Uncovering complex relationships, however, remains the most challenging task as it requires compiling and efficiently querying data from various sources. Here, we describe the Stress Response Array Profiler (StRAP), an open-source, web-based resource for storage, profiling, visualization, and sharing of cancer genomic data. StRAP houses multi-cancer microarray data with major emphasis on radiotherapy studies, and takes a systems biology approach towards the integration, comparison, and cross-validation of multiple cancer profiling studies. The database is a comprehensive platform for comparative analysis of gene expression data. For effective use of arrays, we provide user-friendly and interactive visualization tools that can display the data and query results. StRAP is web-based, platform-independent, and freely accessible at  DNA microarrays are successfully being used to classify tumors and identify novel biomarkers associated with cancer  We develop StRAP, a free web-accessible resource to address the need to query, compare, profile, and visualize results from different microarray experiments. StRAP hosts data from diverse cancer studies (currently from 12 different tissue types), and will be further extended in the future. We used Z scoring method to standardize data, since the internally normalized values do not change with subsequent addition of new datasets. All data are mapped to Entrez Gene identifiers for consistency in comparison. The user-friendly interface facilitates exploration by a wide-range of researchers, including those with little expertise in bioinformatics.In the remainder of this paper we briefly describe StRAP’s construction and core features.The runtime architecture of StRAP is described in http://www.ncbi.nlm.nih.gov/), (2) Pre-processed gene expression microarray molecular profile data (including pre-computed statistics), (3) Metadata on cell lines, and (4) Metadata on platform-associated information.Four main data repositories reside at the backend of StRAP: (1) Gene associated annotation information derived from the National Center for Biotechnology Information .The repositories are stored as a MySQL relational database .Pre-processing and computation of statistical tests are performed in the R environment (http://www.php.net/) and Python (http://www.python.org/). The application is deployed on an Apache HTTP server (http://httpd.apache.org/) at the National Cancer Institute (NCI).The front end interface is a web-based application implemented using R, PHP (Data access and presentation is organized around three main concepts or modules: (1) Genes, (2) Cell lines, and (3) Arrays. Flexible user-defined data queries can be initiated from any of the modules; the data visualization options for the results are displayed in integrated views and may, depending on the query, involve cross-talk between modules. Several links to external resources promote a systems biology approach. The genes module enables gene-centric queries of the StRAP microarray studies. Queries can be based on gene or protein identifiers, synonyms, gene descriptions, or chromosome location. The results include associated arrays and studies, and a compilation of gene-annotation information, spatial localization within the genome visualized in the UCSC Genome browser A typical gene-centric query  to another .The cell lines module provides metadata on available cell lines and associated studies. Queries in this module are tailored to allow selection of complete studies, by tissue of origin, or individual cell line. Comparisons can be made for samples within a study or across studies. .Currently because of the limitation of the number of studies available, it may have limited biological significance. However, the framework of the database is flexible and would allow extensions with data from other types of cancer studies that will help in novel findings.The database will be periodically updated with new studies and features. We plan, for example, to enable construction of interaction networks using literature text-mining, and information from the Human Protein Reference Database (HPRD)


A subset of the information in the database can also be accessed through a freely available web application in the Illumina genomic cloud computing platform BaseSpace at http://www.biomatters.com/apps/melanoma-profiler-for-research. The MelanomaDB database illustrates dysregulation of specific signaling pathways across 310 exome-sequenced melanomas and in individual tumors and identifies the distribution of somatic variants in melanoma. We suggest that MelanomaDB can provide a context in which to interpret the tumor molecular profiles of individual melanoma patients relative to biological information and available drug therapies.Despite on-going research, metastatic melanoma survival rates remain low and treatment options are limited. Researchers can now access a rapidly growing amount of molecular and clinical information about melanoma. This information is becoming difficult to assemble and interpret due to its dispersed nature, yet as it grows it becomes increasingly valuable for understanding melanoma. Integration of this information into a comprehensive resource to aid rational experimental design and patient stratification is needed. As an initial step in this direction, we have assembled a web-accessible melanoma database, MelanomaDB, which incorporates clinical and molecular data from publically available sources, which will be regularly updated as new information becomes available. This database allows complex links to be drawn between many different aspects of melanoma biology: genetic changes  in individual melanomas revealed by DNA sequencing, associations between gene expression and patient survival, data concerning drug targets, biomarkers, druggability, and clinical trials, as well as our own statistical analysis of relationships between molecular pathways and clinical parameters that have been produced using these data sets. The database is freely available at  Melanoma researchers are faced with a rapidly growing amount of useful molecular and clinical data, particularly gene expression information. This rapid growth can be illustrated by surveying the Gene Expression Omnibus (GEO) , an inteUnfortunately, information pertinent to melanoma exists in a diverse range of formats and locations. For example, relevant data about a single gene of interest may include information about the encoded protein’s structure, cellular location, and function, contribution to molecular pathways, drugs that target the protein, the gene, or protein’s utility as a biomarker, genome-wide association studies, mutation frequency, chromosomal aberrations, as well as RNA expression associations with metastasis, treatment response and patient survival, clinical SNP associations, and the results of literature mining. Even within the single data type of tumor DNA sequencing, a variety of methods have been used to implicate genes in melanoma initiation and progression, and these different methods produce data in differing formats. Ideally, all these diverse forms of data could be used by researchers in an integrated fashion to triangulate in on clinically important genes.As a further challenge, genomic information in melanoma is particularly dense due to the high mutation rate found in melanomas of sun-exposed skin . This isTumor development involves multiple genes encoding proteins and non-coding RNAs operating in molecular pathways. Therefore, inference of molecular pathway activity from tumor genomic data using methods such as gene set analysis (GSA)  is usefuBRAF V600E or V600K containing tumors and the majority of treated patients show relatively short term remission, with their relapse almost certainly caused by re-activation of the MAPK pathway, commonly through mutations in NRAS or PDGFRB  pathway molecule BRAF . Howeverr PDGFRB . We propIn the work described here, we have amalgamated a diverse range of genomic and clinical melanoma data, on the scales of both patient population and individual tumor into a single resource. This resource is provided as a downloadable file that can be searched and filtered using any spread sheet application. To facilitate use of this resource in the context of molecular pathways, we also provide a web-accessible SQL database named MelanomaDB, through which researchers can perform GSA using integrated melanoma data of several types. A subset of the information in the database can also be accessed through a freely available web application in the Illumina genomic cloud computing platform BaseSpace. While other disease-specific databases exist for other cancers such as lung  and ovarTo facilitate an integrative analysis of melanoma information we combined a variety of melanoma data in the form of gene sets, attempting to collect information for all genes in the genome. These melanoma gene sets were groups of genes that shared biological or clinical relevance for melanoma, derived from five types of publically available information: drug and biomarker information, druggability, literature relationship strength, disease-specific survival, and somatic mutation data. Drug information includes information on compounds and the proteins they target, while Druggability information comprises of estimations of the degree to which proteins are amenable to targeting by drugs, and protein characteristics relevant to this. A detailed description of this information is available in Data Sheet 1 in Supplementary Material.http://genesetdb.auckland.ac.nz/melanomadb/help.htmlFurther explanations of the gene sets used are in the MelanomaDB help page at http://genesetdb.auckland.ac.nz/melanomadb/help.htmlDrug information was taken from online databases DrugBank version 3 , KEGG DRDruggability data was sourced from the Sophic Integrated Druggable Genome Database , EBI’s DInformation on Literature Relationship strength was derived from the IRIDESCENT  and GAMMGAMMA conducts a meta-analysis of gene expression behavior across 16,000 wide-ranging microarray experiments to identify genes that are consistently and specifically co-expressed across heterogeneous experimental conditions. In this way GAMMA extends the connections in IRIDESCENT’s association network to genes without any published associations to melanoma by identifying which of these genes are consistently co-expressed with multiple known melanoma genes. To date, GAMMA has been used successfully to identify phenotypes and/or disease relevance for several previously uncharacterized genes –45.affy package in the R statistical software  