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Simon Kasif

Publications and source records attributed to Simon Kasif.

At least 19 recordsLinked to original sources
PubMed2007

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Algorithms↗
PubMed2006

Biological context networks: a mosaic view of the interactome.

Network models are a fundamental tool for the visualization and analysis of molecular interactions occurring in biological systems. While broadly illuminating the molecular machinery of the cell, graphical representations of protein interaction networks mask complex patterns of interaction that depend on temporal, spatial, or condition-specific contexts. In this paper, we introduce a novel graph construct called a biological context network that explicitly captures these changing patterns of interaction from one biological context to another. We consider known gene ontology biological process and cellular component annotations as a proxy for context, and show that aggregating small process-specific protein interaction sub-networks leads to the emergence of observed scale-free properties. The biological context model also provides the basis for characterizing proteins in terms of several context-specific measures, including 'interactive promiscuity,' which identifies proteins whose interacting partners vary from one context to another. We show that such context-sensitive measures are significantly better predictors of knockout lethality than node degree, reaching better than 70% accuracy among the top scoring proteins.

Protein Interaction Mapping↗
PubMed2006

Towards the identification of essential genes using targeted genome sequencing and comparative analysis.

BACKGROUND: The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value. RESULTS: We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature. CONCLUSION: Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability.

Animals↗
PubMed2006

The complete genome sequence of a dog: a perspective.

A complete, high-quality reference sequence of a dog genome was recently produced by a team of researchers led by the Broad Institute, achieving another major milestone in deciphering the genomic landscape of mammalian organisms. The genome sequence provides an indispensable resource for comparative analysis and novel insights into dog and human evolution and history. Together with the survey sequence of a poodle previously published in 2003, the two dog genome sequences allowed identification of more than 2.5 million single nucleotide polymorphisms within and between dog breeds, which can be used in evolutionary analysis, behavioral studies and disease gene mapping.(1)

Animals↗
PubMed2005

Genes involved in complex adaptive processes tend to have highly conserved upstream regions in mammalian genomes.

BACKGROUND: Recent advances in genome sequencing suggest a remarkable conservation in gene content of mammalian organisms. The similarity in gene repertoire present in different organisms has increased interest in studying regulatory mechanisms of gene expression aimed at elucidating the differences in phenotypes. In particular, a proximal promoter region contains a large number of regulatory elements that control the expression of its downstream gene. Although many studies have focused on identification of these elements, a broader picture on the complexity of transcriptional regulation of different biological processes has not been addressed in mammals. The regulatory complexity may strongly correlate with gene function, as different evolutionary forces must act on the regulatory systems under different biological conditions. We investigate this hypothesis by comparing the conservation of promoters upstream of genes classified in different functional categories. RESULTS: By conducting a rank correlation analysis between functional annotation and upstream sequence alignment scores obtained by human-mouse and human-dog comparison, we found a significantly greater conservation of the upstream sequence of genes involved in development, cell communication, neural functions and signaling processes than those involved in more basic processes shared with unicellular organisms such as metabolism and ribosomal function. This observation persists after controlling for G+C content. Considering conservation as a functional signature, we hypothesize a higher density of cis-regulatory elements upstream of genes participating in complex and adaptive processes. CONCLUSION: We identified a class of functions that are associated with either high or low promoter conservation in mammals. We detected a significant tendency that points to complex and adaptive processes were associated with higher promoter conservation, despite the fact that they have emerged relatively recently during evolution. We described and contrasted several hypotheses that provide a deeper insight into how transcriptional complexity might have been emerged during evolution.

Animals↗
PubMed2005

Phylogenetic detection of conserved gene clusters in microbial genomes.

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.

Algorithms↗
PubMed2005

Less is more: towards an optimal universal description of protein folds.

