Jacob & Monod – trancription repressors and enhancers

A very interesting story about the way of talented people towards novel discoveries and nobel prises. Their experiments laid foundation to textbook examples of transcriptional regulation. Two reviews, one about transcriptional repressors and another about enhancers, brief latest findings of the field.

Curr Biol. 2010 Sep 14;20(17):R718-23.

Jacob and Monod: from operons to EvoDevo.

Gann A.

PMID: 20833316

Curr Biol. 2010 Sep 14;20(17):R764-71.

Transcriptional repression: conserved and evolved features.

Payankaulam S, Li LM, Arnosti DN.

Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48910, USA.

Abstract

The regulation of gene expression by transcriptional repression is an ancient and conserved mechanism that manifests itself in diverse ways. Here we summarize conserved pathways for transcriptional repression prevalent throughout all forms of life, as well as indirect mechanisms that appear to have originated in eukaryotes, consistent with the unique chromatin environment of eukaryotic genes. The direct interactions between transcriptional repressors and the core transcriptional machinery in bacteria and archaea are sufficient to generate a sophisticated suite of mechanisms that provide flexible control. These direct interactions contrast with the activity of corepressors, which provide an additional regulatory control in eukaryotes. Their modulation of chromatin structure represents an indirect pathway to downregulate transcription, and their diversity and modulation provide additional complexity suited to the requirements of elaborate eukaryotic repression patterns. New findings indicate that corepressors are not necessarily restricted to generating a single stereotypic output, but can rather exhibit diverse functional responses depending on the context in which they are recruited, providing a hitherto unsuspected additional source of diversity in transcriptional control. Mechanisms within eukaryotes appear to be highly conserved, with novel aspects chiefly represented by addition of lineage-specific corepressor scaffolds that provide additional opportunities for recruiting the same core machinery.

Copyright © 2010 Elsevier Ltd. All rights reserved.

PMID: 20833321

Curr Biol. 2010 Sep 14;20(17):R754-63.

Transcriptional enhancers in animal development and evolution.

Levine M.

Department of Molecular and Cell Biology, University of California-Berkeley, CA 94720, USA. mlevine@berkeley.edu

Abstract

Regulatory DNAs serve as templates to bring weakly interacting transcription factors into close proximity so they can work synergistically to switch genes on and off in time and space. Most of these regulatory DNAs are enhancers that can work over long distances–a million base pairs or more in mammals–to control gene expression. Critical enhancers are sometimes even found within the introns of neighboring genes. This review summarizes well-defined examples of enhancers controlling key processes in animal development. Potential mechanisms of transcriptional synergy are discussed with regard to enhancer structure and contemporary ChIP-sequencing assays, whereby just a small fraction of the observed binding sites represent bona fide regulatory DNAs. Finally, there is a discussion of how enhancer evolution can produce novelty in animal morphology and of the prospects for reconstructing transitions in animal evolution by introducing derived enhancers in basal ancestors.

Copyright © 2010 Elsevier Ltd. All rights reserved.

PMID: 20833320

Putting epigenome comparison into practice

In addition to the whole issue about epigenetics there’s a good overview of directions for epigenetics data comparison, along with a list of tools. From Galaxy and EpiGRAPH to Genboree and CLC Genomic Workbench, accompanied with a comprehensive table explaining different layers of complexity of epigenetics data and analysis.

 Nat Biotechnol. 2010 Oct;28(10):1053-6.

Putting epigenome comparison into practice.

Milosavljevic A.

Aleksandar Milosavljevic is at The NIH Epigenomics Roadmap Data Analysis and Coordination Center, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, Texas, USA.

