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http://ki.se/en/meb/twingene-and-genomeeutwin

In collaboration with GenomeEUtwin, the TwinGene project investigates the importance of quantitative trait loci and environmental factors for cardiovascular disease. It is well known that genetic factors are of considerable importance for some familial lipid syndromes and that Type A Behavior pattern and increased lipid levels infer increased risk for cardiovascular disease. It is furthermore known that genetic factors are of importance levels of blood lipid biomarkers. The interplay of genetic and environmental effects for these risk factors in a normal population is less well understood and virtually unknown for the elderly. In the TwinGene project twins born before 1958 are contacted to participate. Health and medication data are collected from self-reported questionnaires, and blood sampling material is mailed to the subject who then contacts a local health care center for blood sampling and a health check-up. In the simple health check-up, height, weight, circumference of waist and hip, and blood pressure are measured. Blood is sampled for DNA extraction, serum collection and clinical chemistry tests of C-reactive protein, total cholesterol, triglycerides, HDL and LDL cholesterol, apolipo��protein A1 and B, glucose and HbA1C. The TwinGene cohort contains more than 10000 of the expected final number of 16000 individuals. Molecular genetic techniques are being used to identify Quantitative Trait Loci (QTLs) for cardiovascular disease and biomarkers in the TwinGene participants. Genome-wide linkage and association studies are ongoing. DZ twins have been genome-scanned with 1000 STS markers and a subset of 300 MZ twins have been genome-scanned with Illumina 317K SNP platform. Association of positional candidate SNPs arising from these genomscans are planned. The TwinGene project is associated with the large European collaboration denoted GenomEUtwin (www.genomeutwin.org, see below) which since 2002 has aimed at gathering genetic data on twins in Europe and setting up the infrastructure needed to enable pooling of data and joint analyses. It has been the funding source for obtaining the genome scan data. Types of samples: * EDTA whole blood * DNA * Serum Number of sample donors: 12 044 (sample collection completed)

Proper citation: KI Biobank - TwinGene (RRID:SCR_006006) Copy   

•   Source: SciCrunch Registry

http://hcsquared.eu/home

HC2 is an EU funded project that aims to promote, support and help define future lines of research in Human Computer Confluence (HCC). HCC is the study of the intersection of HCI, Cognitive Neuroscience, VR/AR, Presence, Pervasive Computing and how they can enable new forms of sensing, perception, interaction and understanding. In a sense it is the study of the disappearing interface. HCC, Human-Computer Confluence, is an ambitious research program studying how the emerging symbiotic relation between humans and computing devices can enable radically new forms of sensing, perception, interaction, and understanding. The horizontal character of HCC makes it a fascinating and fertile interdisciplinary field, but it can also compromise its growth, with researchers scattered across disciplines and groups worldwide. To address this we are building a community of HCC researchers. There are lots of ways you can join in. Add your name to the HCC Players Map, take advantage of our Exchange Program to work with colleagues at your favorite lab, sign up for our Summer School or just follow us on Twitter and LinkedIn to see what''s happening. In order to foster interdisciplinary research and promote HCC research we have set up an Exchange Program. Students that wish to apply for financial support from our Exchange Program should follow the steps provided. The Exchange Program is open to all graduate students (Masters and PhD). A maximum of 500 Euro support will be provided per student.

