Enhancing Scholarly Publications: Developing Hybrid Monographs in the Humanities and Social Sciences

Enhancing publications has a long history but is gaining acceleration as authors and publishers explore electronic tablets as devices for dissemination and presentation. Enhancement of scholarly publications, in contrast, more often takes place in a Web environment and is coupled with presentation of supplementary materials related to research. The approach to enhancing scholarly publications presented in this article goes a step further and involves the interlinking of the “objects” of a document: datasets, supplementary materials, secondary analyses, and post-publication interventions. This approach connects the user-centricity of Web 2.0 with the Semantic Web. It aims at facilitating long-term content structure through standardized formats intended to improve interoperability between concepts and terms within and across knowledge domains. We explored this conception of enhancement on a small set of books prepared for traditional academic publishers. While the project was primarily an exercise in development, the conclusion section of the article reflects on areas where conceptual and empirical studies could be initiated to complement this new direction in scholarly publishing.&nbsp

The implicitome: A resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing

Gateways to the FANTOM5 promoter level mammalian expression atlas

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0560-6) contains supplementary material, which is available to authorized users

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X's gene content, gene expression, and evolution

Preserving sequence annotations across reference sequences

Background: Matching and comparing sequence annotations of different reference sequences is vital to genomics research, yet many annotation formats do not specify the reference sequence types or versions used. This makes the integration of annotations from different sources difficult and error prone. Results: As part of our effort to create linked data for interoperable sequence annotations, we present an RDF data model for sequence annotation using the ontological framework established by the OBO Foundry ontologies and the Basic Formal Ontology (BFO). We defined reference sequences as the common domain of integration for sequence annotations, and identified three semantic relationships between sequence annotations. In doing so, we created the Reference Sequence Annotation to compensate for gaps in the SO and in its mapping to BFO, particularly for annotations that refer to versions of consensus reference sequences. Moreover, we present three integration models for sequence annotations using different reference assemblies. Conclusions: We demonstrated a working example of a sequence annotation instance, and how this instance can be linked to other annotations on different reference sequences. Sequence annotations in this format are semantically rich and can be integrated easily with different assemblies. We also identify other challenges of modeling reference sequences with the BFO

The distinct transcriptomes of slow and fast adult muscles are delineated by noncoding RNAs

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a Huntington’s Disease case study

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Nanopublications for exposing experimental data in the life-sciences: a Huntington's Disease case study

Data from high throughput experiments often produce far more results than can ever appear in the main text or tables of a single research article. In these cases, the majority of new associations are often archived either as supplemental information in an arbitrary format or in publisher-independent databases that can be difficult to find. These data are not only lost from scientific discourse, but are also elusive to automated search, retrieval and processing. Here, we use the nanopublication model to make scientific assertions that were concluded from a workflow analysis of Huntington’s Disease data machine-readable, interoperable, and citable. We followed the nanopublication guidelines to semantically model our assertions as well as their provenance metadata and authorship. We demonstrate interoperability by linking nanopublication provenance to the Research Object model. These results indicate that nanopublications can provide an incentive for researchers to expose data that is interoperable and machine-readable for future use and preservation for which they can get credits for their effort. Nanopublications can have a leading role into hypotheses generation offering opportunities to produce large-scale data integration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2041-1480-6-5) contains supplementary material, which is available to authorized users

Genome Annotation using Nanopublications: An Approach to Interoperability of Genetic Data

<p>With the wide spread use of Next Generation Sequencing (NGS) technologies, the primary bottleneck of genetic research has shifted from data production to data analysis. However, annotated datasets produced by different research groups are often in different formats, making genetic comparisons and integration with other datasets challenging and time consuming tasks. Here, we propose a new data interoperability approach that provides unambiguous (machine readable) description of genomic annotations based on a novel method of data publishing called nanopublication. A nanopublication is a schema built on top of existing semantic web technologies that consists of three components: an individual assertion (i.e., the genomic annotation); provenance (containing links to the experimental information and data processing steps); and publication info (information about data ownership and rights, allowing each genomic annotation to be citable and its scientific impact tracked ). We use nanopublications to demonstrate automatic interoperability between individual genomic annotations from the FANTOM5 consortium (transcription start sites) and the Leiden Open Variation Database (genetic variants). The nanopublications can also be integrated with the data of the other semantic web frameworks like COEUS. Exposing legacy information and new NGS data as nanopublications promises tremendous scaling advantages when integrating very large and heterogeneous genetic datasets.</p

Data from: The implicitome: a resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations