It is remarkable how many students, young faculty, and even senior faculty hanker after a Nobel Prize. Somehow, they think that it is possible to structure their scientific careers so that the culmination will bring this much sought-after honor. Some even think that as a Nobel laureate myself, I may have the key to success—some secrets that I can share and so greatly improve their odds of success. Unfortunately, I must begin by disappointing everyone. There is only one path that should be followed. It is summed up in Rule 1, but some of the other Rules may prove helpful—or if not helpful, then at least amusing. 1. Never Start Your Career by Aiming for a Nobel Prize Don’t even hope for it or think about it. Just focus on doing the very best science that you can. Ask good questions, use innovative methods to answer them, and look for those unexpected results that may reveal some unexpected aspect of nature. If you are successful in your research career, then you will make lots of discoveries and have a very happy life. If you are lucky, you will make a big discovery that may even bag you a prize or two. But only if you are extraordinarily lucky will you stand any chance of winning a Nobel Prize. They are very elusive.

BACKGROUND: Microbial genomes contain an abundance of genes with conserved proximity forming clusters on the chromosome. However, the conservation can be a result of many factors such as vertical inheritance, or functional selection. Thus, identification of conserved gene clusters that are under functional selection provides an effective channel for gene annotation, microarray screening, and pathway reconstruction. The problem of devising a robust method to identify these conserved gene clusters and to evaluate the significance of the conservation in multiple genomes has a number of implications for comparative, evolutionary and functional genomics as well as synthetic biology. RESULTS: In this paper we describe a new method for detecting conserved gene clusters that incorporates the information captured by a genome phylogenetic tree. We show that our method can overcome the common problem of overestimation of significance due to the bias in the genome database and thereby achieve better accuracy when detecting functionally connected gene clusters. Our results can be accessed at database GeneChords http://genomics10.bu.edu/GeneChords. CONCLUSION: The methodology described in this paper gives a scalable framework for discovering conserved gene clusters in microbial genomes. It serves as a platform for many other functional genomic analyses in microorganisms, such as operon prediction, regulatory site prediction, functional annotation of genes, evolutionary origin and development of gene clusters.

Abstract A brief history of the early days of publishing in the bioinformatics field is presented. Contact: [email protected]

No abstract available.

VISA (VIsual Sequence Analysis) is a software package that displays global similarities within a set of related protein sequences. The program identifies amino acid patterns that are common to many members of the set of sequences and displays them as a series of histograms. Individual peaks on the display can be assigned a color and analogous peaks in the other sequences are then automatically marked in the same color. This can be repeated for each significant peak and leads to a display in which major matching segments of multiple amino acid sequences appear as dominant peaks of the histograms with matching colors. These peaks usually correspond to the conserved sequence motifs that are characteristic of particular proteins. An extensive set of software tools is included to help the localization, visualization and analysis of the global similarities displayed. VISA provides a graphic overview of the sequence similarity that can help to understand the architecture of the protein family and can be helpful while designing experiments to probe function.