Biomedical subjects

Richard M Karp

Publications and source records attributed to Richard M Karp.

12 recordsLinked to original sources

PubMed2006

Efficient algorithms for detecting signaling pathways in protein interaction networks.

The interpretation of large-scale protein network data depends on our ability to identify significant substructures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths and trees in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths and trees in networks under several biologically motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths and trees in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in about three hours.

Algorithms↗

PubMed2005

Conserved patterns of protein interaction in multiple species.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.

Amino Acid Sequence↗

PubMed2004

MotifPrototyper: a Bayesian profile model for motif families.

In this article, we address the problem of modeling generic features of structurally but not textually related DNA motifs, that is, motifs whose consensus sequences are entirely different but nevertheless share "metasequence features" reflecting similarities in the DNA-binding domains of their associated protein recognizers. We present MotifPrototyper, a profile Bayesian model that can capture structural properties typical of particular families of motifs. Each family corresponds to transcription regulatory proteins with similar types of structural signatures in their DNA-binding domains. We show how to train MotifPrototypers from biologically identified motifs categorized according to the TRANSFAC categorization of transcription factors and present empirical results of motif classification, motif parameter estimation, and de novo motif detection by using the learned profile models.

Amino Acid Motifs↗

PubMed2004

Towards optimally multiplexed applications of universal arrays.

We study a design and optimization problem that occurs, for example, when single nucleotide polymorphisms (SNPs) are to be genotyped using a universal DNA tag array. The problem of optimizing the universal array to avoid disruptive cross-hybridization between universal components of the system was addressed in previous work. Cross-hybridization can, however, also occur assay specifically, due to unwanted complementarity involving assay-specific components. Here we examine the problem of identifying the most economic experimental configuration of the assay-specific components that avoids cross-hybridization. Our formalization translates this problem into the problem of covering the vertices of one side of a bipartite graph by a minimum number of balanced subgraphs of maximum degree 1. We show that the general problem is NP-complete. However, in the real biological setting, the vertices that need to be covered have degrees bounded by d. We exploit this restriction and develop an O(d)-approximation algorithm for the problem. We also give an O(d)-approximation for a variant of the problem in which the covering subgraphs are required to be vertex disjoint. In addition, we propose a stochastic model for the input data and use it to prove a lower bound on the cover size. We complement our theoretical analysis by implementing two heuristic approaches and testing their performance on synthetic data as well as on simulated SNP data.

Algorithms↗

PubMed2004

Logos: a modular bayesian model for de novo motif detection.

The complexity of the global organization and internal structure of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection, it is necessary to model the complex dependencies within and among motifs and to incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal motif Sequence model for biopolymer sequences, which provides a principled framework for developing, modularizing, extending and computing expressive motif models for complex biopolymer sequence analysis. LOGOS consists of two interacting submodels: HMDM, a local alignment model capturing biological prior knowledge and positional dependency within the motif local structure; and HMM, a global motif distribution model modeling frequencies and dependencies of motif occurrences. Model parameters can be fit using training motifs within an empirical Bayesian framework. A variational EM algorithm is developed for de novo motif detection. LOGOS improves over existing models that ignore biological priors and dependencies in motif structures and motif occurrences, and demonstrates superior performance on both semi-realistic test data and cis-regulatory sequences from yeast and Drosophila genomes with regard to sensitivity, specificity, flexibility and extensibility.

Algorithms↗

PubMed2003

Conserved pathways within bacteria and yeast as revealed by global protein network alignment.

We implement a strategy for aligning two protein-protein interaction networks that combines interaction topology and protein sequence similarity to identify conserved interaction pathways and complexes. Using this approach we show that the protein-protein interaction networks of two distantly related species, Saccharomyces cerevisiae and Helicobacter pylori, harbor a large complement of evolutionarily conserved pathways, and that a large number of pathways appears to have duplicated and specialized within yeast. Analysis of these findings reveals many well characterized interaction pathways as well as many unanticipated pathways, the significance of which is reinforced by their presence in the networks of both species.

Bacteria↗

PubMed2003

The restriction scaffold problem.

Most shotgun sequencing projects undergo a long and costly phase of finishing, in which a partial assembly forms several contigs whose order, orientation, and relative distance is unknown. We propose here a new technique that supplements the shotgun assembly data by experimentally simple and commonly used complete restriction digests of the target. By computationally combining information from the contig sequences and the fragment sizes measured for several different enzymes, we seek to form a "scaffold" on which the contigs will be placed in their correct orientation, order, and distance. We give a heuristic search algorithm for solving the problem and report on promising preliminary simulation results. The key to the success of the search scheme is the very rapid solution of two time-critical subproblems that are solved to optimality in linear time. Our simulations indicate that with noise levels of some 3% relative error in measuring fragment sizes, using six enzymes, most datasets of 13 contigs spanning 300kb can be correctly ordered, and the remaining ones have most of their pairs of neighboring contigs correct. Hence, the technique has a potential to provide real help to finishing. Even without closing all gaps, the ability to order and orient the contigs correctly makes the partial assembly both more accessible and more useful for biologists.

Computational Biology↗

PubMed2003

CREME: a framework for identifying cis-regulatory modules in human-mouse conserved segments.

