome sequences that a person inherits from the two parents represent the most fundamentally useful type of genetic information for the study of heritable diseases and the development of personalized medicine. Because of the difficulty in obtaining long-range phase information, current sequencing methods are unable to provide this information. Here, we introduce and show feasibility of a scalable approach capable of generating genomic sequences completely phased across the entire chromosome.

Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVF BT , by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003–2006, followed by external validation with 634 cycles from 2007–2008, respectively. We tested whether this model could predict the probability of having a live birth in the subsequent treatment (C2). By using nondeterministic methods to identify prognostic factors and their relative nonredundant contribution, we generated a prediction model, IVF BT , that was superior to the age-based control by providing over 1,000-fold improvement to fit new data ( p BT provided predictions that were more accurate for ∼83% of C1 and ∼60% of C2 cycles that were out of the range predicted by age. Over half of those patients were reclassified to have higher live birth probabilities. We showed that data from a prior cycle could be used effectively to provide personalized and validated live birth probabilities in a subsequent cycle. Our approach may be replicated and further validated in other IVF clinics.

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. Availability: http://biogibbs.stanford.edu/∼jiangh/browser/ Contact: jiangh@stanford.edu

ParaLearn is a scalable, parallel FPGA-based system for learning interaction networks using Bayesian statistics. ParaLearn includes problem specific parallel/scalable algorithms, system software and hardware architecture to address this complex problem.Learning interaction networks from data uncovers causal relationships and allows scientists to predict and explain a system's behavior. Interaction networks have applications in many fields, though we will discuss them particularly in the field of personalized medicine where state of the art high-throughput experiments generate extensive data on gene expression, DNA sequencing and protein abundance. In this paper we demonstrate how ParaLearn models Signaling Networks in human T-Cells.We show greater than 2000 fold speedup on a Field Programmable Gate Array when compared to a baseline conventional implementation on a General Purpose Processor (GPP), a 2.38 fold speedup compared to a heavily optimized parallel GPP implementation, and between 2.74 and 6.15 fold power savings over the optimized GPP. Through using current generation FPGA technology and caching optimizations, we further project speedups of up to 8.15 fold, relative to the optimized GPP. Compared to software approaches, ParaLearn is faster, more power efficient, and can support novel learning algorithms that substantially improve the precision and robustness of the results.

We introduce an extension of the P\'olya tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the construction gives rise to random measures that are absolutely continuous with piecewise smooth densities on partitions that can adapt to fit the data. The resulting "optional P\'{o}lya tree" distribution has large support in total variation topology and yields posterior distributions that are also optional P\'{o}lya trees with computable parameter values., Comment: Published in at http://dx.doi.org/10.1214/09-AOS755 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Many genes initially identified for their roles in cell fate determination or signaling during development can have a significant impact on tumorigenesis. In the developing cerebellum, Sonic hedgehog (Shh) stimulates the proliferation of granule neuron precursor cells (GNPs) by activating the Gli transcription factors. Inappropriate activation of Shh target genes results in unrestrained cell division and eventually medulloblastoma, the most common pediatric brain malignancy. We find dramatic differences in the gene networks that are directly driven by the Gli1 transcription factor in GNPs and medulloblastoma. Gli1 binding location analysis revealed hundreds of genomic loci bound by Gli1 in normal and cancer cells. Only one third of the genes bound by Gli1 in GNPs were also bound in tumor cells. Correlation with gene expression levels indicated that 116 genes were preferentially transcribed in tumors, whereas 132 genes were target genes in both GNPs and medulloblastoma. Quantitative PCR and in situ hybridization for some putative target genes support their direct regulation by Gli. The results indicate that transformation of normal GNPs into deadly tumor cells is accompanied by a distinct set of Gli-regulated genes and may provide candidates for targeted therapies.

SUMMARY An asymptotic expansion for marginal posterior densities of nonlinear functions of parameters is derived. The density in question is proportional to an integral over a lower dimensional manifold with the integrand including an appropriate change-in-volume term, Laplace's method is applied to obtain the expansion. Avoiding the difficult step of the explicit parameterization of the manifold, we carry out the expansion via an implicit and local parameterization, and the derivatives required can be evaluated recursively. An example indicates a considerable improvement by including the term of order O(n-1).

Motivation: Splice junction microarrays and RNA-seq are two popular ways of quantifying splice variants within a cell. Unfortunately, isoform expressions cannot always be determined from the expressions of individual exons and splice junctions. While this issue has been noted before, the extent of the problem on various platforms has not yet been explored, nor have potential remedies been presented. Results: We propose criteria that will guarantee identifiability of an isoform deconvolution model on exon and splice junction arrays and in RNA-Seq. We show that up to 97% of 2256 alternatively spliced human genes selected from the RefSeq database lead to identifiable gene models in RNA-seq, with similar results in mouse. However, in the Human Exon array only 26% of these genes lead to identifiable models, and even in the most comprehensive splice junction array only 69% lead to identifiable models. Contact: whwong@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The disconnectivity graph (DG) is widely used to represent energy landscapes. Although powerful numerical methods have been developed to construct DGs for continuous potential-energy surfaces, they have difficulties in applications to discrete Hamiltonians as the case of spin-glass models. When the configuration space is large, brute force enumeration of all configurations to build a DG is not practical. We propose an alternative approach to construct DGs based on recursive partition of Monte Carlo samples from microcanonical ensembles. To characterize energy landscapes, we define the local density of states (LDOS) on a DG, with which one can compute many thermodynamic properties over local energy basins for any temperature. Estimation of LDOS is developed with DG construction. We further propose the concepts of tree entropy and local escape probability, both of which are functions of local density of states, to capture the symmetry and the roughness of a Boltzmann distribution, respectively. Our approach is applied to a study of the Sherrington-Kirkpatrick spin-glass model with N varying between 20 and 100 spins. We observe that the energy landscape is extremely asymmetric and there exists a sharp increase in local escape probability preceding the transition from spin glass to paramagnetic phase.

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.

A Bayesian method for ECT (emission computed tomography) image reconstruction has been developed to incorporate a priori information derived from the spatially correlated CT and MR (magnetic resonance) images. These anatomic maps, showing boundaries between regions that exhibit distinctly different characteristics, can be incorporated into the Bayesian method, thus improving the spatial resolution and noise properties. The correlated structural information can also be used as templates for deriving correcting factors for the effect of photon attenuation, thus improving the quantitative accuracy and noise properties. This novel image reconstruction method using the concept of sensor fusion has been validated by computer simulation studies. Results from these studies indicate that significant improvements in image quality can be achieved. >

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.

Defining the energy function as the negative logarithm of the density, we explore the energy landscape of a distribution via the tree of sublevel sets of its energy. This tree represents the hierarchy among the connected components of the sublevel sets. We propose ways to annotate the tree so that it provides information on both topological and statistical aspects of the distribution, such as the local energy minima (local modes), their local domains and volumes, and the barriers between them. We develop a computational method to estimate the tree and reconstruct the energy landscape from Monte Carlo samples simulated at a wide energy range of a distribution. This method can be applied to any arbitrary distribution on a space with defined connectedness. We test the method on multimodal distributions and posterior distributions to show that our estimated trees are accurate compared to theoretical values. When used to perform Bayesian inference of DNA sequence segmentation, this approach reveals much more information than the standard approach based on marginal posterior distributions., Comment: Published in at http://dx.doi.org/10.1214/08-AOAS196 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Background Compared to the emerging embryonic stem cell (ESC) gene network, little is known about the dynamic gene network that directs reprogramming in the early embryo. We hypothesized that Oct4, an ESC pluripotency regulator that is also highly expressed at the 1- to 2-cell stages in embryos, may be a critical regulator of the earliest gene network in the embryo. Methodology/Principal Findings Using antisense morpholino oligonucleotide (MO)-mediated gene knockdown, we show that Oct4 is required for development prior to the blastocyst stage. Specifically, Oct4 has a novel and critical role in regulating genes that encode transcriptional and post-transcriptional regulators as early as the 2-cell stage. Our data suggest that the key function of Oct4 may be to switch the developmental program from one that is predominantly regulated by post-transcriptional control to one that depends on the transcriptional network. Further, we propose to rank candidate genes quantitatively based on the inter-embryo variation in their differential expression in response to Oct4 knockdown. Of over 30 genes analyzed according to this proposed paradigm, Rest and Mta2, both of which have established pluripotency functions in ESCs, were found to be the most tightly regulated by Oct4 at the 2-cell stage. Conclusions/Significance We show that the Oct4-regulated gene set at the 1- to 2-cell stages of early embryo development is large and distinct from its established network in ESCs. Further, our experimental approach can be applied to dissect the gene regulatory network of Oct4 and other pluripotency regulators to deconstruct the dynamic developmental program in the early embryo.

