Dataset Information

SAIL--a software system for sample and phenotype availability across biobanks and cohorts.

ABSTRACT: The Sample avAILability system-SAIL-is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables.SAIL is available under Aferro-GPL open source license: https://github.com/sail.

SUBMITTER: Gostev M

PROVIDER: S-EPMC3035801 | biostudies-other | 2011 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

SAIL--a software system for sample and phenotype availability across biobanks and cohorts.

Gostev Mikhail M Fernandez-Banet Julio J Rung Johan J Dietrich Joern J Prokopenko Inga I Ripatti Samuli S McCarthy Mark I MI Brazma Alvis A Krestyaninova Maria M

Bioinformatics (Oxford, England) 20101217 4

<h4>Summary</h4>The Sample avAILability system-SAIL-is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on ...[more]

PMID: 21169373

Similar Datasets

Project description:With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3-85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3-20 years) for afni_refacer and the oldest (44-85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

Dataset Information

SAIL--a software system for sample and phenotype availability across biobanks and cohorts.

Publications

SAIL--a software system for sample and phenotype availability across biobanks and cohorts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets