CiteULike: idonaldson's library [108 articles]

ChIPping away at gene regulation.

CE Massie — 2008-07-08T09:53:22-00:00

EMBO reports, Vol. 9, No. 4. (April 2008), pp. 337-343.

The coordinated regulation of gene expression in higher eukaryotes is complex and poorly understood. Recent technological advances have allowed the first insights into these networks on a genome-wide scale. These investigations have identified transcription factor target sites in the genome and successfully predicted cooperative interactions with other factors. However, a detailed understanding of the processes that coordinate gene expression remains elusive. Here, we highlight the advances that have been made using current methods, and the need for new technologies to address the gaps in our knowledge and to map these complex pathways further.

DiRE: identifying distant regulatory elements of co-expressed genes

Valer Gotea — 2008-07-08T08:08:21-00:00

Nucl. Acids Res., Vol. 36, No. suppl_2. (1 July 2008), pp. W133-139.

Regulation of gene expression in eukaryotic genomes is established through a complex cooperative activity of proximal promoters and distant regulatory elements (REs) such as enhancers, repressors and silencers. We have developed a web server named DiRE, based on the Enhancer Identification (EI) method, for predicting distant regulatory elements in higher eukaryotic genomes, namely for determining their chromosomal location and functional characteristics. The server uses gene co-expression data, comparative genomics and profiles of transcription factor binding sites (TFBSs) to determine TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is its ability to detect REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs and it also scores the association of individual transcription factors (TFs) with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data. The DiRE web server is freely available at http://dire.dcode.org. 10.1093/nar/gkn300

A survey of motif discovery methods in an integrated framework

Geir Sandve — 2006-04-08T22:18:54-00:00

Biology Direct, Vol. 1, No. 1. (06 April 2006), 11.

BACKGROUND:There has been a growing interest in computational discovery of regulatory elements, and a multitude of motif discovery methods have been proposed. Computational motif discovery has been used with some success in simple organisms like yeast. However, as we move to higher organisms with more complex genomes, more sensitive methods are needed. Several recent methods try to integrate additional sources of information, including microarray experiments (gene expression and ChIP-chip). There is also a growing awareness that regulatory elements work in combination, and that this combinatorial behavior must be modeled for successful motif discovery. However, the multitude of methods and approaches makes it difficult to get a good understanding of the current status of the field. RESULTS:This paper presents a survey of methods for motif discovery in DNA, based on a structured and well defined framework that integrates all relevant elements. Existing methods are discussed according to this framework. CONCLUSION:The survey shows that although no single method takes all relevant elements into consideration, a very large number of different models treating the various elements separately have been tried. Very often the choices that have been made are not explicitly stated, making it difficult to compare different implementations. Also, the tests that have been used are often not comparable. Therefore, a stringent framework and improved test methods are needed to evaluate the different approaches in order to conclude which ones are most promising. Reviewers: This article was reviewed by Eugene V. Koonin, Philipp Bucher and Frank Eisenhaber.

Retroviral promoters in the human genome

Andrew Conley — 2008-07-07T09:53:47-00:00

Bioinformatics, Vol. 24, No. 14. (15 July 2008), pp. 1563-1567.

Motivation: Endogenous retrovirus (ERV) elements have been shown to contribute promoter sequences that can initiate transcription of adjacent human genes. However, the extent to which retroviral sequences initiate transcription within the human genome is currently unknown. We analyzed genome sequence and high-throughput expression data to systematically evaluate the presence of retroviral promoters in the human genome. Results: We report the existence of 51 197 ERV-derived promoter sequences that initiate transcription within the human genome, including 1743 cases where transcription is initiated from ERV sequences that are located in gene proximal promoter or 5' untranslated regions (UTRs). A total of 114 of the ERV-derived transcription start sites can be demonstrated to drive transcription of 97 human genes, producing chimeric transcripts that are initiated within ERV long terminal repeat (LTR) sequences and read-through into known gene sequences. ERV promoters drive tissue-specific and lineage-specific patterns of gene expression and contribute to expression divergence between paralogs. These data illustrate the potential of retroviral sequences to regulate human transcription on a large scale consistent with a substantial effect of ERVs on the function and evolution of the human genome. Contact: king.jordan@biology.gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn243

Nucleosome positioning from tiling microarray data

Moran Yassour — 2008-06-27T10:37:35-00:00

Bioinformatics, Vol. 24, No. 13. (1 July 2008), pp. i139-146.

Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5-10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed. Results: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions. We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA. Contact: nir@cs.huji.ac.il 10.1093/bioinformatics/btn151

Cross-species de novo identification of cis-regulatory modules with GibbsModule: application to gene regulation in embryonic stem cells

Dan Xie — 2008-05-27T08:52:20-00:00

Genome Res. (15 May 2008), gr.072769.107.

We introduce the GibbsModule algorithm for de novo detection of cis-regulatory motifs and modules in eukaryote genomes. GibbsModule models the co-expressed genes within one species as sharing a core cis-regulatory motif and each homologous gene group as sharing a homologous cis-regulatory module (CRM), characterized by a similar composition of motifs. Without using a pre-determined alignment result, GibbsModule iteratively updates the core motif shared by co-expressed genes and traces the homologous CRMs that contain the core motif. GibbsModule achieved substantial improvements in both precision and recall as compared to peer algorithms on a number of synthetic and real datasets. Applying GibbsModule to analyze the binding regions of the Kruppel-like factor (Klf) transcription factor in embryonic stem cells (ESCs), we discovered a motif that differs from a previously published Klf motif identified by a SELEX experiment, but the new motif is consistent with mutagenesis analysis. Sox2 motif was found to be a collaborating motif to the Klf motif in ESCs. We used quantitative chromatin immunoprecipitation (ChIP) analysis to test whether GibbsModule could distinguish functional and non-functional binding sites. All 7 tested binding sites in GibbsModule predicted CRMs had higher ChIP signals as compared to the other 7 tested binding sites located outside of predicted CRMs. GibbsModule is available at http://biocomp.bioen.uiuc.edu/GibbsModule. 10.1101/gr.072769.107

ConTra: a promoter alignment analysis tool for identification of transcription factor binding sites across species

Bart Hooghe — 2008-05-06T14:55:58-00:00

Nucl. Acids Res. (3 May 2008), gkn195.

