Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

Sochat, Vanessa; Prybol, Cameron J.; Kurtzer, Gregory M.

doi:10.1371/journal.pone.0188511

Cited by 46 publications

(33 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of the steps involved in preparing and preprocessing the MRI data employed recently developed tools and workflows aimed at enhancing standardization and reproducibility of task-based fMRI studies [for a similar preprocessing pipeline, see 96]. Following successful acquisition, all study data were arranged according to the BIDS specification [97] using the tool (version 0.6.0.dev1; freely available from https://github.com/nipy/heudiconv) running inside a container [98, 99] to facilitate further analysis and sharing of the data. Dicoms were converted to the NIfTI-1 format using [version 1.0.20190410 GCC6.3.0; 100].…”

Section: Methodsmentioning

confidence: 99%

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

Wittkuhn

2020

Preprint

View full text Add to dashboard Cite

Neural computations are often anatomically localized and executed on sub-second time scales. Understanding the brain therefore requires methods that offer sufficient spatial and temporal resolution. This poses a particular challenge for the study of the human brain because non-invasive methods have either high temporal or spatial resolution, but not both. Here, we introduce a novel multivariate analysis method for conventional blood-oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) that allows to study sequentially activated neural patterns separated by less than 100 ms with anatomical precision. Human participants underwent fMRI and were presented with sequences of visual stimuli separated by 32 to 2048 ms. Probabilistic pattern classifiers were trained on fMRI data to detect the presence of image-specific activation patterns in early visual and ventral temporal cortex. The classifiers were then applied to data recorded during sequences of the same images presented at increasing speeds. Our results show that probabilistic classifier time courses allowed to detect neural representations and their order, even when images were separated by only 32 ms. Moreover, the frequency spectrum of the statistical sequentiality metric distinguished between sequence speeds on sub-second versus supra-second time scales. These results survived when data with high levels of noise and rare sequence events at unknown times were analyzed. Our method promises to lay the groundwork for novel investigations of fast neural computations in the human brain, such as hippocampal replay.

show abstract

Section: Methodsmentioning

confidence: 99%

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

Wittkuhn

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…GenomeChronicler is also available as a Singularity container (Kurtzer et al, 2017) with all dependencies pre-installed and ready. This can be obtained from SingularityHub (Sochat et al, 2017) by running the command: singularity pull "shub://PGP-UK/GenomeChronicler" on any machine that has Singularity installed.…”

Section: Using a Pre-compiled Containermentioning

confidence: 99%

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline

et al. 2020

View full text Add to dashboard Cite

show abstract

“…GenomeChronicler is also available as a Singularity container (Kurtzer, Sochat, and Bauer 2017) with all dependencies pre-installed and ready. This can be obtained from SingularityHub (Sochat, Prybol, and Kurtzer 2017) by running the command: singularity pull shub://PGP-UK/GenomeChronicler on any machine that has Singularity installed.…”

Section: Using a Pre-compiled Containermentioning

confidence: 99%

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline

Guerra-Assunção

Conde

Moghul

et al. 2020

Preprint

View full text Add to dashboard Cite

In recent years, there has been a significant increase in whole genome sequencing data of individual genomes produced by research projects as well as direct to consumer service providers. While many of these sources provide their users with an interpretation of the data, there is a lack of free, open tools for generating similar reports exploring the data in an easy to understand manner.GenomeChronicler was written as part of the Personal Genome Project UK (PGP-UK) to address this need. PGP-UK provides genomic, transcriptomic, epigenomic and self-reported phenotypic data under an open-access model with full ethical approval. As a result, the reports generated by GenomeChronicler are intended for research purposes only and include information relating to potentially beneficial and potentially harmful variants, but without clinical curation.GenomeChronicler can be used with data from whole genome or whole exome sequencing producing a genome report containing information on variant statistics, ancestry and known associated phenotypic traits. Example reports are available from the PGP-UK data page (personalgenomes.org.uk/data).The objective of this method is to leverage on existing resources to find known phenotypes associated with the genotypes detected in each sample. The provided trait data is based primarily upon information available in SNPedia, but also collates data from ClinVar, GETevidence and gnomAD to provide additional details on potential health implications, presence of genotype in other PGP participants and population frequency of each genotype.The whole pipeline is self-contained, and runs without internet connection, making it a good choice for privacy conscious projects that can run GenomeChronicler within their off-line safe-haven environments. GenomeChronicler can be run for one sample at a time, or in parallel making use of the nextflow workflow manager.The source code is available from GitHub (https://github.com/PGP-UK/GenomeChronicler), container recipes are available for Docker and Singularity, as well as a pre-built container from SingularityHub (https://singularity-hub.org/collections/3664) enabling easy deployment in a variety of settings. Users without access to computational resources to run GenomeChronicler can access the software from the Lifebit CloudOS platform (https://cloudos.lifebit.ai) enabling the production of reports and variant calls from raw sequencing data in a scalable fashion.

show abstract

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

Cited by 46 publications

References 17 publications

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline

Contact Info

Product

Resources

About