CiteULike: lp2's library [234 articles]

The environmental contribution to gene expression profiles

Greg Gibson — 2008-07-18T07:35:34-00:00

Nature Reviews Genetics, Vol. 9, No. 8., pp. 575-581.

The evolution of courtship behaviors through the origination of a new gene in Drosophila

2008-07-18T10:06:42-00:00

PNAS

Symbiotic gut microbes modulate human metabolic phenotypes.

M Li — 2008-02-24T21:53:59-00:00

Proc Natl Acad Sci U S A, Vol. 105, No. 6. (12 February 2008), pp. 2117-2122.

Humans have evolved intimate symbiotic relationships with a consortium of gut microbes (microbiome) and individual variations in the microbiome influence host health, may be implicated in disease etiology, and affect drug metabolism, toxicity, and efficacy. However, the molecular basis of these microbe-host interactions and the roles of individual bacterial species are obscure. We now demonstrate a"transgenomic" approach to link gut microbiome and metabolic phenotype (metabotype) variation. We have used a combination of spectroscopic, microbiomic, and multivariate statistical tools to analyze fecal and urinary samples from seven Chinese individuals (sampled twice) and to model the microbial-host metabolic connectivities. At the species level, we found structural differences in the Chinese family gut microbiomes and those reported for American volunteers, which is consistent with population microbial cometabolic differences reported in epidemiological studies. We also introduce the concept of functional metagenomics, defined as "the characterization of key functional members of the microbiome that most influence host metabolism and hence health." For example, Faecalibacterium prausnitzii population variation is associated with modulation of eight urinary metabolites of diverse structure, indicating that this species is a highly functionally active member of the microbiome, influencing numerous host pathways. Other species were identified showing different and varied metabolic interactions. Our approach for understanding the dynamic basis of host-microbiome symbiosis provides a foundation for the development of functional metagenomics as a probe of systemic effects of drugs and diet that are of relevance to personal and public health care solutions.

Evolution of Mammals and Their Gut Microbes

Ruth Ley — 2008-06-20T11:27:07-00:00

Science, Vol. 320, No. 5883. (20 June 2008), pp. 1647-1651.

Mammals are metagenomic in that they are composed of not only their own gene complements but also those of all of their associated microbes. To understand the coevolution of the mammals and their indigenous microbial communities, we conducted a network-based analysis of bacterial 16S ribosomal RNA gene sequences from the fecal microbiota of humans and 59 other mammalian species living in two zoos and in the wild. The results indicate that host diet and phylogeny both influence bacterial diversity, which increases from carnivory to omnivory to herbivory; that bacterial communities codiversified with their hosts; and that the gut microbiota of humans living a modern life-style is typical of omnivorous primates. 10.1126/science.1155725

Conservation of small RNA pathways in platypus

Elizabeth Murchison — 2008-05-12T17:33:33-00:00

Genome Res. (7 May 2008), gr.073056.107.

Small RNA pathways play evolutionarily conserved roles in gene regulation and defense from parasitic nucleic acids. The character and expression patterns of small RNAs show conservation throughout animal lineages, but specific animal clades also show variations on these recurring themes, including species-specific small RNAs. The monotremes, with only platypus and four species of echidna as extant members, represent the basal branch of the mammalian lineage. Here, we examine the small RNA pathways of monotremes by deep sequencing of six platypus and echidna tissues. We find that highly conserved microRNA species display their signature tissue-specific expression patterns. In addition, we find a large rapidly evolving cluster of microRNAs on platypus chromosome X1, which is unique to monotremes. Platypus and echidna testes contain a robust Piwi-interacting (piRNA) system, which appears to be participating in ongoing transposon defense. 10.1101/gr.073056.107

The new paradigm of flow cell sequencing

Robert Holt — 2008-06-02T17:49:08-00:00

Genome Res., Vol. 18, No. 6. (1 June 2008), pp. 839-846.