PMID: 20944597



Focus on Epigenetics

Epigenetics and epigenomcs are main themes featured in this “Nature Biotechnology” issue. DNA methylation, histone modification, nucleosome positioning, how these modifications influence different states of cellular development, which technologies are available and how analysis of such data can be approached. MeDIP, BS, MRE, ChIP – now these technologies are tghtly associated with sequencing. Besides excellent education about epigenetecs these 3-5 page commentaries provide lots of specifics, i.e. references to databases and efforts to unify epigenetics data collection and processing. NIH Roadmap Epigenomics Mapping Consortium (http://www.roadmapepigenomics.org/), Data access @ NCBI epigenomics portal (http://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics/), Data Analysis and Coordination Center (http://www.epigenomeatlas.org/) – several of many resources. Given that besides methylation we have >100 other genome modifications there’s a lot of exciting work to do! A must, since it is the future of biotechnology.

http://www.nature.com/nbt/journal/v28/n10/index.html  

Oncology’s energetic pipeline

Short report about Warburg effect in cancer, how and hypothesized why tumors switch from aerobic to anaerobic metabolism. Molecular biology behind it, and industry answer by different drugs. Interesting description of new component, dichloroacetate (DCA), which shifts metabolism from glycolysis to oxidative phosphorylation.

Nat Biotechnol. 2010 Sep;28(9):888-91.

Oncology’s energetic pipeline.

Garber K.

PMID: 20829819

The four hundred years of planetary science since Galileo and Kepler

As the title implies – scientific but entertaining reading about development of astronomy, directions humanity is going, and technologies that are developed for space exploration. A good reminder we’re living in the same little world, fragile and beautiful…

Nature. 2010 Jul 29;466(7306):575-84.

The four hundred years of planetary science since Galileo and Kepler.

Burns JA.

Department of Astronomy and College of Engineering, 328 Space Sciences Building, Cornell University, Ithaca, New York 14853-6801, USA. jab16@cornell.edu

Abstract

For 350 years after Galileo’s discoveries, ground-based telescopes and theoretical modelling furnished everything we knew about the Sun’s planetary retinue. Over the past five decades, however, spacecraft visits to many targets transformed these early notions, revealing the diversity of Solar System bodies and displaying active planetary processes at work. Violent events have punctuated the histories of many planets and satellites, changing them substantially since their birth. Contemporary knowledge has finally allowed testable models of the Solar System’s origin to be developed and potential abodes for extraterrestrial life to be explored. Future planetary research should involve focused studies of selected targets, including exoplanets.

PMID: 20671701

Next generation sequencing in functional genomics.

An outstanding review about types of sequencing technologies and what can we learn from it. They discuss “functional genomics” and figure 2 shows a pipeline of multi-level analysis and integration of different layers of data, although no specifics provided. A special focus is given to the description of transcriptional regulation and new knowledge we can get with ChIP sequencing.

The second paper is more practical description of challenges we’re facing with sequencing data analysis. They frame the nature of the data, the nture of algorithms and computational power needed, what types of computing may solve these challenges. Cloud computing is an answer, but htere are many types of computational technology one can use for this purpose. From Amazon EC2 cloud to specifically geared biological computer clouds – wery informative intro into high-dimensional and multi-threading data analysis.

Brief Bioinform. 2010 Sep;11(5):499-511. Epub 2010 May 25.

Next generation sequencing in functional genomics.

Werner T.

Genomatix Software GmbH, D-80335 München, Germany. werner@genomatix.de

Abstract

Genome-wide sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to the genomic sequence. Now next generation sequencing (NGS) extends that reach into multiple almost complete genomes of the same species, revealing more and more details about how individual genomes as well as individual aspects of their regulation differ from each other. The inclusion of NGS-based transcriptome sequencing, chromatin-immunoprecipitation (ChIP) of transcription factor binding and epigenetic analyses (usually based on DNA methylation or histone modification ChIP) completes the picture with unprecedented resolution enabling the detection of even subtle differences such as alternative splicing of individual exons. Functional genomics aims at the elucidation of the molecular basis of biological functions and requires analyses that go far beyond the primary analysis of the reads such as mapping to a genome template sequence. The various and complex interactions between the genome, gene products and metabolites define biological function, which necessitates inclusion of results other than sequence tags in quite elaborative approaches. However, the extra efforts pay off in revealing mechanisms as well as providing the foundation for new strategies in systems biology and personalized medicine. This review emphasizes the particular contribution NGS-based technologies make to functional genomics research with a special focus on gene regulation by transcription factor binding sites.

PMID: 20501549

Nat Rev Genet. 2010 Sep;11(9):647-57.

Computational solutions to large-scale data management and analysis.

Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP.

