CiteULike: heliopais's library [416 articles]

Microarray-based expression profiling and informatics

Richard Simon — 2008-02-15T11:46:03-00:00

Current Opinion in Biotechnology, Vol. 19, No. 1. (February 2008), pp. 26-29.

Microarray-based expression profiling is a powerful technology for studying biological mechanisms and for developing clinically valuable predictive classifiers. The high-dimensional read-out for each sample assayed makes it possible to do new kinds of studies but also increases the risks of misleading conclusions. We review here the current state-of-the-art for design and analysis of microarray-based investigations.

Identification of transcriptional regulators using binding site enrichment analysis.

TM Kim — 2008-01-03T23:13:40-00:00

In Silico Biol, Vol. 6, No. 6. (2006), pp. 531-544.

To understand the transcriptional regulatory network in eukaryotic cells, it is essential to identify functional cis-regulatory sequences that interact with trans-acting factors. A number of algorithms have been developed to predict common cis-regulatory elements for co-regulated genes with similar expression patterns. However, previous methods usually deal with disjoint gene groups partitioned or clustered by arbitrary cutoffs, which might cause information losses. To preclude the defining step of gene set, we adopted enrichment analysis and termed the method binding site enrichment analysis (BSEA). BSEA was first applied for publicly available ChIP-on-chip data of c-MYC, MAX and E2F transcription factors, identifying significant enrichment for signatures of corresponding factors and potential co-activators. Using time-scaled expression profiling of 3T3-L1 adipogenesis, we observed enrichment for signatures of known adipogenic factors such as C/EBPalpha, C/EBPbeta and PPARgamma, temporally coincident with previous reports. BSEA was also applied to tissue-specific expression profiles of human and mouse, identifying well-known tissue-specific transcription factors such as HNF-4 in liver and MEF-2 in heart along with other putative tissue-specific regulators. With extended versatility coping with various kinds of microarray dataset, BSEA can identify key regulators for global microarray data in which transcriptional regulation plays a major role. As a generalized method, BSEA would help to elucidate the transcriptional regulatory networks, the primary challenges in functional genomics.

DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene list

Da Huang — 2007-09-05T21:41:52-00:00

Genome Biology, Vol. 8 (04 September 2007), R183.

Circumventing the cut-off for enrichment analysis

Eitan Rubin — 2008-05-15T11:21:06-00:00

Brief Bioinform, Vol. 7, No. 2. (1 June 2006), pp. 202-203.

Three tools for threshold-free enrichment analysis of microarray data are introduced: GSEA (gene set enrichment analysis), ermineJ and DRIM (discovering rank imbalanced motifs). GSEA offers an interface to a specific algorithm and a well-defined pipeline for the identifying enrichment in diverse gene sets and the creation of signature profiles. ermineJ offers a combined front end to three different algorithms, two of which perform a cut-off-free enrichment analysis. DRIM comprises an implementation of a new algorithm and is specifically designed for the search of new transcription-factor-binding sites based on expression patterns. Together, these tools demonstrate an emerging trend in high-throughput data analysis--the joint analysis of raw results with external knowledge. 10.1093/bib/bbl013

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

VK Mootha — 2006-06-04T00:12:05-00:00

Nat Genet, Vol. 34, No. 3. (July 2003), pp. 267-273.

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

Statistical concerns about the GSEA procedure.

D Damian — 2006-07-24T16:23:51-00:00

Nat Genet, Vol. 36, No. 7. (July 2004)

GeneTrail--advanced gene set enrichment analysis.

C Backes — 2007-08-18T04:43:43-00:00

Nucleic Acids Res, Vol. 35, No. Web Server issue. (1 July 2007)

We present a comprehensive and efficient gene set analysis tool, called 'GeneTrail' that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, 'Over-Representation Analysis' (ORA) comparing a reference set of genes to a test set, and 'Gene Set Enrichment Analysis' (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de.

Gene-set approach for expression pattern analysis

Dougu Nam — 2008-03-04T10:44:00-00:00

Brief Bioinform (17 January 2008), bbn001.

Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expression data, and a number of methods and tools have been developed intensively in recent years. We review those methods and currently available tools, classify them according to the statistical methods employed, and discuss their pros and cons. We also discuss several interesting extensions to the methods. 10.1093/bib/bbn001

Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Tae-Min Kim — 2007-11-18T09:57:08-00:00

BMC Bioinformatics, Vol. 8 (17 November 2007), 453.

PAGE: parametric analysis of gene set enrichment.

SY Kim — 2006-07-24T16:20:10-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. RESULTS: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. CONCLUSION: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Prediction of microRNA targets.

P Mazière — 2007-05-30T07:26:25-00:00

Drug Discov Today, Vol. 12, No. 11-12. (June 2007), pp. 452-458.

Recently, microRNAs (miRNAs) have been shown to be important regulators of genes in many organisms and have already been implicated in a growing number of diseases. MiRNAs are short (21-23 nucleotides) RNAs that bind to the 3' untranslated regions of target genes. This binding event causes translational repression of the target gene and, evidence now suggests, also stimulates rapid degradation of the target transcript. miRNAs represent a new species of regulator, controlling the levels of potentially large numbers of proteins, many of which might be important drug targets. The expression of miRNAs shows that they are highly differentially expressed, with specific miRNAs active in certain tissues at certain times. In many cancers, miRNA expression is significantly altered, and this has been shown to be a useful diagnostic tool. Several computational approaches have been developed for the prediction of miRNA targets.

Using expression profiling data to identify human microRNA targets

Jim Huang — 2007-11-29T18:47:18-00:00

Nat Meth, Vol. 4, No. 12. (December 2007), pp. 1045-1049.

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.

Transcription factors controlling osteoblastogenesis

Pierre Marie — 2008-04-25T14:06:38-00:00

Archives of Biochemistry and Biophysics, Vol. In Press, Corrected Proof

The recent development of molecular biology and mouse genetics and the analysis of the skeletal phenotype induced by genetic mutations in humans led to a better understanding of the role of transcription factors that govern bone formation. This review summarizes the role of transcription factors in osteoblastogenesis and provides an integrated perspective on how the activities of multiple classes of factors are coordinated for the complex process of developing the osteoblast phenotype. The roles of Runx2, the principal transcriptional regulator of osteoblast differentiation, Osterix, [beta]-Catenin and ATF which act downstream of Runx2, and other transcription factors that contribute to the control of osteoblastogenesis including the AP1, C/EBPs, PPAR[gamma] and homeodomain, helix-loop-helix proteins are discussed. This review also updates the regulation of transcription factor expression by signaling factors and hormones that control osteoblastogenesis.

Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors

Tania Schroeder — 2007-10-16T08:21:18-00:00

BMC Genomics, Vol. 8 (09 October 2007), 362.

Microarray gene expression profiling of human osteoarthritic bone suggests altered bone remodelling, WNT and TGF beta/BMP signalling

Blair Hopwood — 2007-10-01T06:39:53-00:00

Arthritis Research & Therapy, Vol. 9 (27 September 2007), R100.

A Twist Code Determines the Onset of Osteoblast Differentiation

Peter Bialek — 2008-04-25T13:17:16-00:00

Developmental Cell, Vol. 6, No. 3. (March 2004), pp. 423-435.

Runx2 is necessary and sufficient for osteoblast differentiation, yet its expression precedes the appearance of osteoblasts by 4 days. Here we show that Twist proteins transiently inhibit Runx2 function during skeletogenesis. Twist-1 and -2 are expressed in Runx2-expressing cells throughout the skeleton early during development, and osteoblast-specific gene expression occurs only after their expression decreases. Double heterozygotes for Twist-1 and Runx2 deletion have none of the skull abnormalities observed in Runx2+/- mice, a Twist-2 null background rescues the clavicle phenotype of Runx2+/- mice, and Twist-1 or -2 deficiency leads to premature osteoblast differentiation. Furthermore, Twist-1 overexpression inhibits osteoblast differentiation without affecting Runx2 expression. Twist proteins' antiosteogenic function is mediated by a novel domain, the Twist box, which interacts with the Runx2 DNA binding domain to inhibit its function. In vivo mutagenesis confirms the antiosteogenic function of the Twist box. Thus, relief of inhibition by Twist proteins is a mandatory event precluding osteoblast differentiation.

