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Identification of co-regulated transcripts affecting male body size in Drosophila -: art. no. R53

GENOME BIOLOGY 2005年第6期6卷 1-15页

作者： Coffman, CJ Wayne, ML Nuzhdin, SV Higgins, LA McIntyre, LM Duke Univ Med Ctr Dept Biostat & Bioinformat Durham NC 27710 USA Vet Adm Med Ctr Hlth Serv Res & Dev Biostat Unit Durham NC 27705 USA Univ Florida Dept Zool Gainesville FL 32611 USA Univ Calif Davis Dept Ecol & Evolut Davis CA 95616 USA Purdue Univ Dept Agron W Lafayette IN 47907 USA

Factor analysis is an analytic approach that describes the covariation among a set of genes through the estimation of ' factors', which may be, for example, transcription factors, microRNAs ( miRNAs), and so on, by which the genes are co- regulated. Factor analysis gives a direct mechanism by which to relate gene networks to complex traits. Using simulated data, we found that factor analysis clearly identifies the number and structure of factors and outperforms hierarchical cluster analysis. Noise genes, genes that are not correlated with any factor, can be distinguished even when factor structure is complex. Applied to body size in Drosophila simulans, an evolutionarily important complex trait, a factor was directly associated with body size.

关键词： Quantitative Trait Locus Body Size Transcript Abundance Hierarchical Cluster Analysis additional data file

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Metabolic changes in schizophrenia and human brain evolution

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GENOME BIOLOGY 2008年第8期9卷 R124-R124页

作者： Khaitovich, Philipp Lockstone, Helen E. Wayland, Matthew T. Tsang, Tsz M. Jayatilaka, Samantha D. Guo, Arfu J. Zhou, Jie Somel, Mehmet Harris, Laura W. Holmes, Elaine Paeaebo, Svante Bahn, Sabine Chinese Acad Sci Shanghai Inst Biol Sci Partner Inst Computat Biol Shanghai 200031 Peoples R China Max Planck Inst Evolutionary Anthropol D-04103 Leipzig Germany Univ Cambridge Inst Biotechnol Cambridge CB2 1QT England Univ London Imperial Coll Sci Technol & Med Div SORA Dept Biomol Med London SW7 2AZ England Univ Sci & Technol China Hefei 230026 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 PR Peoples R China

Background: Despite decades of research, the molecular changes responsible for the evolution of human cognitive abilities remain unknown. Comparative evolutionary studies provide detailed information about DNA sequence and mRNA expression differences between humans and other primates but, in the absence of other information, it has proved very difficult to identify molecular pathways relevant to human cognition. Results: Here, we compare changes in gene expression and metabolite concentrations in the human brain and compare them t o the changes seen in a disorder known to affect human cognitive abilities, schizophrenia. We find that both genes and metabolites relating to energy metabolism and energy-expensive brain functions are altered in schizophrenia and, at the same time, appear to have changed rapidly during recent human evolution, probably as a result of positive selection. Conclusion: Our findings, along with several previous studies, suggest that the evolution of human cognitive abilities was accompanied by adaptive changes in brain metabolism, potentially pushing the human brain to the limit of its metabolic capabilities.

关键词： Schizophrenia Gene Ontology additional data file Schizophrenia Patient Brodmann Area

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Community-wide analysis of microbial genome sequence signatures

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GENOME BIOLOGY 2009年第8期10卷 R85-R85页

作者： Dick, Gregory J. Andersson, Anders F. Baker, Brett J. Simmons, Sheri L. Yelton, A. Pepper Banfield, Jillian F. Univ Calif Berkeley Dept Earth & Planetary Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA

Background: Analyses of DNA sequences from cultivated microorganisms have revealed genome-wide, taxa-specific nucleotide compositional characteristics, referred to as genome signatures. These signatures have far-reaching implications for understanding genome evolution and potential application in classification of metagenomic sequence fragments. However, little is known regarding the distribution of genome signatures in natural microbial communities or the extent to which environmental factors shape them. Results: We analyzed metagenomic sequence data from two acidophilic biofilm communities, including composite genomes reconstructed for nine archaea, three bacteria, and numerous associated viruses, as well as thousands of unassigned fragments from strain variants and low-abundance organisms. Genome signatures, in the form of tetranucleotide frequencies analyzed by emergent self-organizing maps, segregated sequences from all known populations sharing <50-60% average amino acid identity and revealed previously unknown genomic clusters corresponding to low-abundance organisms and a putative plasmid. Signatures were pervasive genome wide. Clusters were resolved because intra-genome differences resulting from translational selection or protein adaptation to the intracellular (pH similar to 5) versus extracellular (pH similar to 1) environment were small relative to inter-genome differences. We found that these genome signatures stem from multiple influences but are primarily manifested through codon composition, which we propose is the result of genome-specific mutational biases. Conclusions: An important conclusion is that shared environmental pressures and interactions among coevolving organisms do not obscure genome signatures in acid mine drainage communities. Thus, genome signatures can be used to assign sequence fragments to populations, between pairs of genomes correlates with the difference predicted based on codon composition. additional data file 7 shows tetra

