Karolinska Institutet

About: Karolinska Institutet is a education organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Population & Cancer. The organization has 46212 authors who have published 121142 publications receiving 6008130 citations.

The BioMart community portal: an innovative alternative to large, centralized data repositories

Abstract: The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Heritability of Adult Body Height: A Comparative Study of Twin Cohorts in Eight Countries

Abstract: A major component of variation in body height is due to genetic differences, but environmental factors have a substantial contributory effect. In this study we aimed to analyse whether the genetic architecture of body height varies between affluent western societies. We analysed twin data from eight countries comprising 30,111 complete twin pairs by using the univariate genetic model of the Mx statistical package. Body height and zygosity were self-reported in seven populations and measured directly in one population. We found that there was substantial variation in mean body height between countries; body height was least in Italy (177 cm in men and 163 cm in women) and greatest in the Netherlands (184 cm and 171 cm, respectively). In men there was no corresponding variation in heritability of body height, heritability estimates ranging from 0.87 to 0.93 in populations under an additive genes/unique environment (AE) model. Among women the heritability estimates were generally lower than among men with greater variation between countries, ranging from 0.68 to 0.84 when an additive genes/shared environment/unique environment (ACE) model was used. In four populations where an AE model fit equally well or better, heritability ranged from 0.89 to 0.93. This difference between the sexes was mainly due to the effect of the shared environmental component of variance, which appears to be more important among women than among men in our study populations. Our results indicate that, in general, there are only minor differences in the genetic architecture of height between affluent Caucasian populations, especially among men.

The nf-core framework for community-curated bioinformatics pipelines.

Abstract: To the Editor — The standardization, portability and reproducibility of analysis pipelines are key issues within the bioinformatics community. Most bioinformatics pipelines are designed for use on-premises; as a result, the associated software dependencies and execution logic are likely to be tightly coupled with proprietary computing environments. This can make it difficult or even impossible for others to reproduce the ensuing results, which is a fundamental requirement for the validation of scientific findings. Here, we introduce the nf-core framework as a means for the development of collaborative, peerreviewed, best-practice analysis pipelines (Fig. 1). All nf-core pipelines are written in Nextflow and so inherit the ability to be executed on most computational infrastructures, as well as having native support for container technologies such as Docker and Singularity. The nf-core community (Supplementary Fig. 1) has developed a suite of tools that automate pipeline creation, testing, deployment and synchronization. Our goal is to provide a framework for high-quality bioinformatics pipelines that can be used across all institutions and research facilities. Being able to reproduce scientific results is the central tenet of the scientific method. However, moving toward FAIR (findable, accessible, interoperable and reusable) research methods1 in data-driven science is complex2,3. Central repositories, such as bio. tools4, omictools5 and the Galaxy toolshed6, make it possible to find existing pipelines and their associated tools. However, it is still notoriously challenging to develop analysis pipelines that are fully reproducible and interoperable across multiple systems and institutions — primarily because of differences in hardware, operating systems and software versions. Although the recommended guidelines for some analysis pipelines have become standardized (for example, GATK best practices7), the actual implementations are usually developed on a case-by-case basis. As such, there is often little incentive to test, document and implement pipelines in a way that permits their reuse by other researchers. This can hamper sustainable sharing of data and tools, and results in a proliferation of heterogeneous analysis pipelines, making it difficult for newcomers to find what they need to address a specific analysis question. As the scale of -omics data and their associated analytical tools has grown, the scientific community is increasingly moving toward the use of specialized workflow management systems to build analysis pipelines8. They separate the requirements of the underlying compute infrastructure from the analysis and workflow description, introducing a higher degree of portability as compared to custom in-house scripts. One such popular tool is Nextflow9. Using Nextflow, software packages can be bundled with analysis pipelines using built-in integration for package managers, such as Conda, and containerization platforms, such as Docker and Singularity. Moreover, support for most common highperformance-computing batch schedulers and cloud providers allows simple deployment of analysis pipelines on almost any infrastructure. The opportunity to run pipelines locally during initial development and then to proceed seamlessly to largescale computational resources in highperformance-computing or cloud settings provides users and developers with great flexibility. The nf-core community project collects a curated set of best-practice analysis pipelines built using Nextflow. Similar projects Participate