DNA sequencing is in a period of rapid change, in which capillary sequencing is no longer the technology of choice for most ultra-high-throughput applications. A new generation of instruments that utilize primed synthesis in flow cells to obtain, simultaneously, the sequence of millions of different DNA templates has changed the field. We compare and contrast these new sequencing platforms in terms of stage of development, instrument configuration, template format, sequencing chemistry, throughput capability, operating cost, data handling issues, and error models. While these platforms outperform capillary instruments in terms of bases per day and cost per base, the short length of sequence reads obtained from most instruments and the limited number of samples that can be run simultaneously imposes some practical constraints on sequencing applications. However, recently developed methods for paired-end sequencing and for array-based direct selection of desired templates from complex mixtures extend the utility of these platforms for genome analysis. Given the ever increasing demand for DNA sequence information, we can expect continuous improvement of this new generation of instruments and their eventual replacement by even more powerful technology. 10.1101/gr.073262.107

A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation

S Eddy — 2008-06-02T08:22:15-00:00

PLoS Computational Biology, Vol. 5, No. 4. (May 2008)

EM-Random Forest and New Measures of Variable Importance for Multi-Locus Quantitative Trait Linkage Analysis.

Sophia S F Lee — 2008-05-30T12:31:32-00:00

Bioinformatics (Oxford, England) (21 May 2008)

MOTIVATION: We developed an EM-Random Forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data. RESULTS: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman-Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs. AVAILABILITY: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF. CONTACT: bull@mshri.on.ca SUPPLEMENTARY INFORMATION: www.infornomics.utoronto.ca/downloads/EMRF.

Statistical Power of Expression Quantitative Trait Loci for Mapping of Complex Trait Loci in Natural Populations

Paul Schliekelman — 2008-04-26T13:29:42-00:00

Genetics, Vol. 178, No. 4. (1 April 2008), pp. 2201-2216.

A number of recent genomewide surveys have found numerous QTL for gene expression, often with intermediate to high heritability values. As a result, there is currently a great deal of interest in genetical genomics--that is, the combination of genomewide expression data and molecular marker data to elucidate the genetics of complex traits. To date, most genetical genomics studies have focused on generating candidate genes for previously known trait loci or have otherwise leveraged existing knowledge about trait-related genes. The purpose of this study is to explore the potential for genetical genomics approaches in the context of genomewide scans for complex trait loci. I explore the expected strength of association between expression-level traits and a clinical trait, as a function of the underlying genetic model in natural populations. I give calculations of statistical power for detecting differential expression between affected and unaffected individuals. I model both reactive and causative expression-level traits with both additive and multiplicative multilocus models for the relationship between phenotype and genotype and explore a variety of assumptions about dominance, number of segregating loci, and other parameters. There are two key results. If a transcript is causative for the disease (in the sense that disease risk depends directly on transcript level), then the power to detect association between transcript and disease is quite good. Sample sizes on the order of 100 are sufficient for 80% power. On the other hand, if the transcript is reactive to a disease locus, then the correlation between expression-level traits and disease is low unless the expression-level trait shares several causative loci with the disease--that is, the expression-level trait itself is a complex trait. Thus, there is a trade-off between the power to show association between a reactive expression-level trait and the clinical trait of interest and the power to map expression-level QTL (eQTL) for that expression-level trait. Gene expression-level traits that are most strongly correlated with the clinical trait will themselves be complex traits and therefore often hard to map. Likewise, the expression-level traits that are easiest to map will tend to have a low correlation with the clinical trait. These results show some fundamental principles for understanding power in eQTL-based mapping studies. 10.1534/genetics.107.076687

Finding genes that underlie complex traits.

AM Glazier — 2007-03-26T10:45:32-00:00

Science, Vol. 298, No. 5602. (20 December 2002), pp. 2345-2349.

Phenotypic variation among organisms is central to evolutionary adaptations underlying natural and artificial selection, and also determines individual susceptibility to common diseases. These types of complex traits pose special challenges for genetic analysis because of gene-gene and gene-environment interactions, genetic heterogeneity, low penetrance, and limited statistical power. Emerging genome resources and technologies are enabling systematic identification of genes underlying these complex traits. We propose standards for proof of gene discovery in complex traits and evaluate the nature of the genes identified to date. These proof-of-concept studies demonstrate the insights that can be expected from the accelerating pace of gene discovery in this field.

The allelic architecture of human disease genes: common disease-common variant... or not?

Jonathan Pritchard — 2008-05-19T12:32:54-00:00

Hum. Mol. Genet., Vol. 11, No. 20. (1 October 2002), pp. 2417-2423.

