CiteULike: SeungjoonOh's library [18 articles]

The hallmarks of cancer.

D Hanahan — 2005-10-31T05:07:59-00:00

Cell, Vol. 100, No. 1. (7 January 2000), pp. 57-70.

Oncogene-induced senescence pathways weave an intricate tapestry.

P Yaswen — 2007-03-14T07:11:15-00:00

Cell, Vol. 128, No. 2. (26 January 2007), pp. 233-234.

The induction of cellular senescence by activated oncogenes acts as a barrier to cell transformation. Now, identify a key component of a senescence pathway that prevents tumorigenesis in a mouse model of skin cancer. They show that the p38-regulated/activated protein kinase (PRAK) induces senescence downstream of oncogenic Ras by directly phosphorylating and activating the tumor-suppressor protein p53.

Epithelial stem cells: turning over new leaves.

C Blanpain — 2007-03-14T07:11:03-00:00

Cell, Vol. 128, No. 3. (9 February 2007), pp. 445-458.

Most epithelial tissues self-renew throughout adult life due to the presence of multipotent stem cells and/or unipotent progenitor cells. Epithelial stem cells are specified during development and are controlled by epithelial-mesenchymal interactions. Despite morphological and functional differences among epithelia, common signaling pathways appear to control epithelial stem cell maintenance, activation, lineage determination, and differentiation. Additionally, deregulation of these pathways can lead to human disorders including cancer. Understanding epithelial stem cell biology has major clinical implications for the diagnosis, prevention, and treatment of human diseases, as well as for regenerative medicine.

The epigenomics of cancer.

PA Jones — 2007-03-14T07:10:48-00:00

Cell, Vol. 128, No. 4. (23 February 2007), pp. 683-692.

Aberrant gene function and altered patterns of gene expression are key features of cancer. Growing evidence shows that acquired epigenetic abnormalities participate with genetic alterations to cause this dysregulation. Here, we review recent advances in understanding how epigenetic alterations participate in the earliest stages of neoplasia, including stem/precursor cell contributions, and discuss the growing implications of these advances for strategies to control cancer.

Cancer biology: Gone but not forgotten

Norman Sharpless — 2007-01-25T23:40:44-00:00

Nature (24 January 2007)

Analysis of promoter regions of co-expressed genes identified by microarray analysis

Srinivas Veerla — 2006-08-17T14:08:51-00:00

BMC Bioinformatics, Vol. 7 (17 August 2006), 384.

Characterization of binding sites of eukaryotic transcription factors.

J Qian — 2007-02-20T06:42:29-00:00

Genomics Proteomics Bioinformatics, Vol. 4, No. 2. (May 2006), pp. 67-79.

To explore the nature of eukaryotic transcription factor (TF) binding sites and determine how they differ from surrounding DNA sequences, we examined four features associated with DNA-binding sites: G+C content, pattern complexity, palindromic structure, and Markov sequence ordering. Our analysis of the regulatory motifs obtained from the TRANSFAC database, using yeast intergenic sequences as background, revealed that these four features show variable enrichment in motif sequences. For example, motif sequences were more likely to have palindromic structure than were background sequences. In addition, these features were tightly localized to the regulatory motifs, indicating that they are a property of the motif sequences themselves and are not shared by the general promoter "environment" in which the regulatory motifs reside. By breaking down the motif sequences according to the TF classes to which they bind, more specific associations were identified. Finally, we found that some correlations, such as G+C content enrichment, were species-specific, while others, such as complexity enrichment, were universal across the species examined. The quantitative analysis provided here should increase our understanding of protein-DNA interactions and also help facilitate the discovery of regulatory motifs through bioinformatics.

Identifying cis-regulatory modules by combining comparative and compositional analysis of DNA.

Nora Pierstorff — 2006-10-18T18:52:57-00:00

Bioinformatics (10 October 2006)

MOTIVATION: Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction. RESULTS: We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some "false positive" predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs. AVAILABILITY: The program CisPlusFinder can be downloaded at http://jakob.genetik.uni-koeln.de/bioinformatik/people/nora/nora.html. All software is licensed under the Lesser GNU Public License (LGPL).

A supervised hidden Markov model framework for efficiently segmenting tiling array data in transcriptional and ChIP-chip experiments: systematically incorporating validated biological knowledge.

Jiang Du — 2006-10-23T15:32:54-00:00

Bioinformatics (12 October 2006)

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array data sets into "active regions" (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. Methodology: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments. SUPPLEMENTARY INFORMATION: The supplementary materials are available at http://tiling.gersteinlab.org/hmm/.

Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques.

Laura Elnitski — 2006-10-24T00:34:10-00:00

Genome Res (19 October 2006)

Fields such as genomics and systems biology are built on the synergism between computational and experimental techniques. This type of synergism is especially important in accomplishing goals like identifying all functional transcription factor binding sites in vertebrate genomes. Precise detection of these elements is a prerequisite to deciphering the complex regulatory networks that direct tissue specific and lineage specific patterns of gene expression. This review summarizes approaches for in silico, in vitro, and in vivo identification of transcription factor binding sites. A variety of techniques useful for localized- and high-throughput analyses are discussed here, with emphasis on aspects of data generation and verification.

Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae

Illes Farkas — 2006-10-28T13:33:40-00:00

BMC Bioinformatics, Vol. 7 (28 October 2006), 478.

