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Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled "An intrinsically disordered protein user community proposal for ELIXIR" held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders. BW was funded by a grant from the BBSRC [BB/P024092/1]. AB, SO and PLM were supported by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number [U24HG007822] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health), by European Molecular Biology Laboratory (EMBL) and by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation SERI. AG was supported by the European Molecular Biology Laboratory (EMBL) and the Wellcome Trust [104948]. AE was supported by a Vetenskapsrådet grant [2016-03798]. EP was supported by Danmarks Grundforskningsfond [DNRF125] and a Carlsberg Foundation Distinguished Fellowship [CF18-0314]. JLS was supported by the Israel I-CORE Project: Integrated Structural Cell Biology and by Instruct-ERIC. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement [778247] and COST Action BM1405 NGP-net.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

// Pinelopi Samara 1 , Vivi Miriagou 2 , Michael Zachariadis 1,3 , Olga Mavrofrydi 1 , Vasilis J. Promponas 4 , Skarlatos G. Dedos 1 , Panagiota Papazafiri 1 , Hubert Kalbacher 5 , Wolfgang Voelter 5 and Ourania Tsitsilonis 1 1 Department of Biology, National and Kapodistrian University of Athens, Athens, Greece 2 Laboratory of Bacteriology, Hellenic Pasteur Institute, Athens, Greece 3 Institute of Biosciences and Applications, National Center for Scientific Research “Demokritos”, Agia Paraskevi, Athens, Greece 4 Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus 5 Interfaculty Institute of Biochemistry, University of Tubingen, Tubingen, Germany Correspondence to: Ourania Tsitsilonis, email: // Pinelopi Samara, email: // Keywords : apoptosis, biomarker, Klebsiella pneumoniae, proTα(100-109), sepsis, Immunology and Microbiology Section, Immune response, Immunity Received : December 15, 2016 Accepted : April 17, 2017 Published : May 24, 2017 Abstract Sepsis is a life-threatening condition that requires urgent care. Thus, the identification of specific and sensitive biomarkers for its early diagnosis and management are of clinical importance. The alarmin prothymosin alpha (proTα) and its decapeptide proTα(100-109) are immunostimulatory peptides related to cell death. In this study, we generated bacterial models of sepsis in mice using two Klebsiella pneumoniae strains (L-78 and ATCC 43816) and monitored sepsis progression using proTα(100-109) as a biomarker. Serum concentration of proTα(100-109) gradually increased as sepsis progressed in mice infected with L-78, a strain which, unlike ATCC 43816, was phagocytosed by monocytes/macrophages. Analysis of splenocytes from L-78-infected animals revealed that post-infection spleen monocytes/macrophages were gradually driven to caspase-3-mediated apoptosis. These results were verified in vitro in L-78-infected human monocytes/macrophages. Efficient phagocytosis of L-78 by monocytes stimulated their apoptosis and the concentration of proTα(100-109) in culture supernatants increased. Human macrophages strongly phagocytosed L-78, but resisted cell death. This is the first report suggesting that high levels of proTα(100-109) correlate, both in vitro and in vivo , with increased percentages of cell apoptosis. Moreover, we showed that low levels of proTα(100-109) early post-infection likely correlate with sepsis resolution and thus, the decapeptide could eventually serve as an early surrogate biomarker for predicting bacteria-induced sepsis outcome.

Bacterial virulence may rely on secondary metabolism, but core metabolism genes are assumed to be necessary primarily for bacterial growth. To assess this assumption, we correlated the genome, the transcriptome and the pathogenicity of 30 fully sequenced Pseudomonas strains using two Drosophila and one mouse infection assay. In accordance with previous studies gene presence-absence does not explain differences in virulence among P. aeruginosa strains, but merely between P. aeruginosa and other Pseudomonas species. Similarly, classical gene expression analysis of highly vs. lowly pathogenic P. aeruginosa strains identifies many virulence factors, and only a few metabolism genes related to virulence. Nevertheless, assessing the virulence of 553 core metabolic and 95 random non-metabolic gene mutants of P. aeruginosa PA14, we found 16.5% of the core metabolic and 8.5% of the non-metabolic genes to be necessary for full virulence. Strikingly, 11.8% of the core metabolism genes exhibit defects in virulence that cannot be attributed to auxotrophy. The compromised in virulence metabolic gene mutants were mapped in multiple pathways and exhibited further defects in acute virulence phenotypes and in a mouse lung infection model. Functional transcriptomics re-analysis of core metabolism at the pathway level, reveals amino-acid, succinate, citramalate, and chorismate biosynthesis and beta-oxidation as important for full virulence and expression of these pathways indicative of virulence in various strains. Thus, P. aeruginosa virulence variation, which to this point remains unpredictably combinatorial at the gene level, can be dissected at the pathway level via combinatorial trancriptome and functional core metabolism analysis.