We describe a method, microarray analysis of differential splicing (MADS), for discovery of differential alternative splicing from exon-tiling microarray data. MADS incorporates a series of low-level analysis algorithms motivated by the “probe-rich” design of exon arrays, including background correction, iterative probe selection, and removal of sequence-specific cross-hybridization to off-target transcripts. We used MADS to analyze Affymetrix Exon 1.0 array data on a mouse neuroblastoma cell line after shRNA-mediated knockdown of the splicing factor polypyrimidine tract binding protein (PTB). From a list of exons with predetermined inclusion/exclusion profiles in response to PTB depletion, MADS recognized all exons known to have large changes in transcript inclusion levels and offered improvement over Affymetrix's analysis procedure. We also identified numerous novel PTB-dependent splicing events. Thirty novel events were tested by RT-PCR and 27 were confirmed. This work demonstrates that the exon-tiling microarray design is an efficient and powerful approach for global, unbiased analysis of pre-mRNA splicing.

Background Hundreds of thousands of human embryos are cultured yearly at in vitro fertilization (IVF) centers worldwide, yet the vast majority fail to develop in culture or following transfer to the uterus. However, human embryo phenotypes have not been formally defined, and current criteria for embryo transfer largely focus on characteristics of individual embryos. We hypothesized that embryo cohort-specific variables describing sibling embryos as a group may predict developmental competence as measured by IVF cycle outcomes and serve to define human embryo phenotypes. Methodology/Principal Findings We retrieved data for all 1117 IVF cycles performed in 2005 at Stanford University Medical Center, and further analyzed clinical data from the 665 fresh IVF, non-donor cycles and their associated 4144 embryos. Thirty variables representing patient characteristics, clinical diagnoses, treatment protocol, and embryo parameters were analyzed in an unbiased manner by regression tree models, based on dichotomous pregnancy outcomes defined by positive serum ß-human chorionic gonadotropin (ß-hCG). IVF cycle outcomes were most accurately predicted at ∼70% by four non-redundant, embryo cohort-specific variables that, remarkably, were more informative than any measures of individual, transferred embryos: Total number of embryos, number of 8-cell embryos, rate (percentage) of cleavage arrest in the cohort and day 3 follicle stimulating hormone (FSH) level. While three of these variables captured the effects of other significant variables, only the rate of cleavage arrest was independent of any known variables. Conclusions/Significance Our findings support defining human embryo phenotypes by non-redundant, prognostic variables that are specific to sibling embryos in a cohort.

We present CisGenome, a software system for analyzing genome-wide chromatin immunoprecipitation (ChIP) data. CisGenome is designed to meet all basic needs of ChIP data analyses, including visualization, data normalization, peak detection, false discovery rate computation, gene-peak association, and sequence and motif analysis. In addition to implementing previously published ChIP-microarray (ChIP-chip) analysis methods, the software contains statistical methods designed specifically for ChlP sequencing (ChIP-seq) data obtained by coupling ChIP with massively parallel sequencing. The modular design of CisGenome enables it to support interactive analyses through a graphic user interface as well as customized batch-mode computation for advanced data mining. A built-in browser allows visualization of array images, signals, gene structure, conservation, and DNA sequence and motif information. We demonstrate the use of these tools by a comparative analysis of ChIP-chip and ChIP-seq data for the transcription factor NRSF/REST, a study of ChIP-seq analysis with or without a negative control sample, and an analysis of a new motif in Nanog- and Sox2-binding regions.

