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1.  NFIX Regulates Neural Progenitor Cell Differentiation During Hippocampal Morphogenesis 
Cerebral Cortex (New York, NY)  2012;24(1):261-279.
Neural progenitor cells have the ability to give rise to neurons and glia in the embryonic, postnatal and adult brain. During development, the program regulating whether these cells divide and self-renew or exit the cell cycle and differentiate is tightly controlled, and imbalances to the normal trajectory of this process can lead to severe functional consequences. However, our understanding of the molecular regulation of these fundamental events remains limited. Moreover, processes underpinning development of the postnatal neurogenic niches within the cortex remain poorly defined. Here, we demonstrate that Nuclear factor one X (NFIX) is expressed by neural progenitor cells within the embryonic hippocampus, and that progenitor cell differentiation is delayed within Nfix−/− mice. Moreover, we reveal that the morphology of the dentate gyrus in postnatal Nfix−/− mice is abnormal, with fewer subgranular zone neural progenitor cells being generated in the absence of this transcription factor. Mechanistically, we demonstrate that the progenitor cell maintenance factor Sry-related HMG box 9 (SOX9) is upregulated in the hippocampus of Nfix−/− mice and demonstrate that NFIX can repress Sox9 promoter-driven transcription. Collectively, our findings demonstrate that NFIX plays a central role in hippocampal morphogenesis, regulating the formation of neuronal and glial populations within this structure.
PMCID: PMC3862270  PMID: 23042739
glia; glial fibrillary acidic protein; neural progenitor cell; nuclear factor one X; SOX9
2.  Genome-wide in silico prediction of gene expression 
Bioinformatics  2012;28(21):2789-2796.
Motivation: Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.
Results: In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using in vivo TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use no TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on in vivo TF binding data.
Availability and implementation: All scripts, motifs and data presented in this article are available online at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3476338  PMID: 22954627
3.  Epigenetic priors for identifying active transcription factor binding sites 
Bioinformatics  2011;28(1):56-62.
Motivation Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored.
Results We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consistently outperforms a simple binary filter based on the same data. We also show that our approach performs competitively with a more complex method, CENTIPEDE, and suggest that the relative simplicity of the log-posterior odds scoring method makes it an appealing and very general method for identifying functional TFBSs on the basis of DNA and epigenetic evidence.
Availability and implementation: FIMO, part of the MEME Suite software toolkit, now supports log-posterior odds scoring using position-specific priors for motif search. A web server and source code are available at Utilities for creating priors are at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3244768  PMID: 22072382
4.  Tissue-specific prediction of directly regulated genes 
Bioinformatics  2011;27(17):2354-2360.
Direct binding by a transcription factor (TF) to the proximal promoter of a gene is a strong evidence that the TF regulates the gene. Assaying the genome-wide binding of every TF in every cell type and condition is currently impractical. Histone modifications correlate with tissue/cell/condition-specific (‘tissue specific’) TF binding, so histone ChIP-seq data can be combined with traditional position weight matrix (PWM) methods to make tissue-specific predictions of TF–promoter interactions.
Results: We use supervised learning to train a naïve Bayes predictor of TF–promoter binding. The predictor's features are the histone modification levels and a PWM-based score for the promoter. Training and testing uses sets of promoters labeled using TF ChIP-seq data, and we use cross-validation on 23 such datasets to measure the accuracy. A PWM+histone naïve Bayes predictor using a single histone modification (H3K4me3) is substantially more accurate than a PWM score or a conservation-based score (phylogenetic motif model). The naïve Bayes predictor is more accurate (on average) at all sensitivity levels, and makes only half as many false positive predictions at sensitivity levels from 10% to 80%. On average, it correctly predicts 80% of bound promoters at a false positive rate of 20%. Accuracy does not diminish when we test the predictor in a different cell type (and species) from training. Accuracy is barely diminished even when we train the predictor without using TF ChIP-seq data.
Availability: Our tissue-specific predictor of promoters bound by a TF is called Dr Gene and is available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3157924  PMID: 21724591
5.  Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data 
BMC Bioinformatics  2010;11:165.
A major goal of molecular biology is determining the mechanisms that control the transcription of genes. Motif Enrichment Analysis (MEA) seeks to determine which DNA-binding transcription factors control the transcription of a set of genes by detecting enrichment of known binding motifs in the genes' regulatory regions. Typically, the biologist specifies a set of genes believed to be co-regulated and a library of known DNA-binding models for transcription factors, and MEA determines which (if any) of the factors may be direct regulators of the genes. Since the number of factors with known DNA-binding models is rapidly increasing as a result of high-throughput technologies, MEA is becoming increasingly useful. In this paper, we explore ways to make MEA applicable in more settings, and evaluate the efficacy of a number of MEA approaches.
We first define a mathematical framework for Motif Enrichment Analysis that relaxes the requirement that the biologist input a selected set of genes. Instead, the input consists of all regulatory regions, each labeled with the level of a biological signal. We then define and implement a number of motif enrichment analysis methods. Some of these methods require a user-specified signal threshold, some identify an optimum threshold in a data-driven way and two of our methods are threshold-free. We evaluate these methods, along with two existing methods (Clover and PASTAA), using yeast ChIP-chip data. Our novel threshold-free method based on linear regression performs best in our evaluation, followed by the data-driven PASTAA algorithm. The Clover algorithm performs as well as PASTAA if the user-specified threshold is chosen optimally. Data-driven methods based on three statistical tests–Fisher Exact Test, rank-sum test, and multi-hypergeometric test—perform poorly, even when the threshold is chosen optimally. These methods (and Clover) perform even worse when unrestricted data-driven threshold determination is used.
Our novel, threshold-free linear regression method works well on ChIP-chip data. Methods using data-driven threshold determination can perform poorly unless the range of thresholds is limited a priori. The limits implemented in PASTAA, however, appear to be well-chosen. Our novel algorithms—AME (Analysis of Motif Enrichment)—are available at
PMCID: PMC2868005  PMID: 20356413

Results 1-5 (5)