Linkage disequilibrium (LD) plays a central role in current and proposed methods for mapping complex disease genes. LD-based methods work best when there is a single susceptibility allele at any given disease locus, and generally perform very poorly if there is substantial allelic heterogeneity. The extent of allelic heterogeneity at typical complex disease loci is not yet known, but predictions about allelic heterogeneity have important implications for the design of future mapping studies, including the proposed genome-wide association studies. In this article, we review the available data and models relating to the number and frequencies of susceptibility alleles at complex disease loci--the allelic architecture' of human disease genes. We also show that the predicted frequency spectrum of disease variants at a gene depends crucially on the method of ascertainment, for example from prior linkage scans or from surveys of functional candidate loci. 10.1093/hmg/11.20.2417

The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes

Maureen Hillenmeyer — 2008-04-18T07:55:22-00:00

Science, Vol. 320, No. 5874. (18 April 2008), pp. 362-365.

Genetics aims to understand the relation between genotype and phenotype. However, because complete deletion of most yeast genes ([~]80%) has no obvious phenotypic consequence in rich medium, it is difficult to study their functions. To uncover phenotypes for this nonessential fraction of the genome, we performed 1144 chemical genomic assays on the yeast whole-genome heterozygous and homozygous deletion collections and quantified the growth fitness of each deletion strain in the presence of chemical or environmental stress conditions. We found that 97% of gene deletions exhibited a measurable growth phenotype, suggesting that nearly all genes are essential for optimal growth in at least one condition. 10.1126/science.1150021

Genome analysis of the platypus reveals unique signatures of evolution

2008-05-07T23:43:22-00:00

Nature, Vol. 453, No. 7192. (May 2008), pp. 175-183.

Lessons from studying monogenic disease for common disease.

L Peltonen — 2008-05-07T13:30:38-00:00

Human molecular genetics, Vol. 15 Spec No 1 (15 April 2006)

Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol

Jonathan Cohen — 2007-01-15T11:50:51-00:00

Science, Vol. 305, No. 5685. (6 August 2004), pp. 869-872.

Heritable variation in complex traits is generally considered to be conferred by common DNA sequence polymorphisms. We tested whether rare DNA sequence variants collectively contribute to variation in plasma levels of highdensity lipoprotein cholesterol (HDL-C). We sequenced three candidate genes (ABCA1, APOA1, and LCAT) that cause Mendelian forms of low HDL-C levels in individuals from a population-based study. Nonsynonymous sequence variants were significantly more common (16% versus 2%) in individuals with low HDL-C (<fifth percentile) than in those with high HDL-C (>95th percentile). Similar findings were obtained in an independent population, and biochemical studies indicated that most sequence variants in the low HDL-C group were functionally important. Thus, rare alleles with major phenotypic effects contribute significantly to low plasma HDL-C levels in the general population. 10.1126/science.1099870

MicroRNAs keeping cells in formation

Eric Miska — 2008-05-03T19:51:12-00:00

Nature Cell Biology, Vol. 10, No. 5., pp. 501-502.

Mapping the Genetic Architecture of Gene Expression in Human Liver

Eric Schadt — 2008-05-06T08:52:37-00:00

PLoS Biology, Vol. 6, No. 5. (1 May 2008), e107.

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Moving toward a system genetics view of disease.

Solveig Sieberts — 2007-07-28T10:02:22-00:00

Mamm Genome (26 July 2007)

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.

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 Res (29 January 2008)

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.

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

The Argonaute protein family

Julia Höck — 2008-02-27T13:56:51-00:00

Genome Biology, Vol. 9 (26 February 2008), 210.

How to succeed in science: a concise guide for young biomedical scientists. Part I: taking the plunge

Jonathan Yewdell — 2008-04-23T12:46:10-00:00

Nat Rev Mol Cell Biol, Vol. 9, No. 5. (May 2008), pp. 413-416.

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

Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits

Hill — 2008-04-17T07:06:03-00:00

PLoS Genetics, Vol. 4, No. 2. (February 2008)

Innate recognition of non-self nucleic acids

Hongbo Chi — 2008-03-10T17:04:28-00:00

Genome Biology, Vol. 9 (10 March 2008), 211.

Genome assembly forensics: finding the elusive mis-assembly

Adam Phillippy — 2008-03-15T05:14:17-00:00

Genome Biology, Vol. 9 (14 March 2008), R55.

RNA-Binding Protein Dnd1 Inhibits MicroRNA Access to Target mRNA.

Martijn Kedde — 2007-12-26T12:41:03-00:00

Cell (19 December 2007)

MicroRNAs (miRNAs) are inhibitors of gene expression capable of controlling processes in normal development and cancer. In mammals, miRNAs use a seed sequence of 6-8 nucleotides (nt) to associate with 3' untranslated regions (3'UTRs) of mRNAs and inhibit their expression. Intriguingly, occasionally not only the miRNA-targeting site but also sequences in its vicinity are highly conserved throughout evolution. We therefore hypothesized that conserved regions in mRNAs may serve as docking platforms for modulators of miRNA activity. Here we demonstrate that the expression of dead end 1 (Dnd1), an evolutionary conserved RNA-binding protein (RBP), counteracts the function of several miRNAs in human cells and in primordial germ cells of zebrafish by binding mRNAs and prohibiting miRNAs from associating with their target sites. These effects of Dnd1 are mediated through uridine-rich regions present in the miRNA-targeted mRNAs. Thus, our data unravel a novel role of Dnd1 in protecting certain mRNAs from miRNA-mediated repression.

Adaptive Evolution of Newly Emerged Micro-RNA Genes in Drosophila

Jian Lu — 2008-04-16T15:20:47-00:00

Mol Biol Evol, Vol. 25, No. 5. (1 May 2008), pp. 929-938.

How often micro-RNA (miRNA) genes emerged and how fast they evolved soon after their emergence are some of the central questions in the evolution of miRNAs. Because most known miRNA genes are ancient and highly conserved, these questions can be best answered by identifying newly emerged miRNA genes. Among the 78 miRNA genes in Drosophila reported before 2007, only 5 are confirmed to be newly emerged in the genus (although many more can be found in the newly reported data set; e.g., Ruby et al. 2007; Stark et al. 2007; Lu et al. 2008). These new miRNA genes have undergone numerous changes, even in the normally invariant mature sequences. Four of them (the miR-310/311/312/313 cluster, denoted miR-310s) were duplicated from other conserved miRNA genes. The fifth one (miR-303) appears to be a very young gene, originating de novo from a non-miRNA sequence recently. We sequenced these 5 miRNA genes and their neighboring regions from a worldwide collection of Drosophila melanogaster lines. The levels of divergence and polymorphism in these miRNA genes, vis-a-vis those of the neighboring DNA sequences, suggest that these 5 genes are evolving adaptively. Furthermore, the polymorphism pattern of miR-310s in D. melanogaster is indicative of hitchhiking under positive selection. Thus, a large number of adaptive changes over a long period of time may be essential for the evolution of newly emerged miRNA genes. 10.1093/molbev/msn040

Extracting expression modules from perturbational gene expression compendia

Steven Maere — 2008-04-11T05:02:23-00:00

BMC Systems Biology, Vol. 2, No. 1. (2008)

BACKGROUND:Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes. RESULTS:We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to biclusters produced by other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these (often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions.CONCLUSIONS:It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.

g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments.

J Reimand — 2007-07-19T13:49:28-00:00

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

g:Profiler (http://biit.cs.ut.ee/gprofiler/) is a public web server for characterising and manipulating gene lists resulting from mining high-throughput genomic data. g:Profiler has a simple, user-friendly web interface with powerful visualisation for capturing Gene Ontology (GO), pathway, or transcription factor binding site enrichments down to individual gene levels. Besides standard multiple testing corrections, a new improved method for estimating the true effect of multiple testing over complex structures like GO has been introduced. Interpreting ranked gene lists is supported from the same interface with very efficient algorithms. Such ordered lists may arise when studying the most significantly affected genes from high-throughput data or genes co-expressed with the query gene. Other important aspects of practical data analysis are supported by modules tightly integrated with g:Profiler. These are: g:Convert for converting between different database identifiers; g:Orth for finding orthologous genes from other species; and g:Sorter for searching a large body of public gene expression data for co-expression. g:Profiler supports 31 different species, and underlying data is updated regularly from sources like the Ensembl database. Bioinformatics communities wishing to integrate with g:Profiler can use alternative simple textual outputs.

