Derek Ruths

Social media for large studies of behavior

Large-scale studies of human behavior in social media need to be held to higher methodological standards On 3 November 1948, the day after Harry Truman won the United States presidential elections, the Chicago Tribune published one of the most famous erroneous headlines in newspaper history: “Dewey Defeats Truman” (1, 2). The headline was informed by telephone surveys, which had inadvertently undersampled Truman supporters (1). Rather than permanently discrediting the practice of polling, this event led to the development of more sophisticated techniques and higher standards that produce the more accurate and statistically rigorous polls conducted today (3).

Control Profiles of Complex Networks

Real Network Control Understanding how complex networks are controlled has implications for a variety of real-world networks, from traffic safety to transcriptional control. Ruths and Ruths (p. 1373; see the Perspective by Onnela) have developed a theoretical framework for analyzing individual controls within networks based on numbers of sources and sinks for information flow. By this method, the number of controls required by a network can be predicted and direct comparisons for the basis for control across networks of differing size, structure, and function can be made. Although three broad classes of real networks were observed, current, established random models of networks were insufficient to model their control structures. The control profile is a network statistic that may prove useful in approaching the control of complex networks. [Also see Perspective by Onnela] Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.

PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships

BackgroundPhylogenies, i.e., the evolutionary histories of groups of taxa, play a major role in representing the interrelationships among biological entities. Many software tools for reconstructing and evaluating such phylogenies have been proposed, almost all of which assume the underlying evolutionary history to be a tree. While trees give a satisfactory first-order approximation for many families of organisms, other families exhibit evolutionary mechanisms that cannot be represented by trees. Processes such as horizontal gene transfer (HGT), hybrid speciation, and interspecific recombination, collectively referred to as reticulate evolutionary events, result in networks, rather than trees, of relationships. Various software tools have been recently developed to analyze reticulate evolutionary relationships, which include SplitsTree4, LatTrans, EEEP, HorizStory, and T-REX.ResultsIn this paper, we report on the PhyloNet software package, which is a suite of tools for analyzing reticulate evolutionary relationships, or evolutionary networks, which are rooted, directed, acyclic graphs, leaf-labeled by a set of taxa. These tools can be classified into four categories: (1) evolutionary network representation: reading/writing evolutionary networks in a newly devised compact form; (2) evolutionary network characterization: analyzing evolutionary networks in terms of three basic building blocks – trees, clusters, and tripartitions; (3) evolutionary network comparison: comparing two evolutionary networks in terms of topological dissimilarities, as well as fitness to sequence evolution under a maximum parsimony criterion; and (4) evolutionary network reconstruction: reconstructing an evolutionary network from a species tree and a set of gene trees.ConclusionThe software package, PhyloNet, offers an array of utilities to allow for efficient and accurate analysis of evolutionary networks. The software package will help significantly in analyzing large data sets, as well as in studying the performance of evolutionary network reconstruction methods. Further, the software package supports the proposed eNewick format for compact representation of evolutionary networks, a feature that allows for efficient interoperability of evolutionary network software tools. Currently, all utilities in PhyloNet are invoked on the command line.

Confounding Factors in HGT Detection: Statistical Error, Coalescent Effects, and Multiple Solutions

Prokaryotic organisms share genetic material across species boundaries by means of a process known as horizontal gene transfer (HGT). This process has great significance for understanding prokaryotic genome diversification and unraveling their complexities. Phylogeny-based detection of HGT is one of the most commonly used methods for this task, and is based on the fundamental fact that HGT may cause gene trees to disagree with one another, as well as with the species phylogeny. Using these methods, we can compare gene and species trees, and infer a set of HGT events to reconcile the differences among these trees. In this paper, we address three factors that confound the detection of the true HGT events, including the donors and recipients of horizontally transferred genes. First, we study experimentally the effects of error in the estimated gene trees (statistical error) on the accuracy of inferred HGT events. Our results indicate that statistical error leads to overestimation of the number of HGT events, and that HGT detection methods should be designed with unresolved gene trees in mind. Second, we demonstrate, both theoretically and empirically, that based on topological comparison alone, the number of HGT scenarios that reconcile a pair of species/gene trees may be exponential. This number may be reduced when branch lengths in both trees are estimated correctly. This set of results implies that in the absence of additional biological information, and/or a biological model of how HGT occurs, multiple HGT scenarios must be sought, and efficient strategies for how to enumerate such solutions must be developed. Third, we address the issue of lineage sorting, how it confounds HGT detection, and how to incorporate it with HGT into a single stochastic framework that distinguishes between the two events by extending population genetics theories. This result is very important, particularly when analyzing closely related organisms, where coalescent effects may not be ignored when reconciling gene trees. In addition to these three confounding factors, we consider the problem of enumerating all valid coalescent scenarios that constitute plausible species/gene tree reconciliations, and develop a polynomial-time dynamic programming algorithm for solving it. This result bears great significance on reducing the search space for heuristics that seek reconciliation scenarios. Finally, we show, empirically, that the locality of incongruence between a pair of trees has an impact on the numbers of HGT and coalescent reconciliation scenarios.