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome. Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) of Argentina [PICT-2015/3367, PICT-2017/1924]; Ministry of Education, Science and Technological Development of the Republic of Serbia [ON173001]; Vetenskapsrådet [2016-03798]; Hungarian National Research, Development, and Innovation Office (NKFIH) [FK-128133]; Italian Ministry of Health Young Investigator Grant [GR-2011-02347754]; Ministerio de Economía y Competitividad (MINECO) [BIO2016-78310-R]; ICREA (ICREA-Academia 2015); Fundac¸ão para a Ciência e a Tecnologia (FCT, Portugal); European Regional Development Fund [POCI-01-0145-FEDER-031173, POCI-01-0145-FEDER-029221]; Mexican National Council of Science and Technology (CONACYT) [215503]; Elixir-GR, Action ‘Reinforcement of the Research and Innovation Infrastructure’, Operational Programme ‘Competitiveness, Entrepreneurship and Innovation’ [NSRF 2014-2020]. co-financed by Greece and the European Union (European Regional Development Fund); Hungarian Academy of Sciences [PREMIUM-2017-48]; Carlsberg Distinguished Fellowship [CF18-0314]; Danmarks Grundforskningsfond [DNRF125]; National Research, Development and Innovation Office [K-125340]; Research Foundation Flanders (FWO) [G.0328.16N]; Hungarian Academy of Sciences [LP2014-18]; OTKA [K108798 and K124670]. This project has received funding from the European Union’s Horizon 2020 research and innovation programme [778247]. Funding for open access charge: European Union’s Horizon 2020 research and innovation programme [778247]. Conflict of interest statement. None declared.

The widespread occurrence of repetitive stretches of DNA in genomes of organisms across the tree of life imposes fundamental challenges for sequencing, genome assembly, and automated annotation of genes and proteins. This multi-level problem can lead to errors in genome and protein databases that are often not recognized or acknowledged. As a consequence, end users working with sequences with repetitive regions are faced with ‘ready-to-use’ deposited data whose trustworthiness is difficult to determine, let alone to quantify. Here, we provide a review of the problems associated with tandem repeat sequences that originate from different stages during the sequencing-assembly-annotation-deposition workflow, and that may proliferate in public database repositories affecting all downstream analyses. As a case study, we provide examples of the Atlantic cod genome, whose sequencing and assembly were hindered by a particularly high prevalence of tandem repeats. We complement this case study with examples from other species, where mis-annotations and sequencing errors have propagated into protein databases. With this review, we aim to raise the awareness level within the community of database users, and alert scientists working in the underlying workflow of database creation that the data they omit or improperly assemble may well contain important biological information valuable to others.

The linguistic foundations of science and technology include many terms that have been borrowed from ancient languages. In the case of terms with origins in the Greek language, the modern meaning can often differ significantly from the original one. Here we use the PubMed database to demonstrate the prevalence of words of Greek origin in the language of modern science, and call for scientists to exercise care when coining new terms.

Author(s): Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S; Vlassi, Metaxia; Promponas, Vasilis J; Ouzounis, Christos A | Abstract: We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.