was constructed, with genes (or genomic loci in some cases) as rows. The columns of this matrix represent diverse features of biological functions related to melanoma and are described in Data Sheet 1 in Supplementary Material. This melanoma data matrix can be utilized in a variety of ways. Most simply, researchers can access a variety of data pertaining to their particular gene of interest. The melanoma data matrix can also be manipulated with spread sheet software to sort, find, and filter information in order to generate gene lists useful for hypothesis generation.p ≤ 0.001). Although variations in large genes such as Titan (TTN) have been implicated as cancer drivers, these may also occur in so many melanomas due to large gene size increasing the likelihood of passenger mutations. However, the BRAF gene clearly stands out as frequently mutated in melanomas despite its moderate length. The distribution of the number of genes with somatic alterations in each individual tumor was performed using R and is shown in Figure Next, we assembled as much information about somatic variation in individual exome-sequenced and genome-wide-sequenced melanomas as possible. We gathered information about somatic variations in 58 established melanoma cell lines, 119 primary “fresh” cell lines, 38 primary tumors, and 96 metastatic melanoma tumors, which was appended to the information matrix described above . Information about non-synonymous coding mutations, structural rearrangements, and indels was included (intronic and synonymous coding mutations were excluded from the current iteration of this data resource). The information contained in Data Sheet 2 in Supplementary Material was read into the statistical environment R and visualized, as described in the Section “As an example of using the information assembled above, an approach to identifying novel candidate novel drug targets for melanoma using this melanoma data matrix  can be performed by filtering and sorting Data Sheet 2 in Supplementary Material in a spreadsheet application and is described in Figure AKR7A2, AKR7A3, ARIH1, ARPC1A, CD163, DCT, DHRS11, DUS4L, FAH, FSCN1, HS3ST3A1, NRAS, NUP155, PANK2, PRMT3, QTRT1, RAD1, RAE1, SUV39H2, UPP1, USP13. It is interesting to see NRAS on this list, which is a potential melanoma drug candidate but has proved remarkably resistant to drug development efforts to date  were removed. This list of putative melanoma tumor suppressors and oncogenes that remains is shown in Table Similarly, a list of putative melanoma tumor suppressor genes or melanoma oncogenes can be generated using a spreadsheet application from this melanoma data matrix . For example, a list consisting of genes that are mutated in more than 10% of melanoma metastases and have shorter melanoma-free patient survival associated with their low (putative tumor suppressor) or high (oncogene) RNA expression. Known tumor suppressors and oncogenes that were identified by this strategy  for enrichment analysis , this list of 245 genes was found to be significantly enriched for several gene sets including sets associated with the extracellular matrix (ECM), cell adhesion, and collagen fibril organization. We encourage users to use a spreadsheet application and simple web tools such as GeneSetDB to perform their own exploration of Data Sheet 2 in Supplementary Material.http://genesetdb.auckland.ac.nz/melanomadb/about.htmlIn order to make use of this assembly of melanoma information and its regular updating easier, we converted this melanoma data matrix  into a web-accessible SQL database. This database, named MelanomaDB, features melanoma gene sets derived from Data Sheet 2 in Supplementary Material and directly links into a molecular pathway/GSA meta-database previously generated by our research group named GeneSetDB . Using Mhttp://www.biomatters.com/apps/melanoma-profiler-for-research (click on “sample project” and navigate using green tabs at top of screen). This BaseSpace application performs variant calling against reference sequences for a user-defined tumor, then uses information from MelanomaDB to identify molecular pathways that genes which contain non-synonymous variants constitute. These pathways are visualized relative to targeting drugs and other clinically related information using pathway diagrams, heatmaps, and waterfall plots, in comparison to the 310 melanomas described above. We hope that this app may be of particular use to researchers involved in generating new melanoma tumor sequences.A subset of the information in MelanomaDB was also included in a freely available Illumina BaseSpace application, which can be accessed at PREX2 . Significantly more of the genes in the KEGG pathway named “Melanoma” carried more somatic variants than expected due to chance alone , in agreement with the known importance of the signaling events represented in this pathway to melanoma formation and progression.The information in MelanomaDB can be used to annotate the signalling pathways contained within the R graphite package . This caBRAF as the only somatic variant in this pathway , downstream of a drug  target  may indicate potential for resistance to the drug.In addition, using a function in the MelanomaDB web tool of the R scripts supplied in Data Sheet 5 in Supplementary Material, somatic alteration of genes in specific molecular pathways can be drawn on a patient-by-patient basis Figure . This alWe then used an R script  to perform gene set enrichment analysis using the GATHER web toolThe approach we have described, while already functioning in a useful way as a melanoma-focused integrated genomic database, provides a template for further development to address the limitations below: (i) It will be important to identify the likely effects of specific somatic variations in the sequenced tumors . In future iterations of MelanomaDB, based on larger numbers of tumors, we will include capacity to dissect the type of genetic alteration such as deletions, coding region mutations, promoter mutations, etc. The database may also be expanded to include the results of analyses from software that predict the effects of coding variants on protein function, such as SIFT , PolyPhe78We have brought together a large collection of melanoma genomic data of several types from published studies and publicly available datasets into an easily utilized data matrix that can be analyzed using a spread sheet application. We also assembled data on tumors from individual patients. We then incorporated this information into a web-accessible SQL database, MelanomaDB, which researchers can use to perform molecular pathway and GSA of melanoma genomic data, and into a BaseSpace application. By way of illustration, we used this information to analyze the mutational and expression patterns of genes encoding proteins in specific directional signaling pathways within individual tumors, and annotated these visualizations with information about existing drugs, druggability, associations between RNA expression and survival, and driver mutations. We hope that this resource will prove increasingly useful when it expands as new tumor data becomes available. In particular, we hope it may provide a context in which to interpret the melanoma molecular profiles of new patients as well as patient-specific molecular pathway disruption. We have demonstrated possible uses of this integrated information, and encourage melanoma researchers to employ these resources.Steven Stones-Havas is an employee, and Cristin G. Print a paid consultant, of the company Biomatters Ltd., which works in the general field of genomic visualization. Biomatters Ltd., generated the freely available BaseSpace application described in this manuscript in a non-commercial collaboration with the other authors.http://www.frontiersin.org/Cancer_Genetics/10.3389/fonc.2013.00184/abstractThe Supplementary Material for this article can be found online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


A central challenge to understanding the ecological and biogeochemical roles of microorganisms in natural and human engineered ecosystems is the reconstruction of metabolic interaction networks from environmental sequence information. The dominant paradigm in metabolic reconstruction is to assign functional annotations using BLAST. Functional annotations are then projected onto symbolic representations of metabolism in the form of KEGG pathways or SEED subsystems.Here we present MetaPathways, an open source pipeline for pathway inference that uses the PathoLogic algorithm to map functional annotations onto the MetaCyc collection of reactions and pathways, and construct environmental Pathway/Genome Databases (ePGDBs) compatible with the editing and navigation features of Pathway Tools. The pipeline accepts assembled or unassembled nucleotide sequences, performs quality assessment and control, predicts and annotates noncoding genes and open reading frames, and produces inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways uses MLTreeMap to build phylogenetic trees for selected taxonomic anchor and functional gene markers, converts General Feature Format (GFF) files into concatenated GenBank files for ePGDB construction based on third-party annotations, and generates useful file formats including Sequin files for direct GenBank submission and gene feature tables summarizing annotations, MLTreeMap trees, and ePGDB pathway coverage summaries for statistical comparisons.http://hallam.microbiology.ubc.ca/MetaPathways.MetaPathways provides users with a modular annotation and analysis pipeline for predicting metabolic interaction networks from environmental sequence information using an alternative to KEGG pathways and SEED subsystems mapping. It is extensible to genomic and transcriptomic datasets from a wide range of sequencing platforms, and generates useful data products for microbial community structure and function analysis. The MetaPathways software package, installation instructions, and example data can be obtained from  Metabolic interactions between microorganisms direct matter and energy transformations integral to ecosystem function -3. PluraWith the expansion of next generation sequencing technologies, increasingly complex datasets are being generated for thousands of environmental samples resulting in analytic bottlenecks with the potential to stymie pathway reconstruction efforts. As a result, on-line services for metabolic reconstruction have been developed to externalize data processing burdens and provide warehousing and visualization tools for environmental sequence information. Popular