Flows of Research Manuscripts Among Scientific Journals Reveal Hidden Submission Patterns

Fathoming Publication Strategies While many studies have tracked numbers of citations after publication, such studies cannot reveal how prepublication dynamics affects subsequent citation history. Calcagno et al. (p. 1065, published online 11 October) surveyed the submission history of more than 80,000 articles published in 16 fields of biology in 2006–2008 and constructed a social network based on manuscript flows among scientific journals. High-impact journals occupied central positions in the network. A majority of manuscripts were published in the first journal to which they were submitted. However, submission history affected the post-publication impact (citation count) of articles, with manuscripts requiring resubmission eventually receiving more citations. A large-scale study of biological manuscript submission history reveals how authors strategically submit their research. The study of science-making is a growing discipline that builds largely on online publication and citation databases, while prepublication processes remain hidden. Here, we report on results from a large-scale survey of the submission process, covering 923 scientific journals from the biological sciences in years 2006 to 2008. Manuscript flows among journals revealed a modular submission network, with high-impact journals preferentially attracting submissions. However, about 75% of published articles were submitted first to the journal that would publish them, and high-impact journals published proportionally more articles that had been resubmitted from another journal. Submission history affected post-publication impact: Resubmissions from other journals received significantly more citations than first-intent submissions, and resubmissions between different journal communities received significantly fewer citations.

RIATA-HGT: a fast and accurate heuristic for reconstructing horizontal gene transfer

Horizontal gene transfer (HGT) plays a major role in microbial genome diversification, and is claimed to be rampant among various groups of genes in bacteria. Further, HGT is a major confounding factor for any attempt to reconstruct bacterial phylogenies. As a result, detecting and reconstructing HGT events in groups of organisms has become a major endeavor in biology. The problem of detecting HGT events based on incongruence between a species tree and a gene tree is computationally very hard (NP-hard). Efficient algorithms exist for solving restricted cases of the problem. We propose RIATA-HGT, the first polynomial-time heuristic to handle all HGT scenarios, without any restrictions. The method accurately infers HGT events based on analyzing incongruence among species and gene trees. Empirical performance of the method on synthetic and biological data is outstanding. Being a heuristic, RIATA-HGT may overestimate the optimal number of HGT events; empirical performance, however, shows that such overestimation is very mild. We have implemented our method and run it on biological and synthetic data. The results we obtained demonstrate very high accuracy of the method. Current version of RIATA-HGT uses the PAUP tool, and we are in the process of implementing a stand-alone version, with a graphical user interface, which will be made public. The tool, in its current implementation, is available from the authors upon request.

Kinome siRNA-phosphoproteomic screen identifies networks regulating AKT signaling.

To identify regulators of intracellular signaling, we targeted 541 kinases and kinase-related molecules with small interfering RNAs (siRNAs), and determined their effects on signaling with a functional proteomics reverse-phase protein array (RPPA) platform assessing 42 phospho and total proteins. The kinome-wide screen demonstrated a strong inverse correlation between phosphorylation of AKT and mitogen-activated protein kinase (MAPK) with 115 genes that, when targeted by siRNAs, demonstrated opposite effects on MAPK and AKT phosphorylation. Network-based analysis identified the MAPK subnetwork of genes along with p70S6K and FRAP1 as the most prominent targets that increased phosphorylation of AKT, a key regulator of cell survival. The regulatory loops induced by the MAPK pathway are dependent on tuberous sclerosis complex 2 but demonstrate a lesser dependence on p70S6K than the previously identified FRAP1 feedback loop. The siRNA screen also revealed novel bi-directionality in the AKT and GSK3 (Glycogen synthase kinase 3) interaction, whereby genetic ablation of GSK3 significantly blocks AKT phosphorylation, an unexpected observation as GSK3 has only been predicted to be downstream of AKT. This method uncovered novel modulators of AKT phosphorylation and facilitated the mapping of regulatory loops.