Pacific Biosciences, Menlo Park, California 94025, USA. eschadt@pacificbiosciences.com

Abstract

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist – such as cloud and heterogeneous computing – to successfully tackle our big data problems.

PMID: 20717155

The Road to the $1,000 Genome

That’s a short report about technology progress and what we’ll encounter on the way to it. While we’ll have sequencing data accumulating the real challenge is to make sense out of it.

… as Bruce Korf, the president of the American College of Medical Genetics, puts it: “We are close to having a $1,000 genome sequence, but this may be accompanied by a $1,000,000 interpretation.”

http://www.bio-itworld.com/2010/09/28/1Kgenome.html

From complete genome sequence to ‘complete’ understanding?

Very good discussion about what genome sequencing can and can not provide. Important note to Peer Bork paper describing “70% hurdle”, that on average one-third of the genes can’t be predicted via traditional methods of genome analysis. Two representative tables with “known unknowns” and “unknown unknowns” genes. Indeed, majority of genes have multiple functions, which can hardly be predicted. Written with healthy sence of humor and sobering thoughts about reality of genome sequencing times.

Trends Biotechnol. 2010 Aug;28(8):398-406.

From complete genome sequence to ‘complete’ understanding?

Galperin MY, Koonin EV.

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. galperin@ncbi.nlm.nih.gov

Abstract

The rapidly accumulating genome sequence data allow researchers to address fundamental biological questions that were not even asked just a few years ago. A major problem in genomics is the widening gap between the rapid progress in genome sequencing and the comparatively slow progress in the functional characterization of sequenced genomes. Here we discuss two key questions of genome biology: whether we need more genomes, and how deep is our understanding of biology based on genomic analysis. We argue that overly specific annotations of gene functions are often less useful than the more generic, but also more robust, functional assignments based on protein family classification. We also discuss problems in understanding the functions of the remaining ‘conserved hypothetical’ genes.

PMID: 20647113

GeneBrowser 2: an application to explore and identify common biological traits in a set of genes.

Very interesting tool combining GO/Pathway/Homology/Position enrichment analysis for a list of genes, simple gene-by-gene information summary, and selection of relevant bibliography. Downside – not possible to export results. But still worth to put in bookmarks. http://bioinformatics.ua.pt/genebrowser2/Default.aspx

Upd: There are some annoying design bugs, which should not affect the analysis, statistics behind it is trivial.

BMC Bioinformatics. 2010 Jul 21;11:389.

GeneBrowser 2: an application to explore and identify common biological traits in a set of genes.

Arrais JP, Fernandes J, Pereira J, Oliveira JL.

Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.

Abstract

BACKGROUND: The development of high-throughput laboratory techniques created a demand for computer-assisted result analysis tools. Many of these techniques return lists of genes whose interpretation requires finding relevant biological roles for the problem at hand. The required information is typically available in public databases, and usually, this information must be manually retrieved to complement the analysis. This process is a very time-consuming task that should be automated as much as possible.

RESULTS: GeneBrowser is a web-based tool that, for a given list of genes, combines data from several public databases with visualisation and analysis methods to help identify the most relevant and common biological characteristics. The functionalities provided include the following: a central point with the most relevant biological information for each inserted gene; a list of the most related papers in PubMed and gene expression studies in ArrayExpress; and an extended approach to functional analysis applied to Gene Ontology, homologies, gene chromosomal localisation and pathways.

CONCLUSIONS: GeneBrowser provides a unique entry point to several visualisation and analysis methods, providing fast and easy analysis of a set of genes. GeneBrowser fills the gap between Web portals that analyse one gene at a time and functional analysis tools that are limited in scope and usually desktop-based.

PMID: 20663121

The BioPAX community standard for pathway data sharing.

Object-oriented language for representing any kind of pathways. Structures are similar to that of gene ontology, and are very flexible. Origin of this language, its integration with a family of similar efforts, tools and programming libraries for integration of BioPAX capabilities. Given that major bioinformatics tools (Cytoscape, Visant, CellDesigner etc.) support it, and many plan to integrate it this in an excellent effort to comprehensive programming on pathway level.

Nat Biotechnol. 2010 Sep;28(9):935-42. Epub 2010 Sep 9.

The BioPAX community standard for pathway data sharing.

Multi-author group

[1] Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. [2] Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara, Turkey.

