Journal of Computational Biology

MetaCluster 4.0: A Novel Binning Algorithm for NGS Reads and Huge Number of Species.

Publication Date: 2012 Feb PMID: 22300323
Authors: Wang, Y. - Leung, H. C. - Yiu, S. M. - Chin, F. Y.
Journal: J Comput Biol

Abstract Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets. Supplementary Material is available at www.liebertonline.com/cmb .

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A linearized constraint-based approach for modeling signaling networks.

Publication Date: 2012 Feb PMID: 22300322
Authors: Vardi, L. - Ruppin, E. - Sharan, R.
Journal: J Comput Biol

Abstract With the unparalleled increase in the availability of biological data over the last couple of decades, accurate and computable models are becoming increasingly important for unraveling complex biological phenomena. Past efforts to model signaling networks have utilized various computational methods, including Boolean and constraint-based modeling (CBM) approaches. These approaches are based on solving mixed integer linear programs; hence, they may not scale up for the analysis of large networks and are not amenable for applications based on sampling the full spectrum of the solution space. Here we propose a new CBM approach that is fully linear and does not involve integer variables, thereby overcoming the aforementioned limitations. We describe a novel optimization procedure for model construction and demonstrate the utility of our approach on a reconstructed model of the human epidermal growth factor receptor (EGFR) pathway, spanning 322 species and 211 connections. We compare our model's predictions to experimental phosphorylation data and to the predictions inferred via an additional Boolean-based EGFR signaling model. Our results show high prediction accuracy (75%) and high similarity to the Boolean model. Considering the marked computational advantages in terms of scalability and sampling utilization obtained by having a linear mode, these results demonstrate the potential promise of this framework for the study of cellular signaling.

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Efficient manipulations of synonymous mutations for controlling translation rate: an analytical approach.

Publication Date: 2012 Feb PMID: 22300321
Authors: Dana, A. - Tuller, T.
Journal: J Comput Biol

Abstract Gene translation is a central process in all living organism with important ramifications to almost every biomedical field. Previous systems evolutionary studies in the field have demonstrated that in many organisms coding sequence features undergo selection to optimize this process. In the current study, we report for the first time analytical proofs related to the various aspects of this process and its optimality. Among our results we show that coding sequences with mono- tonic increasing profiles of translation efficiency (i.e., with slower codons near the 5'UTR), mathematically optimize ribosomal allocation by minimizing the number of ribosomes needed for translating a codon per time unit. Thus, the genomic translation efficiency profile reported in previous studies for many organisms is optimal in this sense. In addition, we show that improving translation efficiency of a codon in a gene may result in a decrease in the translation rate of other genes, demonstrating that the relation between codon bias and protein translation rate is less trivial than was assumed before. Based on these observations we describe an efficient heuristic for designing coding sequences with specific translation efficiency and minimal ribosomal allocation for heterologous gene expression. We demonstrate how this heuristic can be used in biotechnology for engineering a heterologous gene before expressing it in a new host.

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Dynamic Modeling of miRNA-mediated Feed-Forward Loops.

Publication Date: 2012 Feb PMID: 22300320
Authors: Eduati, F. - di Camillo, B. - Karbiener, M. - Scheideler, M. - Cora, D. - Caselle, M. - Toffolo, G.
Journal: J Comput Biol

Abstract Given the important role of microRNAs (miRNAs) in genome-wide regulation of gene expression, increasing interest is devoted to mixed transcriptional and post-transcriptional regulatory networks analyzing the combinatorial effect of transcription factors (TFs) and miRNAs on target genes. In particular, miRNAs are known to be involved in feed-forward loops (FFLs), where a TF regulates a miRNA and they both regulate a target gene. Different algorithms have been proposed to identify miRNA targets, based on pairing between the 5' region of the miRNA and the 3'UTR of the target gene, and correlation between miRNA host genes and target mRNA expression data. Here we propose a quantitative approach integrating an existing method for mixed FFL identification based on sequence analysis with differential equation modeling approach that permits us to select active FFLs based on their dynamics. Different models are assessed based on their ability to properly reproduce miRNA and mRNA expression data in terms of identification criteria, namely: goodness of fit, precision of the estimates, and comparison with submodels. In comparison with standard approaches based on correlation, our method improves in specificity. As a case study, we applied our method to adipogenic differentiation gene expression data providing potential novel players in this regulatory network. Supplementary Material for this article is available at www.liebertonline.com/cmb .