关键词： Codon Usage Acid Mine Drainage additional data file Codon Bias Sequence Fragment

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ProCAT: a data analysis approach for protein microarrays

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GENOME BIOLOGY 2006年第11期7卷 R110-R110页

作者： Zhu, Xiaowei Gerstein, Mark Snyder, Michael Yale Univ Program Computat Biol & Bioinformat New Haven CT 06511 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06511 USA Yale Univ Dept Comp Sci New Haven CT 06511 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06511 USA

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.

关键词： Window Size additional data file Background Correction Median Absolute Deviation Protein Microarrays

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Analysis of the mouse embryonic stem cell regulatory networks obtained by ChIP-chip and ChIP-PET

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GENOME BIOLOGY 2008年第8期9卷 R126-R126页

作者： Mathur, Divya Danford, Timothy W. Boyer, Laurie A. Young, Richard A. Gifford, David K. Jaenisch, Rudolf Cambridge Ctr Whitehead Inst Biomed Res Cambridge MA 02142 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

Background: Genomewide approaches have begun to reveal the transcriptional networks responsible for pluripotency in embryonic stem (ES) cells. Chromatin Immunoprecipitation (ChIP) followed either by hybridization to a microarray platform (ChIP-chip) or by DNA sequencing (ChIP-PET), has identified binding targets of the ES cell transcription factors OCT4 and NANOG in humans and mice, respectively. These studies have provided an outline of the transcriptional framework involved in maintaining pluripotency. Recent evidence with comparing multiple technologies suggests that expanding these datasets using different platforms would be a useful resource for examining the mechanisms underlying pluripotency regulation. Results: We have now identified OCT4 and NANOG genomic targets in mouse ES cells by ChIPchip and provided the means to compare these data with previously reported ChIP-PET results in mouse ES cells. We have mapped the sequences of OCT4 and NANOG binding events from each dataset to genomic coordinates, providing a valuable resource to facilitate a better understanding of the ES cell regulatory circuitry. Interestingly, although considerable differences are observed in OCT4 and NANOG occupancy as identified by each method, a substantial number of targets in both datasets are enriched for genes that have known roles in cell fate specification and that are differentially expressed upon Oct4 or Nanog knockdown. Conclusion: This study suggests that each dataset is a partial representation of the overall ES cell regulatory circuitry, and through integrating binding data obtained by ChIP chip and ChIP-PET, the methods presented here provide a useful means for integrating datasets obtained by different techniques in the future.

关键词： Gene Ontology Embryonic Stem Cell additional data file Human Embryonic Stem Cell Mouse Embryonic Stem Cell

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Supervised harvesting of expression trees

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GENOME BIOLOGY 2001年第1期2卷 1-12页

作者： Hastie, Trevor Tibshirani, Robert Botstein, David Brown, Patrick Stanford Univ Dept Stat Stanford CA 94305 USA Stanford Univ Dept Hlth Res & Policy Stanford CA 94305 USA Stanford Univ Dept Genet Stanford CA 94305 USA Stanford Univ Dept Biochem Stanford CA 94305 USA

Background: We propose a new method for supervised learning from gene expression data. We call it 'tree harvesting'. This technique starts with a hierarchical clustering of genes, then models the outcome variable as a sum of the average expression profiles of chosen clusters and their products. It can be applied to many different kinds of outcome measures such as censored survival times, or a response falling in two or more classes (for example, cancer classes). The method can discover genes that have strong effects on their own, and genes that interact with other genes. Results: We illustrate the method on data from a lymphoma study, and on a dataset containing samples from eight different cancers. It identified some potentially interesting gene clusters. In simulation studies we found that the procedure may require a large number of experimental samples to successfully discover interactions. Conclusions: Tree harvesting is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worthy of further investigation.

