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Biomedical subjects

Ron Shamir

Publications and source records attributed to Ron Shamir.

At least 19 recordsLinked to original sources
PubMed2025

Integrating mutation, copy number, and gene expression data to identify driver genes of recurrent chromosome-arm losses.

Aneuploidy is a hallmark of cancer, yet the genes driving recurrent chromosome-arm losses remain largely unknown. We present a systematic framework integrating mutation, copy number, and gene expression data to identify candidate driver genes of cancer type-specific recurrent chromosome-arm losses across 20 cancer types, using ∼7,500 tumors from The Cancer Genome Atlas. By analyzing focal deletions and point mutations that co-occur, or are mutually exclusive, with chromosome-arm losses, we pinpoint 322 candidate drivers associated with 159 recurring events. Our approach identifies known aneuploidy drivers such as TP53 and PTEN, while revealing multiple additional candidates, including tumor suppressors not previously linked to aneuploidy. We leverage expression changes associated with chromosome-arm losses to propose cancer-promoting pathway-level alterations. Integrating these findings highlights key candidate drivers that underlie the observed expression alterations, reinforcing their biological relevance. We provide a comprehensive catalog of candidate driver genes for recurrently lost chromosome-arms in human cancer.

Humans↗
PubMed2006

Assessing the number of ancestral alternatively spliced exons in the human genome.

BACKGROUND: It is estimated that between 35% and 74% of all human genes undergo alternative splicing. However, as a gene that undergoes alternative splicing can have between one and dozens of alternative exons, the number of alternatively spliced genes by itself is not informative enough. An additional parameter, which was not addressed so far, is therefore the number of human exons that undergo alternative splicing. We have previously described an accurate machine-learning method allowing the detection of conserved alternatively spliced exons without using ESTs, which relies on specific features of the exon and its genomic vicinity that distinguish alternatively spliced exons from constitutive ones. RESULTS: In this study we use the above-described approach to calculate that 7.2% (+/- 1.1%) of all human exons that are conserved in mouse are alternatively spliced in both species. CONCLUSION: This number is the first estimation for the extent of ancestral alternatively spliced exons in the human genome.

Algorithms↗
PubMed2006

Quantification of protein half-lives in the budding yeast proteome.

A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the half-life of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.

Fungal Proteins↗
PubMed2006

E2F1 identified by promoter and biochemical analysis as a central target of glioblastoma cell-cycle arrest in response to Ras inhibition.

Active Ras contributes to the malignant phenotype of glioblastoma multiforme. Recent studies showed that the Ras inhibitor farnesyl thiosalicylic acid downregulates the transcription factor hypoxia-inducible factor-1alpha, causing shutdown of glycolysis in U87 glioblastoma cells. Farnesyl thiosalicylic acid also inhibited the growth of U87 cells. The way in which Ras inhibition affects U87 cell proliferation was not clear. Here we applied a computational method in which gene expression profile clustering is combined with promoter sequence analysis to obtain global dissection of the transcriptional response to farnesyl thiosalicylic acid in U87 cells. The analysis revealed a prominent Ras-dependent cell-cycle arrest response, in which a major component is highly enriched for the binding-site signature of the transcription factor E2F1. Electrophoretic mobility shift assays together with E2F-luciferase reporter assays showed that E2F1 was inactivated by the Ras inhibitor. Inhibition of Ras by farnesyl thiosalicylic acid promoted proteasomal degradation of cyclin D1, with a concomitant decrease in phosphorylated retinoblastoma protein accompanied by downregulation of E2F1 and decreased expression of key E2F1-regulated genes critical for cell-cycle progression. U87 cell growth arrest induced by farnesyl thiosalicylic acid was overridden by constitutive expression of E2F1. Thus, downregulation of E2F1 and of hypoxia-inducible factor-1alpha represents 2 distinct arms of the antioncogenic effect of Ras inhibitors in glioblastoma.

Cell Cycle↗
PubMed2006

A fast method for computing high-significance disease association in large population-based studies.

Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000-100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable.

Algorithms↗
PubMed2006

A specific RAD51 haplotype increases breast cancer risk in Jewish non-Ashkenazi high-risk women.

While the precise genes involved in determining familial breast cancer risk in addition to BRCA1/2 are mostly unknown, one strong candidate is RAD51. Jewish non-Ashkenazi women at high-risk for breast/ovarian cancer and ethnically matched controls were genotyped using four single nucleotide polymorphisms spanning the RAD51 genomic region, and the resulting haplotypes were constructed using the GERBIL algorithm. A total of 314 individuals were genotyped: 184 non-Ashkenazi high-risk women (119 with breast cancer), and 130 unaffected, average-risk ethnically matched controls. Using GEBRIL, three frequent haplotypes were constructed. One of the haplotypes (TGTA - coined haplotype 3) was present in 7.3% (19/260 haplotypes) of controls (n=130) and in 16.8% (40/238 haplotypes) of high-risk breast cancer patients (n=119, P=0.001). A specific RAD51 haplotype is more prevalent among non-Ashkenazi Jewish high-risk women than in average-risk population.