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An S-System Parameter Estimation Method (SPEM) for Biological Networks.

Publication Date: 2012 Feb PMID: 22300319
Authors: Yang, X. - Dent, J. E. - Nardini, C.
Journal: J Comput Biol

Abstract Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package ( http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html ), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.

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Large-scale elucidation of drug response pathways in humans.

Publication Date: 2012 Feb PMID: 22300318
Authors: Silberberg, Y. - Gottlieb, A. - Kupiec, M. - Ruppin, E. - Sharan, R.
Journal: J Comput Biol

Abstract Elucidating signaling pathways is a fundamental step in understanding cellular processes and developing new therapeutic strategies. Here we introduce a method for the large-scale elucidation of signaling pathways involved in cellular response to drugs. Combining drug targets, drug response expression profiles, and the human physical interaction network, we infer 99 human drug response pathways and study their properties. Based on the newly inferred pathways, we develop a pathway-based drug-drug similarity measure and compare it to two common, gold standard drug-drug similarity measures. Remarkably, our measure provides better correspondence to these gold standards than similarity measures that are based on associations between drugs and known pathways, or on drug-specific gene expression profiles. It further improves the prediction of drug side effects and indications, elucidating specific response pathways that may be associated with these drug properties. Supplementary Material for this article is available at www.liebertonline.com/cmb .

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Structural Bistability of the GAL Regulatory Network and Characterization of its Domains of Attraction.

Publication Date: 2012 Feb PMID: 22300317
Authors: Cosentino, C. - Salerno, L. - Passanti, A. - Merola, A. - Bates, D. G. - Amato, F.
Journal: J Comput Biol

Abstract Bistability is a system-level property, exploited by many biomolecular interaction networks as a key mechanism to accomplish different cellular functions (e.g., differentiation, cell cycle, switch-like response to external stimuli). Bistability has also been experimentally found to occur in the regulatory network of the galactose metabolic pathway in the model organism Saccharomyces cerevisiae. In this yeast, bistability generates a persistent memory of the type of carbon source available in the extracellular medium: under the same experimental conditions, cells previously grown with different nutrients generate different responses and get stably locked into two distinct steady states. The molecular interactions of the GAL regulatory network have been thoroughly dissected through wet-lab experiments; thus, this system provides a formidable benchmark to our ability to characterize and reproduce in silico the behavior of bistable biological systems. To this aim, a number of models have been proposed in the literature; however, we found that they are not able to replicate the persistent memory behavior observed in (Acar et al., 2005 ). The present study proposes a novel model of the GAL regulatory network, which, in addition to reproducing in silico the experimental findings, can be formally analyzed for structural multistability of the network, using chemical reaction network theory (CRNT), and allows the characterization of the domains of attraction (DA). This work provides further insights into the GAL system and proposes an easily generalizable approach to the study of bistability-associated behaviors in biological systems.

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Fast, sensitive discovery of conserved genome-wide motifs.

Publication Date: 2012 Feb PMID: 22300316
Authors: Ihuegbu, N. E. - Stormo, G. D. - Buhler, J.
Journal: J Comput Biol

Abstract Regulatory sites that control gene expression are essential to the proper functioning of cells, and identifying them is critical for modeling regulatory networks. We have developed Magma (Multiple Aligner of Genomic Multiple Alignments), a software tool for multiple species, multiple gene motif discovery. Magma identifies putative regulatory sites that are conserved across multiple species and occur near multiple genes throughout a reference genome. Magma takes as input multiple alignments that can include gaps. It uses efficient clustering methods that make it about 70 times faster than PhyloNet, a previous program for this task, with slightly greater sensitivity. We ran Magma on all non-coding DNA conserved between Caenorhabditis elegans and five additional species, about 70 Mbp in total, in <4 h. We obtained 2,309 motifs with lengths of 6-20 bp, each occurring at least 10 times throughout the genome, which collectively covered about 566 kbp of the genomes, approximately 0.8% of the input. Predicted sites occurred in all types of non-coding sequence but were especially enriched in the promoter regions. Comparisons to several experimental datasets show that Magma motifs correspond to a variety of known regulatory motifs.