The differentiation of human embryonic stem cells (hESCs) into functional hepatocytes provides a powerful in vitro model system for studying the molecular mechanisms governing liver development. Furthermore, a well-characterized renewable supply of hepatocytes differentiated from hESCs could be used for in vitro assays of drug metabolism and toxicology, screening of potential antiviral agents, and cell-based therapies to treat liver disease. In this study, we describe a protocol for the differentiation of hESCs toward hepatic cells with complex cellular morphologies. Putative hepatic cells were identified and isolated using a lentiviral vector, containing the α-fetoprotein promoter driving enhanced green fluorescent protein expression (AFP:eGFP). Whole-genome transcriptional profiling was performed on triplicate samples of AFP:eGFP+ and AFP:eGFP− cell populations using the recently released Affymetrix Exon Array ST 1.0 (Santa Clara, CA, http://www.affymetrix.com). Statistical analysis of the transcriptional profiles demonstrated that the AFP:eGFP+ population is highly enriched for genes characteristic of hepatic cells. These data provide a unique insight into the complex process of hepatocyte differentiation, point to signaling pathways that may be manipulated to more efficiently direct the differentiation of hESCs toward mature hepatocytes, and identify molecular markers that may be used for further dissection of hepatic cell differentiation from hESCs. Disclosure of potential conflicts of interest is found at the end of this article.

In this paper, we propose a stochastic control approach to the problem of pairs trading. We model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem. We are able to obtain the optimal solution to this control problem in closed form via the corresponding Hamilton-Jacobi-Bellman equation. We also provide closed form maximum-likelihood estimation values for the parameters in the model. The approach is illustrated with a numerical example involving simulated data for a pair of stocks.

In this paper, we present a parameter identification algorithm for the discovery of a genetic regulatory network. The genetic network is modeled, via a mechanistic approach, as a nonlinear stochastic regulatory network, in which transcription, translation and degradation processes are described as discrete stochastic events which depend nonlinearly on the number of molecules inside the cell. The system depends on several unknowns, namely the rates of transcription, the rates of translation and the degradation rates. Furthermore, the system is observable through the measure, at regular time intervals, of the factorial cumulants of the molecule counts. The unknown parameters are uncovered by studying the system output response to an arbitrary command input. The parameter search is posed as an optimization program, in which the cost function is the deviation between the observed factorial cumulants and the model output, and in which the constraint is the parameterized ordinary differential equation (ODE) governing the time evolution of the factorial cumulants. The optimization problem is solved via an adjoint-based quasi- Newton algorithm. The command input is found to have an important impact on the parameter search: Oscillatory input signals yield better parameter discovery than flat input signals. Finally, numerical results are presented for two systems: a Hill feedback and a Michaelis Menten process.

Glioblastoma (GBM) is a highly lethal brain tumour presenting as one of two subtypes with distinct clinical histories and molecular profiles. The primary GBM subtype presents acutely as a high-grade disease that typically harbours mutations in EGFR, PTEN and INK4A/ARF (also known as CDKN2A), and the secondary GBM subtype evolves from the slow progression of a low-grade disease that classically possesses PDGF and TP53 events. Here we show that concomitant central nervous system (CNS)-specific deletion of p53 and Pten in the mouse CNS generates a penetrant acute-onset high-grade malignant glioma phenotype with notable clinical, pathological and molecular resemblance to primary GBM in humans. This genetic observation prompted TP53 and PTEN mutational analysis in human primary GBM, demonstrating unexpectedly frequent inactivating mutations of TP53 as well as the expected PTEN mutations. Integrated transcriptomic profiling, in silico promoter analysis and functional studies of murine neural stem cells (NSCs) established that dual, but not singular, inactivation of p53 and Pten promotes an undifferentiated state with high renewal potential and drives increased Myc protein levels and its associated signature. Functional studies validated increased Myc activity as a potent contributor to the impaired differentiation and enhanced renewal of NSCs doubly null for p53 and Pten (p53(-/-) Pten(-/-)) as well as tumour neurospheres (TNSs) derived from this model. Myc also serves to maintain robust tumorigenic potential of p53(-/-) Pten(-/-) TNSs. These murine modelling studies, together with confirmatory transcriptomic/promoter studies in human primary GBM, validate a pathogenetic role of a common tumour suppressor mutation profile in human primary GBM and establish Myc as an important target for cooperative actions of p53 and Pten in the regulation of normal and malignant stem/progenitor cell differentiation, self-renewal and tumorigenic potential.

