TPX: Biomedical literature search made easy

TPX is a web-based PubMed search enhancement tool that enables faster article searching using analysis and exploration features. These features include identification of relevant biomedical concepts from search results with linkouts to source databases, concept based article categorization, concept assisted search and filtering, query refinement. A distinguishing feature here is the ability to add user-defined concept names and/or concept types for named entity recognition. The tool allows contextual exploration of knowledge sources by providing concept association maps derived from the MEDLINE repository. It also has a full-text search mode that can be configured on request to access local text repositories, incorporating entity co-occurrence search at sentence/paragraph levels. Local text files can also be analyzed on-the-fly. Availability: http://tpx.atc.tcs.com     

Molecular motors: single-molecule recordings made easy

A fusion protein of kinesin and gelsolin binds a short actin filament which can be visualized using a standard fluorescence microscope. This technique has provided new insight into the mechanism of kinesin action, and in principle it can be extended to allow single-molecule assays of any protein.     

concatenator: sequence data matrices handling made easy

concatenator is a simple and user-friendly software that implements two very useful functions for phylogenetics data analysis. It concatenates NEXUS files of several fragments in a single NEXUS file ready to be used in phylogenetics software, such as paup and mrbayes and it converts FASTA sequence data files to NEXUS and vice-versa. Additionally, concatenated files can be prepared for partition tests in paup. It is freely available in http://cobig2.fc.ul.pt.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.     

CTree: comparison of clusters between phylogenetic trees made easy

SUMMARY: CTree has been designed for the quantification of clusters within viral phylogenetic tree topologies. Clusters are stored as individual data structures from which statistical data, such as the Subtype Diversity Ratio (SDR), Subtype Diversity Variance (SDV) and pairwise distances can be extracted. This simplifies the quantification of tree topologies in relation to inter- and intra-cluster diversity. Here the novel features incorporated within CTree, including the implementation of a heuristic algorithm for identifying clusters, are outlined along with the more usual features found within general tree viewing software. AVAILABILITY: CTree is available as an executable jar file from: http://www.manchester.ac.uk/bioinformatics/ctree     

PDZ domains: the building blocks regulating tumorigenesis

Over 250 PDZ (PSD95/Dlg/ZO-1) domain-containing proteins have been described in the human proteome. As many of these possess multiple PDZ domains, the potential combinations of associations with proteins that possess PBMs (PDZ-binding motifs) are vast. However, PDZ domain recognition is a highly specific process, and much less promiscuous than originally thought. Furthermore, a large number of PDZ domain-containing proteins have been linked directly to the control of processes whose loss, or inappropriate activation, contribute to the development of human malignancies. These regulate processes as diverse as cytoskeletal organization, cell polarity, cell proliferation and many signal transduction pathways. In the present review, we discuss how PBM-PDZ recognition and imbalances therein can perturb cellular homoeostasis and ultimately contribute to malignant progression.     

GenePath: a system for automated construction of genetic networks from mutant data

MOTIVATION: Genetic networks are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. RESULTS: We have developed GenePath, an intelligent assistant that automates the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints in the search for a plausible genetic network. GenePath formalizes genetic data analysis, facilitates the consideration of all the available data in a consistent manner, and the examination of the large number of possible consequences of planned experiments. It also provides an explanation mechanism that traces every finding to the pertinent data. AVAILABILITY: GenePath can be accessed at http://genepath.org. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://genepath.org/bi-.supp.     

DNA building blocks: keeping control of manufacture

Ribonucleotide reductase (RNR) is the only source for de novo production of the four deoxyribonucleoside triphosphate (dNTP) building blocks needed for DNA synthesis and repair. It is crucial that these dNTP pools are carefully balanced, since mutation rates increase when dNTP levels are either unbalanced or elevated. RNR is the major player in this homeostasis, and with its four different substrates, four different allosteric effectors and two different effector binding sites, it has one of the most sophisticated allosteric regulations known today. In the past few years, the structures of RNRs from several bacteria, yeast and man have been determined in the presence of allosteric effectors and substrates, revealing new information about the mechanisms behind the allosteric regulation. A common theme for all studied RNRs is a flexible loop that mediates modulatory effects from the allosteric specificity site (s-site) to the catalytic site for discrimination between the four substrates. Much less is known about the allosteric activity site (a-site), which functions as an on-off switch for the enzyme's overall activity by binding ATP (activator) or dATP (inhibitor). The two nucleotides induce formation of different enzyme oligomers, and a recent structure of a dATP-inhibited α(6)β(2) complex from yeast suggested how its subunits interacted non-productively. Interestingly, the oligomers formed and the details of their allosteric regulation differ between eukaryotes and Escherichia coli. Nevertheless, these differences serve a common purpose in an essential enzyme whose allosteric regulation might date back to the era when the molecular mechanisms behind the central dogma evolved.     

Greedily building protein networks with confidence

MOTIVATION: With genome sequences complete for human and model organisms, it is essential to understand how individual genes and proteins are organized into biological networks. Much of the organization is revealed by proteomics experiments that now generate torrents of data. Extracting relevant complexes and pathways from high-throughput proteomics data sets has posed a challenge, however, and new methods to identify and extract networks are essential. We focus on the problem of building pathways starting from known proteins of interest. RESULTS: We have developed an efficient, greedy algorithm, SEEDY, that extracts biologically relevant biological networks from protein-protein interaction data, building out from selected seed proteins. The algorithm relies on our previous study establishing statistical confidence levels for interactions generated by two-hybrid screens and inferred from mass spectrometric identification of protein complexes. We demonstrate the ability to extract known yeast complexes from high-throughput protein interaction data with a tunable parameter that governs the trade-off between sensitivity and selectivity. DNA damage repair pathways are presented as a detailed example. We highlight the ability to join heterogeneous data sets, in this case protein-protein interactions and genetic interactions, and the appearance of cross-talk between pathways caused by re-use of shared components. SIGNIFICANCE AND COMPARISON: The significance of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E interactions, a single adjustable parameter controls the size of the pathways that are generated, and an associated P-value indicates the statistical confidence that the pathways are enriched for proteins with a coherent function. Previous approaches have focused on extracting sub-networks by identifying motifs enriched in known biological networks. SEEDY provides the complementary ability to perform a directed search based on proteins of interest. AVAILABILITY: SEEDY software (Perl source), data tables and confidence score models (R source) are freely available from the author.     

Ingeneue: a versatile tool for reconstituting genetic networks,with examples from the segment polarity network

Here we describe a software tool for synthesizing molecular genetic data into models of genetic networks. Our software program Ingeneue, written in Java, lets the user quickly turn a map of a genetic network into a dynamical model consisting of a set of ordinary differential equations. We developed Ingeneue as part of an ongoing effort to explore the design and evolvability of genetic networks. Ingeneue has three principal advantages over other available mathematical software: it automates instantiation of the same network model in each cell in a 2-D sheet of cells; it constructs model equations from pre-made building blocks corresponding to common biochemical processes; and it automates searches through parameter space, sensitivity analyses, and other common tasks. Here we discuss the structure of the software and some of the issues we have dealt with. We conclude with some examples of results we have achieved with Ingeneue for the Drosophila segment polarity network.     

Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.     

DNA containing non-nucleosidic phenanthrene building blocks with asymmetrical linkers

The synthesis and hybridization properties of oligonucleotides containing phenanthrene building blocks with non-nucleosidic linkers of different length are described. It was found that the length of the linkers, as well as the combination of unequal linkers can have a substantial influence on the thermal stability of the modified DNA.