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Binding profiles of chromatin-modifying proteins are predictive for transcriptional activity and promoter-proximal pausing.

Publication Date: 2012 Feb PMID: 22300315
Authors: Sakoparnig, T. - Kockmann, T. - Paro, R. - Beisel, C. - Beerenwinkel, N.
Journal: J Comput Biol

ABSTRACT The establishment and maintenance of proper gene expression patterns is essential for stable cell differentiation. Using unsupervised learning techniques, chromatin states have been linked to discrete gene expression states, but these models cannot predict continuous gene expression levels, nor do they reveal detailed insight into the chromatin-based control of gene expression. Here, we employ regularized regression techniques to link, in a quantitative manner, binding profiles of chromatin proteins to gene expression levels and promoter-proximal pausing of RNA polymerase II in Drosophila melanogaster on a genome-wide scale. We apply stability selection to reliably detect interactions of chromatin features and predict several known, suggested, and novel proteins and protein pairs as transcriptional activators or repressors. Our integrative analysis reveals new insights into the complex interplay of transcriptional regulators in the context of gene expression. Supplementary Material is available at www.libertonline.com/cmb .

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Anti-Cooperative and Cooperative Protein-Protein Interactions between TetR Isoforms on Synthetic Enhancers.

Publication Date: 2012 Feb PMID: 22300314
Authors: Amit, R.
Journal: J Comput Biol

Abstract Protein-protein interactions play an important role in determining the regulatory output of cis regulatory regions. In this work, we revisit the regulatory output functions recorded for the synthetic enhancers that contain binding sites for TetR. We use our thermodynamic model as an analysis tool to infer that two different types of interactions may take place between the TetR molecules. First, a strong mutually exclusive anti-cooperative interaction precludes the synthetic enhancer from being occupied by more than one AT (the aTc bound TetR isoform) protein, and a second weak cooperative interaction exists between the aTc-free TetR isoform (T). Consequently, this work highlights the power of the synthetic enhancer approach as a tool for studying protein-protein interactions via an experimentally verifiable prediction for the general mode of binding of the TetR repressor.

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Optimization approaches for the in silico discovery of optimal targets for gene over/underexpression.

Publication Date: 2012 Feb PMID: 22300313
Authors: Goncalves, E. - Pereira, R. - Rocha, I. - Rocha, M.
Journal: J Comput Biol

Abstract Metabolic engineering (ME) efforts have been recently boosted by the increase in the number of annotated genomes and by the development of several genome-scale metabolic models for microbes of interest in industrial biotechnology. Based on these efforts, strain optimization methods have been proposed to reach the best set of genetic changes to apply to selected host microbes, in order to create strains that are able to overproduce metabolites of industrial interest. Previous work in strain optimization has been mostly based in finding sets of gene (or reaction) deletions that lead to desired phenotypes in computational simulations. In this work, we focus on enlarging the set of possible genetic changes, considering gene over and underexpression. A gene is considered under (over) expressed if its expression value is constrained to be significantly lower (higher) than the one in the wild-type strain, used as a reference. A method is proposed to propagate relative gene expression values to flux constraints over related reactions, making use of the available transcriptional/translational information. The algorithms chosen for the optimization tasks are metaheuristics such as Evolutionary Algorithms (EA) and Simulated Annealing (SA), based on previous successful work on gene knockout optimization. These methods were modified appropriately to accommodate the novel optimization tasks and were applied to study the optimization of succinic and lactic acid production using Escherichia coli as the host. The results are compared with previous ones obtained in gene knockout optimization, thus showing the usefulness of the approach. The methods proposed in this work were implemented in a novel plug-in for OptFlux, an open-source software framework for ME. Supplementary Material is available at www.liebertonline.com/cmb .

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Preface: RECOMB Systems Biology, Regulatory Genomics, and DREAM 2011 Special Issue.

Publication Date: 2012 Feb PMID: 22300312
Authors: Califano, A. - Kellis, M. - Stolovitzky, G.
Journal: J Comput Biol

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