on-line services for metabolic reconstruction include Integrated Microbial Genomes and Metagenomes (IMG/M), Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA), and Metagenome Rapid Annotation using Subsystem Technology (MG-RAST). Both IMG/M ,13 and CPathway Tools ,19 is a Here we extend the PGDB concept for cellular organisms to microbial community structure and function through the introduction of MetaPathways, a modular pipeline for pathway inference that uses the PathoLogic algorithm to build environmental PGDBs (ePGDBs) compatible with the editing and navigation features of Pathway Tools. The pipeline accepts assembled contig or unassembled nucleotide sequences, performs quality control and coverage estimates, predicts and annotates noncoding genes and open reading frames, and produces concatenated GenBank files used as inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways uses MLTreeMap  to buildMetaPathways is a modular pipeline written in Python that calls software components written in C/C++, Perl, and Python. Required input files for MetaPathways include metagenomic or metatranscriptomic sequence data in one of several file formats . The pipeline consists of five operational stages including 1) Quality control (QC) and open reading frame (ORF) prediction 2) ORF annotation, (3) Modular analysis (4) ePGDB construction, and (5) Pathway Export . A mapping file (.mapping.txt) is exported relating original input sequence names with sequential names. ORFs are predicted using the Prokaryotic Dynamic Programming Genefinding Algorithm , which can detect incomplete or fragmentary ORFs . CoordinConceptually translated ORFs are queried against user-defined reference protein databases including KEGG , COG 2828, RefSeBLAST represents a computational burden that can limit pipeline performance on big datasets when implemented on local machines. Therefore, we have adopted a representational state transfer (REST) design supporting implementation on external Sun Grid Engine servers or supercomputers . A user-Tabular BLAST results returned from local or external resources are used to assign product descriptions to predicted ORFs based on an internal heuristic to standardize product descriptions. For each ORF, the top e-value from each reference database is selected and given an “information score” based on the number of distinct enzymatic words and a preference to Enzyme Commission (EC) numbers (+10 score). Functional annotations with the highest information score are appended to the ORF description and exported as a tabular file (.annotated.gff). Predicted ORFs with no BLAST hits are annotated as “hypothetical protein.” In addition, BLAST summaries of functional annotations at different hierarchical levels (Cite KEGG/COG) are exported for KEGG and COG databases (.{DB}.stats.txt). Following functional annotation of predicted ORFs, nucleotide sequences are queried against reference nucleotide databases including SILVA  and GreeMetaPathways currently implements three modular analyses using existing or derived files as input . The first analytic module implements tRNA-scan (version 1.4) to identify relevant tRNAs from QC nucleotide sequences . ResultiThe annotated ORF file (.annotated.gff) is parsed and separated into four files including (1) an annotation file containing gene product information, (2) a nucleotide sequence file in .fasta format, (3) a genetic-elements file, and (4) a PGDB parameters file (/ptools/). For the purposes of ePGDB construction, nucleotide input files are concatenated to form a single “chromosomal” element defining a composite genome. Concatenation is necessary to improve Pathway Tools performance on input files containing thousands of genetic-elements in batch mode. PathoLogic uses these input files to predict metabolic pathways based on defined biochemical rules  resulting in ePGDB construction and export to the local Pathway Tools internal library ($Pathway_Tools/user/).Environmental PGDBs and their contents are accessible, internally or externally, through a built-in web server, allowing the knowledge of genes, proteins, metabolic and regulatory networks embedded within them to be queried, compared, curated and shared in a distributed fashion via the Internet. In addition to powerful search and retrieval functions, Pathway Tools provides a metabolic encyclopedia, based on primary literature citations encompassing more than 1900 evolutionary conserved sub-pathways within the MetaCyc schema ,24,38. TInformation is extracted from ePGDBs including ORF identities, enzyme abundance, and pathway coverage and exported in tabular format . A receipt and time-stamp for each successful pipeline execution is created containing the specific parameter settings used in ePGDB creation (.run_parameters.txt).MetaPathways performance was evaluated using unassembled  and assembled (Illumina HiSeq) genomic sequence information sourced from a naphtha-degrading, methanogenic enrichment culture . As an alternative to comprehensive all-against-all homology searches, future pipeline implementations will also incorporate scalable and distributed clustering algorithms enabling functional annotation based on hierarchical cluster assignments ,52-54.Aside from runtime improvements, additional data transformation and visual analysis modules expanding on existing taxonomic binning and marker gene identification components are needed. These include coverage statistics for assembled sequence information, data matrices and interactive visualizations indicating numerical abundance and taxonomic distribution of enzymatic steps, self-organizing maps and automated methods to append single cell or population genome assemblies to the NCBI hierarchy for more accurate taxonomic binning. Additional reference databases for 5S, 7S and 23S RNA genes and updates to the current MetaCyc database that include more biogeochemically relevant pathways are needed to improve BLAST and cluster-based annotation efforts. Finally, more experience and operational insight is needed in constructing, comparing and interpreting ePGDBs to identify potential sources of error and inform ongoing Pathway Tools development efforts.http://github.com/hallamlab/MetaPathways/ or http://hallam.microbiology.ubc.ca/MetaPathways.MetaPathways provides users with a modular annotation and analysis pipeline for predicting metabolic interaction networks from environmental sequence information. It is extensible to genomic and transcriptomic datasets from multiple sequencing platforms, and generates useful data products for microbial community structure and functional analysis including phylogenetic trees, taxonomic bins and tabular annotation files. The pipeline provides local and external computing solutions for implementing BLAST/LAST homology searches, resolves data handling issues associated with .gbk and .gff file conversion and NCBI submission, and generates ePGBDs using Pathway Tools for pathway inference and interactive visualization. The MetaPathways software, installation instructions, tutorials and example data can be obtained from Project Name: MetaPathways 1.0.Project Home Page:http://hallam.microbiology.ubc.ca/MetaPathways.Operating system(s): Linux/Unix, Mac OSX 10.6.x or later, Windows XP.Programming Languages: C/C++, Python 2.7+, Perl.Other Requirements: GCC compiler, NCBI BLAST 2.2.25+, Pathway Tools 16.0.License: GNU GPL, Academic licenses needed for BLAST and Pathway Tools.ePGDB: Environmental Pathway/Genome Database; ORF: Open reading frame; EC: Enzyme commission; REST: Representational state transfer.The authors are unaware of any competing interests.KMK was the primary pipeline developer and co-wrote the paper. NWH assisted with pipeline development and co-wrote the paper. APP helped conceptualize and implement initial pipeline implementations. SJH conceived pipeline architecture, provided essential feedback on data products, integration and formatting, and co-wrote the paper. All authors read and approved the final manuscript.MetaPathways validation summary based on a comparison of three sequencing methods on a common sample.Click here for fileSource genome statistics for simulated metagenomes sim1 and sim2.Click here for fileConfusion tables for classification analysis of simulated metagenomes sim1 and sim2 at progressively larger sequence coverage.Click here for file


In the past few years, programmed cell death (PCD) has become a popular research area due to its fundamental aspects and its links to human diseases. Yeast has been used as a model for studying PCD, since the discovery of morphological markers of apoptotic cell death in yeast in 1997. Increasing knowledge in identification of components and molecular pathways created a need for organization of information. To meet the demands from the research community, we have developed a curated yeast apoptosis database, yApoptosis. The database structurally collects an extensively curated set of apoptosis, PCD and related genes, their genomic information, supporting literature and relevant external links. A web interface including necessary functions is provided to access and download the data. In addition, we included several networks where the apoptosis genes or proteins are involved, and present them graphically and interactively to facilitate rapid visualization. We also promote continuous inputs and curation by experts. yApoptosis is a highly specific resource for sharing information online, which supports researches and studies in the field of yeast apoptosis and cell death.Database URL:http://www.ycelldeath.com/yapoptosis/ Saccharomyces cerevisiae (baker’s yeast or budding yeast) is a unicellular organism that has long been used as a common model organism for eukaryal cell and molecular biology. It has been extensively used to study many cellular processes such as cell metabolism, cell division and cell death. Apoptosis, one of the modes of programmed cell death (PCD), was initially extensively studied in mammalian cells, but it is also found in unicellular organisms, and was first described in yeast by Madeo et al. ; however, its focus is on cell death proteins in metazoan cells    and has Here, we introduce an online database for yeast apoptosis, yApoptosis, that contains a list of apoptosis and PCD-related genes , curated apoptosis networks and protein complexes where these apoptosis genes participate and a network of co-regulated interacting proteins. This information can be accessed through the web interface with multiple functions.yApoptosis is a database dedicated to apoptosis genes, proteins and processes in yeast. The main aim of