Despite the controversy regarding the importance of protein N-linked glycosylation in species of the genus Plasmodium, genes potentially encoding core subunits of the oligosaccharyltransferase (OST) complex have already been characterized in completely sequenced genomes of malaria parasites. Nevertheless, the currently established notion is that only four out of eight subunits of the OST complex-which is considered conserved across eukaryotes-are present in Plasmodium species. In this study, we carefully conduct computational analysis to provide unequivocal evidence that all components of the OST complex, with the exception of Swp1/Ribophorin II, can be reliably identified within completely sequenced plasmodial genomes. In fact, most of the subunits currently considered as absent from Plasmodium refer to uncharacterized protein sequences already existing in sequence databases. Interestingly, the main reason why the unusually short Ost4 subunit (36 residues long in yeast) has not been identified so far in plasmodia (and possibly other species) is the failure of gene-prediction pipelines to detect such a short coding sequence. We further identify elusive OST subunits in select protist species with completely sequenced genomes. Thus, our work highlights the necessity of a systematic approach towards the characterization of OST subunits across eukaryotes. This is necessary both for obtaining a concrete picture of the evolution of the OST complex but also for elucidating its possible role in eukaryotic pathogens.

Summary: The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed ® and related biological databases. Herein, we describe BioTextQuest + , a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest + enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest + addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest + through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. Availability: The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest . Contact: g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary information: Supplementary data are available at Bioinformatics online.

Eukaryotic cells are defined by compartments through which the trafficking of macromolecules is mediated by large complexes, such as the nuclear pore, transport vesicles and intraflagellar transport. The assembly and maintenance of these complexes is facilitated by endomembrane coatomers, long suspected to be divergently related on the basis of structural and more recently phylogenomic analysis. By performing supervised walks in sequence space across coatomer superfamilies, we uncover subtle sequence patterns that have remained elusive to date, ultimately unifying eukaryotic coatomers by divergent evolution. The conserved residues shared by 3,502 endomembrane coatomer components are mapped onto the solenoid superhelix of nucleoporin and COPII protein structures, thus determining the invariant elements of coatomer architecture. This ancient structural motif can be considered as a universal signature connecting eukaryotic coatomers involved in multiple cellular processes across cell physiology and human disease.

We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of two or more unknown spectral density functions has a parametric linear combination of cosines. Maximum likelihood inference is worked out in detail and it is shown that its output yields several distance measures among independent stationary time series. These similarity indices are suitable for clustering time series data based on their second order properties. Other spectral domain based distances are investigated as well and we compare all methods and distances to the problem of producing segmentations of bacterial outer membrane proteins consistent with their transmembrane topology. Protein sequences are transformed to time series data by employing numerical scales of physicochemical parameters. We also present interesting results on the prediction of transmembrane β-strands, based on the clustering outcome, for a representative set of bacterial outer membrane proteins with given three-dimensional structure. © 2010 Elsevier Ireland Ltd. 100 2 132 143 Cited By :2

Clustering of stationary time series has become an important tool in many scientific applications, like medicine, finance, etc. Time series clustering methods are based on the calculation of suitable similarity measures which identify the distance between two or more time series. These measures are either computed in the time domain or in the spectral domain. Since the computation of time domain measures is rather cumbersome we resort to spectral domain methods. A new measure of distance is proposed and it is based on the so-called cepstral coefficients which carry information about the log spectrum of a stationary time series. These coefficients are estimated by means of a semiparametric model which assumes that the log-likelihood ratio of two or more unknown spectral densities has a linear parametric form. After estimation, the estimated cepstral distance measure is given as an input to a clustering method to produce the disjoint groups of data. Simulated examples show that the method yields good results, even when the processes are not necessarily linear. These cepstral-based clustering algorithms are applied to biological time series. In particular, the proposed methodology effectively identifies distinct and biologically relevant classes of amino acid sequences with the same physicochemical properties, such as hydrophobicity. © 2008 Elsevier Ltd. All rights reserved. 41 7 2398 2412 Cited By :15

Predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal with this class of problems. In this paper, we present the development and implementation of the Scaled Conjugate Gradient (SCG) learning algorithm for BRNN architectures. The model has been tested on the Protein Secondary Structure Prediction (PSSP) and Transmembrane Protein Topology Prediction problems (TMPTP). Our method currently achieves preliminary results close to 73% correct predictions for the PSSP problem and close to 79% for the TMPTP problem, which are expected to increase with larger datasets, external rules, ensemble methods and filtering techniques. Importantly, the SCG algorithm is training the BRNN architecture approximately 3 times faster than the Backpropagation Through Time (BPTT) algorithm. © Springer International Publishing Switzerland 2016. 9886 LNCS 123 131 Sponsors: Conference code: 180929

