CplexA: a Mathematica package to study macromolecular-assembly control of gene expression

Summary: Macromolecular assembly vertebrates essential cellular processes, such as gene regulation and signal transduction. A major challenge for conventional computational methods to study these processes is tackling the exponential increase of the number of configurational states with the number of components. CplexA is a Mathematica package that uses functional programming to efficiently compute probabilities and average properties over such exponentially large number of states from the energetics of the interactions. The package is particularly suited to study gene expression at complex promoters controlled by multiple, local and distal, DNA binding sites for transcription factors. Availability: CplexA is freely available together with documentation at http://sourceforge.net/projects/cplexa/.Comment: 28 pages. Includes Mathematica, Matlab, and Python implementation tutorials. Software can be downloaded at http://cplexa.sourceforge.net

Making Concrete from Recycled Materials

Making concrete out of recycled concrete aggregate, or RCA, can be a sustainable and cost-saving alternative to other aggregates. However, the quality of concrete made with RCA is dependent on the quality of the recycled material used. OTREC researchers Jason Ideker of Oregon State University and Jennifer Tanner of the University of Wyoming, with graduate student researchers Matthew P. Adams and Angela Jones, sought to determine some of the primary concerns involved with the use of RCA and to arrive at methods of assessing its durability for use in new concrete. Alkali-silica reaction occurs in concrete over time, causing it to expand and eventually weaken. There are standards for testing aggregate to determine its susceptibility to this reaction, but these testing standards were developed for traditional aggregates, not for RCA. Phase 1 of this research project involved accelerated laboratory tests related to assessing alkali-silica reactivity (ASR) of RCA. A team of four laboratories performed testing using the same materials in order to determine the variation between different laboratories. Phase II of the research sought to provide corroboration of those results by testing an additional three aggregates in two laboratories. In addition, field specimens and mitigation studies were conducted. There are methods to control ASR in concrete which contains traditional aggregates; another goal of this research was to investigate how effective those methods are in concrete incorporating RCA

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

Inferring gene ontologies from pairwise similarity data.

MotivationWhile the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene-gene pairwise similarities from -omics data; infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; and respect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge-none has been evaluated for GO inference.MethodsWe consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method's ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast.ResultsFor task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20-25% precision, recall).ConclusionThis study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data

Network-based stratification of tumor mutations.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

Optimization of synchronization in gradient clustered networks

We consider complex clustered networks with a gradient structure, where sizes of the clusters are distributed unevenly. Such networks describe more closely actual networks in biophysical systems and in technological applications than previous models. Theoretical analysis predicts that the network synchronizability can be optimized by the strength of the gradient field but only when the gradient field points from large to small clusters. A remarkable finding is that, if the gradient field is sufficiently strong, synchronizability of the network is mainly determined by the properties of the subnetworks in the two largest clusters. These results are verified by numerical eigenvalue analysis and by direct simulation of synchronization dynamics on coupled-oscillator networks.Comment: PRE, 76, 056113 (2007

Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

Challenges in identifying cancer genes by analysis of exome sequencing data.

Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13-60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed

Cytoscape: the network visualization tool for GenomeSpace workflows.

Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013