Breast Neoplasms↗
PubMed2006

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.

Cell Physiological Phenomena↗
PubMed2005

Deciphering transcriptional regulatory elements that encode specific cell cycle phasing by comparative genomics analysis.

Transcriptional regulation is a major tier in the periodic engine that mobilizes cell cycle progression. The availability of complete genome sequences of multiple organisms holds promise for significantly improving the specificity of computational identification of functional elements. Here, we applied a comparative genomics analysis to decipher transcriptional regulatory elements that control cell cycle phasing. We analyzed genome-wide promoter sequences from 12 organisms, including worm, fly, fish, rodents and human, and identified conserved transcriptional modules that determine the expression of genes in specific cell cycle phases. We demonstrate that a canonical E2F signal encodes for expression highly specific to the G1/S phase, and that a cis-regulatory module comprising CHR-NF-Y elements dictates expression that is restricted to the G2 and G2/M phases. B-Myb binding site signatures occur in many of the CHR-NF-Y target genes, suggesting a specific role for this triplet in the regulation of the cell cycle transcriptional program. Remarkably, E2F signals are conserved in promoters of G1/S genes in all organisms from worm to human. The CHR-NF-Y module is conserved in promoters of G2/M regulated genes in all analyzed vertebrates. Our results reveal novel modules that determine specific cell cycle phasing, and identify their respective putative target genes with remarkably high specificity.

Animals↗
PubMed2005

A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs.

BACKGROUND: In recent years, intensive computational efforts have been directed towards the discovery of promoter motifs that correlate with mRNA expression profiles. Nevertheless, it is still not always possible to predict steady-state mRNA expression levels based on promoter signals alone, suggesting that other factors may be involved. Other genic regions, in particular 3' UTRs, which are known to exert regulatory effects especially through controlling RNA stability and localization, were less comprehensively investigated, and deciphering regulatory motifs within them is thus crucial. RESULTS: By analyzing 3' UTR sequences and mRNA decay profiles of Saccharomyces cerevisiae genes, we derived a catalog of 53 sequence motifs that may be implicated in stabilization or destabilization of mRNAs. Some of the motifs correspond to known RNA-binding protein sites, and one of them may act in destabilization of ribosome biogenesis genes during stress response. In addition, we present for the first time a catalog of 23 motifs associated with subcellular localization. A significant proportion of the 3' UTR motifs is highly conserved in orthologous yeast genes, and some of the motifs are strikingly similar to recently published mammalian 3' UTR motifs. We classified all genes into those regulated only at transcription initiation level, only at degradation level, and those regulated by a combination of both. Interestingly, different biological functionalities and expression patterns correspond to such classification. CONCLUSION: The present motif catalogs are a first step towards the understanding of the regulation of mRNA degradation and subcellular localization, two important processes which--together with transcription regulation--determine the cell transcriptome.

3' Untranslated Regions↗
PubMed2005

EXPANDER--an integrative program suite for microarray data analysis.

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander.

Algorithms↗
PubMed2005

Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast.

Transcriptional modules of coregulated genes play a key role in regulatory networks. Comparative studies show that modules of coexpressed genes are conserved across taxa. However, little is known about the mechanisms underlying the evolution of module regulation. Here, we explore the evolution of cis-regulatory programs associated with conserved modules by integrating expression profiles for two yeast species and sequence data for a total of 17 fungal genomes. We show that although the cis-elements accompanying certain conserved modules are strictly conserved, those of other conserved modules are remarkably diverged. In particular, we infer the evolutionary history of the regulatory program governing ribosomal modules. We show how a cis-element emerged concurrently in dozens of promoters of ribosomal protein genes, followed by the loss of a more ancient cis-element. We suggest that this formation of an intermediate redundant regulatory program allows conserved transcriptional modules to gradually switch from one regulatory mechanism to another while maintaining their functionality. Our work provides a general framework for the study of the dynamics of promoter evolution at the level of transcriptional modules and may help in understanding the evolvability and increased redundancy of transcriptional regulation in higher organisms.

Computational Biology↗
PubMed2005

Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

BACKGROUND: Gene-expression microarrays and RNA interferences (RNAi) are among the most prominent techniques in functional genomics. The combination of the two holds promise for systematic, large-scale dissection of transcriptional networks. Recent studies, however, raise the concern that nonspecific responses to small interfering RNAs (siRNAs) might obscure the consequences of silencing the gene of interest, throwing into question the ability of this experimental strategy to achieve precise network dissections. RESULTS: We used microarrays and RNAi to dissect a transcriptional network induced by DNA damage in a human cellular system. We recorded expression profiles with and without exposure of the cells to a radiomimetic drug that induces DNA double-strand breaks (DSBs). Profiles were measured in control cells and in cells knocked-down for the Rel-A subunit of NFkappaB and for p53, two pivotal stress-induced transcription factors, and for the protein kinase ATM, the major transducer of the cellular responses to DSBs. We observed that NFkappaB and p53 mediated most of the damage-induced gene activation; that they controlled the activation of largely disjoint sets of genes; and that ATM was required for the activation of both pathways. Applying computational promoter analysis, we demonstrated that the dissection of the network into ATM/NFkappaB and ATM/p53-mediated arms was highly accurate. CONCLUSIONS: Our results demonstrate that the combined experimental strategy of expression arrays and RNAi is indeed a powerful method for the dissection of complex transcriptional networks, and that computational promoter analysis can provide a strong complementary means for assessing the accuracy of this dissection.