Background Transcriptome studies have revealed that many eukaryotic genomes are pervasively transcribed producing numerous long non-coding RNAs (lncRNAs). However, only a few lncRNAs have been ascribed a cellular role thus far, with most regulating the expression of adjacent genes. Even less lncRNAs have been annotated as essential hence implying that the majority may be functionally redundant. Therefore, the function of lncRNAs could be illuminated through systematic analysis of their synthetic genetic interactions (GIs). Results Here, we employ synthetic genetic array (SGA) in Saccharomyces cerevisiae to identify GIs between long intergenic non-coding RNAs (lincRNAs) and protein-coding genes. We first validate this approach by demonstrating that the telomerase RNA TLC1 displays a GI network that corresponds to its well-described function in telomere length maintenance. We subsequently performed SGA screens on a set of uncharacterised lincRNAs and uncover their connection to diverse cellular processes. One of these lincRNAs, SUT457, exhibits a GI profile associating it to telomere organisation and we consistently demonstrate that SUT457 is required for telomeric overhang homeostasis through an Exo1-dependent pathway. Furthermore, the GI profile of SUT457 is distinct from that of its neighbouring genes suggesting a function independent to its genomic location. Accordingly, we show that ectopic expression of this lincRNA suppresses telomeric overhang accumulation in sut457Δ cells assigning a trans-acting role for SUT457 in telomere biology. Conclusions Overall, our work proposes that systematic application of this genetic approach could determine the functional significance of individual lncRNAs in yeast and other complex organisms. Electronic supplementary material The online version of this article (doi:10.1186/s12915-016-0325-7) contains supplementary material, which is available to authorized users.

Atg8-family proteins are the best-studied proteins of the core autophagic machinery. They are essential for the elongation and closure of the phagophore into a proper autophagosome. Moreover, Atg8-family proteins are associated with the phagophore from the initiation of the autophagic process to, or just prior to, the fusion between autophagosomes with lysosomes. In addition to their implication in autophagosome biogenesis, they are crucial for selective autophagy through their ability to interact with selective autophagy receptor proteins necessary for the specific targeting of substrates for autophagic degradation. In the past few years it has been revealed that Atg8-interacting proteins include not only receptors but also components of the core autophagic machinery, proteins associated with vesicles and their transport, and specific proteins that are selectively degraded by autophagy. Atg8-interacting proteins contain a short linear LC3-interacting region/LC3 recognition sequence/Atg8-interacting motif (LIR/LRS/AIM) motif which is responsible for their interaction with Atg8-family proteins. These proteins are referred to as LIR-containing proteins (LIRCPs). So far, many experimental efforts have been carried out to identify new LIRCPs, leading to the characterization of some of them in the past 10 years. Given the need for the identification of LIRCPs in various organisms, we developed the iLIR database (https://ilir.warwick.ac.uk) as a freely available web resource, listing all the putative canonical LIRCPs identified in silico in the proteomes of 8 model organisms using the iLIR server, combined with a Gene Ontology (GO) term analysis. Additionally, a curated text-mining analysis of the literature permitted us to identify novel putative LICRPs in mammals that have not previously been associated with autophagy. © 2016 The Author(s). Published with license by Taylor & Francis. © Anne-Claire Jacomin, Siva Samavedam, Vasilis Promponas, and Ioannis P. Nezis. 12 1945 1953 Cited By :3

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Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources. This paper presents the overall design and implementation of a web based scientific workflow system for virtual screening called, the Life Sciences Informatics (LiSIs) platform. The LiSIs platform consists of the following layers: the input layer covering the data file input the pre-processing layer covering the descriptors calculation, and the docking preparation components the processing layer covering the attribute filtering, compound similarity, substructure matching, docking prediction, predictive modelling and molecular clustering post-processing layer covering the output reformatting and binary file merging components output layer covering the storage component. The potential of LiSIs platform has been demonstrated through two case studies designed to illustrate the preparation of tools for the identification of promising chemical structures. The first case study involved the development of a Quantitative Structure Activity Relationship (QSAR) model on a literature dataset while the second case study implemented a docking-based virtual screening experiment. Our results show that VS workflows utilizing docking, predictive models and other in silico tools as implemented in the LiSIs platform can identify compounds in line with expert expectations. We anticipate that the deployment of LiSIs, as currently implemented and available for use, can enable drug discovery researchers to more easily use state of the art computational techniques in their search for promising chemical compounds. The LiSIs platform is freely accessible (i) under the GRANATUM platform at: http://www.granatum.org and (ii) directly at: http://lisis.cs.ucy.ac.cy. © 2015 Bentham Science Publishers. 18 281 295 Cited By :2

Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources. This paper presents the overall design and implementation of a web based scientific workflow system for virtual screening called, the Life Sciences Informatics (LiSIs) platform. The LiSIs platform consists of the following layers: the input layer covering the data file input the pre-processing layer covering the descriptors calculation, and the docking preparation components the processing layer covering the attribute filtering, compound similarity, substructure matching, docking prediction, predictive modelling and molecular clustering post-processing layer covering the output reformatting and binary file merging components output layer covering the storage component. The potential of LiSIs platform has been demonstrated through two case studies designed to illustrate the preparation of tools for the identification of promising chemical structures. The first case study involved the development of a Quantitative Structure Activity Relationship (QSAR) model on a literature dataset while the second case study implemented a docking-based virtual screening experiment. Our results show that VS workflows utilizing docking, predictive models and other in silico tools as implemented in the LiSIs platform can identify compounds in line with expert expectations. We anticipate that the deployment of LiSIs, as currently implemented and available for use, can enable drug discovery researchers to more easily use state of the art computational techniques in their search for promising chemical compounds. The LiSIs platform is freely accessible (i) under the GRANATUM platform at: http://www.granatum.org and (ii) directly at: http://lisis.cs.ucy.ac.cy. © 2015 Bentham Science Publishers. 18 3 281 295 Cited By :2

ID: 743 In: Bioinformatics volume 31 issue 13 page 2208. Summary: Motivation: Local compositionally biased and low complexity regions (LCRs) in amino acid sequences have initially attracted the interest of researchers due to their implication in generating artifacts in sequence database searches. There is accumulating evidence of the biological significance of LCRs both in physiological and in pathological situations. Nonetheless, LCR-related algorithms and tools have not gained wide appreciation across the research community, partly due to the fact that only a handful of user-friendly software is currently freely available.Results: We developed LCR-eXXXplorer, an extensible online platform attempting to fill this gap. LCR-eXXXplorer offers tools for displaying LCRs from the UniProt/SwissProt knowledgebase, in combination with other relevant protein features, predicted or experimentally verified. Moreover, users may perform powerful queries against a custom designed sequence/LCR-centric database. We anticipate that LCR-eXXXplorer will be a useful starting point in research efforts for the elucidation of the structure, function and evolution of proteins with LCRs.Availability and implementation: LCR-eXXXplorer is freely available at the URL http://repeat.biol.ucy.ac.cy/lcr-exxxplorer.Contact: vprobon@ucy.ac.cySupplementary information: Supplementary data are available at Bioinformatics online.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

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A simple yet powerful programming tool enabling in silico experimentation, end-to-end data management through web services as well as use of grid and cloud processing power is scientific workflows. This technology is receiving considerable interest in recent years primarily due to its ability to promote and support scientific collaboration among large distributed research teams. The paper reviews the Scientific Workflows Management Systems (SWMS) field and investigates in detail popular open source workflow systems used commonly in life sciences informatics. Emphasis is placed on features which make these systems attractive for scientific use, e.g. user friendliness, use of distributed resources, reusability, provenance, collaboration, data integration, etc. Our conclusions indicate that although SWMS, including open source ones, have several open issues, their unique features and strong momentum clearly suggest that it is only a matter of time before they are adopted in even more scientific fields. © 2012 IEEE. 552 558 Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206 Cited By :3

Computer-aided drug discovery techniques have been widely used in recent years to support the development of new pharmaceuticals. Virtual screening, the computational counterpart of experimental screening, attempts to replicate the results from in vitro and in vivo methods through the use of in silico models and tools. This paper presents the LISIs platform a web based scientific workflow system for virtual screening that has been implemented primarily for the discovery of chemoprevention agents. We describe the overall design of the system as well as the implementation of its various components. Indicative results from early applications of the system are also presented to illustrate its potential uses and functionalities. © 2012 IEEE. 439 446 Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206 Cited By :4