Common Single-Nucleotide Polymorphisms Act in Concert to Affect Plasma Levels of High-Density Lipoprotein Cholesterol

V Spirin — 2008-04-13T19:35:40-00:00

American Journal of Human Genetics, Vol. 81 (2007), pp. 1298-1303.

Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1

Christopher — 2008-04-09T08:53:39-00:00

Nature Genetics (April 2008)

A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25

Rayjean Hung — 2008-04-02T18:44:58-00:00

Nature, Vol. 452, No. 7187. (3 April 2008), pp. 633-637.

A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules

Joshua Stuart — 2005-09-24T15:56:37-00:00

Science, Vol. 302, No. 5643. (10 October 2003), pp. 249-255.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Manyof these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentallyconfirmed the predictions implied bysome of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

Singular value decomposition for genome-wide expression data processing and modeling.

O Alter — 2006-04-24T09:27:19-00:00

Proc Natl Acad Sci U S A, Vol. 97, No. 18. (29 August 2000), pp. 10101-10106.

We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Variations in DNA elucidate molecular networks that cause disease

Yanqing Chen — 2008-03-18T04:25:40-00:00

Nature (16 March 2008)

Genetics of gene expression and its effect on disease

Valur Emilsson — 2008-03-18T04:25:40-00:00

Nature (16 March 2008)

Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome.

DP Locke — 2008-02-01T05:37:26-00:00

Am J Hum Genet, Vol. 79, No. 2. (August 2006), pp. 275-290.

Studies of copy-number variation and linkage disequilibrium (LD) have typically excluded complex regions of the genome that are rich in duplications and prone to rearrangement. In an attempt to assess the heritability and LD of copy-number polymorphisms (CNPs) in duplication-rich regions of the genome, we profiled copy-number variation in 130 putative "rearrangement hotspot regions" among 269 individuals of European, Yoruba, Chinese, and Japanese ancestry analyzed by the International HapMap Consortium. Eighty-four hotspot regions, corresponding to 257 bacterial artificial chromosome (BAC) probes, showed evidence of copy-number differences. Despite a predisposing genetic architecture, no polymorphism was ever observed in the remaining 46 "rearrangement hotspots," and we suggest these represent excellent candidate sites for pathogenic rearrangements. We used a combination of BAC-based and high-density customized oligonucleotide arrays to resolve the molecular basis of structural rearrangements. For common variants (frequency >10%), we observed a distinct bias against copy-number losses, suggesting that deletions are subject to purifying selection. Heritability estimates did not differ significantly from 1.0 among the majority (30 of 34) of loci analyzed, consistent with normal Mendelian inheritance. Some of the CNPs in duplication-rich regions showed strong LD with nearby single-nucleotide polymorphisms (SNPs) and were observed to segregate on ancestral SNP haplotypes. However, LD with the best available SNP markers was weaker than has been reported for deletion polymorphisms in less complex regions of the genome. These observations may be accounted for by a low density of SNP data in duplicated regions, challenges in mapping and typing the CNPs, and the possibility that CNPs in these regions have rearranged on multiple haplotype backgrounds. Our results underscore the need for complete maps of genetic variation in duplication-rich regions of the genome.

The Geometry of Musical Chords

Dmitri Tymoczko — 2006-09-05T04:52:06-00:00

Science, Vol. 313, No. 5783. (7 July 2006), pp. 72-74.

A musical chord can be represented as a point in a geometrical space called an orbifold. Line segments represent mappings from the notes of one chord to those of another. Composers in a wide range of styles have exploited the non-Euclidean geometry of these spaces, typically by using short line segments between structurally similar chords. Such line segments exist only when chords are nearly symmetrical under translation, reflection, or permutation. Paradigmatically consonant and dissonant chords possess different near-symmetries and suggest different musical uses. 10.1126/science.1126287

Medical sequencing at the extremes of human body mass.

N Ahituv — 2007-05-28T19:38:53-00:00

Am J Hum Genet, Vol. 80, No. 4. (April 2007), pp. 779-791.