Transcription factors (TFs) are key components in signaling pathways, and the presence of their binding sites in the promoter regions of DNA is essential for their regulation of the expression of the corresponding genes. Orthologous promoter sequences are commonly used to increase the specificity with which potentially functional transcription factor binding sites (TFBSs) are recognized and to detect possibly important similarities or differences between the different species. The ConTra (conserved TFBSs) web server provides the biologist at the bench with a user-friendly tool to interactively visualize TFBSs predicted using either TransFac (1) or JASPAR (2) position weight matrix libraries, on a promoter alignment of choice. The visualization can be preceded by a simple scoring analysis to explore which TFs are the most likely to bind to the promoter of interest. The ConTra web server is available at http://bioit.dmbr.ugent.be/ConTra/index.php. 10.1093/nar/gkn195

Inferring the role of transcription factors in regulatory networks

Philippe Veber — 2008-05-07T00:09:18-00:00

BMC Bioinformatics, Vol. 9 (06 May 2008), 228.

The biological function of some human transcription factor binding motifs varies with position relative to the transcription start site

Kannan Tharakaraman — 2008-03-26T13:08:41-00:00

Nucl. Acids Res. (26 March 2008), gkn137.

A number of previous studies have predicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks like the transcriptional start site (TSS). The studies' methods are generally too computationally intensive for genome-scale investigation, so the full potential of positional regulomics' to discover TFBSs and determine their function remains unknown. Because databases often annotate the genomic landmarks in DNA sequences, the methodical exploitation of positional regulomics has become increasingly urgent. Accordingly, we examined a set of 7914 human putative promoter regions (PPRs) with a known TSS. Our methods identified 1226 eight-letter DNA words with significant positional preferences with respect to the TSS, of which only 608 of the 1226 words matched known TFBSs. Many groups of genes whose PPRs contained a common word displayed similar expression profiles and related biological functions, however. Most interestingly, our results included 78 words, each of which clustered significantly in two or three different positions relative to the TSS. Often, the gene groups corresponding to different positional clusters of the same word corresponded to diverse functions, e.g. activation or repression in different tissues. Thus, different clusters of the same word likely reflect the phenomenon of positional regulation', i.e. a word's regulatory function can vary with its position relative to a genomic landmark, a conclusion inaccessible to methods based purely on sequence. Further integrative analysis of words co-occurring in PPRs also yielded 24 different groups of genes, likely identifying cis-regulatory modules de novo. Whereas comparative genomics requires precise sequence alignments, positional regulomics exploits genomic landmarks to provide a poor man's alignment'. By exploiting the phenomenon of positional regulation, it uses position to differentiate the biological functions of subsets of TFBSs sharing a common sequence motif. 10.1093/nar/gkn137

Detecting cis-regulatory binding sites for cooperatively binding proteins

Liesbeth van Oeffelen — 2008-05-06T13:26:56-00:00

Nucl. Acids Res., Vol. 36, No. 8. (1 May 2008), e46.

Several methods are available to predict cis-regulatory modules in DNA based on position weight matrices. However, the performance of these methods generally depends on a number of additional parameters that cannot be derived from sequences and are difficult to estimate because they have no physical meaning. As the best way to detect cis-regulatory modules is the way in which the proteins recognize them, we developed a new scoring method that utilizes the underlying physical binding model. This method requires no additional parameter to account for multiple binding sites; and the only necessary parameters to model homotypic cooperative interactions are the distances between adjacent protein binding sites in basepairs, and the corresponding cooperative binding constants. The heterotypic cooperative binding model requires one more parameter per cooperatively binding protein, which is the concentration multiplied by the partition function of this protein. In a case study on the bacterial ferric uptake regulator, we show that our scoring method for homotypic cooperatively binding proteins significantly outperforms other PWM-based methods where biophysical cooperativity is not taken into account. 10.1093/nar/gkn140

W-AlignACE: An improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data.

Xin Chen — 2008-03-09T19:38:12-00:00

Bioinformatics (5 March 2008)

MOTIVATION: Position weight matrices (PWMs) are widely used to depict the DNA binding preferences of transcription factors (TFs) in computational molecular biology and regulatory genomics. Thus, learning an accurate PWM to characterize the binding sites of a specific TF is a fundamental problem that plays an important role in modeling regulatory motifs and also in discovering the regulatory targets of TFs. RESULTS: We study the question of how to learn a more accurate PWM from both binding sequences and gene expression (or ChIPchip) data, and propose to find a PWM such that the likelihood of simultaneously observing both binding sequences and their associated gene expression (or ChIP-chip) data is maximized. To solve the above maximum likelihood problem, a sequence weighting scheme is thus introduced based on the observation that binding sites inducing drastic fold changes in mRNA expression (or showing strong binding ratios in ChIP experiments) are likely to represent a true motif. We have incorporated this new learning approach into the popular motif finding program AlignACE. The modified program, called W-AlignACE, is compared with three other programs (AlignACE, MDscan, and MotifRegressor) on a variety of datasets, including simulated data, mRNA expression and ChIP-chip data. These tests demonstrate that W-AlignACE is an effective tool for discovering TF binding motifs from gene expression (or ChIP-chip) data and, in particular, has the ability to find very weak motifs like DIG1 and GAL4. AVAILABILITY: http://www.ntu.edu.sg/home/ChenXin/Gibbs CONTACT: chenxin@ntu.edu.sg Supplementary materials: Available at Bioinformatics Online.

How many human genes can be defined as housekeeping with current expression data?

Jiang Zhu — 2008-04-16T11:53:22-00:00

BMC Genomics, Vol. 9, No. 1. (2008)

BACKGROUND:Housekeeping (HK) genes are ubiquitously expressed in all tissue/cell types and constitute a basal transcriptome for the maintenance of basic cellular functions. Partitioning transcriptomes into HK and tissue-specific (TS) genes relatively is fundamental for studying gene expression and cellular differentiation. Although many studies have aimed at large-scale and thorough categorization of human HK genes, a meaningful consensus has yet to be reached.RESULTS:We collected two latest gene expression datasets (both EST and microarray data) from public databases and analyzed the gene expression profiles in 18 human tissues that have been well-documented by both two data types. Benchmarked by a manually-curated HK gene collection (HK408), we demonstrated that present data from EST sampling was far from saturated, and the inadequacy has limited the gene detectability and our understanding of TS expressions. Due to a likely over-stringent threshold, microarray data showed higher false negative rate compared with EST data, leading to a significant underestimation of HK genes. Based on EST data, we found that 40.0% of the currently annotated human genes were universally expressed in at least 16 of 18 tissues, as compared to only 5.1% specifically expressed in a single tissue. Our current EST-based estimate on human HK genes ranged from 3,140 to 6,909 in number, a ten-fold increase in comparison with previous microarray-based estimates.CONCLUSIONS:We concluded that a significant fraction of human genes, at least in the currently annotated data depositories, was broadly expressed. Our understanding of tissue-specific expression was still preliminary and required much more large-scale and high-quality transcriptomic data in future studies. The new HK gene list categorized in this study will be useful for genome-wide analyses on structural and functional features of HK genes.

Transcription factor and microRNA motif discovery: The Amadeus platform and a compendium of metazoan target sets.

Chaim Linhart — 2008-04-18T07:36:36-00:00

Genome research (14 April 2008)

We present a three-fold contribution to the computational task of motif discovery, a key component in the effort of delineating the regulatory map of a genome: 1) We constructed a comprehensive large-scale, publicly-available compendium of transcription factor and microRNA target gene sets derived from diverse high-throughput experiments in several metazoans. We used the compendium as a benchmark for motif discovery tools. 2) We developed Amadeus, a highly efficient, user-friendly software platform for genome-scale detection of novel motifs, applicable to a wide range of motif discovery tasks. Amadeus improves upon extant tools in terms of accuracy, running time, output information and ease-of-use, and is the only program that attained a high success rate on the metazoan compendium. 3) We demonstrate that by searching for motifs based on their genome-wide localization or chromosomal distributions (without using a pre-defined target set), Amadeus uncovers diverse known phenomena, as well as novel regulatory motifs.

Transposable elements and the evolution of regulatory networks.

Cédric Feschotte — 2008-03-31T08:19:51-00:00

Nat Rev Genet (27 March 2008)

The control and coordination of eukaryotic gene expression rely on transcriptional and post-transcriptional regulatory networks. Although progress has been made in mapping the components and deciphering the function of these networks, the mechanisms by which such intricate circuits originate and evolve remain poorly understood. Here I revisit and expand earlier models and propose that genomic repeats, and in particular transposable elements, have been a rich source of material for the assembly and tinkering of eukaryotic gene regulatory systems.

Chromhome: A rich internet application for accessing comparative chromosome homology maps

Sridevi Nagarajan — 2008-03-27T05:57:20-00:00

BMC Bioinformatics, Vol. 9 (26 March 2008), 168.

Dynamic Regulation of Nucleosome Positioning in the Human Genome

Dustin Schones — 2008-03-07T11:55:52-00:00

Cell, Vol. 132, No. 5. (7 March 2008), pp. 887-898.

Summary The positioning of nucleosomes with respect to DNA plays an important role in regulating transcription. However, nucleosome mapping has been performed for only limited genomic regions in humans. We have generated genome-wide maps of nucleosome positions in both resting and activated human CD4+ T cells by direct sequencing of nucleosome ends using the Solexa high-throughput sequencing technique. We find that nucleosome phasing relative to the transcription start sites is directly correlated to RNA polymerase II (Pol II) binding. Furthermore, the first nucleosome downstream of a start site exhibits differential positioning in active and silent genes. TCR signaling induces extensive nucleosome reorganization in promoters and enhancers to allow transcriptional activation or repression. Our results suggest that H2A.Z-containing and modified nucleosomes are preferentially lost from the -1 nucleosome position. Our data provide a comprehensive view of the nucleosome landscape and its dynamic regulation in the human genome.

Motif discovery in tissue-specific regulatory sequences using directed information.

A Rao — 2008-03-17T11:03:35-00:00

EURASIP J Bioinform Syst Biol (2007)

Motif discovery for the identification of functional regulatory elements underlying gene expression is a challenging problem. Sequence inspection often leads to discovery of novel motifs (including transcription factor sites) with previously uncharacterized function in gene expression. Coupled with the complexity underlying tissue-specific gene expression, there are several motifs that are putatively responsible for expression in a certain cell type. This has important implications in understanding fundamental biological processes such as development and disease progression. In this work, we present an approach to the identification of motifs (not necessarily transcription factor sites) and examine its application to some questions in current bioinformatics research. These motifs are seen to discriminate tissue-specific gene promoter or regulatory regions from those that are not tissue-specific. There are two main contributions of this work. Firstly, we propose the use of directed information for such classification constrained motif discovery, and then use the selected features with a support vector machine (SVM) classifier to find the tissue specificity of any sequence of interest. Such analysis yields several novel interesting motifs that merit further experimental characterization. Furthermore, this approach leads to a principled framework for the prospective examination of any chosen motif to be discriminatory motif for a group of coexpressed/coregulated genes, thereby integrating sequence and expression perspectives. We hypothesize that the discovery of these motifs would enable the large-scale investigation for the tissue-specific regulatory role of any conserved sequence element identified from genome-wide studies.

An efficient method for statistical significance calculation of transcription factor binding sites.

Z Qian — 2008-03-01T21:46:46-00:00

Bioinformation, Vol. 2, No. 5. (2007), pp. 169-174.