yApoptosis is to collect and organize apoptosis- and PCD-related information and to present it in a user-friendly format, so that it can facilitate collecting and searching for information about yeast cell death. We encourage participation from the research community to use yApoptosis as an online platform for communication, sharing and contributing useful information. Therefore, this platform will also provide opportunities for stronger collaborations in the field of yeast cell death research.http://www.mysql.com/). The web interface for browsing, searching and connecting to the back-end database was constructed on PHP (http://php.net/) and JavaScript (http://en.wikipedia.org/wiki/JavaScript). To provide interactive data visualizations including zoom, pan and clickable nodes, Google Maps API (https://developers.google.com/maps/) is used for graphical representation of the apoptosis networks .yApoptosis is a database and a web interface. The data are stored and maintained using the database management system MySQL  its product (protein) is assigned with the gene ontology (GO)  term ‘apA network is a set of nodes connected by edges. For biological networks, nodes can represent different kinds of molecules  or events , whereas edges describe relationships between nodes, which include protein interactions, regulations, transport and conversions. In yApoptosis, to facilitate visualization of the apoptotic process and to show how apoptosis genes interact in different contexts, we graphically present four different networks .http://en.wikipedia.org/wiki/XML) and SBML (http://www.wolfram.com/mathematica/), MATLAB (http://www.mathworks.se/products/matlab/) and COPASI , genes, proteins and processes in different locations or compartments , which subsequently lead to apoptosis. The network was reconstructed based on extensive literature study and it was drawn using CellDesigner . Only geand SBML , which md COPASI ].et al.  are the largest data sets available . The stuet al.  . CombiniHere, we first generated a yeast integrated network of protein–protein and transcriptional regulations interactions  and thenCurrently we provide a contact form and updates on the ‘Feedback’ page, which is a collaborative platform for users. In the future, if the research community expresses interest, we plan to propose an interactive forum or message board for discussions. So far, the updates have included corrections, curations and general comments about the improvement of yApoptosis, which are sent directly to the developer team.yApoptosis allows three main accessing methods to query for a particular apoptosis gene A: ‘browsyApoptosis is a user-friendly database dedicated to research of yeast cell death. To our knowledge, it is the first resource that structurally collects apoptotis and related genes of yeast and shares information through a user-friendly web interface. In addition to data from literature, we also include additional information based on computational predictions. Moreover, we provide a communication channel to encourage a collaborative online environment among researchers in the field.http://www.ycelldeath.com), an online platform that will collect information on different aspects of yeast PCD. The plan is to continuously extend the database to include relevant data and information not only for apoptosis but also for other yeast cell death processes including necrosis, autophagy, stress and ageing. Moreover, relevant experimental data and recommended protocols will be integrated as additional information. To make it an even more powerful resource, it will incorporate bioinformatics tools for facilitating data analysis and visualization.The yApoptosis is the first part of yCellDeath (Yeast cell death database;


The World-Wide Molecular Matrix (WWMM) is a ten year project to create a peer-to-peer (P2P) system for the publication and collection of chemical objects, including over 250, 000 molecules. It has now been instantiated in a number of repositories which include data encoded in Chemical Markup Language (CML) and linked by URIs and RDF. The technical specification and implementation is now complete. We discuss the types of architecture required to implement nodes in the WWMM and consider the social issues involved in adoption. The World-Wide Molecular Matrix (WWMM) was conceived in 2001 in the spirit of the about-to-be launched UK eScience  and otheWe have often used the term "chemical semantic web" which is effectively synonymous with the WWMM, the preferred term in this article.This article describes the evolution of the WWMM. Some of the early ideas  were ahead of implementability but are now linked into general semantic web approaches. The paper therefore represents an evolving vision of a distributed decentralised system.per se, contributed little to the semantic web in our fields.The eScience programme held out the vision of a total network ("Grid") of linked computing resources, with provision for high-speed access and interchange of data. We assumed that this would be a semantic network where many of the resources would not be bytes and CPU but would be structured information. We were grateful to receive early funding from the eScience project ("Molecular Standards for the Grid") but havLike many early ideas, it is impossible to predict the requirements for successful autonomous growth and it has taken approximately 10 years for the initial ideas of the WWMM to become an early reality today. The semantic web and, in its wake, the WWMM, have had to wait for the time to be right for them to flourish. This requires a complex mixture of different requirements:• A widely-distributed toolchain in at least alpha.• A critical mass of early adopters.• A general realisation that this was an imperative whose time was bound to come.In bioscience these ideas have been taken up at an early stage and many semantic resources have been created. There is a large amount of public investment in bioscience information technology driven in part by the Genome publications but also by the realisation that machines were going to be essential for discovery linking and simple inferences from semi-structured knowledge. We believed, optimistically and perhaps naively, that the same philosophy would be taken up in chemical disciplines. Some chemists had led the field of AI in the early 1970s  and simiThe Napster and similar models worked because of a fortunate combination of circumstances. Almost all nodes were read-and-publish, in that they would consume information they wanted (music tracks) and would install a re-publication server as part of their "bargain" to the community. In addition, the metadata for music is relatively simple and was already widely used. The title of a track or artist generally identifies more or less precisely what is required. The P2P model survives in systems such as Skype  and BitTe.g. molecules) while others might represent the work created in a particular institution. We also expect that there will be specialist sites for aggregating and indexing. This is a potential model for publication of data and metadata. In 2004 we had anticipated that some of the roles of the WWMM would be exemplified by the infrastructure and ecology of university institutional repositories but in reality these are poorly linked and there is no re-use and re-purposing of content.The WWMM started with a more complex challenge. The metadata for molecules  is not as simple as discovering music on the web. But the biggest challenge was that software would have to be written, which could be trivially distributed and where clients could legitimately and safely offer services without needing to know the details of installation. Nevertheless, the original (2001) concept has lost none of its validity. We envisage an ecology of sites (using a common syntactic and semantic infrastructure) which store a variety of objects in different numbers and with different attributes, and offer them to the world for re-use. Some sites can be expected to provide monocultural collections of certain types of object  and EBI By contrast, in chemistry, the only mandatory deposition of domain-specific data is in crystallography. Some of this is published openly on publishers' websites , but apIn science, repositories seem to work best where there is a central unifying concept found in every entry. For example, Swiss-Prot  is basede.g. PubChem [The major current repositories of chemical information are generally run outside the community input of chemists and related disciplines Very few are fully Open .e.g. InChI). InChI doesn't fall short because of which information it chooses to include, it falls short because it chooses. In contrast, the CAS system is more flexible and can be assigned to a wide range of chemical substances. We recommend the use of relatively short alphanumeric identifiers. Chemists have a long tradition of using numeric identifiers  and for most systems sequential numbering seems to be the best way of minting identifiers.Identifier systems are essential but very challenging. Semantic identifiers will always fail to represent general concepts, because the decision of which aspects of the concept are important to its identity are fixed, or from a fixed set . Without this, name collisions are certain. Moreover, without authorities to maintain identifier systems, they inevitably decay. We hope that this paper may stimulate persistent non-profit organisations  to create Open identifier systems. In the absence of this, the most likely solution will be through web persistence as in Wikipedia .In chemistry the "molecule" has become a central concept for aggregation. We note that there is much semantic and ontological confusion between substance, compound, connection table, and other concepts describing chemical objects and their composition. Thus, for example, the InChI only formally relates to a connection table, and works where there is a pragmatic correlation between connections tables and the composition of substances. It breaks down where a substance may contain components with different connection tables, where the connection table is dynamic, or where different substances can occur in different macroscopic forms. The technology of WWMM can support concepts such as molecule (connection table) and substance independently.e.g. "aluminium chloride", "glucose", "diamond") we must rely on an authority to provide a controlled identifier system. The system in commonest use is the Chemical Abstracts registry number (CAS number)[The WWMM paradigm relies on a unique identifier system for discovering and asserting