MOTIVATION: Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as 'protein parts' to describe fold-sized structures. RESULTS: The dictionary was constructed by aligning representative structures of all known folds, clustering similar substructures and selecting the most descriptive substructures in a minimum description length fashion. We show that the dictionary is compact and descriptive, capable of describing a substantial fraction of all known protein folds. We performed simulations using independent sets of training and testing folds. Dictionaries generated using the training set had high coverage over the folds in the testing set, suggesting that dictionary entries reflect general features of protein structures and should be capable of describing novel protein folds.

Algorithms↗
PubMed2005

Computational tradeoffs in multiplex PCR assay design for SNP genotyping.

BACKGROUND: Multiplex PCR is a key technology for detecting infectious microorganisms, whole-genome sequencing, forensic analysis, and for enabling flexible yet low-cost genotyping. However, the design of a multiplex PCR assays requires the consideration of multiple competing objectives and physical constraints, and extensive computational analysis must be performed in order to identify the possible formation of primer-dimers that can negatively impact product yield. RESULTS: This paper examines the computational design limits of multiplex PCR in the context of SNP genotyping and examines tradeoffs associated with several key design factors including multiplexing level (the number of primer pairs per tube), coverage (the % of SNP whose associated primers are actually assigned to one of several available tube), and tube-size uniformity. We also examine how design performance depends on the total number of available SNPs from which to choose, and primer stringency criterial. We show that finding high-multiplexing/high-coverage designs is subject to a computational phase transition, becoming dramatically more difficult when the probability of primer pair interaction exceeds a critical threshold. The precise location of this critical transition point depends on the number of available SNPs and the level of multiplexing required. We also demonstrate how coverage performance is impacted by the number of available snps, primer selection criteria, and target multiplexing levels. CONCLUSION: The presence of a phase transition suggests limits to scaling Multiplex PCR performance for high-throughput genomics applications. Achieving broad SNP coverage rapidly transitions from being very easy to very hard as the target multiplexing level (# of primer pairs per tube) increases. The onset of a phase transition can be "delayed" by having a larger pool of SNPs, or loosening primer selection constraints so as to increase the number of candidate primer pairs per SNP, though the latter may produce other adverse effects. The resulting design performance tradeoffs define a benchmark that can serve as the basis for comparing competing multiplex PCR design optimization algorithms and can also provide general rules-of-thumb to experimentalists seeking to understand the performance limits of standard multiplex PCR.

Algorithms↗
PubMed2005

MuPlex: multi-objective multiplex PCR assay design.

We have developed a web-enabled system called MuPlex that aids researchers in the design of multiplex PCR assays. Multiplex PCR is a key technology for an endless list of applications, including detecting infectious microorganisms, whole-genome sequencing and closure, forensic analysis and for enabling flexible yet low-cost genotyping. However, the design of a multiplex PCR assays is computationally challenging because it involves tradeoffs among competing objectives, and extensive computational analysis is required in order to screen out primer-pair cross interactions. With MuPlex, users specify a set of DNA sequences along with primer selection criteria, interaction parameters and the target multiplexing level. MuPlex designs a set of multiplex PCR assays designed to cover as many of the input sequences as possible. MuPlex provides multiple solution alternatives that reveal tradeoffs among competing objectives. MuPlex is uniquely designed for large-scale multiplex PCR assay design in an automated high-throughput environment, where high coverage of potentially thousands of single nucleotide polymorphisms is required. The server is available at http://genomics14.bu.edu:8080/MuPlex/MuPlex.html.

Algorithms↗
PubMed2005

GEMS: a web server for biclustering analysis of expression data.

The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Biclustering of gene expression data (also called co-clustering or two-way clustering) is a non-trivial but promising methodology for the identification of gene groups that show a coherent expression profile across a subset of conditions. Thus, biclustering is a natural methodology as a screen for genes that are functionally related, participate in the same pathways, affected by the same drug or pathological condition, or genes that form modules that are potentially co-regulated by a small group of transcription factors. We have developed a web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data. Users may upload expression data and specify a set of criteria. GEMS then performs bicluster mining based on a Gibbs sampling paradigm. The web server provides a flexible and an useful platform for the discovery of co-expressed and potentially co-regulated gene modules. GEMS is an open source software and is available at http://genomics10.bu.edu/terrence/gems/.