A Bayesian method that incorporates a priori information to improve the resulting image quality in positron emission tomography is described. This approach utilizes a Gibbs prior to describe correlation of neighboring regions and takes into account the effect of limited spatial resolution. The Gibbs prior includes features depicting the similarity of intensities in neighboring pixels within homogeneous regions and line sites outlined as boundaries between regions. The effect of limited spatial resolution is incorporated into the probability density functions relating image cells to detector bins. Other physical factors, in principle, can be included as well. A highly efficient computational technique, the iterative conditional averages method, was employed for computing the point estimates. Significant improvements in image quality can be anticipated. >

The traditional one-sample nonparametric estimator of the cumulative hazard function is extended to allow for time-varying discrete covariates. A proof for the consistency and asymptotic normality of the estimator, as well as a derivation of the asymptotic standard error, is provided. Numerical results show that the estimator performs reasonably well.

Cis-regulatory modules (CRMs) composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. Comparative genomic studies have shown that these regulatory elements are more conserved across species due to evolutionary constraints. We propose a statistical method to combine module structure and cross-species orthology in de novo motif discovery. We use a hidden Markov model (HMM) to capture the module structure in each species and couple these HMMs through multiple-species alignment. Evolutionary models are incorporated to consider correlated structures among aligned sequence positions across different species. Based on our model, we develop a Markov chain Monte Carlo approach, MultiModule, to discover CRMs and their component motifs simultaneously in groups of orthologous sequences from multiple species. Our method is tested on both simulated and biological data sets in mammals and Drosophila, where significant improvement over other motif and module discovery methods is observed., Published at http://dx.doi.org/10.1214/07-AOAS103 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Microarray data from multiple species have been used to study evolutionary constraints on gene expression. Expression measurements from conventional microarray platforms such as the 3' expression arrays are strongly affected by platform-dependent probe effects that may introduce apparent but misleading discrepancies between species. In this manuscript, we assess the conservation of mammalian gene expression in adult tissues using data from a high-density exon array platform. The exon arrays have more than 6 million probes on a single array targeting all exons in a genome. We find that, unlike 3' array data, gene expression measurements from exon arrays reveal patterns of gene expression that are highly conserved between humans and mice in multiple tissues. Our analysis provides strong evidence for widespread stabilizing selection pressure on transcript abundance during mammalian evolution.

Identifying somatic mutations is a key analysis in cancer research. The challenge lies in the impure and heterogeneous nature of the tumor samples. Oftentimes, an algorithm works well for one tumor but poorly for another. Here, we present an ensemble approach that integrates multiple algorithms and demonstrate its performance and high accuracy with validation from both synthetic data and real data. Our approach incorporates state-of-the-art callers including MuTect, SomaticSniper, VarScan2, JointSNVMix2, and VarDict for somatic mutation detection. Each of these algorithms has its unique strength, capable of detecting variants that are missed by some others. The call sets are combined based on 70 independent sequencing and genomic features, which are then used by an adaptively boosted decision tree learner. The learner is trained with a sophisticated simulated data to discriminate true mutations from very noisy data of the tumor samples. In our latest submission to the ICGC-TCGA DREAM Mutation Calling Challenge (the Challenge), our approach obtained an unprecedented somatic SNV detection accuracy of 97.1% with a recall of 94.2% and a precision of 99.9%. The synthetic data was a tumor-normal pair of samples with 30x sequencing depth each. The tumor sample was synthesized by spiking in a whole spectrum of variants ranging from SNVs/Indels to SVs, resulting in an SNV allele frequency (VAF) of 25%. We further validated our approach with “in silico titration”. The titration mixed two different real genomes at different proportions with validated ground truths to generate different sample conditions, ranging from the simplest case where the normal and tumor were pure to the more challenging case where the tumor and normal tissues cross contaminated. From an VAF of 50%, 25% to 15%, our approach achieved an accuracy of 95.7%, 92.5%, and 85.3% respectively based on cross validation, consistent with the results from the Challenge. Finally, we validated our approach with three widely-used and published cancer datasets, obtained from TCGA and EGA, including a whole-genome sequenced malignant melanoma cell line, a whole-genome sequenced chronic lymphocytic leukemia cell line, and a whole-exome sequenced colon adenocarcinoma patient sample with experimentally validated somatic mutations. Our approach was trained on the data from the Challenge and applied to the aforementioned samples to measure its accuracy. Our results showed that we achieved a recall of 98.9%, 89.1% and 87.9% respectively. Although precision on real data cannot be measured without a comprehensive whole-genome experimental validation, our comparatively smaller call sets compared to all other methods considered implying that it has the highest precision among all. We extended our study of the above three validation approaches, namely synthetic genomes, in silico titration, and real samples, to compare with all the five individual callers for accuracy performance. We found that our approach had the highest accuracy when compared to any individual caller. To conclude, our approach is shown to have high accuracy in different types and conditions of tumor samples and by far the best in its class. Citation Format: Li Tai Fang, Pegah T. Afshar, John C. Mu, Narges Bani Asadi, Wing H. Wong, Hugo Y. K. Lam. An ensemble approach to accurately detect somatic mutations via adaptive boosting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-306. doi:10.1158/1538-7445.AM2015-LB-306