MOTIVATION: The binding of transcription factors to specific regulatory sequence elements is a primary mechanism for controlling gene transcription. Recent findings suggest a modular organization of binding sites for transcription factors that cooperate in the regulation of genes. In this work we establish a framework for finding recurrent cis-regulatory modules in the promoters of a selected set of genes and scoring their statistical significance. RESULTS: Proceeding from a database of identified binding site motifs and their genomic locations we seek motifs whose frequency in the selected promoters is different than in a background promoter set. We present several statistical tests designed for this purpose. We provide a hashing algorithm for detecting combinations of these motifs that co-occur in clusters within the selected promoters. The significance of such co-occurrences is evaluated using novel statistical scores. Our methods are combined in CREME, a suite of software which includes a browser for viewing the pattern of occurrence of selected cis-regulatory modules. We applied our methodology to find modules within human-mouse conserved promoter segments, focusing on cell cycle regulated genes and stress response related genes. To validate the biological significance of the identified modules we tested whether the associated genes tended to be co-expressed or share similar function. In the cell cycle set five of the seven identified sets of genes were coherently expressed. On the stress response data four of the six detected sets fell predominantly into well-defined functional sub-categories.

Algorithms↗

PubMed2003

Efficient reconstruction of haplotype structure via perfect phylogeny.

Each person's genome contains two copies of each chromosome, one inherited from the father and the other from the mother. A person's genotype specifies the pair of bases at each site, but does not specify which base occurs on which chromosome. The sequence of each chromosome separately is called a haplotype. The determination of the haplotypes within a population is essential for understanding genetic variation and the inheritance of complex diseases. The haplotype mapping project, a successor to the human genome project, seeks to determine the common haplotypes in the human population. Since experimental determination of a person's genotype is less expensive than determining its component haplotypes, algorithms are required for computing haplotypes from genotypes. Two observations aid in this process: first, the human genome contains short blocks within which only a few different haplotypes occur; second, as suggested by Gusfield, it is reasonable to assume that the haplotypes observed within a block have evolved according to a perfect phylogeny, in which at most one mutation event has occurred at any site, and no recombination occurred at the given region. We present a simple and efficient polynomial-time algorithm for inferring haplotypes from the genotypes of a set of individuals assuming a perfect phylogeny. Using a reduction to 2-SAT we extend this algorithm to handle constraints that apply when we have genotypes from both parents and child. We also present a hardness result for the problem of removing the minimum number of individuals from a population to ensure that the genotypes of the remaining individuals are consistent with a perfect phylogeny. Our algorithms have been tested on real data and give biologically meaningful results. Our webserver (http://www.cs.columbia.edu/compbio/hap/) is publicly available for predicting haplotypes from genotype data and partitioning genotype data into blocks.

Adult↗

PubMed2003

LOGOS: a modular Bayesian model for de novo motif detection.

The complexity of the global organization and internal structures of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection it is necessary to model the complex dependencies within and among motifs and incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal motif Sequence model for biopolymer sequences, which provides a principled framework for developing, modularizing, extending and computing expressive motif models for complex biopolymer sequence analysis. LOGOS consists of two interacting submodels: HMDM, a local alignment model capturing biological prior knowledge and positional dependence within the motif local structure; and HMM, a global motif distribution model modeling frequencies and dependencies of motif occurrences. Model parameters can be fit using training motifs within an empirical Bayesian framework. A variational EM algorithm is developed for de novo motif detection. LOGOS improves over existing models that ignore biological priors and dependencies in motif structures and motif occurrences, and demonstrates superior performance on both semi-realistic test data and cis-regulatory sequences from yeast and Drosophila sequences with regard to sensitivity, specificity, flexibility and extensibility.

Algorithms↗

PubMed2003

Keynote address: the role of algorithmic research in computational genomics.

In the early 1990s, after more than three decades of studying algorithms within the frame work of theoretical computer science, I shifted my focus to alogrithmic problems arising in genomics. There is a fundamental difference between the views of algorithms in the two fields: in theoretical computer science the input-output behavior of an algorithm is rigorously specified in advance, whereas in computational biology an algorithm is merely a vehicle for discovering Nature's ground truth. In order to be effective in computational genomics I have had to radically change my approach to research. On the occasion of this keynote address I will share some of the lessons I have learned, in the hope of making the way easier for computer scientists and mathematicians entering this field. These lessons will be encapsulated in a list of aphorisms, accompanied by illustrative examples.

Algorithms↗

PubMedDate not supplied

Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data.

Mounting evidence shows that many protein complexes are conserved in evolution. Here we use conservation to find complexes that are common to the yeast S. cerevisiae and the bacteria H. pylori. Our analysis combines protein interaction data that are available for each of the two species and orthology information based on protein sequence comparison. We develop a detailed probabilistic model for protein complexes in a single species and a model for the conservation of complexes between two species. Using these models, one can recast the question of finding conserved complexes as a problem of searching for heavy subgraphs in an edge- and node-weighted graph, whose nodes are orthologous protein pairs. We tested this approach on the data currently available for yeast and bacteria and detected 11 significantly conserved complexes. Several of these complexes match very well with prior experimental knowledge on complexes in yeast only and serve for validation of our methodology. The complexes suggest new functions for a variety of uncharacterized proteins. By identifying a conserved complex whose yeast proteins function predominantly in the nuclear pore complex, we propose that the corresponding bacterial proteins function as a coherent cellular membrane transport system. We also compare our results to two alternative methods for detecting complexes and demonstrate that our methodology obtains a much higher specificity.

Bacterial Proteins↗