Integrating transcription factor binding site information with gene expression datasets.

Ian B Jeffery — 2006-11-29T13:10:22-00:00

Bioinformatics (24 November 2006)

MOTIVATION: Microarrays are widely used to measure gene expression differences between sets of biological samples. Many of these differences will be due to differences in the activities of transcription factors. In principle, these differences can be detected by associating motifs in promoters with differences in gene expression levels between the groups. In practice, this is hard to do. RESULTS: We combine correspondence analysis, between group analysis and co-inertia analysis to determine which motifs, from a database of promoter motifs, are strongly associated with differences in gene expression levels. Given a database of motifs and gene expression levels from a set of arrays, the method produces a ranked list of motifs associated with any specified split in the arrays. We give an example using the Gene Atlas compendium of gene expression levels for human tissues where we search for motifs that are associated with expression in central nervous system (CNS) or muscle tissues. Most of the motifs that we find are known from previous work to be strongly associated with expression in CNS or muscle. We give a second example using a published prostate cancer data set where we can simply and clearly find which transcriptional pathways are associated with differences between benign and metastatic samples. AVAILABILITY: The source code is freely available upon request from the authors.

Beyond microarrays: Finding key transcription factors controlling signal transduction pathways

Alexdander Kel — 2006-11-24T03:20:32-00:00

BMC Bioinformatics, Vol. 7, No. Suppl 2. (2006)

BACKGROUND:Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments.RESULTS:We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells.CONCLUSION:We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC(R) and TRANSPATH(R)). The corresponding software and databases are available at http://www.gene-regulation.com.

Tissue-specific regulatory elements in mammalian promoters

Andrew Smith — 2007-01-16T11:00:19-00:00

Mol Syst Biol, Vol. 3 (16 January 2007)

Reconstructing dynamic regulatory maps.

J Ernst — 2007-01-19T09:21:39-00:00

Mol Syst Biol, Vol. 3 (2007)

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

EDGEdb: a transcription factor-DNA interaction database for the analysis of C. elegans differential gene expression

Inmaculada Barrasa — 2007-01-19T05:59:12-00:00

BMC Genomics, Vol. 8 (18 January 2007), 21.

Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster

Thomas Down — 2007-01-23T14:07:06-00:00

PLoS Computational Biology, Vol. 3, No. 1. (1 January 2007), e7.

A key step in understanding gene regulation is to identify the repertoire of transcription factor binding motifs (TFBMs) that form the building blocks of promoters and other regulatory elements. Identifying these experimentally is very laborious, and the number of TFBMs discovered remains relatively small, especially when compared with the hundreds of transcription factor genes predicted in metazoan genomes. We have used a recently developed statistical motif discovery approach, NestedMICA, to detect candidate TFBMs from a large set of Drosophila melanogaster promoter regions. Of the 120 motifs inferred in our initial analysis, 25 were statistically significant matches to previously reported motifs, while 87 appeared to be novel. Analysis of sequence conservation and motif positioning suggested that the great majority of these discovered motifs are predictive of functional elements in the genome. Many motifs showed associations with specific patterns of gene expression in the D. melanogaster embryo, and we were able to obtain confident annotation of expression patterns for 25 of our motifs, including eight of the novel motifs. The motifs are available through Tiffin, a new database of DNA sequence motifs. We have discovered many new motifs that are overrepresented in D. melanogaster promoter regions, and offer several independent lines of evidence that these are novel TFBMs. Our motif dictionary provides a solid foundation for further investigation of regulatory elements in Drosophila, and demonstrates techniques that should be applicable in other species. We suggest that further improvements in computational motif discovery should narrow the gap between the set of known motifs and the total number of transcription factors in metazoan genomes.

Statistical significance of cis-regulatory modules

Dustin Schones — 2007-01-23T13:59:47-00:00

BMC Bioinformatics, Vol. 8, No. 1. (2007)

BACKGROUND:It is becoming increasingly important for researchers to be able to scan through large genomic regions for transcription factor binding sites or clusters of binding sites forming cis-regulatory modules. Correspondingly, there has been a push to develop algorithms for the rapid detection and assessment of cis-regulatory modules. While various algorithms for this purpose have been introduced, most are not well suited for rapid, large scale scanning.RESULTS:We introduce methods designed for the detection and statistical evaluation of cis-regulatory modules, modeled as either clusters of individual binding sites or as combinations of sites with constrained organization. In order to determine the statistical significance of module sites, we first need a method to determine the statistical significance of single transcription factor binding site matches. We introduce a straightforward method of estimating the statistical significance of single site matches using a database of known promoters to produce data structures that can be used to estimate p-values for binding site matches. We next introduce a technique to calculate the statistical significance of the arrangement of binding sites within a module using a max-gap model. If the module scanned for has defined organizational parameters, the probability of the module is corrected to account for organizational constraints. The statistical significance of single site matches and the architecture of sites within the module can be combined to provide an overall estimation of statistical significance of cis-regulatory module sites.CONCLUSIONS:The methods introduced in this paper allow for the detection and statistical evaluation of single transcription factor binding sites and cis-regulatory modules. The features described are implemented in the Search Tool for Occurrences of Regulatory Motifs (STORM) and MODSTORM software.