Proper citation: HC2: Human-Computer Confluence (RRID:SCR_005549) Copy   

•   Source: SciCrunch Registry

http://www.chernobyltissuebank.com/

The CTB (Chernobyl Tissue Bank) is an international cooperation that collects, stores and disseminates biological samples from tumors and normal tissues from patients for whom the aetiology of their disease is known - exposure to radioiodine in childhood following the accident at the Chernobyl power plant. The main objective of this project is to provide a research resource for both ongoing and future studies of the health consequences of the Chernobyl accident. It seeks to maximize the amount of information obtained from small pieces of tumor by providing multiple aliquots of RNA and DNA extracted from well documented pathological specimens to a number of researchers world-wide and to conserve this valuable material for future generations of scientists. It exists to promote collaborative, rather than competitive, research on a limited biological resource. Tissue is collected to an approved standard operating procedure (SOP) and is snap frozen; the presence or absence of tumor is verified by frozen section. A representative paraffin block is also obtained for each case. Where appropriate, we also collect fresh and paraffin-embedded tissue from loco-regional metastases. Currently we do not issue tissue but provide extracted nucleic acid, paraffin sections and sections from tissue microarrays from this material. The project is coordinated from Imperial College, London and works with Institutes in the Russian Federation (the Medical Radiological Research Centre in Obninsk) and Ukraine (the Institute of Endocrinology and Metabolism in Kiev) to support local scientists and clinicians to manage and run a tissue bank for those patients who have developed thyroid tumors following exposure to radiation from the Chernobyl accident. Belarus was also initially included in the project, but is currently suspended for political reasons.

Proper citation: Chernobyl Tissue Bank (RRID:SCR_010662) Copy   

•   Source: SciCrunch Registry

http://abacas.sourceforge.net

Software that contiguates (align, order, orientate), visualizes and designs primers to close gaps on shotgun assembled contigs based on a reference sequence. ABACAS finds alignment positions and identifies syntenies of assembled contigs against the reference, then generates a pseudomolecule taking overlapping contigs and gaps into account.

Proper citation: ABACAS (RRID:SCR_015852) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/glycocd/

Manually curated, comprehensive repository of clusters of differentiation (CDs) which are a) defined as distinct oligosaccharide sequences as part of either glycoproteins and/or glycosphingolipids and b) defined as proteins which have carbohydrate recognition sites (CRDs) or as carbohydrate binding lectins. The data base is generated by exhaustive search of literature and other online data banks related to carbohydrates and proteins. This data bank is the beginning of an effort to provide concise, relevant information of carbohydrate-related CDs in a user- friendly manner. For users convenience the data bank under menu browse of GlycoCD is arranged in two section namely carbohydrate recognition CDs (CRD CD) and glycan CD. The carbohydrate recognition CD part is the collection of proteins which recognize glycan structures by means of the CRDs. Glycan CD is the part in which CDs are summarized which characterize specific oligosaccharide structures. The GlycoCD databank has been developed with the aim to assist the immunologist, cell biologist as well as the clinician who wants to keep up with the present knowledge in this field of glycobiology.

Proper citation: Glyco-CD (RRID:SCR_001574) Copy   

•   Source: SciCrunch Registry

https://github.com/DiltheyLab/HLA-LA

Software implements new graph alignment model for human leukocyte antigen, based on projection of linear alignments onto variation graph. Enables accurate HLA type inference from whole genome and whole exome Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data and from genome assemblies.

Proper citation: HLA-LA (RRID:SCR_022283) Copy   

•   Source: SciCrunch Registry

https://biofam.github.io/MOFA2/

Software framework for unsupervised integration of multi-omics data sets. Used for discovering principal sources of variation in multi omics data sets.

Proper citation: MOFA (RRID:SCR_022992) Copy   

•   Source: SciCrunch Registry

http://search.driver.research-infrastructures.eu/

Data infrastructure project that merged with OpenAIRE. Cohesive, robust and flexible, pan-European infrastructure for digital repositories, offering sophisticated services and functionalities for researchers, administrators and the general public. Access the network of freely accessible digital repositories with content across academic disciplines with over 3,500,000 scientific publications, found in journal articles, dissertations, books, lectures, reports, etc., harvested regularly from more than 295 repositories, from 38 countries. DRIVER has established a network of relevant experts and Open Access repositories. DRIVER-II will consolidate these efforts and transform the initial testbed into a fully functional, state-of-the art service, extending the network to a larger confederation of repositories. It aims to optimize the way the e-Infrastructure is used to store knowledge, add value to primary research data and information making secondary research more effective, provide a valuable asset for industry, and help bridging research and education. The objectives of DRIVER-II, the second phase of the project, include efforts to expand, enrich, and strengthen the results of DRIVER, in the following areas: * strategic geographic and community expansion by means of the DRIVER confederation * establish a robust, scalable repository infrastructure accompanied by an open source software package D-Net * broader coverage of content through the use of enhanced publications * advanced end-user functionality to support scientific exploration of complex digital objects * larger outreach and advocacy programs * continued repository support * guidelines for interoperability in the larger European digital library community