TGF-[beta] up-regulates serum response factor in activated hepatic stellate cells

Jens Herrmann — 2008-04-25T12:56:23-00:00

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Vol. 1772, No. 11-12. (December 2007), pp. 1250-1257.

In differentiated smooth muscle cells (SMC) the regulation of SMC marker genes (e.g. [alpha]-smooth muscle actin) is mainly conducted by the serum response factor (SRF) and accessory co-factors like myocardin. A number of SMC markers are also expressed in activated hepatic stellate cells which are the main cellular effectors in liver fibrogenesis. In the present study we found that during cellular activation and transdifferentiation the SRF transcription factor is up-regulated by transforming growth factor-[beta], accumulated in the nucleus, and exhibited increased DNA-binding activity. These observations were accompanied by a forced expression of the SRF co-activator myocardin. Specific targeting of SRF by small interference RNA resulted in diminished contents of [alpha]-smooth muscle actin. Therefore, we conclude that hepatic stellate cells retain differentiation capacity to evolve characteristics that are typical for cells of the cardiac and smooth muscle lineages.

BMP1 controls TGFbeta1 activation via cleavage of latent TGFbeta-binding protein

Gaoxiang Ge — 2008-04-25T11:32:30-00:00

J. Cell Biol., Vol. 175, No. 1. (9 October 2006), pp. 111-120.

Transforming growth factor beta1 (TGFbeta1), an important regulator of cell behavior, is secreted as a large latent complex (LLC) in which it is bound to its cleaved prodomain (latency-associated peptide [LAP]) and, via LAP, to latent TGFbeta-binding proteins (LTBPs). The latter target LLCs to the extracellular matrix (ECM). Bone morphogenetic protein 1 (BMP1)-like metalloproteinases play key roles in ECM formation, by converting precursors into mature functional proteins, and in morphogenetic patterning, by cleaving the antagonist Chordin to activate BMP2/4. We provide in vitro and in vivo evidence that BMP1 cleaves LTBP1 at two specific sites, thus liberating LLC from ECM and resulting in consequent activation of TGFbeta1 via cleavage of LAP by non-BMP1-like proteinases. In mouse embryo fibroblasts, LAP cleavage is shown to be predominantly matrix metalloproteinase 2 dependent. TGFbeta1 is a potent inducer of ECM formation and of BMP1 expression. Thus, a role for BMP1-like proteinases in TGFbeta1 activation completes a novel fast-forward loop in vertebrate tissue remodeling. 10.1083/jcb.200606058

Smad transcription factors.

J Massagué — 2006-01-28T00:22:02-00:00

Genes Dev, Vol. 19, No. 23. (1 December 2005), pp. 2783-2810.

Smad transcription factors lie at the core of one of the most versatile cytokine signaling pathways in metazoan biology-the transforming growth factor-beta (TGFbeta) pathway. Recent progress has shed light into the processes of Smad activation and deactivation, nucleocytoplasmic dynamics, and assembly of transcriptional complexes. A rich repertoire of regulatory devices exerts control over each step of the Smad pathway. This knowledge is enabling work on more complex questions about the organization, integration, and modulation of Smad-dependent transcriptional programs. We are beginning to uncover self-enabled gene response cascades, graded Smad response mechanisms, and Smad-dependent synexpression groups. Our growing understanding of TGFbeta signaling through the Smad pathway provides general principles for how animal cells translate complex inputs into concrete behavior.

Factor analysis for gene regulatory networks and transcription factor activity profiles

Iosifina Pournara — 2007-02-24T09:49:22-00:00

BMC Bioinformatics, Vol. 8 (23 February 2007), 61.

Non-transcriptional pathway features reconstructed from secondary effects of RNA interference

Florian Markowetz — 2005-10-28T10:10:29-00:00

Bioinformatics, Vol. 21, No. 21. (1 November 2005), pp. 4026-4032.