Algorithms↗
PubMed2005

Characterization of two new aminopeptidases in Escherichia coli.

Two genes in the Escherichia coli genome, ypdE and ypdF, have been cloned and expressed, and their products have been purified. YpdF is shown to be a metalloenzyme with Xaa-Pro aminopeptidase activity and limited methionine aminopeptidase activity. Genes homologous to ypdF are widely distributed in bacterial species. The unique feature in the sequences of the products of these genes is a conserved C-terminal domain and a variable N-terminal domain. Full or partial deletion of the N terminus in YpdF leads to the loss of enzymatic activity. The conserved C-terminal domain is homologous to that of the methionyl aminopeptidase (encoded by map) in E. coli. However, YpdF and Map differ in their preference for the amino acid next to the initial methionine in the peptide substrates. The implication of this difference is discussed. ypdE is the immediate downstream gene of ypdF, and its start codon overlaps with the stop codon of ypdF by 1 base. YpdE is shown to be a metalloaminopeptidase and has a broad exoaminopeptidase activity.

Amino Acid Sequence↗
PubMed2004

Identification of genes with fast-evolving regions in microbial genomes.

Complete sequences of multiple strains of the same microbial species provide an invaluable source for studying the evolutionary dynamics between orthologous genes over a relatively short time scale. Usually the intensity of the selection pressure is inferred from a comparison between the nonsynonymous substitution rate and the synonymous substitution rate. In this paper, we propose an alternative method for detecting genes with one or more fast-evolving regions from pairwise comparisons of orthologous genes. Our method looks for regions with overrepresented nonsynonymous mutations along the alignment, and requires a higher nonsynonymous evolution rate in those regions than the neutral evolution rate. It identifies gene targets under intensive selection pressure that are not detected from the conventional rate comparison analysis. For those identified genes with known annotations, most of them have a clear role in processes such as bacterial defense and host-pathogen interactions. Gene sets reported from our method provide a measure of the phenotypic divergence between two closely related genomes.

Amino Acid Sequence↗
PubMed2004

GC/AT-content spikes as genomic punctuation marks.

Large-scale analysis of the GC-content distribution at the gene level reveals both common features and basic differences in genomes of different groups of species. Sharp changes in GC content are detected at the transcription boundaries for all species analyzed, including human, mouse, rat, chicken, fruit fly, and worm. However, two substantially distinct groups of GC-content profiles can be recognized: warm-blooded vertebrates including human, mouse, rat, and chicken, and invertebrates including fruit fly and worm. In vertebrates, sharp positive and negative spikes of GC content are observed at the transcription start and stop sites, respectively, and there is also a progressive decrease in GC content from the 5' untranslated region to the 3' untranslated region along the gene. In invertebrates, the positive and negative GC-content spikes at the transcription start and stop sites are preceded by spikes of opposite value, and the highest GC content is found in the coding regions of the genes. Cross-correlation analysis indicates high frequencies of GC-content spikes at transcription start and stop sites. The strong conservation of this genomic feature seen in comparisons of the human/mouse and human/rat orthologs, and the clustering of genes with GC-content spikes on chromosomes imply a biological function. The GC-content spikes at transcription boundaries may reflect a general principle of genomic punctuation. Our analysis also provides means for identifying these GC-content spikes in individual genomic sequences.

Adenosine↗
PubMed2004

Segmentally variable genes: a new perspective on adaptation.

