Scoring Protein Relationships in Functional Interaction Networks Predicted from Sequence Data

The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins

Lorentz breaking Effective Field Theory and observational tests

Analogue models of gravity have provided an experimentally realizable test field for our ideas on quantum field theory in curved spacetimes but they have also inspired the investigation of possible departures from exact Lorentz invariance at microscopic scales. In this role they have joined, and sometime anticipated, several quantum gravity models characterized by Lorentz breaking phenomenology. A crucial difference between these speculations and other ones associated to quantum gravity scenarios, is the possibility to carry out observational and experimental tests which have nowadays led to a broad range of constraints on departures from Lorentz invariance. We shall review here the effective field theory approach to Lorentz breaking in the matter sector, present the constraints provided by the available observations and finally discuss the implications of the persisting uncertainty on the composition of the ultra high energy cosmic rays for the constraints on the higher order, analogue gravity inspired, Lorentz violations.Comment: 47 pages, 4 figures. Lecture Notes for the IX SIGRAV School on "Analogue Gravity", Como (Italy), May 2011. V.3. Typo corrected, references adde

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Relativistic quantum clocks

The conflict between quantum theory and the theory of relativity is exemplified in their treatment of time. We examine the ways in which their conceptions differ, and describe a semiclassical clock model combining elements of both theories. The results obtained with this clock model in flat spacetime are reviewed, and the problem of generalizing the model to curved spacetime is discussed, before briefly describing an experimental setup which could be used to test of the model. Taking an operationalist view, where time is that which is measured by a clock, we discuss the conclusions that can be drawn from these results, and what clues they contain for a full quantum relativistic theory of time.Comment: 12 pages, 4 figures. Invited contribution for the proceedings for "Workshop on Time in Physics" Zurich 201

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

Application of COMPOCHIP Microarray to Investigate the Bacterial Communities of Different Composts

A microarray spotted with 369 different 16S rRNA gene probes specific to microorganisms involved in the degradation process of organic waste during composting was developed. The microarray was tested with pure cultures, and of the 30,258 individual probe-target hybridization reactions performed, there were only 188 false positive (0.62%) and 22 false negative signals (0.07%). Labeled target DNA was prepared by polymerase chain reaction amplification of 16S rRNA genes using a Cy5-labeled universal bacterial forward primer and a universal reverse primer. The COMPOCHIP microarray was applied to three different compost types (green compost, manure mix compost, and anaerobic digestate compost) of different maturity (2, 8, and 16 weeks), and differences in the microorganisms in the three compost types and maturity stages were observed. Multivariate analysis showed that the bacterial composition of the three composts was different at the beginning of the composting process and became more similar upon maturation. Certain probes (targeting Sphingobacterium, Actinomyces, Xylella/Xanthomonas/ Stenotrophomonas, Microbacterium, Verrucomicrobia, Planctomycetes, Low G + C and Alphaproteobacteria) were more influential in discriminating between different composts. Results from denaturing gradient gel electrophoresis supported those of microarray analysis. This study showed that the COMPOCHIP array is a suitable tool to study bacterial communities in composts

Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

Background: Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets. Results: The focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets. Conclusions: The MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website

Embryogenic potential and expression of embryogenesis-related genes in conifers are affected by treatment with a histone deacetylase inhibitor

Somatic embryogenesis is used for vegetative propagation of conifers. Embryogenic cultures can be established from zygotic embryos; however, the embryogenic potential decreases during germination. In Arabidopsis, LEAFY COTYLEDON (LEC) genes are expressed during the embryonic stage, and must be repressed to allow germination. Treatment with the histone deacetylase inhibitor trichostatin A (TSA) causes de-repression of LEC genes. ABSCISICACID3 (ABI3) and its Zeamays ortholog VIVIPAROUS1 (VP1) act together with the LEC genes to promote embryo maturation. In this study, we have asked the question whether TSA treatment in a conifer affects the embryogenic potential and the expression of embryogenesis-related genes. We isolated two conifer LEC1-type HAP3 genes, HAP3A and HAP3B, from Picea abies and Pinus sylvestris. A comparative phylogenetic analysis of plant HAP3 genes suggests that HAP3A and HAP3B are paralogous genes originating from a duplication event in the conifer lineage. The expression of HAP3A is high, in both somatic and zygotic embryos, during early embryo development, but decreases during late embryogeny. In contrast, the expression of VP1 is initially low but increases during late embryogeny. After exposure to TSA, germinating somatic embryos of P. abies maintain the competence to differentiate embryogenic tissue, and simultaneously the germination progression is partially inhibited. Furthermore, when embryogenic cultures of P. abies are exposed to TSA during embryo maturation, the maturation process is arrested and the expression levels of PaHAP3A and PaVP1 are maintained, suggesting a possible link between chromatin structure and expression of embryogenesis-related genes in conifers

A new pairwise kernel for biological network inference with support vector machines

International audienceBACKGROUND: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). RESULTS: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. CONCLUSION: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data