the identity of objects. This works well where the connection table is a complete description and identification of the substance, but where it fails (S number) but thise.g. the Internet Archive's Wayback Machine [e.g. our current CLaRION project [e.g. for data-oriented peer review, or can aggregate and re-use objects of interest. Site D specialises in one particular facet of objects or properties. This is exemplified by CrystalEye which trawls the web and extracts only crystal structures and collates and systematizes them.Figure  Machine ) where i project  which ise.g. Google, Open StreetMap [This vision, in 2004, was ahead of the technology to implement it, although we created some early prototypes of parts of the system. In both closed and Open systems, the successes have largely been through centralised sites . These treetMap  and the treetMap  reportede.g. pressure at which a boiling point was measured) are not recorded. In some cases it is unclear what the molar unit is. For example, some programs calculate the extensive properties for a complete unit cell (e.g. Na4Cl4). These properties are supported by a system of dictionaries (see the sibling article in this issue).The WWMM is also designed to hold properties of chemical molecules and substances. In many cases, these concepts are very well defined and managed by community definitions such as the IUPAC Gold Book . Howevere.g. chemical reactions and processes, such as chemical syntheses) have been excluded from the initial version of the WWMM until their semantic representation has been more explored within the community.Concepts which are relations between objects . In Quixote a calculation is the same regardless of which laboratory carries it out and duplications of calculations have the same canonical representation. We believe that there will be a demand for molecules from CrystalEye and Quixote and that these will be excellent exemplars and workbench for crystallographers, scientists and computational scientists interested in P2P systems and distributed repositories.The Linked Open Data (LOD) concept and movement has demonstrated the vision of a cloud of interlinked resources, and many of the bioscience databases feature prominently. In 2011, there are still very few Open data resources in chemistry. To be a full member of this graph, a resource has to have a public identifier system (URI) and a license that allows essentially total freedom of access and re-use. "Free resources" (where there is no right of re-use) cannot be included. The following current resources could be transformed into LOD nodes:etc.)• Bioscience databases • NMRShiftDB (a volunteer-driven collection of Open NMR spectra)• A subset of the ChemSpider resource • Chemical entries in Wikipedia• CrystalEye • Computational chemistry from the Quixote projectThe data so far has been primarily from current aggregators and voluntary collections. The WWMM concept also included the idea that scientists would publish their data directly onto the web as they carried out experiments or calculations. Although a very small percentage of the community, chemistry has been among the leaders in developing this idea and J-C Bradley  and, morTim Berners-Lee originally introduced the four principles of linked data :1. Use URIs as names for things.2. Use HTTP URIs so that people can look up those names.3. When someone looks up a URI, provide useful information, using the standards .4. Include links to other URIs so that they can discover more things.e.g. not in an aeroplane) and where the maintainer of the resource can guarantee 24/7 availability. If this latter condition cannot be met, then either the system breaks (perhaps temporarily) or it has to provide a fall-through mechanism of aliased addresses. CML was originally designed with the clear W3C principle that names and addresses were distinct but we are attracted by the conflated URI vision which we believe will work for much of chemistry. Given at least a partial implementation of principle 2, then we endeavour to satisfy principle 3 by using RDF and SPARQL where appropriate. Principle 4 is a fundamental part of the WWMM and follows practice in, for example, bioscience where most resources have copious links to others. Whether or not resources are normalised is a problem that we have not yet explored in depth.The modern WWMM adopts principle 1 completely. All things including not only data but metadata such as dictionaries are completely supported by URIs. Principle 2 brings certain problems. In the initial design of HTML and XML there was a strong architectural differences between URLs (addresses) and URIs (identifiers) and this formal distinction has to remain in many fields. Tim Berners-Lee simplified this to principle 2 on the basis that everything of interest could have both an address and a URI, and that they could be conflated into the same string. For this to happen, the identified object must be sufficiently stable and conceptually bounded that it is effectively describable as a single persistent object.  The single address requirement can also be problematic. Principle 2 only fails to break when the user or user agent has pervasive access to the web . There are a small number of spectral identifiers in NMRShiftDB and a small number of reaction identifiers in KEGG, confined to biological transformations. The CrystalEye collection does not have an identifier system yet although the Crystallography Open Database (COD)[Semantic data requires a minimum of an identifier system. Many published collections of information do not generate identifiers and are only accessible and identifiable through their web addresses. This is a fragile design and it is essential that components of the WWMM have unique permanent identifiers. The traditional use of chemical identifiers has been restricted to large authorities such as CAS, Beilstein , RTECS , and morase (COD) does. Thase (COD) codes aretc.) are more easily understood and processed in XML form (e.g. CML). The WWMM, therefore, is a mixture of CML components linked together and annotated by RDF triples. As the semantic web develops new approaches to indexing and describing RDF we can expect the flavour of RDF to evolve. In 2011, it is still unclear exactly what triple-store or other RDF technology is required to support large amounts of RDF, but we believe that for local collections (e.g. the output of a laboratory) there are now many good OS RDF engines.In principle, LOD can be completely held as RDF triples. However, many components of chemistry  provided by high-level ontological frameworks such as OWL, OBO , Cyc 5353etc. InIt will be fundamental to have an indexing and discovery system. Because of the non-textual nature of much chemistry, current search engines such as Bing and Google will not be able to index much of the WWMM material. We therefore need distributed search technologies and in the first instance will rely on RDF and on conventional chemical substructure search. We have designed the system such that it is possible for scientists to add indexers (plug-ins) to a repository to create domain-specific searchable metadata. For example, it is not easy to search on the web for a compound containing between 10-15 carbon atoms, but if a repository exposes a carbon-count field as RDF then it is straightforward to retrieve entries using an RDF query containing combinations of index fields. More complex chemical concepts can also be indexed, such as peaks in NMR spectra, cavities in crystals or HOMO-LUMO gaps in theoretical calculations.The architecture of the WWMM is built on a number of web standards and protocols, described in detail below:• SWORD deposit: publish data to server• Atom archive feeds: syndicate published data• HTTP content negotiation: retrieve data in human and machine understandable formats• OAI-ORE/RDF: machine understandable representation of the dataetc.The Atom Publishing Protocol (AtomPub ) provideWeb feeds are widely used to provide users with notifications of updated content. Typically a feed document lists recent content - such as active news items, or the list of articles in the current issue of a journal - and by monitoring ("subscribing to") a feed, users can be alerted when new content is published. Entries in a feed document typically contain the title and summary of an item, along with a link to the full resource. Earlier iterations of the WWMM made use of RSS feeds  to alerte.g. multiple languages, data formats, size, resolution etc.) of a resource (i.e. a data object or service identified by a URI) from the same URI, based on the preferences expressed by a client, through a mechanism called content negotiation  and XHTML (application/xhtml+xml) content, or less strongly (q = 0.9) XML (application/xml). If none of these are available it will accept anything else (*/*).The WWMM uses content negotiation to publish data in formats that are both human and machine readable. The URI for a resource published on the WWMM can be resolve to alternative representations - an HTML or XHTML 'splash' page for humans, or an RDF representation (application/rdf+xml) for machines.This request by a web browser (such as Mozilla Firefox)GET/crystals/211721 HTTP/1.1Host: http://crystaleye.ch.cam.ac.ukAccept: text/html, application/xhtml+xml, application/xml;q = 0.9, */*;q = 0.8will return a human-friendly HTML page describing the crystal structure, while the following request for the same resource by a machine agentGET/crystals/211721 HTTP/1.1Host: http://crystaleye.ch.cam.ac.ukAccept: application/rdf+xmlcan return a machine understandable RDF representation of the data.e.g. a crystal structure of NMR spectrum and the aggregate resource  is a stae Figure .The OAI-ORE model includes three classes of object: Aggregation, Aggregated Resource and Resource Map. Aggregations are an abstract concept, containing one or more Aggregated Resource. An Aggregation may be serialized into a number of different formats, and each of these serializations is termed a Resource Map. Each Resource Map has a unique URI, distinct from the Aggregation's URI, in order for the different representations of the Aggregation to be resolvable.As well as describing an Aggregation, a Resource Map may contain additional data about the Aggregation and the individual Aggregated Resources Figure .etc.)We have developed a large number of software components of varying complexity with much inter-dependence between the components. We embrace agile development practices and our software development environment is built upon existing technologies. Substantial use is made of