GS2: an efficiently computable measure of GO-based similarity of gene sets

Motivation: The growing availability of genome-scale datasets has attracted increasing attention to the development of computational methods for automated inference of functional similarities among genes and their products. One class of such methods measures the functional similarity of genes based on their distance in the Gene Ontology (GO). To measure the functional relatedness of a gene set, these measures consider every pair of genes in the set, and the average of all pairwise distances is calculated. However, as more data becomes available and gene sets used for analysis become larger, such pair-based calculation becomes prohibitive. Results: In this article, we propose GS2 (GO-based similarity of gene sets), a novel GO-based measure of gene set similarity that is computable in linear time in the size of the gene set. The measure quantifies the similarity of the GO annotations among a set of genes by averaging the contribution of each genes GO terms and their ancestor terms with respect to the GO vocabulary graph. To study the performance of our method, we compared our measure with an established pair-based measure when run on gene sets with varying degrees of functional similarities. In addition to a significant speed improvement, our method produced comparable similarity scores to the established method. Our method is available as a web-based tool and an open-source Python library. Availability: The web-based tools and Python code are available at: http://bioserver.cs.rice.edu/gs2. Contact: troy.ruths@rice.edu

Hypothesis generation in signaling networks.

Biological signaling networks comprise the chemical processes by which cells detect and respond to changes in their environment. Such networks have been implicated in the regulation of important cellular activities, including cellular reproduction, mobility, and death. Though technological and scientific advances have facilitated the rapid accumulation of information about signaling networks, utilizing these massive information resources has become infeasible except through computational methods and computer-based tools. To date, visualization and simulation tools have received significant emphasis. In this paper, we present a graph-theoretic formalization of biological signaling network models that are in wide but informal use, and formulate two problems on the graph: the Constrained Downstream and Minimum Knockout Problems. Solutions to these problems yield qualitative tools for generating hypotheses about the networks, which can then be experimentally tested in a laboratory setting. Using established graph algorithms, we provide a solution to the Constrained Downstream Problem. We also show that the Minimum Knockout Problem is NP-Hard, propose a heuristic, and assess its performance. In tests on the Epidermal Growth Factor Receptor (EGFR) network, we find that our heuristic reports the correct solution to the problem in seconds. Source code for the implementations of both solutions is available from the authors upon request.

Identification of a Novel Endoplasmic Reticulum Stress Response Element Regulated by XBP1

Background: Endoplasmic reticulum (ER) stress maintains cellular protein homeostasis. Results: A novel ER stress-responsive element, ERSE-26, identified in 38 genes, is regulated by sXBP1 during ER stress. Conclusion: ER stress increases levels of prion and other proteins not previously known to be involved in the ER stress response. Significance: ERSE-26 implicates novel genes regulated by the ER stress response. Understanding the regulatory mechanisms mediating PRNP gene expression is highly relevant to elucidating normal cellular prion protein (PrP) function(s) and the transmissibility of prion protein neurodegenerative diseases. Here, luciferase reporter assays showed that an endoplasmic reticulum stress element (ERSE)-like element, CCAAT-N26-CCACG in the human PRNP promoter, is regulated by ER stress and X-box-binding protein 1 (XBP1) but not by activating transcription factor 6 α (ATF6α). Bioinformatics identified the ERSE-26 motif in 37 other human genes in the absence of canonical ERSE sites except for three genes. Several of these genes are associated with a synaptic function or are involved in oxidative stress. Brefeldin A, tunicamycin, and thapsigargin ER stressors induced gene expression of PRNP and four randomly chosen ERSE-26-containing genes, ERLEC1, GADD45B, SESN2, and SLC38A5, in primary human neuron cultures or in the breast carcinoma MCF-7 cell line, although the level of the response depends on the gene analyzed, the genetic background of the cells, the cell type, and the ER stressor. Overexpression of XBP1 increased, whereas siRNA knockdown of XBP1 considerably reduced, PRNP and ERLEC1 mRNA levels in MCF-7 cells. Taken together, these results identify a novel ER stress regulator, which implicates the ER stress response in previously unrecognized cellular functions.