关键词： Large Cluster additional data file True Cluster Tree Harvesting Cancer Class

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Protein interactions in human genetic diseases

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GENOME BIOLOGY 2008年第1期9卷 R9-R9页

作者： Schuster-Bockler, Benjamin Bateman, Alex Wellcome Trust Sanger Inst Hinxton CB10 1SA England

We present a novel method that combines protein structure information with protein interaction data to identify residues that form part of an interaction interface. Our prediction method can retrieve interaction hotspots with an accuracy of 60% (at a 20% false positive rate). The method was applied to all mutations in the Online Mendelian Inheritance in Man (OMIM) database, predicting 1,428 mutations to be related to an interaction defect. Combining predicted and hand-curated sets, we discuss how mutations affect protein interactions in general.

关键词： additional data file Obligate Interaction Griscelli Syndrome Stoichiometric Imbalance Sequence Identity Cutoff

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Using orthologous and paralogous proteins to identify specificity determining residues.

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GENOME BIOLOGY 2002年第3期3卷 1-20页

作者： Mirny, Leonid A. Gelfand, Mikhail S. MIT Harvard MIT Div Hlth Sci & Technol Cambridge MA 02139 USA IntegratedGenom Moscow Moscow 117333 Russia

Background Concepts of orthology and paralogy are become increasingly important as whole-genome comparison allows their identification in complete genomes. Functional specificity of proteins is assumed to be conserved among orthologs and is different among paralogs. We used this assumption to identify residues which determine specificity of protein-DNA and protein-ligand recognition. Finding such residues is crucial for understanding mechanisms of molecular recognition and for rational protein and drug design. Results Assuming conservation of specificity among orthologs and different specificity of paralogs, we identify residues which correlate with this grouping by specificity. The method is taking advantage of complete genomes to find multiple orthologs and paralogs. The central part of this method is a procedure to compute statistical significance of the predictions. The procedure is based on a simple statistical model of protein evolution. When applied to a large family of bacterial transcription factors, our method identified 12 residues that are presumed to determine the protein-DNA and protein-ligand recognition specificity. Structural analysis of the proteins and available experimental results strongly support our predictions. Our results suggest new experiments aimed at rational re-design of specificity in bacterial transcription factors by a minimal number of mutations. Conclusions While sets of orthologous and paralogous proteins can be easily derived from complete genomic sequences, our method can identify putative specificity determinants in such proteins.

关键词： Mutual Information additional data file Specificity Determinant Orthologous Protein Identify Amino Acid

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NetGrep: fast network schema searches in interactomes

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GENOME BIOLOGY 2008年第9期9卷 R138-R138页

作者： Banks, Eric Nabieva, Elena Peterson, Ryan Singh, Mona Princeton Univ Dept Comp Sci Princeton NJ 08540 USA Princeton Univ Carl Icahn Lab Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA

NetGrep (http://***/singhlab/netgrep/) is a system for searching protein interaction networks for matches to user-supplied 'network schemas'. Each schema consists of descriptions of proteins (for example, their molecular functions or putative domains) along with the desired topology and types of interactions among them. Schemas can thus describe domain-domain interactions, signaling and regulatory pathways, or more complex network patterns. NetGrep provides an advanced graphical interface for specifying schemas and fast algorithms for extracting their matches.

关键词： Gene Ontology additional data file Biological Network Network Motif Protein Feature

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Evolution of protein complexes by duplication of homomeric interactions

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GENOME BIOLOGY 2007年第4期8卷 R51-R51页

作者： Pereira-Leal, Jose B. Levy, Emmanuel D. Kamp, Christel Teichmann, Sarah A. Inst Gulbenkian Ciencias P-2781901 Oeiras Portugal MRC Mol Biol Lab Cambridge CB2 2QH England Paul Ehrlich Inst Fed Agcy Sera & Vaccines D-63225 Langen Germany

Background: Cellular functions are accomplished by the concerted actions of functional modules. The mechanisms driving the emergence and evolution of these modules are still unclear. Here we investigate the evolutionary origins of protein complexes, modules in physical protein-protein interaction networks. Results: We studied protein complexes in Saccharomyces cerevisiae, complexes of known three-dimensional structure in the Protein data Bank and clusters of pairwise protein interactions in the networks of several organisms. We found that duplication of homomeric interactions, a large class of protein interactions, frequently results in the formation of complexes of paralogous proteins. This route is a common mechanism for the evolution of complexes and clusters of protein interactions. Our conclusions are further confirmed by theoretical modelling of network evolution. We propose reasons for why this is favourable in terms of structure and function of protein complexes. Conclusion: Our study provides the first insight into the evolution of functional modularity in protein-protein interaction networks, and the origins of a large class of protein complexes.

关键词： Protein Complex additional data file Cluster Coefficient Protein Interaction Network Domain Architecture

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