Body weight is a quantitative trait with significant heritability in humans. To identify potential genetic contributors to this phenotype, we resequenced the coding exons and splice junctions of 58 genes in 379 obese and 378 lean individuals. Our 96-Mb survey included 21 genes associated with monogenic forms of obesity in humans or mice, as well as 37 genes that function in body weight-related pathways. We found that the monogenic obesity-associated gene group was enriched for rare nonsynonymous variants unique to the obese population compared with the lean population. In addition, computational analysis predicted a greater fraction of deleterious variants within the obese cohort. Together, these data suggest that multiple rare alleles contribute to obesity in the population and provide a medical sequencing-based approach to detect them.

Systematic discovery of in vivo phosphorylation networks.

R Linding — 2007-07-08T16:28:02-00:00

Cell, Vol. 129, No. 7. (29 June 2007), pp. 1415-1426.

Protein kinases control cellular decision processes by phosphorylating specific substrates. Thousands of in vivo phosphorylation sites have been identified, mostly by proteome-wide mapping. However, systematically matching these sites to specific kinases is presently infeasible, due to limited specificity of consensus motifs, and the influence of contextual factors, such as protein scaffolds, localization, and expression, on cellular substrate specificity. We have developed an approach (NetworKIN) that augments motif-based predictions with the network context of kinases and phosphoproteins. The latter provides 60%-80% of the computational capability to assign in vivo substrate specificity. NetworKIN pinpoints kinases responsible for specific phosphorylations and yields a 2.5-fold improvement in the accuracy with which phosphorylation networks can be constructed. Applying this approach to DNA damage signaling, we show that 53BP1 and Rad50 are phosphorylated by CDK1 and ATM, respectively. We describe a scalable strategy to evaluate predictions, which suggests that BCLAF1 is a GSK-3 substrate.

On the Utility of Linkage Disequilibrium as a Statistic for Identifying Targets of Positive Selection in Nonequilibrium Populations

Jeffrey Jensen — 2007-08-23T19:49:42-00:00

Genetics, Vol. 176, No. 4. (1 August 2007), pp. 2371-2379.

A critically important challenge in empirical population genetics is distinguishing neutral nonequilibrium processes from selective forces that produce similar patterns of variation. We here examine the extent to which linkage disequilibrium (i.e., nonrandom associations between markers) improves this discrimination. We show that patterns of linkage disequilibrium recently proposed to be unique to hitchhiking models are replicated under nonequilibrium neutral models. We also demonstrate that jointly considering spatial patterns of association among variants alongside the site-frequency spectrum is nonetheless of value. Through a comparison of models of equilibrium neutrality, nonequilibrium neutrality, equilibrium hitchhiking, nonequilibrium hitchhiking, and recurrent hitchhiking, we evaluate a linkage disequilibrium (LD) statistic (omegamax) that appears to have power to identify regions recently shaped by positive selection. Most notably, for demographic parameters relevant to non-African populations of Drosophila melanogaster, we demonstrate that selected loci are distinguishable from neutral loci using this statistic. 10.1534/genetics.106.069450

Independent component analysis reveals new and biologically significant structures in micro array data

Attila Frigyesi — 2006-06-08T22:15:14-00:00

BMC Bioinformatics, Vol. 7 (08 June 2006), 290.

Bayesian sparse hidden components analysis for transcription regulation networks

Chiara Sabatti — 2006-03-16T11:16:14-00:00

Bioinformatics, Vol. 22, No. 6. (15 March 2006), pp. 739-746.

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

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.

Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors

Gael Yvert — 2006-05-09T19:48:36-00:00

Nat Genet, Vol. 35, No. 1. (2003), pp. 57-64.

Integrated approaches to uncovering transcription regulatory networks in mammalian cells.

Kai Tan — 2008-01-18T02:45:40-00:00

Genomics (7 January 2008)

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.

Endogenous human microRNAs that suppress breast cancer metastasis

Sohail Tavazoie — 2008-01-10T06:08:57-00:00

Nature, Vol. 451, No. 7175., pp. 147-152.

The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis

Qihong Huang — 2008-02-02T00:12:11-00:00

Nature Cell Biology, Vol. 10, No. 2. (13 January 2008), pp. 202-210.