'Cells do not care about mathematics' thus concluded a biologist friend after a discussion on the future of biology. And indeed, why should they care? But if we exchange the word 'cell' with 'rock', 'Moon' or 'electrons', do we have to change the sentence also? Starting from this line of thought, we review some recent developments in understanding the stochastic behavior of biological systems. We emphasize the importance of a molecular Signal Generator in the study of genetic networks.

Genome-wide location analysis (ChIP-chip, ChIP-PET) is a powerful technique to study mammalian transcriptional regulation. In order to obtain a basic understanding of the location data generated for mammalian transcription factors and potential issues in their analysis, we conducted a comparative study of eight independent ChIP experiments involving six different transcription factors in human and mouse. Our cross-study comparisons, to the best of our knowledge the first to analyze multiple datasets, revealed the importance of carefully chosen genomic controls in the de novo identification of key transcription factor binding motifs, raised issues about the interpretation of ubiquitously occurring sequence motifs, and demonstrated the clustering tendency of protein-binding regions for certain transcription factors.

Rejoinder to ``Equi-energy sampler with applications in statistical inference and statistical mechanics'' by Kou, Zhou and Wong [math.ST/0507080]

We propose an equi-energy (EE) sampling approach to study protein folding in the two-dimensional hydrophobic-hydrophilic (HP) lattice model. This approach enables efficient exploration of the global energy landscape and provides accurate estimates of the density of states, which then allows us to conduct a detailed study of the thermodynamics of HP protein folding, in particular, on the temperature dependence of the transition from folding to unfolding and on how sequence composition affects this phenomenon. With no extra cost, this approach also provides estimates on global energy minima and ground states. Without using any prior structural information of the protein the EE sampler is able to find the ground states that match the best known results in most benchmark cases. The numerical results demonstrate it as a powerful method to study lattice protein folding models.

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples., Synopsis A key event in the generation of many cancers is loss of heterozygosity (LOH) of chromosomal regions containing tumor suppressor genes, whereby one parent's version of the tumor suppressor is lost. As we develop a better understanding of the molecular mechanisms that generate different cancers, a description of the LOH events underlying these cancers is forming an important part of their classification. Generally, detection of LOH relies on comparison of the tumor's genome to the normal genome of the individual. Unfortunately, for many tumors, including most experimental models of cancer, the normal genome is not available. Therefore, the authors have developed a hidden Markov model-based method that evaluates the probability of LOH at all sites throughout the genome, based on high-resolution genotyping of only the tumor. They were able to achieve high levels of accuracy, specifically by taking into account the haplotype block structure of the genome. Application of this method to a set of 34 prostate cancer samples allowed the authors to identify the locations of the known and suspected tumor suppressor genes that are targeted by LOH.