existing Open Source utilities, tools and libraries such as Apache Commons , RestletWe endeavour to write the code with high test coverage , and aim for test-driven development. We run a Jenkins continuous integration  server ae.g. in materials properties prediction).The WWMM is predicated on a critical mass of users who are prepared to develop both content and technology. The precise path for the evolution will depend on a mixture of what technologies are available, the familiarity of the community and the resources that are available. It may also depend on the perceived business models and the uptake by significant producers and consumers. The earliest experiment is in our Quixote community where we are producing semantic computational chemistry and disseminating this from RDF-aware servers. It is likely that different members of the community will play different roles. Some may wish to upload their results to a semantifier which deposits them in a given repository ("push"). Others may wish to aggregate legacy data and re-disseminate it ("pull"). For example, a University Department or group might wish to expose its results on its own webpages to enhance the reputation and provide re-usable material. A national lab might act as an aggregator for a sub-community of scientists (The future of the WWMM will depend on a number of factors which we cannot predict:a) The change from "walled garden" providers to Open collections.b) Citizen science. The most dramatic example in science has been the large collaboration involved in GalaxyZoo , and nowc) The need to link data. Almost all chemical systems at the moment are unsuitable for LOD in both the lack of semantics and the problems of licences. The ChemSpider system is a hybrid in that some of the data are Open and some of the material is exposed in RDF.d) The realisation that chemistry needs community ontologies.e) The high and unsustainable cost of closed data collections.f) The growing dissatisfaction of the upcoming generation of scientists with closed systems.g) The frustration of the non-academic community in the difficulty of obtaining material published in STM publications.h) The desire of scientific publishers and editorial boards to publish the semantic data associated with articles.We understand that the initial collection of 175, 000 molecules in DSpace@Cambridge  is regulThe WWMM is now technically deployable and its critical mass will depend on adopters who need an Open distributed system, and who are prepared to contribute to the infrastructure design, the ontology design and its implementation.The authors declare that they have no competing interests.PMR had the original vision for the WWMM, has participated in and overseen its development and has written the manuscript. SEA has been responsible for much of the current infrastructure and has written the manuscript. OJD has contributed to the development of the infrastructure, provided valuable advice on technical matters and has written the manuscript. JAT was involved in development of the original infrastructure and content production and has written the manuscript. YZ was involved in the development of the original infrastructure and content production. All authors have seen and approved the final version.


As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype. In the relatively short time it has existed it has already transformed the way our world and society work, at a very fundamental level. It has also done so at faster pace than any other massively important technological advancement. Not surprisingly, considering the social creatures we humans are, as our Internet evolved so did the means we use to communicate and interact with the people we deem close. Social networking bloomed and connected together an astonishing number of users. At the same time, sensing technologies like Wireless Sensor Networks (WSNs) are becoming more robust and viable while the sensor nodes themselves are becoming cheaper. Modern mobile phones and smartphones have also brought us mobility in our Internet access and are also a powerful computational resource, with many interesting capabilities. Mobile phones are also becoming even more powerful, common and cheaper. Never, in its entire history, did mankind have a collection of technologies that are capable of connecting and monitor such large numbers of users, simultaneously, at a global scale.While the Web and its associated technologies will continue to evolve at increasingly faster pace, people will always live on the “real world”. Considering our social nature and the importance of “social media” throughout our history, the most likely path in our technological and social evolution is a merge between the real and the virtual: a society where people are connected not only by their physical proximity, but also by their virtual closeness. This implies the development of “people-centric” sensing systems that can acquire, process and share, in a private and secure manner, useful information about social network users. This new trend in our civilization’s evolution is still making its first steps. Our generation is on the verge of a technological revolution that will, in the next few decades, transform our society and open the path to many new market opportunities.We are now seeing the first people-centric applications being released onto the market. In these systems, humans and their activities are the central focus of sensing and data visualization is focused towards the user’s friends and family, rather than research teams or technical staff. As the researchers of the MetroSense project suggest , people-Although we already have the necessary base technology to develop these people-centric systems there are still many challenges associated with their development. Wireless Sensor Networks, for instance, are still mostly confined to research or industrial scenarios, isolated from the “real-world” and far away from useful “day to day” use. One can reason several possible causes that contributed to this situation. There are still financial and technological limitations in sensor node technology: nodes are computationally limited and still relatively expensive. Another reason is that development requires a detailed understanding of low level protocol and network, therefore requiring a great amount of effort to build new applications. This is not a particularly favorable situation in a world where the Web 2.0 and user-generated applications and content are becoming ever more important. In addition, interoperability is another difficult problem created by the many independent proprietary data formats used by different sensor networks. Although it has been proven that it is possible to implement generic communication mechanisms (both IP protocol stack (6LowPAN) and web services) on the sensor nodes, the applicability and efficiency of these technologies still needs further validation from successful real world applications. Because of their limitations, allowing directly access to sensor nodes from the Internet brings many challenges and may not be a suitable solution for all WSN applications. Therefore, it is necessary to have an infrastructure that easily, securely, and efficiently supports the integration and interoperability of sensor networks with Web 2.0 environments.On the other hand, social networks are still very “static”. Access to social networking nowadays is becoming increasingly more mobile. It is not uncommon to see people access their smartphones to share and discuss daily experiences shortly after their occurrence, updating thoughts and responding to feedback from their friends as the situation develops and the user’s life continues. Current users can announce social events to their group of friends, share experiences through photos and comments, and show their opinions and hobbies through “likes” and their own “private wall”. Despite the general public’s interest in these social services, their current functionality does not reflect the true dynamic of people’s relationships and personal lives. Instead of being pre-determined and unique events in time, social group activities can, in fact, happen very frequently and, most of the time, spontaneously. Current systems are not capable of providing this “real-time” component to social networking, which diminishes its true potential.With these new social trends and current technological limitations in mind, we propose SocialSense, a system that combines social networking with mobile sensing technologies to expand and provide greater functionality to social networks and other web applications. In short, this system will focus on bringing a “real-time” component to social networking, by sharing and exploring real-time information about the users’ daily actions, social activities and visited locations. By using body-area networks and mobile phones, we intend to research and develop mechanisms that focus on “people-centric sensing” and provide a seamless connection between the virtual  and the real (the user’s daily lives). This connection brings greater value not only for the user’s social experience but also for the service provider, as the new types of information available can be mined and analyzed to create more personalized and interesting product advertisement mechanisms.A system on a chip or “system on chip” (SoC or SOC) integrates several electronic system components into a single chip. Research on the topic of people-centric sensing is highly dependent on the development of integrated and flexible, yet cheap and comfortable sensors that function with a low energy consumption. This is a challenging task since current sensor motes are still too bulky and expensive. We expect that this situation will change in the near future.The remaining of this document is organized as follows: the next section introduces an overview of our point of view on the research topic where we present our current model and working prototype. Section 3 details our current results while the last section concludes this article.2.et al. at the MIT media laboratory has proved to be much more complex and sophisticated. The authors coined the term “dual reality” [Bridging the real and virtual environments  is a subject that has received a great deal of attention in recent scientific research, with some interesting experiences being performed in this area. The Beyond Broadcast conference, for example, not only took place in Cambridge, but also on an island in the 3-D virtual world Second Life. This was accomplished by streaming video and audio from the real-world conference to the Berkman