Abstract

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

PMID: 20829833

What is systems biology?

Description of the variety of systems biology definition. Three domains of systems biology – diversity, simplicity, complexity. Definition what is not systems biology, ex negativo. Connection of systems biology with syntetic biology – in a way of Richard Feynman’s famous quote “What I cannot build, I cannot understand”, systems and syntetic biologies go hand in hand to being able to understand biological systems on a level of reproducing them.

What is systems biology?

Rainer Breitling ^1,2*

• ¹ Faculty of Biomedical and Life Sciences, University of Glasgow, Scotland, UK
• ² Groningen Bioinformatics Centre, University of Groningen, Groningen, Netherlands

Systems biology is increasingly popular, but to many biologists it remains unclear what this new discipline actually encompasses. This brief personal perspective starts by outlining the asthetic qualities that motivate systems biologists, discusses which activities do not belong to the core of systems biology, and finally explores the crucial link with synthetic biology. It concludes by attempting to define systems biology as the research endeavor that aims at providing the scientific foundation for successful synthetic biology.

Integration and visualization of systems biology data in context of the genome.

Another genome browser, built in Java and interfaced with R, Perl, SQL. Discussion has an impressive list of a dosen of available browsers, besides UCSC. Main advantage – interoperability and integrative properties.

http://gaggle.systemsbiology.net/docs/geese/genomebrowser/

BMC Bioinformatics. 2010 Jul 19;11:382.

Integration and visualization of systems biology data in context of the genome.

Bare JC, Koide T, Reiss DJ, Tenenbaum D, Baliga NS.

Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.

Abstract

BACKGROUND: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment.

RESULTS: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data.A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome.

CONCLUSIONS: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

PMID: 20642854

Next-generation genomics: an integrative approach

Excellent overview of new technologies and ways of integrating them. Brief description of layers of data (transcriptome, epigenome, interactome), corresponding technologies, and examples how they can be integrated. Abundant discussion of sequencing and methods of analysis,, including tools. Suggested flow chart for data analysis (Figure 4). A must to get current update of technological state and what we have in front of us.

Nat Rev Genet. 2010 Jun 8;11(7):476-486. [Epub ahead of print]

Next-generation genomics: an integrative approach.

Hawkins RD, Hon GC, Ren B.

[1] Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653, USA. [2] These authors contributed equally to this work.

Abstract

Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods – including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing (RNA-seq) – acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.

PMID: 20531367

Next-generation genomics: an integrative approach.

Excellent overview of new technologies and ways of integrating them. Brief description of layers of data (transcriptome, epigenome, interactome), corresponding technologies, and examples how they can be integrated. Abundant discussion of sequencing and methods of analysis,, including tools.Suggested flow chart for data analysis (Figure 4). A must to get current update of technological state and what we have in front of us.

Nat Rev Genet. 2010 Jun 8;11(7):476-486. [Epub ahead of print]

Next-generation genomics: an integrative approach.

Hawkins RD, Hon GC, Ren B.

[1] Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653, USA. [2] These authors contributed equally to this work.

Abstract

Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods – including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing (RNA-seq) – acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.

PMID: 20531367

AACR centennial series: the biology of cancer metastasis: historical perspective.

 Timeline, hypotheses and discoveries on the path of cancer research. Steps of metastasis, involvement of other cells/organs/tissues. Role of microenvironment, signaling molecules and pathways. Very comprehensive.

Cancer Res. 2010 Jul 15;70(14):5649-69. Epub 2010 Jul 7.

AACR centennial series: the biology of cancer metastasis: historical perspective.

Talmadge JE, Fidler IJ.

The University of Nebraska Medical Center, Transplantation Immunology Laboratory, Omaha, Nebraska, USA.