How to decide which are the most pertinent overly-represented features during gene set enrichment analysis

Roland Barriot — 2007-09-11T14:08:08-00:00

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

BACKGROUND:The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.RESULTS:We present a generic framework to efficiently identify the most pertinent over-represented features in a set of genes. We propose a formal representation of gene sets based on the theory of partially ordered sets (posets), and give a formal definition of target set pertinence. Algorithms and compact representations of target sets are provided for the generation and the evaluation of the pertinent target sets. The relevance of our method is illustrated through the search for enriched GO annotations in the proteins involved in a multiprotein complex. The results obtained demonstrate the gain in terms of pertinence (up to 64% redundancy removed), space requirements (up to 73% less storage) and efficiency (up to 98% less comparisons).CONCLUSIONS:The generic framework presented in this article provides a formal approach to adequately represent available data and efficiently search for pertinent over-represented features in a set of genes or proteins. The formalism and the pertinence definition can be directly used by most of the methods and tools currently available for feature enrichment analysis.

A multivariate extension of the gene set enrichment analysis.

L Klebanov — 2007-10-17T18:05:03-00:00

J Bioinform Comput Biol, Vol. 5, No. 5. (October 2007), pp. 1139-1153.

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.

Extensions to gene set enrichment

Zhen Jiang — 2007-02-02T17:42:27-00:00

Bioinformatics, Vol. 23, No. 3. (1 February 2007), pp. 306-313.

Motivation: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture changes in the expression of pre-defined sets of genes. We propose number of extensions to GSEA, including the use of different statistics to describe the association between genes and phenotypes of interest. We make use of dimension reduction procedures, such as principle component analysis, to identify gene sets with correlated expression. We also address issues that arise when gene sets overlap. Results: Our proposals extend the range of applicability of GSEA and allow for adjustments based on other covariates. We have provided a well-defined procedure to address interpretation issues that can raise when gene sets have substantial overlap. We have shown how standard dimension reduction methods, such as PCA, can be used to help further interpret GSEA. Contact: zjiang@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btl599

Comparative evaluation of gene-set analysis methods

Qi Liu — 2007-11-07T21:43:18-00:00

BMC Bioinformatics, Vol. 8 (07 November 2007), 431.

Analyzing gene expression data in terms of gene sets: methodological issues.

Jelle J Goeman — 2007-02-18T10:19:26-00:00

Bioinformatics (15 February 2007)

MOTIVATION: Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This paper aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing. RESULTS: We identify some crucial assumptions which are needed by the majority of methods. P-values derived from methods that use a model which takes the genes as the sampling unit are easily misinterpreted, as they are based on a statistical model that does not resemble the biological experiment actually performed. Furthermore, because these models are based on a crucial and unrealistic independence assumption between genes, the p-values derived from such methods can be wildly anti-conservative, as a simulation experiment shows. We also argue that methods that competitively test each gene set against the rest of the genes create an unnecessary rift between single gene testing and gene set testing.

Pathway Analysis of Microarray Data via Regression

AJ Adewale — 2008-03-13T09:21:31-00:00

Journal of Computational Biology, Vol. 0, No. 0. (0), pp. 1-9.

Pathway analysis of microarray data evaluates gene expression profiles of a priori defined biological pathways in association with a phenotype of interest. We propose a unified pathway-analysis method that can be used for diverse phenotypes including binary, multiclass, continuous, count, rate, and censored survival phenotypes. The proposed method also allows covariate adjustments and correlation in the phenotype variable that is encountered in longitudinal, cluster-sampled, and paired designs. These are accomplished by combining the regression-based test statistic for each individual gene in a pathway of interest into a pathway-level test statistic. Applications of the proposed method are illustrated with two real pathway-analysis examples: one evaluating relapse-associated gene expression involving a matched-pair binary phenotype in children with acute lymphoblastic leukemia; and the other investigating gene expression in breast cancer tissues in relation to patients' survival (a censored survival phenotype). Implementations for various phenotypes are available in R. Additionally, an Excel Add-in for a user-friendly interface is currently being developed.

Improving gene set analysis of microarray data by SAM-GS

Irina Dinu — 2007-07-06T09:43:50-00:00

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

BACKGROUND:Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). RESULTS:Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53. CONCLUSIONS:We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use at http://www.ualberta.ca/~yyasui/homepage.html.

miRiad Roles for the miR-17-92 Cluster in Development and Disease

Joshua Mendell — 2008-04-23T14:51:34-00:00

Cell, Vol. 133, No. 2. (18 April 2008), pp. 217-222.

MicroRNAs (miRNAs) encoded by the miR-17-92 cluster and its paralogs are known to act as oncogenes. Expression of these miRNAs promotes cell proliferation, suppresses apoptosis of cancer cells, and induces tumor angiogenesis. New work reveals essential functions for these miRNAs not only in tumor formation but also during normal development of the heart, lungs, and immune system.

The effect of replication on gene expression microarray experiments

Paul Pavlidis — 2008-04-11T11:00:15-00:00

Bioinformatics, Vol. 19, No. 13. (1 September 2003), pp. 1620-1627.

Motivation: We examine the effect of replication on the detection of apparently differentially expressed genes in gene expression microarray experiments. Our analysis is based on a random sampling approach using real data sets from 16 published studies. We consider both the ability to find genes that meet particular statistical criteria as well as the stability of the results in the face of changing levels of replication. Results: While dependent on the data source, our findings suggest that stable results are typically not obtained until at least five biological replicates have been used. Conversely, for most studies, 10-15 replicates yield results that are quite stable, and there is less improvement in stability as the number of replicates is further increased. Our methods will be of use in evaluating existing data sets and in helping to design new studies. Supplementary information: http://microarray.cpmc.columbia.edu/pavlidis/pub/gxrep 10.1093/bioinformatics/btg227

Hidden layers of human small RNAs

Hideya Kawaji — 2008-04-10T12:00:11-00:00

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

BACKGROUND:Small RNA attracts increasing interest based on the discovery of RNA silencing and the rapid progress of our understanding of these phenomena. Although recent studies suggest the possible existence of yet undiscovered types of small RNAs in higher organisms, many studies to profile small RNA have focused on miRNA and/or siRNA rather than on the exploration of additional classes of RNAs. RESULTS:Here, we explored human small RNAs by unbiased sequencing of RNAs with sizes of 19-40 nt. We provide substantial evidences for the existence of independent classes of small RNAs. Our data shows that well-characterized non-coding RNA, such as tRNA, snoRNA, and snRNA are cleaved at sites specific to the class of ncRNA. In particular, tRNA cleavage is regulated depending on tRNA type and tissue expression. We also found small RNAs mapped to genomic regions that are transcribed in both directions by bidirectional promoters, indicating that the small RNAs are a product of dsRNA formation and their subsequent cleavage. Their partial similarity with ribosomal RNAs (rRNAs) suggests unrevealed functions of ribosomal DNA or interstitial rRNA. Further examination revealed six novel miRNAs. CONCLUSIONS:Our results underscore the complexity of the small RNA world and the biogenesis of small RNAs.

TeXshade: shading and labeling of multiple sequence alignments using LaTeX2e

Eric Beitz — 2007-09-09T15:13:04-00:00

Bioinformatics, Vol. 16, No. 2. (1 February 2000), pp. 135-139.

Motivation: Typesetting, shading and labeling of nucleotide and peptide alignments using standard word processing or graphics software is time consuming. Available automatic sequence shading programs usually do not allow manual application of additional shadings or labels. Hence, a flexible alignment shading package was designed for both calculated and manual shading, using the macro language of the scientific typesetting software LaTeX2e. Results: TeXshade is the first TeX-based alignment shading software featuring, in addition to standard identity and similarity shading, special modes for the display of functional aspects such as charge, hydropathy or solvent accessibility. A plenitude of commands for manual shading, graphical labels, re-arrangements of the sequence order, numbering, legends etc. is implemented. Further, TeXshade allows the inclusion and display of secondary structure predictions in the DSSP-, STRIDE- and PHD-format. Availability: From http://homepages.uni-tuebingen.de/beitz/tse.html(macro package and on-line documentation) Contact: eric.beitz@uni-tuebingen.de 10.1093/bioinformatics/16.2.135

A scoring matrix approach to detecting miRNA target sites

Simon Moxon — 2008-03-31T17:46:38-00:00

Algorithms for Molecular Biology, Vol. 3 (31 March 2008), 3.

Histone deacetylase inhibition accelerates the early events of stem cell differentiation: transcriptomic and epigenetic analysis

Efthimia Karantzali — 2008-04-05T05:54:48-00:00

Genome Biology, Vol. 9 (04 April 2008), R65.

Piecing Together the Mosaic of Early Mammalian Development through MicroRNAs

Adriana Blakaj — 2008-04-04T13:04:59-00:00

J. Biol. Chem., Vol. 283, No. 15. (11 April 2008), pp. 9505-9508.

The microRNA (miRNA) pathway represents an integral component of the gene regulation circuitry that controls development. In recent years, the role of miRNAs in embryonic stem (ES) cells and mammalian embryogenesis has begun to be explored. A few dozens of miRNAs expressed in mammalian ES cells, either exclusively or nonexclusively, have been cloned. The overall role of miRNAs in ES cells and embryonic development has been assessed by examining the effect of knocking out Dicer, an RNase III enzyme required for miRNA and small interfering RNA biogenesis, as well as DGCR8, a nuclear protein specifically involved in miRNA biogenesis. In addition, the role of a cluster of miRNAs specifically expressed in ES cells, the miR-290295 group, has been investigated by the knock-out approach. These analyses have revealed the crucial role of miRNAs in ES cell differentiation, lineage specification, and organogenesis, especially neurogenesis and cardiogenesis. Systematic investigation of the role of miRNAs in ES cells and embryos will allow us to find missing pieces of the mosaic of early development. 10.1074/jbc.R800002200

Multilevel Regulation of Gene Expression by MicroRNAs

Eugene Makeyev — 2008-03-28T15:03:59-00:00

Science, Vol. 319, No. 5871. (28 March 2008), pp. 1789-1790.

MicroRNAs (miRNAs) are [~]22-nucleotide-long noncoding RNAs that normally function by suppressing translation and destabilizing messenger RNAs bearing complementary target sequences. Some miRNAs are expressed in a cell- or tissue-specific manner and may contribute to the establishment and/or maintenance of cellular identity. Recent studies indicate that tissue-specific miRNAs may function at multiple hierarchical levels of gene regulatory networks, from targeting hundreds of effector genes incompatible with the differentiated state to controlling the levels of global regulators of transcription and alternative pre-mRNA splicing. This multilevel regulation may allow individual miRNAs to profoundly affect the gene expression program of differentiated cells. 10.1126/science.1152326

Gene Regulation by Transcription Factors and MicroRNAs

Oliver Hobert — 2008-03-28T15:00:47-00:00

Science, Vol. 319, No. 5871. (28 March 2008), pp. 1785-1786.

The properties of a cell are determined by the genetic information encoded in its genome. Understanding how such information is differentially and dynamically retrieved to define distinct cell types and cellular states is a major challenge facing molecular biology. Gene regulatory factors that control the expression of genomic information come in a variety of flavors, with transcription factors and microRNAs representing the most numerous gene regulatory factors in multicellular genomes. Here, I review common principles of transcription factor and microRNA-mediated gene regulatory events and discuss conceptual differences in how these factors control gene expression. 10.1126/science.1151651

The Effect of Central Loops in miRNA:MRE Duplexes on the Efficiency of miRNA-Mediated Gene Regulation.

W Ye — 2008-03-18T02:56:30-00:00

PLoS ONE, Vol. 3, No. 3. (2008)

MicroRNAs (miRNAs) guide posttranscriptional repression of mRNAs. Hundreds of miRNAs have been identified but the target identification of mammalian mRNAs is still a difficult task due to a poor understanding of the interaction between miRNAs and the miRNA recognizing element (MRE). In recent research, the importance of the 5' end of the miRNA:MRE duplex has been emphasized and the effect of the tail region addressed, but the role of the central loop has largely remained unexplored. Here we examined the effect of the loop region in miRNA:MRE duplexes and found that the location of the central loop is one of the important factors affecting the efficiency of gene regulation mediated by miRNAs. It was further determined that the addition of a loop score combining both location and size as a new criterion for predicting MREs and their cognate miRNAs significantly decreased the false positive rates and increased the specificity of MRE prediction.

Empirical Bayes models for multiple probe type microarrays at the probe level

Magnus Astrand — 2008-03-21T05:49:07-00:00

BMC Bioinformatics, Vol. 9 (20 March 2008), 156.

MicroRNA expression profiling of the developing murine molar tooth germ and the developing murine submandibular salivary gland

Anne-Marthe Jevnaker — 2008-03-19T10:16:34-00:00

Archives of Oral Biology, Vol. In Press, Corrected Proof

Using microarrays, miRNA expression profiles have been established at selected times during development (E15.5, P0 and P5) of the murine first molar mandibular tooth germ and the right submandibular salivary gland (E15.5, P0, P5 and P25). Microarray data was validated using real-time PCR, also facilitating RT-PCR profiling of nine selected miRNAs. In general, good agreement between microarray data and real-time PCR data was found. Further, miRNA expression profiles of foetal and adult liver were also investigated, and found to agree with published data. In tooth germ and salivary gland up to 88 different miRNAs were detected. In all tissues examined miRNA expression was highly dynamic; miRNA profiles changing extensively with time of development. Additionally, the expression of some miRNAs was tissue-specific. Bioinformatic analysis of clusters of miRNAs was attempted using the miRGate software, the results suggesting miRNAs to be involved in the regulation of essential developmental processes, e.g., epithelical cell proliferation, mesodermal cell fate determination and salivary gland morphogenesis.

Selective Blockade of MicroRNA Processing by Lin-28.

Srinivas R Viswanathan — 2008-02-25T08:32:02-00:00

Science (21 February 2008)

MicroRNAs (miRNAs) play critical roles in development, and dysregulation of miRNA expression has been observed in human malignancies. Recent evidence suggests that the processing of several primary miRNA transcripts (pri-miRNAs) is blocked post-transcriptionally in embryonic stem (ES) cells, embryonal carcinoma (EC) cells, and primary tumors. Here we show that Lin-28, a developmentally regulated RNA-binding protein, selectively blocks the processing of pri-let-7 miRNAs in embryonic cells. Using in vitro and in vivo studies, we demonstrate that Lin-28 is necessary and sufficient for blocking Microprocessor-mediated cleavage of pri-let-7 miRNAs. Our results identify Lin-28 as a negative regulator of miRNA biogenesis and suggest that Lin-28 may play a central role in blocking miRNA-mediated differentiation in stem cells and certain cancers.

Predators Make (Temporary) Escape from Coevolutionary Arms Race

Liza Gross — 2008-03-11T13:34:46-00:00

PLoS Biology, Vol. 6, No. 3. (1 March 2008), e75.

Can subtle changes in gene expression be consistently detected with different microarray platforms?

Paola Pedotti — 2008-03-11T10:04:19-00:00

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

BACKGROUND:The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle.RESULTS:Gene expression profiles in the hippocampus of five wild-type and five transgenic deltaC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms.CONCLUSIONS:The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.

Targeted Deletion Reveals Essential and Overlapping Functions of the miR-17~92 Family of miRNA Clusters

Andrea Ventura — 2008-03-07T10:14:22-00:00

Cell, Vol. 132, No. 5. (7 March 2008), pp. 875-886.

Summary miR-17~92, miR-106b~25, and miR-106a~363 belong to a family of highly conserved miRNA clusters. Amplification and overexpression of miR-17~92 is observed in human cancers, and its oncogenic properties have been confirmed in a mouse model of B cell lymphoma. Here we show that mice deficient for miR-17~92 die shortly after birth with lung hypoplasia and a ventricular septal defect. The miR-17~92 cluster is also essential for B cell development. Absence of miR-17~92 leads to increased levels of the proapoptotic protein Bim and inhibits B cell development at the pro-B to pre-B transition. Furthermore, while ablation of miR-106b~25 or miR-106a~363 has no obvious phenotypic consequences, compound mutant embryos lacking both miR-106b~25 and miR-17~92 die at midgestation. These results provide key insights into the physiologic functions of this family of microRNAs and suggest a link between the oncogenic properties of miR-17~92 and its functions during B lymphopoiesis and lung development.

MicroRNAs flex their muscles

Eva van Rooij — 2008-03-06T15:10:04-00:00

Trends in Genetics, Vol. In Press, Corrected Proof

MicroRNAs negatively regulate gene expression by promoting mRNA degradation and inhibiting mRNA translation. Recent studies have uncovered a cadre of muscle-specific microRNAs that regulate diverse aspects of muscle function, including myoblast proliferation, differentiation, contractility and stress responsiveness. These myogenic microRNAs, which are encoded by bicistronic transcripts or are nestled within introns of myosin genes, modulate muscle functions by fine-tuning gene expression patterns or acting as `on-off' switches. Muscle-specific microRNAs also participate in numerous diseases, including cardiac hypertrophy, heart failure, cardiac arrhythmias, congenital heart disease and muscular dystrophy. The myriad roles of microRNAs in muscle biology pose interesting prospects for their therapeutic manipulation in muscle disease.

Embryonic Stem Cell-Specific MicroRNAs

Hristo Houbaviy — 2008-03-06T15:05:47-00:00

Developmental Cell, Vol. 5, No. 2. (August 2003), pp. 351-358.

We have identified microRNAs (miRNAs) in undifferentiated and differentiated mouse embryonic stem (ES) cells. Some of these appear to be ES cell specific, have related sequences, and are encoded by genomic loci clustered within 2.2 kb of each other. Their expression is repressed as ES cells differentiate into embryoid bodies and is undetectable in adult mouse organs. In contrast, the levels of many previously described miRNAs remain constant or increase upon differentiation. Our results suggest that miRNAs may have a role in the maintenance of the pluripotent cell state and in the regulation of early mammalian development.

A skin microRNA promotes differentiation by repressing ‘stemness’

Rui Yi — 2008-03-03T04:03:40-00:00

Nature (02 March 2008)

MicroRNA Mirn140 modulates Pdgf signaling during palatogenesis.

Johann Eberhart — 2008-02-13T07:40:42-00:00

Nature Genetics (10 February 2008)

Disruption of signaling pathways such as those mediated by sonic hedgehog (Shh) or platelet-derived growth factor (Pdgf) causes craniofacial abnormalities, including cleft palate. The role that microRNAs play in modulating palatogenesis, however, is completely unknown. We show that, in zebrafish, the microRNA Mirn140 negatively regulates Pdgf signaling during palatal development, and we provide a mechanism for how disruption of Pdgf signaling causes palatal clefting. The pdgf receptor alpha (pdgfra) 3' UTR contained a Mirn140 binding site functioning in the negative regulation of Pdgfra protein levels in vivo. pdgfra mutants and Mirn140-injected embryos shared a range of facial defects, including clefting of the crest-derived cartilages that develop in the roof of the larval mouth. Concomitantly, the oral ectoderm beneath where these cartilages develop lost pitx2 and shha expression. Mirn140 modulated Pdgf-mediated attraction of cranial neural crest cells to the oral ectoderm, where crest-derived signals were necessary for oral ectodermal gene expression. Mirn140 loss of function elevated Pdgfra protein levels, altered palatal shape and caused neural crest cells to accumulate around the optic stalk, a source of the ligand Pdgfaa. These results suggest that the conserved regulatory interactions of mirn140 and pdgfra define an ancient mechanism of palatogenesis, and they provide candidate genes for cleft palate.

MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis.

Zhongmin Tian — 2008-02-01T07:56:51-00:00

Genome Research, Vol. 18, No. 3. (29 January 2008), pp. 404-411.

Mammalian genomes contain several hundred highly conserved genes encoding microRNAs. In silico analysis has predicted that a typical microRNA may regulate the expression of hundreds of target genes, suggesting miRNAs might have broad biological significance. A major challenge is to obtain experimental evidence for predicted microRNA-target pairs. We reasoned that reciprocal expression of a microRNA and a predicted target within a physiological context would support the presence and relevance of a microRNA-target pair. We used microRNA microarray and proteomic techniques to analyze the cortex and the medulla of rat kidneys. Of the 377 microRNAs analyzed, we identified 6 as enriched in the renal cortex and 11 in the renal medulla. From approximately 2100 detectable protein spots in two-dimensional gels, we identified 58 proteins as more abundant in the renal cortex and 72 in the renal medulla. The differential expression of several microRNAs and proteins was verified by real-time PCR and Western blot analyses, respectively. Several pairs of reciprocally expressed microRNAs and proteins were predicted to be microRNA-target pairs by TargetScan, PicTar, or miRanda. Seven pairs were predicted by two algorithms and two pairs by all three algorithms. The identification of reciprocal expression of microRNAs and their computationally predicted targets in the rat kidney provides a unique molecular basis for further exploring the biological role of microRNA. In addition, this study establishes a differential profile of microRNA expression between the renal cortex and the renal medulla and greatly expands the known differential proteome profiles between the two kidney regions.