Clustering methods are used routinely to form groups of objects with similar characteristics. Collections of time series datasets appear in several biological applications. Some of these applications require grouping the observed time series data to homogeneous clusters. We review methods for time series frequency domain based clustering with emphasis on applications. Our point of view is that an appropriate notion of clustering for time series data can be developed by means of the spectral density function and its sample counterpart, the periodogram. For the development of frequency domain based clustering algorithms, it is required to define suitable similarity (or dissimilarity) measures. We review several such measures and we discuss various clustering algorithms in this context. Biological applications of time series frequency domain clustering are studied along with interesting complementary approaches. © 2011 Blackwell Publishing Ltd. 33 5 744 756 Cited By :2

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement. © 2006 IEEE. 9 3 731 739 Cited By :7

ID: 745 In: Briefings in bioinformatics volume 15 issue 3 page 443. Summary: More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.

Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings. © 2012 IEEE. vii ix Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206

The ability to monitor interactions between individuals over time can provide us with information on life histories, mating systems, behavioural interactions between individuals and ecological interactions with the environment. Tracking individuals over time has traditionally been a time- and often a cost-intensive exercise, and certain types of animals are particularly hard to monitor. Here we use canonical discriminant analysis (CDA) to identify individual Mexican Ant-thrushes using data extracted with a semi-automated procedure from song recordings. We test the ability of CDA to identify individuals over time, using recordings obtained over a 4-year period. CDA correctly identified songs of 12 individual birds 93.3% of the time from recordings in one year (2009), while including songs of 18 individuals as training data. Predicting singers in one year using recordings from other years indicated some instances of variation, with correct classification in the range of 67-88% one individual was responsible for the great majority (66%) of classification errors. We produce temporal maps of the study plot showing that considerably more information was provided by identifying individuals from their songs than by ringing and re-sighting colour-ringed individuals. The spatial data show site fidelity in males, but medium-term pair bonds and an apparently large number of female floaters. Recordings can be used to monitor intra- and intersexual interactions of animals, their movements over time, their interactions with the environment and their population dynamics. © 2011 The Authors. Journal compilation © 2011 British Ornithologists' Union. 153 255 268 Cited By :31

Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is currently considered as one of the optimal computational neural network type architectures for addressing the problem. In this paper, we implement the same BRNN architecture, but we use a modified training procedure. More specifically, our aim is to identify the effect of the contribution of local versus global information, by varying the length of the segment on which the Recurrent Neural Networks operate for each residue position considered. For training the network, the backpropagation learning algorithm with an online training procedure is used, where the weight updates occur for every amino acid, as opposed to Baldi et al. [1], where the weight updates are applied after the presentation of the entire protein. Our results with a single BRNN are better than Baldi et al. [1] by three percentage points (Q3) and comparable to results of [1] when they use an ensemble of 6 BRNNs. In addition, our results improve even further when sequence-to-structure output is filtered in a post-processing step, with a novel Hidden Markov Model-based approach. © 2010 IFIP. 339 AICT 128 137 Sponsors: Cyprus University of Technology Frederick University Cyprus Tourism Organization Conference code: 82446 Cited By :4

EuroKUP (Urine and Kidney Proteomics www.eurokup.org) is a COST (European Cooperation in the field of Scientific and Technical research: www.cost.esf.org Action fostering amulti-disciplinary network of investigators from 25 countries and focusing on facilitating translational proteomic research in kidney diseases. Four Working Groups focusing respectively on defining clinically important research questions in kidney diseases, kidney tissue proteomics, urine proteomics and bioinformatics have been generated. The EuroKUP members had their second combined Working Group and Management Committee (MC) meeting in Nafplio, Greece from March 29 to 30, 2009. This report summarizes the main presentations, discussions and agreed action points during this meeting. These refer to the design of collaborative projects and clinical center networks for specific kidney diseases establishment of guidelines for kidney tissue proteomics analysis by laser-based imaging- and laser capture microdissection-MS development and characterization of a "standard" urine specimen to be used for assessment of platform capability and data comparability in clinical proteomics applications definition of statistical requirements in biomarker discovery studies and development of a specialized kidney and urine ontology. Various training activities are planned involving training schools on laser capture microdissection- and imaging-MS, workshops on ontologies as well as short-term travel grants for junior investigators. © 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. 3 1017 1022 Cited By :8