Abstract

Metastasis resistant to therapy is the major cause of death from cancer. Despite almost 200 years of study, the process of tumor metastasis remains controversial. Stephen Paget initially identified the role of host-tumor interactions on the basis of a review of autopsy records. His “seed and soil” hypothesis was substantiated a century later with experimental studies, and numerous reports have confirmed these seminal observations. An improved understanding of the metastatic process and the attributes of the cells selected by this process is critical for the treatment of patients with systemic disease. In many patients, metastasis has occurred by the time of diagnosis, so metastasis prevention may not be relevant. Treating systemic disease and identifying patients with early disease should be our goal. Revitalized research in the past three decades has focused on new discoveries in the biology of metastasis. Even though our understanding of molecular events that regulate metastasis has improved, the contributions and timing of molecular lesion(s) involved in metastasis pathogenesis remain unclear. Review of the history of pioneering observations and discussion of current controversies should increase understanding of the complex and multifactorial interactions between the host and selected tumor cells that contribute to fatal metastasis and should lead to the design of successful therapy.

PMID: 20610625

An introduction to sample size and power

Simple and clear few pages to refresh some forgotten knowledge about power analysis, sample size, Type I and II errors etc. Elegant examples, straightforward definitions. It pays to read for learning such programs as PS: Power and Sample Size Calculation and G*Power 3. 

J Dev Behav Pediatr. 2008 Dec;29(6):516-22.

An introduction to sample size and power.

Bernstein BA.

Department of Pediatrics, St. Francis Hospital and Medical Center, University of Connecticut School of Medicine, Hartford, CT 06105, USA. BBernste@StFrancisCare.org

PMID: 19077847

Data vs. Hypothesis

Two great scientists, Robert Weinberg and Todd Golub, lay out their view of thich science we shall pursue in a recent Nature issue. Still, one does not contradict the other – from high-dimensional high-throughput data one can make applicable hypotheses.

Point: Hypotheses first

Counterpoint: Data first

Breast cancer stem cells revealed

A summary of an important finding of two markers, CD44 and CD24, distinguishing tumor initiating stem cells. CD44 up & CD24 down – stem cells, and vice versa. An important finding to eradicate cancer in its roots. Reference to the original paper is in this comment.

Proc Natl Acad Sci U S A. 2003 Apr 1;100(7):3547-9. Epub 2003 Mar 25.

Breast cancer stem cells revealed.

Dick JE.

Division of Cell and Molecular Biology, University Health Network, and Department of Molecular Genetics and Microbiology, University of Toronto, 620 University Avenue Toronto, ON, Canada M5G 2C1. jdick@uhnres.utoronto.ca

Comment on:

• Proc Natl Acad Sci U S A. 2003 Apr 1;100(7):3983-8.

 

PMID: 12657737

A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes.

Testing of commonly accepted view that differentially expressed genes regulated by common transcription factors. Very good introduction with references to studies addressing this hypothesis and to 8 tools built around it. They used oPOSSUM, Jaspar, and Weeder tools.

BMC Bioinformatics. 2010 May 20;11:267.

A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes.

Meng G, Mosig A, Vingron M.

CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, 200031, Shanghai, China. gfmeng@picb.ac.cn

Abstract

BACKGROUND: Observed co-expression of a group of genes is frequently attributed to co-regulation by shared transcription factors. This assumption has led to the hypothesis that promoters of co-expressed genes should share common regulatory motifs, which forms the basis for numerous computational tools that search for these motifs. While frequently explored for yeast, the validity of the underlying hypothesis has not been assessed systematically in mammals. This demonstrates the need for a systematic and quantitative evaluation to what degree co-expressed genes share over-represented motifs for mammals. RESULTS: We identified 33 experiments for human and mouse in the ArrayExpress Database where transcription factors were manipulated and which exhibited a significant number of differentially expressed genes. We checked for over-representation of transcription factor binding sites in up- or down-regulated genes using the over-representation analysis tool oPOSSUM. In 25 out of 33 experiments, this procedure identified the binding matrices of the affected transcription factors. We also carried out de novo prediction of regulatory motifs shared by differentially expressed genes. Again, the detected motifs shared significant similarity with the matrices of the affected transcription factors. CONCLUSIONS: Our results support the claim that functional regulatory motifs are over-represented in sets of differentially expressed genes and that they can be detected with computational methods.

PMID: 20487530

Q&A: Cancer: clues from cell metabolism.

Nature. 2010 Jun 3;465(7298):562-4.

Review in form of questions and answers, about metabolism and how cancer alters it.

Q&A: Cancer: clues from cell metabolism.

Kaelin WG Jr, Thompson CB.

PMID: 20520704