Ataxia Telangiectasia Mutated Proteins↗
PubMed2005

A global view of pleiotropy and phenotypically derived gene function in yeast.

Pleiotropy, the ability of a single mutant gene to cause multiple mutant phenotypes, is a relatively common but poorly understood phenomenon in biology. Perhaps the greatest challenge in the analysis of pleiotropic genes is determining whether phenotypes associated with a mutation result from the loss of a single function or of multiple functions encoded by the same gene. Here we estimate the degree of pleiotropy in yeast by measuring the phenotypes of 4710 mutants under 21 environmental conditions, finding that it is significantly higher than predicted by chance. We use a biclustering algorithm to group pleiotropic genes by common phenotype profiles. Comparisons of these clusters to biological process classifications, synthetic lethal interactions, and protein complex data support the hypothesis that this method can be used to genetically define cellular functions. Applying these functional classifications to pleiotropic genes, we are able to dissect phenotypes into groups associated with specific gene functions.

Algorithms↗
PubMed2005

Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium.

Biological systems are orchestrated by heterogeneous regulatory programs that control complex processes and adapt to a dynamic environment. Recent advances in high-throughput experimental methods provide genome-wide perspectives on such regulatory programs. A considerable amount of data on the behavior of model systems in a variety of conditions is rapidly accumulating. Still, the dominant paradigm is to analyze new genome-wide experiments separately from any other extant data, for example, by clustering the new data alone. Here we introduce a new methodology for analyzing the results of a new functional genomic study vis-à-vis a large compendium of previously published results from heterogeneous experimental techniques. We demonstrate our methodology on Saccharomyces cerevisiae, using a compendium of some 2000 experiments from 60 different publications. Most importantly, we show how the integrated analysis reveals unexpected connections among biological processes, and differentiates between novel and known effects in the analyzed experiments. Such characterization is impossible when new data sets are studied in isolation. Our results exemplify the power of the integrative approach in the analysis of genomic scale data sets and call for a paradigm shift in their study.

Algorithms↗
PubMed2005

A block-free hidden Markov model for genotypes and its application to disease association.

We present a new stochastic model for genotype generation. The model offers a compromise between rigid block structure and no structure altogether: It reflects a general blocky structure of haplotypes, but also allows for "exchange" of haplotypes at nonboundary SNP sites; it also accommodates rare haplotypes and mutations. We use a hidden Markov model and infer its parameters by an expectation-maximization algorithm. The algorithm was implemented in a software package called HINT (haplotype inference tool) and tested on 58 datasets of genotypes. To evaluate the utility of the model in association studies, we used biological human data to create a simple disease association search scenario. When comparing HINT to three other models, HINT predicted association most accurately.

Algorithms↗
PubMed2005

The degenerate primer design problem: theory and applications.

A PCR primer sequence is called degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program called HYDEN for designing highly degenerate primers for a set of genomic sequences. We report on the success of the program in several applications, one of which is an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, HYDEN was used to design primers with degeneracies up to 10(10) that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.

Algorithms↗
PubMed2005

Tag SNP selection in genotype data for maximizing SNP prediction accuracy.

MOTIVATION: The search for genetic regions associated with complex diseases, such as cancer or Alzheimer's disease, is an important challenge that may lead to better diagnosis and treatment. The existence of millions of DNA variations, primarily single nucleotide polymorphisms (SNPs), may allow the fine dissection of such associations. However, studies seeking disease association are limited by the cost of genotyping SNPs. Therefore, it is essential to find a small subset of informative SNPs (tag SNPs) that may be used as good representatives of the rest of the SNPs. RESULTS: We define a new natural measure for evaluating the prediction accuracy of a set of tag SNPs, and use it to develop a new method for tag SNPs selection. Our method is based on a novel algorithm that predicts the values of the rest of the SNPs given the tag SNPs. In contrast to most previous methods, our prediction algorithm uses the genotype information and not the haplotype information of the tag SNPs. Our method is very efficient, and it does not rely on having a block partition of the genomic region. We compared our method with two state-of-the-art tag SNP selection algorithms on 58 different genotype datasets from four different sources. Our method consistently found tag SNPs with considerably better prediction ability than the other methods. AVAILABILITY: The software is available from the authors on request.

Algorithms↗