Proper citation: Digital Repository Infrastructure Vision for European Research (RRID:SCR_002752) Copy   

•   Source: SciCrunch Registry

https://github.com/INCF/csa

Software tool for description of connectivity in small and large scale neuronal network models. It provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. Can be used as component of neuronal network simulators or other tools.

Proper citation: Connection-set algebra (RRID:SCR_017397) Copy   

•   Source: SciCrunch Registry

https://lsbr.niams.nih.gov/bsoft/

Software package and a platform for the processing of electron micrographs in structural biology. Supports different image file formats used in electron microscopy (including MRC, SPIDER, IMAGIC, SUPRIM, and PIF)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bsoft (RRID:SCR_016503) Copy   

•   Source: SciCrunch Registry

http://scipion.i2pc.es/

Software framework for image processing to obtain 3D models of macromolecular complexes using Electron Microscopy. Open-source project for integration, reproducibility and validation in 3D electron microscopy. It integrates several software packages to execute workflows combining different software tools, while taking care of formats and conversions. Electron Microscopy (3DEM). waiting for pdf from Joe

Proper citation: SCIPION (RRID:SCR_016738) Copy   

•   Source: SciCrunch Registry

http://simbioms.org/

SIMBioMS (System for Information Management in BioMedical Studies) is a multi-module solution for data management in biomedical studies. Any research concerning human samples and/or utilizing high-throughput technologies yields such amount of information that conventional data storage solution might not be sufficient. We offer here three software modules: * Sample Information Management System (SIMS), * Assay Information Management System (AIMS) * Sample avAILability system (SAIL) * Emanta Administration tool (Emanta) All three software modules were developed as a part of the integrated EU project MolPAGE (Molecular Phenotyping to Accelerate Genomic Epidemiology) and the collaborative research project ENGAGE (European Network of Genomic and Genetic Epidemiology). SIMS and AIMS can work either as united system or as two completely independent components. In turn, SAIL is an independent web-based system for indexing of phenotypes availability in different cohorts and collections. All systems are packaged in such a way that they can easily be installed either as local (e.g. on a laptop) or as centralized databases (to be used by a group of people). SIMS and AIMS benefit from customizable interface, editable vocabularies and a choice of options for tackling data confidentiality issues. The systems provides a user with efficient means of control over data exchange process and at the same time helps to format the metadata in compliance with the standards accepted in functional genomics. Since SIMBioMS is an open source project, source files can be downloaded and changed by the user if needed.

Proper citation: SIMBioMS (RRID:SCR_005745) Copy   

•   Source: SciCrunch Registry

https://CRAN.R-project.org/package=APIS

Software R package to assign offspring to their parents without any prior information other than offspring and parental genotypes, and user-defined, acceptable error rate among assigned offspring. Auto-adaptive parentage inference software that tolerates missing parents.

Proper citation: APIS (RRID:SCR_025445) Copy   

•   Source: SciCrunch Registry

https://github.com/farkkilab/tribus

Software tool for cell type based analysis of multiplexed imaging data. Interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. Recovers fine-grained cell types, matching the gold standard annotations by human experts, can target ambiguous populations and discover phenotypically distinct cell subtypes.

Proper citation: TRIBUS (RRID:SCR_027367) Copy   

•   Source: SciCrunch Registry