ProfPPIdb: Pairs of physical protein-protein interactions predicted for entire proteomes

Motivation Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. Results We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784

Structural and Functional Analysis of Multi-Interface Domains

10.1371/journal.pone.0050821PLoS ONE71

Pricing European Options with a Log Student's t-Distribution: a Gosset Formula

The distribution of the returns for a stock are not well described by a normal probability density function (pdf). Student's t-distributions, which have fat tails, are known to fit the distributions of the returns. We present pricing of European call or put options using a log Student's t-distribution, which we call a Gosset approach in honour of W.S. Gosset, the author behind the nom de plume Student. The approach that we present can be used to price European options using other distributions and yields the Black-Scholes formula for returns described by a normal pdf.Comment: 12 journal pages, 9 figures and 3 tables (Submitted to Physica A

Processfolio: uniting Academic Literacies and Critical Emancipatory Action Research for practitioner-led inquiry into EAP writing assessment

This paper reports on the design and implementation of an alternative form of writing assessment on a UK English for Academic Purposes (EAP) presessional course. The assessment, termed processfolio, was a response to research inquiry into how writing assessment in a local context negated student agency and inculcated disempowering models of teaching and learning academic writing. The project merged an Academic Literacies approach to writing (Lea and Street, 1998) with a Critical Emancipatory Action Research (Carr and Kemmis, 1986) framework and a Critical Realist(Bhaskar, 1989) perspective. Data collected from the folios and interviews with students and teachers on their experiences of the processfolio found that a small scale intervention has potential for agency to be exercised within the highly constrained context of a UK EAP pre-sessional. New directions in research are proposed which can engage students and teachers to work for change in UK EAP assessment within their internal and external constraints

ADHD and brain anatomy:What do academic textbooks used in the Netherlands tell students?

Studies of brain size of children classified with ADHD appear to reveal smaller brains when compared to ‘normal’ children. Yet, what does this mean? Even with the use of rigorously screened case and control groups, these studies show only small, average group differences between children with and without an ADHD classification. However, academic textbooks used in the Netherlands often portray individual children with an ADHD classification as having a different, malfunctioning brain that necessitates medical intervention. This conceptualisation of ADHD might serve professional interests, but not necessarily the interests of children

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

Background. The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. Results. We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. Conclusions. In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches

Amplification by PCR Artificially Reduces the Proportion of the Rare Biosphere in Microbial Communities

The microbial world has been shown to hold an unimaginable diversity. The use of rRNA genes and PCR amplification to assess microbial community structure and diversity present biases that need to be analyzed in order to understand the risks involved in those estimates. Herein, we show that PCR amplification of specific sequence targets within a community depends on the fractions that those sequences represent to the total DNA template. Using quantitative, real-time, multiplex PCR and specific Taqman probes, the amplification of 16S rRNA genes from four bacterial species within a laboratory community were monitored. Results indicate that the relative amplification efficiency for each bacterial species is a nonlinear function of the fraction that each of those taxa represent within a community or multispecies DNA template. Consequently, the low-proportion taxa in a community are under-represented during PCR-based surveys and a large number of sequences might need to be processed to detect some of the bacterial taxa within the ‘rare biosphere’. The structure of microbial communities from PCR-based surveys is clearly biased against low abundant taxa which are required to decipher the complete extent of microbial diversity in nature

Experimental Animal Models in Periodontology: A Review

In periodontal research, animal studies are complementary to in vitro experiments prior to testing new treatments. Animal models should make possible the validation of hypotheses and prove the safety and efficacy of new regenerating approaches using biomaterials, growth factors or stem cells. A review of the literature was carried out by using electronic databases (PubMed, ISI Web of Science). Numerous animal models in different species such as rats, hamsters, rabbits, ferrets, canines and primates have been used for modeling human periodontal diseases and treatments. However, both the anatomy and physiopathology of animals are different from those of humans, making difficult the evaluation of new therapies. Experimental models have been developed in order to reproduce major periodontal diseases (gingivitis, periodontitis), their pathogenesis and to investigate new surgical techniques. The aim of this review is to define the most pertinent animal models for periodontal research depending on the hypothesis and expected results