Various statistical models have been developed to describe the DNA binding preference of transcription factors, by which putative transcription factor binding sites (TFBS) can be identified according to scores assigned. Statistical significance of these scores, usually known as the p-value, play a critical role in identification. We developed an efficient algorithm to provide precise calculation of the statistical significance, remarkably enhancing the calculation efficiency by reducing the time complexity from an exponent scale to a linear scale, and successfully extended the application of this algorithm to a wide range of models, from the commonly used position weight matrix models to the complicated Bayesian Network models. Further, we calculated p-values of all transcription factor DNA binding sites recorded in the database, JASPAR, and based on these, we investigated some unseen properties of p-values as a whole, such as the p-value distribution of different models and the p-value variance according to changed scoring schemes. We hope that our algorithm and the result of computational experiments would offer an improved solution to the statistical significance of transcription factor binding sites. The software to implement our method can be downloaded from http://pcal.biosino.org/pCal.html.

Assessment of composite motif discovery methods

Kjetil Klepper — 2008-02-26T21:23:01-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery - discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery.RESULTS:We have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise. CONCLUSIONS:Although some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

fdrMotif: Identifying cis-elements by an EM Algorithm Coupled with False Discovery Rate Control.

Leping Li — 2008-02-27T17:11:04-00:00

Bioinformatics (22 February 2008)

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a z-score or p-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. Methodology: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization (e.g., position weight matrix (PWM)) and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E) and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated "ChIP" sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background se-quences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: Supplementary material and C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/. CONTACT: li3@niehs.nih.gov, liangy3@niehs.nih.gov.

Genome-wide approaches to studying chromatin modifications

Dustin Schones — 2008-02-18T13:07:54-00:00

Nat Rev Genet, Vol. 9, No. 3. (March 2008), pp. 179-191.

Ancora: a web resource for exploring highly conserved noncoding elements and their association with developmental regulatory genes

Par Engstrom — 2008-02-17T01:30:08-00:00

Genome Biology, Vol. 9 (15 February 2008), R34.

Transposable elements as drivers of genomic and biological diversity in vertebrates.

A Böhne — 2008-02-25T10:08:02-00:00

Chromosome Res, Vol. 16, No. 1. (2008), pp. 203-215.

Comparative genomics has revealed that major vertebrate lineages contain quantitatively and qualitatively different populations of retrotransposable elements and DNA transposons, with important differences also frequently observed between species of the same lineage. This is essentially due to (i) the differential evolution of ancestral families of transposable elements, with evolutionary scenarios ranging from complete extinction to massive invasion; (ii) the lineage-specific introduction of transposable elements by infection and horizontal transfer, as exemplified by endogenous retroviruses; and (iii) the lineage-specific emergence of new transposable elements, as particularly observed for non-coding retroelements called short interspersed elements (SINEs). During vertebrate evolution, transposable elements have repeatedly contributed regulatory and coding sequences to the host, leading to the emergence of new lineage-specific gene regulations and functions. In all vertebrate lineages, there is evidence of transposable element-mediated genomic rearrangements such as insertions, deletions, inversions and duplications potentially associated with or subsequent to speciation events. Taken together, these observations indicate that transposable elements are major drivers of genomic and biological diversity in vertebrates, with possible important roles in speciation and major evolutionary transitions.

DNA sequence and structural properties as predictors of human and mouse promoters

Pelin Akan — 2008-02-21T08:36:19-00:00

Gene, Vol. 410, No. 1. (29 February 2008), pp. 165-176.

Promoters play a central role in gene regulation, yet our power to discriminate them from non-promoter sequences in higher eukaryotes is mainly restricted to those associated with CpG islands. Here, we examined in silico the promoters of 30,954 human and 18,083 mouse transcripts in the DBTSS database, to assess the impact of particular sequence and structural features (propeller twist, bendability and nucleosome positioning preference) on promoter classification and prediction. Our analysis showed that a stricter-than-traditional definition of CpG islands captures low and high CpG count promoter classes more accurately than the traditional one. We observed that both human and mouse promoter sequences are flexible with the exception of the TATA box and TSS, which are rigid regions irrespective of association with a CpG island. Therefore varying levels of structural flexibility in promoters may affect their accessibility to proteins, and hence their specificity. For all features investigated, averaged values across core promoters discriminated CpG island associated promoters from background, whereas the same did not hold for promoters without a CpG island. However, local changes around - 34 to - 23 (expected position of TATA box) and the TSS were informative in discriminating promoters (both classes) from non-promoter sequences. Additionally, we investigated ATG deserts and observed that they occur in all promoter sets except those with a TATA-box and without a CpG island in human. Interestingly, all mouse promoter sets showed ATG codon depletion irrespective of the presence of a TATA-box, possibly reflecting a weaker contribution to TSS specificity in mouse.

Mapping of transcription factor binding regions in mammalian cells by ChIP: Comparison of array- and sequencing-based technologies

Ghia Euskirchen — 2007-06-15T20:55:55-00:00

Genome Res., Vol. 17, No. 6. (1 June 2007), pp. 898-909.

