This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform.Availability: BMIQ is freely available from http://code.google.com/p/bmiq/.Contact: a.teschendorff@ucl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online

SUMMARY Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N 4 ) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks. With recent advances in cDNA and oligonucleotide microarray technologies (1), it has become possible to measure mRNA expression levels on a genome-wide scale. Data thus collected provide valuable descriptions of gene activities under various biochemical (2) and physiological (3) circumstances and allow one to reverse-engineer the gene networks, i.e., to infer the underlying network structures from experimental measurements. However, naturally occurring gene regulatory networks are embedded in genomes that typically consist of thousands of genes. To extract the topology of such networks and hence isolate the functional subnetworks represents a computationally daunting task; it also requires a very large amount of experimental data, which are expensive to obtain.To circumvent this problem of data deficiency, many current research efforts have focused on clustering, i.e., grouping genes into hierarchical functional units based on correlations in expression patterns (3)(4)(5)(6)(7)(8). This hierarchical approach has been fruitful in identifying coregulated genes in certain functional units (3-6). It has also been generalized to self-organizing maps (7) and supervised learning schemes (8) to cope with the sensitivity to noise and other deficiencies intrinsic to hierarchical clustering (9), at the cost of increasing computational cost. However, a fundamental shortcoming of such clustering schemes is that they are based on the assumptions that: (i) gene regulatory networks are hierarchical in structure (3-6), and (ii) genes performing related biological functions exhibit similar expression patterns (and vice versa). These assumptions may not always be valid. At a structural level, there are data suggesting that gene regulatory networks are not strictly hierarchical in nature; rather, they are interwoven like a web (10), as in the cases of metabolic (11) and protein networks (12), with multiple pathways for similar functions to provide redundancy to protect against mutations and other deleterious effects (13). At a dynamical level, mRNA and protein expression levels for certain genes may not be correlated (14), suggesting a similar lack of strict correlation between gene expression and function. Therefore, although clustering is useful on a local scale to identify isolated coexpressing units, it is not suitable for large-scale reverse engineering.Recently, there have been attempts t...

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding topdown control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.computer model ͉ fMRI ͉ lateral inhibition ͉ prefrontal ͉ short-term memory ͉ parietal

While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds.T he genome projects are rapidly generating extensive lists of the genes and proteins that govern cellular behavior, and the analysis of these lists is providing a wealth of clinically relevant information. Simultaneously, there has been impressive progress made toward the description of the regulatory mechanisms in many cellular systems (1). Transcriptional regulation, used by cells to control gene expression (2, 3), occurs when a regulatory protein increases or decreases the transcription rate through biochemical reactions that enhance or block polymerase binding at the promoter region. Because many genes code for regulatory proteins that can activate or repress other genes, the emerging picture is that of a complex web, or circuit, of interacting genes and proteins. The elucidation of how subcellular processes at the genetic level are manifest in macroscopic phenomena at the phenotypic level will be a major goal of postgenomic research. Many cellular processes are described at the genetic level by diagrams that resemble complex electrical circuits (4), and there has been recent interest in two broad avenues of research relating to such genomic circuitry. At one end of the spectrum is the task of quantifying the fundamental laws of gene regulation. Within the context of the electrical circuit analogy, this question involves the deduction of a set of mesoscopic equations that faithfully quantify the information contained in the genetic circuit. A natural plan of attack is to use a forward engineering approach, whereby relatively simple circuits are designed and tested with respect to a set of equations generated from the underlying biochemistry. Recent work in this area has entailed the successful coupling of dynamical systems analysis with the construction of relatively simple genetic circuits, such as autoregulatory single-gene networks (ref. 5; F. Isaacs, J.H., C. R. Cantor, and J.J.C., unpublished work), genetic toggle switches (6), and genetic oscillators (7).At the other end of the spectrum is the project of deducing the connec...

A conspicuous feature of cortical organization is the wide diversity of inhibitory interneurons; their differential computational functions remain unclear. Here we propose a local cortical circuit in which three major subtypes of interneurons play distinct roles. In a model designed for spatial working memory, stimulus tuning of persistent activity arises from the concerted action of widespread inhibition mediated by perisoma-targeting (parvalbumin-containing) interneurons and localized disinhibition of pyramidal cells via interneurontargeting (calretinin-containing) interneurons. Moreover, resistance against distracting stimuli (a fundamental property of working memory) is dynamically controlled by dendrite-targeting (calbindin-containing) interneurons. The experimental observation of inverted tuning curves of monkey prefrontal neurons recorded during working memory supports a key model prediction. This work suggests a framework for understanding the division of labor and cooperation among different inhibitory cell types in a recurrent cortical circuit. S ynaptic inhibition is of paramount importance to cortical recurrent dynamics, sensory processing, and memory function. The complex inhibitory operation is likely to be accomplished by coordinated action of many subtypes of GABAergic (GABA, ␥-aminobutyric acid) interneurons present in the cortex. Recent years have witnessed a dramatic accumulation of our knowledge about these inhibitory cells, their morphology, physiology, chemical markers, synaptic connections, short-term plasticity, and molecular characteristics (1-6). On the other hand, little is known about specific computations by the diverse interneuron subtypes in animal behavior.To elucidate distinct operations performed by diverse interneurons, we have investigated a cortical microcircuit model that incorporates three interneuron subpopulations. Specifically, we report here a recurrent network model for working memory in the prefrontal cortex (PFC). PFC is a brain system critical to working memory, the ability to hold information actively in the mind for a short period of time (7,8). Understanding the cellular and circuit mechanisms of stimulus-selective persistent activity associated with working memory is a subject of intense current experimental and computational research (9, 10). More generally, persistent activity is believed to be a hallmark of strong recurrency in a cortical microcircuit, therefore modeling a working memory circuit represents a testbed for our investigation of cortical organization and functions. MethodsModel Architecture. The network model represents a local circuit of dorso-lateral prefrontal cortex in monkey. There are four cell populations: pyramidal (P) neurons and three subpopulations of inhibitory cells. Perisoma-targeting, dendrite-targeting, and interneuron-targeting interneurons are assumed to express parvalbumin (PV), calbindin (CB), and calretinin (CR) calcium-binding proteins, respectively (Fig. 1). P cells are four times more numerous than interneurons, and half of...