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The current focus of many functional genomic studies is on the elucidation of gene regulatory networks. The functional analyses of transcription factors and their DNA-binding sites, in conjunction with genome-wide expression profiling, are crucial in understanding of gene regulatory networks. This paper describes an efficient and easy method for characterizing the DNA-binding sequence specificity of novel plant transcription factors. This new method is based on the fusion of a DNA-binding protein (DBP) to 6×His-tagged cellulase D (CELD), which serves both as a means for affinity purification of DBP-DNA complex in the selection of binding sites from a pool of biotinylated random-sequence oligonucleotides and as a reporter for measurement of DNA-binding activity. Thus, it eliminates the use of radioactivity and gel electrophoresis techniques currently used for purification of DBP-DNA complexes and assays of DNA-binding activity. The effectiveness of this method was demonstrated by the success of simultaneous selection of the binding sites of nine plant DBPs from four superfamilies (AP2, bHLH, NAC and MYB). The high-throughput capacity of CELD-based DNA-binding assays allows the quantitative analysis of the binding sequence specificity from a large number of DBP-selected oligonucleotides. The binding sequence specificity of three novel transcription factors (rice OsbHLH66, wheat TaNAC69 and TaMYB80), determined with this method, is presented. This new method provides the capacity of high-throughput analysis on the DNA-binding sequence specificity of a large number of putative transcription factors, predicted on the basis of conserved DNA-binding domains. [ABSTRACT FROM AUTHOR]

The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell-context–specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high-throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities. [ABSTRACT FROM AUTHOR]

The field of proteomics is developing at a rapid pace in the post-genome era. Translational proteomics investigations aim to apply a combination of established methods and new technologies to learn about protein expression profiles predictive of clinical events, therapeutic response, and underlying mechanisms. However, in contrast to genetic studies and in parallel with gene expression studies, the dynamic nature of the proteome in conjunction with the challenges of accounting for post-translational modifications requires the translational proteomics investigator to understand the strengths and limitations of proteomics approaches. In this review, we provide an overview of proteomics approaches and techniques, and proteomics informatics for clinical transplantation investigators. We also review recent publications pertaining to transplantation proteomics, and discuss the implications and utility of urine proteomics for non-invasive investigation of transplant outcomes. [ABSTRACT FROM AUTHOR]

In June 2001, when the Human Genome Project and Celera completed the first maps of the human genome, Francis Collins, head of the government sponsored HGP, warned that only then would the real race begin. Proteomics is more complex by several orders of magnitude than genomics, with no one company, laboratory or consortium remotely able to run the race alone, acknowledged Brian D. Chait, head of the mass spectrometry laboratory at Rockefeller University in New York City. With the publication of the draft of the human genome in the February 16, 2001 issues of Science and Nature came what many had already suspected: instead of the earlier estimate of about 100 000 human genes, the actual count reduced this figure by 75 percent.