Recent progress in mapping transcription factor (TF) binding regions can largely be credited to chromatin immunoprecipitation (ChIP) technologies. We compared strategies for mapping TF binding regions in mammalian cells using two different ChIP schemes: ChIP with DNA microarray analysis (ChIP-chip) and ChIP with DNA sequencing (ChIP-PET). We first investigated parameters central to obtaining robust ChIP-chip data sets by analyzing STAT1 targets in the ENCODE regions of the human genome, and then compared ChIP-chip to ChIP-PET. We devised methods for scoring and comparing results among various tiling arrays and examined parameters such as DNA microarray format, oligonucleotide length, hybridization conditions, and the use of competitor Cot-1 DNA. The best performance was achieved with high-density oligonucleotide arrays, oligonucleotides [≥]50 bases (b), the presence of competitor Cot-1 DNA and hybridizations conducted in microfluidics stations. When target identification was evaluated as a function of array number, 80%-86% of targets were identified with three or more arrays. Comparison of ChIP-chip with ChIP-PET revealed strong agreement for the highest ranked targets with less overlap for the low ranked targets. With advantages and disadvantages unique to each approach, we found that ChIP-chip and ChIP-PET are frequently complementary in their relative abilities to detect STAT1 targets for the lower ranked targets; each method detected validated targets that were missed by the other method. The most comprehensive list of STAT1 binding regions is obtained by merging results from ChIP-chip and ChIP-sequencing. Overall, this study provides information for robust identification, scoring, and validation of TF targets using ChIP-based technologies. 10.1101/gr.5583007

Systematic functional characterization of cis-regulatory motifs in human core promoters

Saurabh Sinha — 2008-02-18T05:00:45-00:00

Genome Res. (6 February 2008), gr.6828808.

A large number of cis-regulatory motifs involved in transcriptional control have been identified, but the regulatory context and biological processes in which many of them function are unknown. Here, we computationally identify the sets of human core promoters targeted by motifs, and systematically characterize their function by using a robust gene-set-based approach and diverse sources of biological data. We find that the target sets of most motifs contain both genes with similar function and genes that are coregulated in vivo, thereby suggesting both the biological process regulated by the motifs and the conditions in which this regulation may occur. Our analysis also identifies many motifs whose target sets are predicted to be regulated by a common microRNA, suggesting a connection between transcriptional and post-transcriptional control processes. Finally, we predict novel roles for uncharacterized motifs in the regulation of specific biological processes and certain types of human cancer, and experimentally validate four such predictions, suggesting regulatory roles for four uncharacterized motifs in cell cycle progression. Our analysis thus provides a concrete framework for uncovering the biological function of cis-regulatory motifs genome wide. 10.1101/gr.6828808

An analysis of the positional distribution of DNA motifs in promoter regions and its biological relevance

Ana Casimiro — 2008-02-08T01:10:44-00:00

BMC Bioinformatics, Vol. 9 (07 February 2008), 89.

Identification of tissue-specific cis-regulatory modules based on interactions between transcription factors

Xueping Yu — 2007-11-10T21:48:55-00:00

BMC Bioinformatics, Vol. 8 (09 November 2007), 437.

Text-mining assisted regulatory annotation

Stein Aerts — 2008-02-13T12:51:42-00:00

Genome Biology, Vol. 9, No. 2. (2008)

BACKGROUND:Decoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes are fundamental challenges in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature. RESULTS:We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that over 30,000 papers harbour unannotated cis-regulatory data. Additionally, we show that DNA sequences can be automatically extracted from full-text articles with high cis-regulatory content and accurately mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process. CONCLUSIONS:Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.

Jane: Suggesting Journals, Finding Experts.

Martijn J Schuemie — 2008-02-07T13:17:07-00:00

Bioinformatics (28 January 2008)

SUMMARY: With an exponentially growing number of articles being published every year, scientists can use some help in determining which journal is most appropriate for publishing their results, and which other scientists can be called upon to review their work. Jane (Journal/Author Name Estimator) is a freely available web-based application that, on the basis of a sample text (e.g., the title and abstract of a manuscript), can suggest journals and experts who have published similar articles. AVAILABILITY: http://biosemantics.org/jane. CONTACT: m.schuemie@erasmusmc.nl.

A novel genome-scale repeat finder geared towards transposons

Xuehui Li — 2008-02-13T13:40:04-00:00

Bioinformatics, Vol. 24, No. 4. (15 February 2008), pp. 468-476.

Motivation: Repeats are ubiquitous in genomes and play important roles in evolution. Transposable elements are a common kind of repeat. Transposon insertions can be nested and make the task of identifying repeats difficult. Results: We develop a novel iterative algorithm, called Greedier, to find repeats in a target genome given a repeat library. Greedier distinguishes itself from existing methods by taking into account the fragmentation of repeats. Each iteration consists of two passes. In the first pass, it identifies the local similarities between the repeat library and the target genome. Greedier then builds graphs from this comparison output. In each graph, a vertex denotes a similar subsequence pair. Edges denote pairs of subsequences that can be connected to form higher similarities. In the second pass, Greedier traverses these graphs greedily to find matches to individual repeat units in the repeat library. It computes a fitness value for each such match denoting the similarity of that match. Matches with fitness values greater than a cutoff are removed, and the rest of the genome is stitched together. The similarity cutoff is then gradually reduced, and the iteration is repeated until no hits are returned from the comparison. Our experiments on the Arabidopsis and rice genomes show that Greedier identifies approximately twice as many transposon bases as those found by cross_match and WindowMasker. Moreover, Greedier masks far fewer false positive bases than either cross_match or WindowMasker. In addition to masking repeats, Greedier also reports potential nested transposon structures. Contact: xli@cise.ufl.edu 10.1093/bioinformatics/btm613

Genome-scale ChIP-chip analysis using 10,000 human cells.

LG Acevedo — 2008-02-12T17:08:35-00:00

Biotechniques, Vol. 43, No. 6. (December 2007), pp. 791-797.

The technique of chromatin immunoprecipitation (ChIP) is a powerful method for identifying in vivo DNA binding sites of transcription factors and for studying chromatin modifications. Unfortunately, the large number of cells needed for the standard ChIP protocol has hindered the analysis of many biologically interesting cell populations that are difficult to obtain in large numbers. New ChIP methods involving the use of carrier chromatin have been developed that allow the one-gene-at-a-time analysis of very small numbers of cells. However such methods are not useful if the resultant sample will be applied to genomic microarrays or used in ChIP-sequencing assays. Therefore, we have miniaturized the ChIP protocol such that as few as 10,000 cells (without the addition of carrier reagents) can be used to obtain enough sample material to analyze the entire human genome. We demonstrate the reproducibility of this MicroChIP technique using 2.1 million feature high-density oligonucleotide arrays and antibodies to RNA polymerase II and to histone H3 trimethylated on lysine 27 or lysine 9.

Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets

David Johnson — 2008-02-11T14:28:53-00:00

Genome Res. (7 February 2008), gr.7080508.

The most widely used method for detecting genome-wide proteinDNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. 10.1101/gr.7080508

GSE: a comprehensive database system for the representation, retrieval, and analysis of microarray data.

T Danford — 2008-02-04T13:19:00-00:00

Pac Symp Biocomput (2008), pp. 539-550.

We present GSE, the Genomic Spatial Event database, a system to store, retrieve, and analyze all types of high-throughput microarray data. GSE handles expression datasets, ChIP-chip data, genomic annotations, functional annotations, the results of our previously published Joint Binding Deconvolution algorithm for ChIP-chip, and precomputed scans for binding events. GSE can manage data associated with multiple species; it can also simultaneously handle data associated with multiple 'builds' of the genome from a single species. The GSE system is built upon a middle software layer for representing streams of biological data; we outline this layer, called GSEBricks, and show how it is used to build an interactive visualization application for ChIP-chip data. The visualizer software is written in Java and communicates with the GSE database system over the network. We also present a system to formulate and record binding hypotheses--simple descriptions of the relationships that may hold between different ChIP-chip experiments. We provide a reference software implementation for the GSE system.

CMARRT: a tool for the analysis of ChIP-chip data from tiling arrays by incorporating the correlation structure.

PF Kuan — 2008-02-04T13:13:44-00:00

Pac Symp Biocomput (2008), pp. 515-526.

Whole genome tiling arrays at a user specified resolution are becoming a versatile tool in genomics. Chromatin immunoprecipitation on microarrays (ChIP-chip) is a powerful application of these arrays. Although there is an increasing number of methods for analyzing ChIP-chip data, perhaps the most simple and commonly used one, due to its computational efficiency, is testing with a moving average statistic. Current moving average methods assume exchangeability of the measurements within an array. They are not tailored to deal with the issues due to array designs such as overlapping probes that result in correlated measurements. We investigate the correlation structure of data from such arrays and propose an extension of the moving average testing via a robust and rapid method called CMARRT. We illustrate the pitfalls of ignoring the correlation structure in simulations and a case study. Our approach is implemented as an R package called CMARRT and can be used with any tiling array platform.

Model-based deconvolution of genome-wide DNA binding.

David J Reiss — 2008-01-03T23:57:48-00:00

Bioinformatics (1 December 2007)

MOTIVATION: Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray (ChIP-Chip) is a routinely used protocol for localizing the genomic targets of DNA-binding proteins. The resolution to which binding sites in this assay can be identified is commonly considered to be limited by two factors: (a) the resolution at which the genomic targets are tiled in the microarray, and (b) the large and variable lengths of the immunoprecipitated DNA fragments. RESULTS: We have developed a generative model of binding sites in ChIP-chip data, and an approach, MeDiChI, for efficiently and robustly learning that model from diverse data sets. We have evaluated MeDiChI's performance using simulated data, as well as on several diverse ChIP-chip data sets collected on widely different tiling array platforms for two different organisms (S. cerevisiae and H. salinarium NRC-1). We find that MeDiChI accurately predicts binding locations to a resolution greater than that of the probe spacing, even for overlapping peaks, and can increase the effective resolution of tiling array data by a factor of 5x or better. Moreover, the method's performance on simulated data provides insights into effectively optimizing the experimental design for increased binding site localization accuracy and efficacy. AVAILABILITY: MeDiChI is available as an open-source R package, including all data, from http://baliga.systemsbiology.net/medichi. CONTACT: dreiss@systemsbiology.org.

TFBS Identification Based on Genetic Algorithm with Combined Representations and Adaptive Post-processing.

Tak-Ming Chan — 2007-12-11T23:52:52-00:00

Bioinformatics (6 December 2007)

MOTIVATION: Identification of transcription factor binding sites (TFBSs) plays an important role in deciphering the mechanisms of gene regulation. Recently, GAME (Wei and Jensen, 2006), a Genetic Algorithm (GA) based approach with iterative post-processing, has shown superior performance in TFBS identification. However, the basic GA in GAME is not elaborately designed, and may be trapped in local optima in real problems. The feature operators are only applied in the post-processing, but the final performance heavily depends on the GA output. Hence, both effectiveness and efficiency of the overall algorithm can be improved by introducing more advanced representations and novel operators in the GA, as well as designing the post-processing in an adaptive way. RESULTS: We propose a novel framework GALF-P, consisting of Genetic Algorithm with Local Filtering (GALF) and adaptive postprocessing techniques (-P), to achieve both effectiveness and efficiency for TFBS identification. GALF combines the position-led and consensus-led representations used separately in current GAs and employs a novel local filtering operator to get rid of false positives within an individual efficiently during the evolutionary process in the GA. Pre-selection is used to maintain diversity and avoid local optima. Post-processing with adaptive adding and removing is developed to handle general cases with arbitrary numbers of instances per sequence. GALF-P shows superior performance to GAME, MEME, BioProspector and BioOptimizer on synthetic datasets with difficult scenarios and real test datasets. GALF-P is also more robust and reliable when further compared with GAME, the current state-of-thearts approach. AVAILABILITY: http://www.cse.cuhk.edu.hk/~tmchan/GALFP/ CONTACT: tmchan@cse.cuhk.edu.hk Supplementary Material: Available at Bioinformatics online.

Predicting expression patterns from regulatory sequence in Drosophila segmentation

Eran Segal — 2008-01-04T06:05:33-00:00

Nature (02 January 2008)

NestedMICA as an ab initio protein motif discovery tool

Mutlu Dogruel — 2008-01-15T03:45:20-00:00

BMC Bioinformatics, Vol. 9 (14 January 2008), 19.

Gene expression profiling by massively parallel sequencing

Tatiana Torres — 2007-11-27T16:15:35-00:00

Genome Res. (21 November 2007), gr.6984908.

Massively parallel sequencing holds great promise for expression profiling, as it combines the high throughput of SAGE with the accuracy of EST sequencing. Nevertheless, until now only very limited information had been available on the suitability of the current technology to meet the requirements. Here, we evaluate the potential of 454 sequencing technology for expression profiling using Drosophila melanogaster. We show that short (< [~]80 bp) and long (> [~]300400 bp) cDNA fragments are under-represented in 454 sequence reads. Nevertheless, sequencing of 3' cDNA fragments generated by nebulization could be used to overcome the length bias of the 454 sequencing technology. Gene expression measurements generated by restriction analysis and nebulization for fragments within the 80- to 300-bp range showed correlations similar to those reported for replicated microarray experiments (0.830.91); 97% of the cDNA fragments could be unambiguously mapped to the genomic DNA, demonstrating the advantage of longer sequence reads. Our analyses suggest that the 454 technology has a large potential for expression profiling, and the high mapping accuracy indicates that it should be possible to compare expression profiles across species. 10.1101/gr.6984908

Functional evolution of the p53 regulatory network through its target response elements

Anil Jegga — 2008-01-14T05:28:21-00:00

Proceedings of the National Academy of Sciences (10 January 2008), 0704694105.

Transcriptional network evolution is central to the development of complex biological systems. Networks can evolve through variation of master regulators and/or by changes in regulation of genes within networks. To gain insight into meaningful evolutionary differences in large networks, it is essential to address the functional consequences of sequence differences in response elements (REs) targeted by transcription factors. Using a combination of custom bioinformatics and multispecies alignment of promoter regions, we investigated the functional evolution of REs in terms of responsiveness to the sequence-specific transcription factor p53, a tumor suppressor and master regulator of stress responses. We identified REs orthologous to known p53 targets in human and rodent cells or alternatively REs related to the established p53 consensus. The orthologous REs were assigned p53 transactivation capabilities based on rules determined from model systems, and a functional heat map was developed to visually summarize conservation of sequence and relative level of responsiveness to p53 for 47 REs in 14 species. Individual REs exhibited marked differences in transactivation potentials and widespread evolutionary turnover. Functional differences were often not predicted from consensus sequence evaluations. Of the established human p53 REs analyzed, 91% had sequence conservation in at least one nonprimate species compared with 67.5% for functional conservation. Surprisingly, there was almost no conservation of functional REs for genes involved in DNA metabolism or repair between humans and rodents, suggesting important differences in p53 stress responses and cancer development. 10.1073/pnas.0704694105

Runx genes are direct targets of Scl/Tal1 in the yolk sac and fetal liver.

Josette-Renee Landry — 2008-01-14T09:45:01-00:00

Blood (9 January 2008)

Transcription factors such as Scl/Tal1, Lmo2 and Runx1 are essential for the development of hematopoietic stem cells (HSCs). However, the precise mechanisms by which these factors interact to form transcriptional networks, as well as the identity of the genes downstream of these regulatory cascades remain largely unknown. To this end, we generated an Scl-/- yolk sac cell line to identify candidate Scl target genes by global expression profiling following re-introduction of a TAT-Scl fusion protein. Bioinformatics analysis resulted in the identification of nine candidate Scl target transcription factor genes, including Runx1 and Runx3. Chromatin immunoprecipitation confirmed that both Runx genes are direct targets of Scl in the fetal liver and that Runx1 is also occupied by Scl in the yolk sac. Furthermore, binding of an Scl-Lmo2-Gata2 complex was demonstrated to occur on the regions flanking the conserved E-boxes of the Runx1 loci and was shown to transactivate the Runx1 element. Together our data provides a key component of the transcriptional network of early haematopoiesis by identifying downstream targets of Scl that can explain key aspects of the early Scl-/- phenotype.

A novel method for high accuracy sumoylation site prediction from protein sequences

Jialin Xu — 2008-01-09T03:42:07-00:00

BMC Bioinformatics, Vol. 9 (08 January 2008), 8.

BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks

Andrei Zinovyev — 2007-11-27T10:46:49-00:00

Bioinformatics (16 November 2007), btm553.

BiNoM (BIological NetwOrk Manager) is a new bioinformatics software which significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX), BiNoM implements a fullfeatured BioPax editor and a method of "interfaces" for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules,cllipboard operations and others. Availability: Last version of BiNoM together with documentation, source code and API is available at http://bioinfo.curie.fr/projects/binom Contact: andrei.zinoyev@curie.fr, binom@curie.fr 10.1093/bioinformatics/btm553

A Hybrid Model for Robust Detection of Transcription Factor Binding Sites.

Sumedha Gunewardena — 2008-01-12T11:11:14-00:00

Bioinformatics (9 January 2008)

MOTIVATION: The short and degenerate nature of transcription factor (TF) binding sites contributes towards a low signal to noise ratio making it very difficult to separate them from their background. In order to tackle this problem one needs to look at ways of capturing the underlying biophysical properties that best discriminates TF binding sites from their background DNA. One such discriminatory property lies in the observed compositional differences in the nucleotide levels of TF binding sites and background DNA which are a result of processes such as purifying selection and selective preferences of TF binding sites for particular nucleotides or a combination of nucleotides over others. RESULTS: In this paper, we present a hybrid model, referred to as a MonoDi-nucleotide model for robustly detecting TF binding sites. It incorporates both mono- and dinucleotide statistics to optimally partition the base positions of an aligned set of TF binding sites (motif) into a non-redundant sequence of mono and / or dinucleotide segments that maximises the odds ratio of the binding sites relative to their background DNA. We tested the MonoDi-nucleotide model on the benchmark dataset compiled by Tompa et al., 2005 for assessing computational tools that predict TF binding sites. The performance of the MonoDi-nucleotide model on this data set compares well to, and in many cases exceeds, the performance of existing tools. This is in part attributed to the significant role played by dinucleotides in discriminating TF binding sites from background DNA. AVAILABILITY: A Matlab implementation of the MonoDi-nucleotide model can be found at http://www.utoronto.ca/zhanglab/MonoDi/. CONTACT: Sumedha@cantab.net, Zhaolei.Zhang@utoronto.ca.

OrthoMaM: A database of orthologous genomic markers for placental mammal phylogenetics

Vincent Ranwez — 2007-12-01T02:47:25-00:00

BMC Evolutionary Biology, Vol. 7 (30 November 2007), 241.

Maintenance of transposon-free regions throughout vertebrate evolution

Cas Simons — 2007-12-20T15:29:40-00:00

BMC Genomics, Vol. 8 (20 December 2007), 470.

Housekeeping and tissue-specific genes differ in simple sequence repeats in the 5'-UTR region

Mark Lawson — 2008-01-04T09:39:38-00:00

Gene, Vol. 407, No. 1-2. (15 January 2008), pp. 54-62.

SSRs (simple sequence repeats) have been shown to have a variety of effects on an organism. In this study, we compared SSRs in housekeeping and tissue-specific genes in human and mouse, in terms of SSR types and distributions in different regions including 5'-UTRs, introns, coding exons, 3'-UTRs, and upstream regions. Among all these regions, SSRs in the 5'-UTR show the most distinction between housekeeping genes and tissue-specific genes in both densities and repeat types. Specifically, SSR densities in 5'-UTRs in housekeeping genes are about 1.7 times higher than those in tissue-specific genes, in contrast to the 0.8-1.2 times differences between the two classes of genes in other regions. Tri-SSRs in 5'-UTRs of housekeeping genes are more GC rich than those of tissue-specific genes and CGG, the dominant type of tri-SSR in 5'-UTR, accounts for 74-79% of the tri-SSRs in housekeeping genes, as compared to 42-57% in tissue-specific genes. 75% of the tri-SSRs in the 5'-UTR of housekeeping genes have 4-5 repeat units, versus the 86-90% in tissue-specific genes. Taken together, our results suggest that SSRs may have an effect on gene expression and may play an important role in contributing to the different expression profiles between housekeeping and tissue-specific genes.

Lyl1 Interacts with CREB1 and Alters Expression of CREB1 Target Genes.

Serban San-Marina — 2008-01-03T12:01:14-00:00

Biochim Biophys Acta (7 December 2007)

The basic helix-loop-helix (bHLH) transcription factor family contains key regulators of cellular proliferation and differentiation as well as the suspected oncoproteins Tal1 and. Tal1 and Lyl1 are aberrantly over-expressed in leukemia as a result of chromosomal translocations, or other genetic or epigenetic events. Protein-protein and protein-DNA interactions described so far are mediated by their highly homologous bHLH domains, while little is known about the function of other protein domains. Hetero-dimers of Tal1 and Lyl1 with E2A or HEB, decrease the rate of E2A or HEB homo-dimer formation and are poor activators of transcription. In vitro, these hetero-dimers also recognize different binding sites from homo-dimer complexes, which may also lead to inappropriate activation or repression of promoters in vivo. Both mechanisms are thought to contribute to the oncogenic potential of Tal1 and Lyl1. Despite their bHLH structural similarity, accumulating evidence suggests that Tal1 and Lyl1 target different genes. This raises the possibility that domains flanking the bHLH region, which are distinct in the two proteins, may participate in target recognition. Here we report that CREB1, a widely-expressed transcription factor and a suspected oncogene in acute myelogenous leukemia (AML) was identified as a binding partner for Lyl1 but not for Tal1. The interaction between Lyl1 and CREB1 involves the N terminal domain of Lyl1 and the Q2 and KID domains of CREB1. The histone acetyl-transferases p300 and CBP are recruited to these complexes in the absence of CREB1 Ser 133 phosphorylation. In the Id1 promoter, Lyl1 complexes direct transcriptional activation. We also found that in addition to Id1, over-expressed Lyl1 can activate other CREB1 target promoters such as Id3, cyclin D3, Brca1, Btg2 and Egr1. Moreover, approximately 50% of all gene promoters identified by ChIP-chip experiments were jointly occupied by CREB1 and Lyl1, further strengthening the association of Lyl1 with Cre binding sites. Given the newly recognized importance of CREB1 in AML, the ability of Lyl1 to modulate promoter responses to CREB1 suggests that it plays a role in the malignant phenotype by occupying different promoters than Tal1.

Genome-wide analysis reveals regulatory role of G4 DNA in gene transcription

Zhuo Du — 2007-12-20T21:42:06-00:00

Genome Res. (20 December 2007), gr.6905408.

G-quadruplex or G4 DNA, a four-stranded DNA structure formed in G-rich sequences, has been hypothesized to be a structural motif involved in gene regulation. In this study, we examined the regulatory role of potential G4 DNA motifs (PG4Ms) located in the putative transcriptional regulatory region (TRR, 500 to +500) of genes across the human genome. We found that PG4Ms in the 500-bp region downstream of the annotated transcription start site (TSS; PG4MD500) are associated with gene expression. Generally, PG4MD500-positive genes are expressed at higher levels than PG4MD500-negative genes, and an increased number of PG4MD500 provides a cumulative effect. This observation was validated by controlling for attributes, including gene family, function, and promoter similarity. We also observed an asymmetric pattern of PG4MD500 distribution between strands, whereby the frequency of PG4MD500 in the coding strand is generally higher than that in the template strand. Further analysis showed that the presence of PG4MD500 and its strand asymmetry are associated with significant enrichment of RNAP II at the putative TRR. On the basis of these results, we propose a model of G4 DNA-mediated stimulation of transcription with the hypothesis that PG4MD500 contributes to gene transcription by maintaining the DNA in an open conformation, while the asymmetric distribution of PG4MD500 considerably reduces the probability of blocking the progression of the RNA polymerase complex on the template strand. Our findings provide a comprehensive view of the regulatory function of G4 DNA in gene transcription. 10.1101/gr.6905408