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Charles DeLisi

Publications and source records attributed to Charles DeLisi.

24 records · Page 2Linked to original sources

The society of genes: networks of functional links between genes from comparative genomics.

BACKGROUND: Comparative genomics provides at least three methods beyond traditional sequence similarity for identifying functional links between genes: the examination of common phylogenetic distributions, the analysis of conserved proximity along the chromosomes of multiple genomes, and observations of fusions of genes into a multidomain gene in another organism. We have previously generated the links according to each of these methods individually for 43 known microbial genomes. Here we combine these results to construct networks of functional associations. RESULTS: We show that the functional networks obtained by applying these methods have different topologies and that the information they provide is largely additive. In particular, the combined networks of functional links contain an average of 57% of an organism's complete genetic complement, uncover substantial portions of known pathways, and suggest the function of previously unannotated genes. In addition, the combined networks are qualitatively different from the networks obtained using individual methods. They have a dominant cluster that contains approximately 80%-90% of the genes, independent of genome size, and the dominant clusters show the small world behavior expected of a biological system, with global connectivity that is nearly random, and local properties that are highly ordered. CONCLUSIONS: When the information on functional linkage provided by three emerging computational methods is combined, the integrated network uncovers large numbers of conserved pathways and identifies clusters of functionally related genes. It therefore shows considerable utility and promise as a tool for understanding genomic structure, and for guiding high throughput experimental investigations.

Chromosome Mapping↗

A computational framework for optimal masking in the synthesis of oligonucleotide microarrays.

High-throughput genomic technologies are revolutionizing modern biology. In particular, DNA microarrays have become one of the most powerful tools for profiling global mRNA expression in different tissues and environmental conditions, and for detecting single nucleotide polymorphisms. The broad applicability of gene expression profiling to the biological and medical realms has generated expanding demand for mass production of microarrays, which in turn has created considerable interest in improving the cost effectiveness of microarray fabrication techniques. We have developed the computational framework for an optimal synthesis strategy for oligonucleotide microarrays. The problem was introduced by Hubbell et al. Here, we formalize the problem, obtain precise bounds on its complexity and devise several computational solutions.

Algorithms↗

Predictome: a database of putative functional links between proteins.

The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods--chromosomal proximity, phylogenetic profiling and domain fusion--and large-scale experimental screenings of protein-protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.

Animals↗

Protein therapeutics: promises and challenges for the 21st century.

Recent advances in massively parallel experimental and computational technologies are leading to radically new approaches to the early phases of the drug production pipeline. The revolution in DNA microarray technologies and the imminent emergence of its analogue for proteins, along with machine learning algorithms, promise rapid acceleration in the identification of potential drug targets, and in high-throughput screens for subpopulation-specific toxicity. Similarly, advances in structural genomics in conjunction with in vitro and in silico evolutionary methods will rapidly accelerate the number of lead drug candidates and substantially augment their target specificity. Taken collectively, these advances will usher in an era of predictive medicine, which will move medical practice from reactive therapy after disease onset, to proactive prevention.

Animals↗

Identifying functional links between genes using conserved chromosomal proximity.

Conservation of proximity of a pair of genes across multiple genomes generally indicates that their functions could be linked. Here, we present a systematic evaluation using 42 complete microbial genomes from 25 phylogenetic groups to test the reliability of this observation in predicting function for genes. We find a relationship between the number of phylogenetic groups in which a gene pair is proximate and the probability that the pair belongs to a common pathway. Our method produces 1586 links between ortholog families substantiated by observed proximity in genomes representing at least three phylogenetic groups. Of the pairs annotated in the KEGG database, 80% are in the same biological pathway in KEGG.

Chromosome Mapping↗

Condition specific transcription factor binding site characterization in Saccharomyces cerevisiae.

MOTIVATION: We demonstrate a computational process by which transcription factor binding sites can be elucidated using genome-wide expression and binding profiles. The profiles direct us to the intergenic locations likely to contain the promoter regions for a given factor. These sequences are multiply and locally aligned to give an anchor motif from which further characterization can take place. RESULTS: We present bases for and assumptions about the variability within these motifs which give rise to potentially more accurate motifs, capture complex binding sites built upon the basis motif, and eliminate the constraints of the currently employed promoter searching protocols. We also present a measure of motif quality based on the occurrence of the putative motifs in regions observed to contain the binding sites. The assumptions, motif generation, quality assessment and comparison allow the user as much control as their a priori knowledge allows. AVAILABILITY: IGRDB and the datasets mentioned herein are available at http://chipdb.wi.mit.edu/

Amino Acid Motifs↗