Genomic sequence variation is the hallmark of life and is key to understanding diversity and adaptation among the numerous microorganisms on earth. Analysis of the sequenced microbial genomes suggests that genes are evolving at many different rates. We have attempted to derive a new classification of genes into three broad categories: lineage-specific genes that evolve rapidly and appear unique to individual species or strains; highly conserved genes that frequently perform housekeeping functions; and partially variable genes that contain highly variable regions, at least 70 amino acids long, interspersed among well-conserved regions. The latter we term segmentally variable genes (SVGs), and we suggest that they are especially interesting targets for biochemical studies. Among these genes are ones necessary to deal with the environment, including genes involved in host-pathogen interactions, defense mechanisms, and intracellular responses to internal and environmental changes. For the most part, the detailed function of these variable regions remains unknown. We propose that they are likely to perform important binding functions responsible for protein-protein, protein-nucleic acid, or protein-small molecule interactions. Discerning their function and identifying their binding partners may offer biologists new insights into the basic mechanisms of adaptation, context-dependent evolution, and the interaction between microbes and their environment.

Acclimatization↗
PubMed2004

Whole-genome annotation by using evidence integration in functional-linkage networks.

The advent of high-throughput biology has catalyzed a remarkable improvement in our ability to identify new genes. A large fraction of newly discovered genes have an unknown functional role, particularly when they are specific to a particular lineage or organism. These genes, currently labeled "hypothetical," might support important biological cell functions and could potentially serve as targets for medical, diagnostic, or pharmacogenomic studies. An important challenge to the scientific community is to associate these newly predicted genes with a biological function that can be validated by experimental screens. In the absence of sequence or structural homology to known genes, we must rely on advanced biotechnological methods, such as DNA chips and protein-protein interaction screens as well as computational techniques to assign putative functions to these genes. In this article, we propose an effective methodology for combining biological evidence obtained in several high-throughput experimental screens and integrating this evidence in a way that provides consistent functional assignments to hypothetical genes. We use the visualization method of propagation diagrams to illustrate the flow of functional evidence that supports the functional assignments produced by the algorithm. Our results contain a number of predictions and furnish strong evidence that integration of functional information is indeed a promising direction for improving the accuracy and robustness of functional genomics.

Chromosome Mapping↗
PubMed2004

Identification of transcription factor binding sites upstream of human genes regulated by the phosphatidylinositol 3-kinase and MEK/ERK signaling pathways.

We have taken an integrated approach in which expression profiling has been combined with the use of small molecule inhibitors and computational analysis of transcription factor binding sites to characterize regulatory sequences of genes that are targets of specific signaling pathways in growth factor-stimulated human cells. T98G cells were stimulated with platelet-derived growth factor (PDGF) and analyzed by DNA microarrays, which identified 74 immediate-early gene transcripts. Cells were then treated with inhibitors to identify subsets of genes that are targets of the phosphatidylinositol 3-kinase (PI3K) and MEK/ERK signaling pathways. Four groups of PDGF-induced genes were defined: independent of PI3K and MEK/ERK signaling, dependent on PI3K signaling, dependent on MEK/ERK signaling, and dependent on both pathways. The upstream regions of all genes in the four groups were scanned using TRANSFAC for putative cis-elements as compared with a background set of non-induced genes. Binding sites for 18 computationally predicted transcription factors were over-represented in the four groups of co-expressed genes compared with the background sequences (p < 0.01). Many of the cis-elements identified were conserved in orthologous mouse genes, and many of the predicted elements and their cognate transcription factors were consistent with previous experimental data. In addition, chromatin immunoprecipitation assays experimentally verified nine predicted SRF binding sites in T98G cells, including a previously unknown SRF site upstream of DUSP5. These results indicate that groups of human genes regulated by discrete intracellular signaling pathways share common cis-regulatory elements.

Animals↗
PubMed2004

topoSNP: a topographic database of non-synonymous single nucleotide polymorphisms with and without known disease association.

The database of topographic mapping of Single Nucleotide Polymorphism (topoSNP) provides an online resource for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. The current release also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data. The database will be updated at regular intervals and can be accessed at: http://gila.bioengr.uic.edu/snp/toposnp.

Computational Biology↗