Speeding disease gene discovery by sequence based candidate prioritization

BACKGROUND: Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning. RESULTS: We examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time. CONCLUSION: PROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies

SUSPECTS: enabling fast and effective prioritization of positional candidates.

SUSPECTS is a web-based server which combines annotation and sequence-based approaches to prioritize disease candidate genes in large regions of interest. It uses multiple lines of evidence to rank genes quickly and effectively while limiting the effect of annotation bias to significantly improve performance

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases

The State of Altmetrics: A Tenth Anniversary Celebration

Altmetric’s mission is to help others understand the influence of research online.We collate what people are saying about published research in sources such as the mainstream media, policy documents, social networks, blogs, and other scholarly and non-scholarly forums to provide a more robust picture of the influence and reach of scholarly work. Altmetric works with some of the biggest publishers, funders, businesses and institutions around the world to deliver this data in an accessible and reliable format. Contents Altmetrics, Ten Years Later, Euan Adie (Altmetric (founder) & Overton) Reflections on Altmetrics, Gemma Derrick (University of Lancaster), Fereshteh Didegah (Karolinska Institutet & Simon Fraser University), Paul Groth (University of Amsterdam), Cameron Neylon (Curtin University), Jason Priem (Our Research), Shenmeng Xu (University of North Carolina at Chapel Hill), Zohreh Zahedi (Leiden University) Worldwide Awareness and Use of Altmetrics, Yin-Leng Theng (Nanyang Technological University) Leveraging Machine Learning on Altmetrics Big Data, Saeed-Ul Hassan (Information Technology University), Naif R. Aljohani (King Abdulaziz University), Timothy D. Bowman (Wayne State University) Altmetrics as Social-Spatial Sensors, Vanash M. Patel (West Hertfordshire Hospitals NHS Trust), Robin Haunschild (Max Planck Institute for Solid State Research), Lutz Bornmann (Administrative Headquarters of the Max Planck Society) Altmetric’s Fable of the Hare and the Tortoise, Mike Taylor (Digital Science) The Future of Altmetrics: A Community Vision, Liesa Ross (Altmetric), Stacy Konkiel (Altmetric

Assessing the impact of predatory journals on policy and guidance documents: a cross-sectional study protocol.

peer reviewed[en] INTRODUCTION: Many predatory journals fail to follow best publication practices. Studies assessing the impact of predatory journals have focused on how these articles are cited in reputable academic journals. However, it is possible that research from predatory journals is cited beyond the academic literature in policy documents and guidelines. Given that research used to inform public policy or government guidelines has the potential for widespread impact, we will examine whether predatory journals have penetrated public policy. METHODS AND ANALYSIS: This is a descriptive study with no hypothesis testing. Policy documents that cite work from the known predatory publisher OMICS will be downloaded from the Overton database. Overton collects policy documents from over 1200 sources worldwide. Policy documents will be evaluated to determine how the predatory journal article is used. We will also extract epidemiological details of the policy documents, including: who funded their development, the discipline the work is relevant to and the name of the organisations producing the policy. The record of scholarly citations of the identified predatory articles will also be examined. Findings will be reported with descriptive statistics using counts and percentages. ETHICS AND DISSEMINATION: No ethical approval was required for this study since it does not involve human or animal research. Study findings will be discussed at workshops on journalology and predatory publishing and will be disseminated through preprint, peer-reviewed literature and conference presentations

Attention! A study of open access vs non-open access articles

<p>There are lots of good reasons to publish in open access journals. Two of the most commonly given ones are the beliefs that OA articles are read more widely and that they generate higher citations.</p> <p>Do open access articles also get higher altmetric counts?</p> <p>We take a look at Nature Communications to see if there is any discernible difference in the quantitative altmetrics between their open access and reader pays articles. We picked Nature Communications to look at as it’s a relatively high volume, multi-disciplinary hybrid journal (at least it was during our study period – it has gone fully OA now) that clearly marks up authors, license and subject areas in its metadata.</p> <p> </p> <p>The dataset behind this post can be found here:</p> <p> </p> <p>http://figshare.com/articles/Altmetrics_data_for_Nature_Communications_articles_Oct_13_Oct_14/1213687</p

New Data, New Metrics: How and When They can be Used [NWB'2016 presentation slides]

<p>The advent of digitization and open science is accelerating the pace of research activity, communication and evaluation. This is also producing many more “signals” across the research process. Inputs such as the grants metadata and equipment use; outputs such as data sets, posters, patents, clinical trial results, and curated researcher profiles; indicators such as altmetrics, article and object-level metrics, and patent citations; and even case study accounts from the researchers themselves, can now be monitored and analysed.</p><p>Understanding these “signals” may help improve both the ways in which research is conducted and how the outcomes are measured and assessed. The latter is especially important with the increased pressure from government and the public to justify research investments and for institutions and researchers to properly articulate the stage-appropriate outcomes of research projects in social, economic and environmental contexts.</p><p>In his presentation, Euan Adie, founder of Digital Science portfolio company Altmetric, will discuss the nature of these “signals”: how institutions can work with funders, researchers and publishers to create research information frameworks. These frameworks may capture these “signals” and enrich them as funding is acquired, and as the research is conducted, results are produced, and its impacts felt.</p