High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.

/pdf/kobas-2-0-a-web-server-for-annotation-and-identification-of-krnh95v775.pdf

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

/pdf/disgenet-a-comprehensive-platform-integrating-information-on-8zrzm0m8xl.pdf

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.

https://science.sciencemag.org/content/sci/338/6104/222.full.pdf

A high-coverage genome sequence from an archaic Denisovan individual

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene-gene and attribute-attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation.Database URL: http://amp.pharm.mssm.edu/Harmonizome.

/pdf/the-harmonizome-a-collection-of-processed-datasets-gathered-2p59rm4moi.pdf

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.

/pdf/genetic-association-database-j966vkr1uo.pdf

Genetic association database

Maize genetics and genomics database

Rewiring the circuitry

EDITORIAL: Herb Brody, Michelle Grayson, Rebecca Dargie, Victoria Kitchener, Stephen Pincock, Nicola Jones, Wilson da Silva, Eric Bender, Mohammed Yahia, Subhra Priyadarshini, Linda Nordling, Hepeng Jia, Rafael Garcia, Claire Ainsworth, Barbara Casassus. EDITORIAL SUPPORT: Nobuko Miyairi, Larissa Kogleck. ART & DESIGN: Wesley Fernandes, Mohamed Ashour, Alisdair Macdonald, Andrea Duffy, Chris Gilloch. WEB & DATA: Bob Edenbach, Olivier Lechevalier, Yuxin Wang, Naomi Nakahara, Masamichi Wada, Jyoti Miglani, Akiko Murakami, Takeshi Ouchi, Maxime Fontaine, Jennie Pao. PRODUCTION: Sue Gray, Karl Smart, Ian Pope, Robert Sullivan. MARKETING: Hannah Phipps. SALES: Nils Moeller, Kate Yoneyama, Kylie Ahern, Janet Cen, Yuki Fujiwara, Maria Kubalova, Krystal Trenchfield, Stella Yan. PROJECT MANAGER: Anastasia Panoutsou. ART DIRECTOR: Kelly Buckheit Krause. PUBLISHING: Nick Campbell, Richard Hughes, David Swinbanks. This first edition of the Nature Index 2014 Global supplement provides a snapshot of results from the new Nature Index. In this supplement, we turn a spotlight on the countries and institutions around the world that contributed to some of the highest quality research over the previous calendar year. The Nature Index provides a new way to look at the scientific literature — and to those research organizations that contribute to it. By looking at articles from only a small group of journals, most favoured by the scientific community as a place to publish their best research, we hope to provide a new level of analysis that is more targeted and hence more malleable. We want users to be able to tease out patterns of research, look at trends, analyse individual strengths, and investigate how institutions and countries collaborate. The story behind the Nature Index is outlined on page S52. In this supplement, our analysis includes layers of information from other data sources, for example demographics, national spend on research and development, changes to science policy and funding, and even altmetrics (online and social media coverage), which help put the Nature Index data into perspective. The top level, global results can be seen on page S56. Drilling down by region (starting with North America on page S60), we highlight the most interesting patterns in research output in this snapshot of the Nature Index. Within each region, we try to identify national hotspots for highquality research, based not just on output quantity but also on a range of indicators — for example, the number of researchers and the ratio of collaborators — that help put the data in context and allow a more nuanced view of these patterns. Focusing down further, we look at some of the notable institutions in each country, and use the Nature Index data to compare and contrast between them, aiming to tease out specific institutional strengths. Above all, our hope is that this supplement, rather than providing some authoritative analysis, will act as a conversation starter and a nucleation point for ideas for further analysis. Every reader of this supplement and user of natureindex.com will have their own specific interests and questions they want to address. We encourage use of the freely-available data to do just that. And we hope the conversation we have started here will prove useful for researchers, institutions, analysts and policy-makers alike.

Index 2014 Global

Dear Sir, We welcome the comments by Haunschild and Bornmann (2014) on the beta release of the Nature Index (natureindex.com). Nature Publishing Group (NPG) actively seeks constructive feedback from the researcher community we serve, and our aim is to iterate and improve the Nature Index in response to such feedback. Currently, the Nature Index is a database that tracks the affiliations of original research articles published in 68 natural science journals independently selected by active scientists as the journals in which they would most like to publish their best research (Campbell and Grayson 2014). A 12-month rolling window of data is organized and made available under a Creative Commons license at natureindex.com. The Nature Index provides absolute counts of high-quality publication productivity at the institutional and national level, and as such is one indicator of high-quality research output across the globe. NPG does not intend the Nature Index to be a ranking and have quite deliberately not referred to it as such anywhere—comparisons of countries or institutions derived from Nature Index data need to be interpreted in context. Nor should the Nature Index be used as a research assessment tool in isolation from other quantitative and qualitative sources of relevant information. The dataset is limited to a relatively small proportion of total research articles, covers the natural sciences only, and outputs are non-normalized. We believe there is value in recording trends of absolute counts of high-quality research outputs, in the same way as it is useful to monitor a country’s gross domestic product (GDP). Tracking the relative contributions that organizations, countries and even specific collaborations or projects make to an absolute measure of high-quality research output is useful, not least to understand the countries and institutions whose strategic direction

Nick Campbell

Papers

Genetic association database

Maize genetics and genomics database

Rewiring the circuitry

Index 2014 Global

A response to `Discussion about the new Nature Index'