Island in Second Life . Anotherreality” ,9 to indreality”  where thThere are also applications of sensor nodes as means of transmitting mobility into virtual worlds. In  a framewHowever, none of the above works could be considered “people-centric”, despite their connection to social networking. In order to better understand the applicability of people-centric sensing systems, let us consider a simple use-case. A social network user goes on a night-out with some of his friends to a local nightclub. He has an activity detection system that runs on his smartphone, possibility accompanied by several small body-area wireless sensors . The small sensors in his body directly communicate with the smartphone through a body-area wireless sensor network and share information regarding several conditions, such as his movement (accelerometer) and heartbeat. The sensors can also detect environment factors such as the temperature. The smartphone runs activity detection algorithms that combine the sensory information  to discern what the user currently doing, in this case, dancing. The smartphone accesses the Internet and communicates with the user’s preferred social network to represent information about the user’s current activities to his trusted group of friends. The information can be represented through a virtual representation of the user and the environment. If the user’s friends also use the same system, it is possible to correlate their close physical proximity with activity detection and deduce that the group is currently engaging in a social group activity . People who belong to this same group of friends who are still at home and were indecisive as to whether or not go to the nightclub can now have an idea of who is attending the social event and where it is happening, and use this information to make their decision. The above use-case is a good example of how a people-centric sensing system could be a part of a regular person’s social life. The term “people-centric sensing” is used by the MetroSense project  to descrPrivacy Exchanging sensitive information such as user’s vital signs, location and current activity is a task that has strict privacy requirements. Privacy is a requirement that affects all application layers: from the sensor’s wireless communication to the exchange of middleware messages. During our research we intend to employ effective privacy mechanisms that ensure the safety of sensitive information.Managing intermittent connectivity Reliability of wireless sensor networks is very important in order to create systems that are robust and stable. However, due to the wireless nature of mobile phone and WSNs communication, there is always a risk of intermittent connectivity due to environment interference, power shortages or communication range. Robust people-centric sensing systems should be capable of maintaining an illusion of smooth and continuous people tracking. Offline mechanisms that can estimate a user’s current activity and location even when there is a shortage of fresh data can maintain an illusion of continuous monitoring in adverse conditions. These offline mechanisms can also be employed in data mining and analysis, since their function is to find patterns in the data.Detection of activity Based on previous work in the area, our research will attempt to improve and design resource aware algorithms that use both the mobile phone and wireless sensors and are directed specifically towards the representation of activities in social networking sites and virtual communities. At the same time, we will also seek interesting and engaging ways of representing user activities and environmental data, through the use of virtual representations of users .The state of the art in the area still represents only the first steps for these people-centric sensing systems. There is plenty of room for improvement in various areas, encouraging future research. In fact, creating such systems still presents several technical challenges that we intend to address with our research, namely:Aside from these technical challenges from an application point-of-view, research on the topic of people-centric sensing is also highly dependent on cheap and comfortable sensors that have a decent battery life. One of the current obstacles in our research is a lack of extremely lightweight and small sensors that can be used in a ubiquitous way, that is, the user forgets that he is using them. The area of SoC is extremely important for the development of new sensor devices that are both extremely flexible, comfortable and with a good battery-life. For example, in  the authWe intend to design a model based on innovative solutions that effectively address all these technical challenges. Small and discreet wireless sensors allow for more diverse kinds of vital-signs data to be used, which permit the creation of accurate activity detection systems. Activity detection amongst groups of friends can be correlated to automatically create social events that can be shared in Social Networks. Localization techniques can be used to permit users to locate their friends in big social events. Offline inference techniques can provide an illusion of seamless connection between the real and the virtual, by trying to infer current location and activity even when there is no fresh data due to a connectivity problem. All of these research approaches attempt to expand the features of social networking sites and increase their functionality beyond the simple sharing of photos and comments.The research herein proposed is based on a testbed already implemented in our labs. In our current model, Wireless Sensor Networks (WSNs) are data and functionality providers, managed by an integration middleware. This platform is called SocialSense and provides a set of services for consumption by external environments using web service APIs. This model facilitates the integration and interoperability of WSNs with virtual world environments, social networks and other web 2.0 applications. Our approach is based on a hybrid gateway-based model that supports both proactive and reactive communication. In proactive communication the sensor nodes periodically collect data and send it to the middleware for processing. In reactive communication, the end users send data/functionality requests to the middleware which then uses the sensor node’s data to react accordingly. A simplified illustration of our current prototype’s architecture is depicted in Our model connects the middleware with the sensor networks through several entities called “proxies”. They receive data packets from the sensor nodes and perform earlier processing tasks, forwarding the information to the middleware for more advanced management processing. In addition, they may also handle specific commands and requests coming from the middleware to satisfy client applications, by performing queries to the sensor nodes. Proxies also include a few components that support mobility. One of the main advantages of this modular approach is that it can easily offer mobility, localization and scalability. Proxies are designed to be lightweight, so that they may be installed on both computers/laptops and modest or limited devices such as smartphones. Each proxy can handle communication with several wireless sensor networks and all proxies eventually connect to the central middleware service.The middleware gateway exposes the functionality and data of sensor networks as web services. It is the central part of our framework, the basis for the integration and interoperability between sensor networks and other Web 2.0 environments. This approach facilitates the development of applications using sensor networks , increases the diversity of possible web applications and promotes the reuse of WSN deployments and software code. In addition, data coming from the sensor networks can easily be stored in a cloud data warehouse for further processing. The middleware contains the necessary intelligence to process messages coming from different sensors and requests coming from different users and applications. It also contains the necessary interfaces to proactively send data to different Web 2.0 applications.The activity detection module is responsible for processing sensor data and running algorithms that are capable of classifying current user activity. Current prototype versions of this module use accelerometer data to perform the detection of very simple activities, such as walking or running. These earlier versions were also implemented as part of the middleware service, but in the future the module will be moved onto a mobile proxy (possibility a smartphone) in order to promote mobility and scalability . As our research advances we will explore the use of more types of data and attempt to design more efficient and accurate mechanisms for detecting activities.Privacy is an area still under very active research and implementation. We are currently implementing a privacy mechanism based on TARF, a trust-aware routing framework for WSNs presented in . TARF isIntermittent connectivity and interference avoidance are areas that were already a subject of previous work within our research group. Namely, the work presented in  addresseSocialSense was also thought to be able to support the use of several medical devices. In our current implementation, we use a small heart beat monitor   is a webThe communication between proxies and sensor nodes is currently implemented independently using any protocols that are appropriate, in order to allow interoperability between different networks. Web services at the “bottom level” are used by the proxies for posting new data to the middleware. The web services provide basic functionality for storing data  within the system in a private database. Examples of such “low level” web services called by proxy devices are the “postTemperature;” and “postLocalizationCoordinates;” services, although there are others of similar functionality. “Higher level” web services are used by web interfaces and applications to retrieve ready-to-use, processed data from the middleware. Our own web interface (that serves as a link between our framework and Facebook) uses several web services, such as “getTemperature;”, “getLocalizationCoordinates;” and “getActivity;” in order to update the user’s avatar position within the map of our testbed, as shown in These web services are responsible for retrieving the most recent data from our database regarding a user’s current location, vital signs or activity. In addition to the web services provided by our own middleware, our current web interface also integrates with the Google Maps API in order to display the users’ location and activity by overlaying a map of our testbed and a small avatar.etc.). In case the user accepts these terms, an “access token” is generated by Facebook and sent to our middleware by the user’s browser through HTTPS. This access token is then used by our middleware to communicate with Facebook and fetch the user’s Facebook ID, which is then compared to the existing IDs in our database. In case the ID exists on the database, the user is given access to sensory data, otherwise, our webservice returns nothing. Thus, only users registered within our system can fetch relevant data. The middleware supports a localization services based on the RSSIs of the received wireless packets and a trilateration algorithm.The interface allows users to select those who will have access to their own activity and location, and we implemented a login-based security mechanism to access data in our middleware. This login-based mechanism relies on Facebook’s own “access token” mechanism: when a user accesses our application, a web page asks for permissions to fetch various types of data  with the Facebook platform. The mashup periodically collects user activity and sensor data and publishes them into the user’s personal wall. The results of this integration are shown in The sensors used for our application were general-purpose Zoleria Z1 motes. These sensors are unfortunately bulky and not wearable. Future iterations of our work would benefit from the use of more comfortable, smaller and application-specific sensors stemming from current research in the field of SoC. A good example of an interesting sensor for people centric applications is the UP bracelet  from Jaw2) is an interesting measurement for this case, since it represents the amount of oxygen that dissolved or carried in the user’s bloodstream. However, if we wanted to use this measurement, we would have to depend on cumbersome sensors that are too big to be comfortable and usable in a social context, as the one shown in From our point of view, ideal sensors for people-centric sensing applications must be very comfortable and lightweight and provide as many measurements as possible. This is a problem, since many interesting vital signs can only be measured with cumbersome technology. To better explain our vision, let us consider a people centric sensing system that has “multiple” solutions. The “basic” solution has only the most important hardware  that acts as a direct bridge to the user’s mobile phone. However, users can acquire more sensors that provide greater capability. A “sports” solution, for example, can provide more sensors for a better assessment of physical exercise. Oxygen saturation  technologies in order to remove the need for both cabling and batteries, drastically reducing the sensor’s overall size. Recently, STMicroelectronics has introduced a 16-kbit memory NFC chip designed to employ energy harvesting technology to power up small devices, thus eliminating the need for an internal power supply . At the Since NFC has a very short range , a “Ring” device could act as a wireless bridge between the SpO2 sensor and the bracelet. This would require embedding Bluetooth Low Energy and NFC onto a single device, while maintaining the overall price as affordable as possible. This solution would be extremely interesting from our point-of-view, since it would provide many different measurements while remaining relatively comfortable for the average user.4.Although the obtained results presented in this work are very simple, they prove that it is feasible to seamlessly merging physical and virtual world and develop new applications by accessing openly exposed sensory information through webservices. They also show that it is feasible to detect users’ activity based on the data from sensors. This work represents our first steps into our research on people-centric sensing.The sensors used on our application prototype were general purpose wireless sensors. These are not adequate for real-world deployments of people-centric sensing systems. Such systems benefit from small sensors that are both comfortable to wear and energy efficient. Future developments in the field of SoC will prove to be important for the development of sensors that are specifically oriented for these kinds of systems. We expect that future sensor technology will become cheaper and smaller, making people-centric systems more attractive to the general populace.


One of the most important aspects of genomic analysis is the prediction of which pathways, both metabolic and non-metabolic, are present in an organism. In IMG, this is carried out by the assignment of IMG terms, which are organized into IMG pathways. Based on manual and automatic assignment of IMG terms, the presence or absence of IMG pathways is automatically inferred. The three categories of pathway assertion are asserted (likely present), not asserted (likely absent), and unknown. In the unknown category, at least one term necessary for the pathway is missing, but an ortholog in another organism has the corresponding term assigned to it. Automatic pathway inference is an important initial step in genome analysis. Gene Product, Modified Protein, and Protein Complex. Gene Product terms are assigned directly to genes. Modified Protein refers to a covalently or non-covalently modified protein, for example a phosphorylated protein or a protein that binds a cofactor. Protein Complex terms are associations of two or more IMG terms, where the components of the complex can be any of the three types of IMG terms. This multi-level organization of IMG terms is designed to disambiguate association between reactions and genes in cases of complex catalysts, such as multi-subunit enzymes, which may require multiple cofactors and undergo a multi-step maturation process. As a result, the information about all genes and pathways required for activity of the catalysts is explicitly recorded in IMG, which enables more precise automated inference of the presence of individual reactions and entire pathways. More information about the rationale for IMG terms can be found in the Using IMG section of the IMG website [The assignment of genes to pathways in IMG begins with the assignment of IMG terms. IMG terms are a set of functional assignments curated by members of the Genome Biology Program (GBP) at the Joint Genome Institute (JGI). There are three types of IMG terms:  website .IMG terms participate as catalysts in IMG reactions, which make up IMG pathways. Some IMG reactions are traditional biochemical reactions corresponding to conversion of small molecules with known stoichiometry, while others describe chemical conversions of unknown or undefined stoichiometry, transport of small molecules or macromolecules, protein-protein interactions or other interactions between other macromolecules, such as conformational changes . Figuremissing, meaning that it can not be found based on similarity to genes that are known to have this activity. In this case, it is possible that a new protein family will be found to have this activity. Another goal of pathway inference is to find new physiological functions for an organism. Genes for a pathway that was thought to be absent may be found in the genome. For example, Ferroglobus placidus is known to perform partial denitrification by reducing nitrate to nitrous oxide, but dinitrogen production was not detected [With the huge number of genomes available, it would be too large a task to go through each genome individually to determine which pathways are present, so the process must be automated. Automated pathway inference has several goals. The first is to predict which pathways are found in a given organism. If physiological studies of the organism have been carried out, then the experimental results can be compared with the automated pathway inference results. Two other goals of pathway inference are 1) to identify errors in the assignment of IMG terms to genes, and 2) to identify “missing” genes. If a pathway is known to be present in an organism but it is not asserted as being present, then one possibility is that a gene should have a term from this pathway assigned to it, but it was not assigned during the annotation process. If a search for a gene that should have this term assigned is successful, the term can then be assigned and the gap filled. If the search is not successful, then this gene function is detected . HoweverThe first step of automated pathway inference in IMG is to check if catalysts for all reactions of a pathway are present in the organism. In some pathways there are alternative catalysts for certain reactions or alternative reactions that differ in their coenzyme specificity . In the example in not asserted. If an ortholog is present for the missing step, then the pathway status is unknown. unknown. Genes with six out of the seven IMG terms involved in the pathway are present, and for the single missing term, 3-dehydroquinate dehydratase, there is an ortholog with this function. This is shown in the top table in the Evidence entry where 7/7 with orthologs is written.If there is at least one term missing from the pathway, there are two possibilities for pathway assignment status – unknown, and not asserted. The assignment of these terms depends on whether or not the genome has a gene with an ortholog that has the assigned function. If no ortholog has the missing function, then the pathway is assigned the status of Automatic IMG pathway assertion is implemented in a Perl program that interacts with the IMG Oracle database. The program iterates through all IMG pathways and organisms and checks whether the organism has genes associated with IMG terms needed in each reaction of the pathway. Authorized users are able to manually curate the pathway assertion status. If a pathway has been manually curated for an organism, then the program will skip this combination of pathway and organism. Automatic pathway assertion is done three times per year, in sync with each major release of IMG.The interpretation of a sequenced genome requires a comparison between experimentally determined physiological data and analysis of the pathways encoded in the genome sequence. Automatic pathway inference is an initial step for analysis of the capabilities of the organism under study. Discrepancies between the automatic predictions and the known biology can serve as starting points for further experimental studies. Automated pathway assertion is currently available in the public version of IMG.