Publications by authors named "Jeremy C Mason"

9 Publications

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OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data.

PLoS One 2020 30;15(12):e0242933. Epub 2020 Dec 30.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242933PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773254PMC
January 2021

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation.

Nat Immunol 2020 01 16;21(1):86-100. Epub 2019 Dec 16.

Wellcome Sanger Institute, Hinxton, UK.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic 'hits', of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated 'immunologic structures', the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.
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http://dx.doi.org/10.1038/s41590-019-0549-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338221PMC
January 2020

Soft windowing application to improve analysis of high-throughput phenotyping data.

Bioinformatics 2020 03;36(5):1492-1500

Korea Mouse Phenotyping Center (KMPC), Korea.

Motivation: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors.

Results: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.

Availability And Implementation: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115897PMC
March 2020

PDX Finder: A portal for patient-derived tumor xenograft model discovery.

Nucleic Acids Res 2019 01;47(D1):D1073-D1079

The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients' tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the 'findability' of their models by participating in the PDX Finder initiative at www.pdxfinder.org.
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http://dx.doi.org/10.1093/nar/gky984DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323912PMC
January 2019

A mouse informatics platform for phenotypic and translational discovery.

Mamm Genome 2015 Oct 28;26(9-10):413-21. Epub 2015 Aug 28.

MRC Mammalian Genetics Unit, MRC Harwell, Harwell Science and Innovation Campus, Harwell, OX11 0RD, UK.

The International Mouse Phenotyping Consortium (IMPC) is providing the world's first functional catalogue of a mammalian genome by characterising a knockout mouse strain for every gene. A robust and highly structured informatics platform has been developed to systematically collate, analyse and disseminate the data produced by the IMPC. As the first phase of the project, in which 5000 new knockout strains are being broadly phenotyped, nears completion, the informatics platform is extending and adapting to support the increasing volume and complexity of the data produced as well as addressing a large volume of users and emerging user groups. An intuitive interface helps researchers explore IMPC data by giving overviews and the ability to find and visualise data that support a phenotype assertion. Dedicated disease pages allow researchers to find new mouse models of human diseases, and novel viewers provide high-resolution images of embryonic and adult dysmorphologies. With each monthly release, the informatics platform will continue to evolve to support the increased data volume and to maintain its position as the primary route of access to IMPC data and as an invaluable resource for clinical and non-clinical researchers.
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http://dx.doi.org/10.1007/s00335-015-9599-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602054PMC
October 2015

PhenStat: A Tool Kit for Standardized Analysis of High Throughput Phenotypic Data.

PLoS One 2015 6;10(7):e0131274. Epub 2015 Jul 6.

Mouse Informatics Group, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.

The lack of reproducibility with animal phenotyping experiments is a growing concern among the biomedical community. One contributing factor is the inadequate description of statistical analysis methods that prevents researchers from replicating results even when the original data are provided. Here we present PhenStat--a freely available R package that provides a variety of statistical methods for the identification of phenotypic associations. The methods have been developed for high throughput phenotyping pipelines implemented across various experimental designs with an emphasis on managing temporal variation. PhenStat is targeted to two user groups: small-scale users who wish to interact and test data from large resources and large-scale users who require an automated statistical analysis pipeline. The software provides guidance to the user for selecting appropriate analysis methods based on the dataset and is designed to allow for additions and modifications as needed. The package was tested on mouse and rat data and is used by the International Mouse Phenotyping Consortium (IMPC). By providing raw data and the version of PhenStat used, resources like the IMPC give users the ability to replicate and explore results within their own computing environment.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0131274PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4493137PMC
April 2016

Applying the ARRIVE Guidelines to an In Vivo Database.

PLoS Biol 2015 May 20;13(5):e1002151. Epub 2015 May 20.

Mammalian Genetics Unit, Medical Research Council, Harwell, United Kingdom.

The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were developed to address the lack of reproducibility in biomedical animal studies and improve the communication of research findings. While intended to guide the preparation of peer-reviewed manuscripts, the principles of transparent reporting are also fundamental for in vivo databases. Here, we describe the benefits and challenges of applying the guidelines for the International Mouse Phenotyping Consortium (IMPC), whose goal is to produce and phenotype 20,000 knockout mouse strains in a reproducible manner across ten research centres. In addition to ensuring the transparency and reproducibility of the IMPC, the solutions to the challenges of applying the ARRIVE guidelines in the context of IMPC will provide a resource to help guide similar initiatives in the future.
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http://dx.doi.org/10.1371/journal.pbio.1002151DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4439173PMC
May 2015

The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data.

Nucleic Acids Res 2014 Jan 4;42(Database issue):D802-9. Epub 2013 Nov 4.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD, UK and Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

The International Mouse Phenotyping Consortium (IMPC) web portal (http://www.mousephenotype.org) provides the biomedical community with a unified point of access to mutant mice and rich collection of related emerging and existing mouse phenotype data. IMPC mouse clinics worldwide follow rigorous highly structured and standardized protocols for the experimentation, collection and dissemination of data. Dedicated 'data wranglers' work with each phenotyping center to collate data and perform quality control of data. An automated statistical analysis pipeline has been developed to identify knockout strains with a significant change in the phenotype parameters. Annotation with biomedical ontologies allows biologists and clinicians to easily find mouse strains with phenotypic traits relevant to their research. Data integration with other resources will provide insights into mammalian gene function and human disease. As phenotype data become available for every gene in the mouse, the IMPC web portal will become an invaluable tool for researchers studying the genetic contributions of genes to human diseases.
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http://dx.doi.org/10.1093/nar/gkt977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964955PMC
January 2014

The IKMC web portal: a central point of entry to data and resources from the International Knockout Mouse Consortium.

Nucleic Acids Res 2011 Jan 6;39(Database issue):D849-55. Epub 2010 Oct 6.

The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.

The International Knockout Mouse Consortium (IKMC) aims to mutate all protein-coding genes in the mouse using a combination of gene targeting and gene trapping in mouse embryonic stem (ES) cells and to make the generated resources readily available to the research community. The IKMC database and web portal (www.knockoutmouse.org) serves as the central public web site for IKMC data and facilitates the coordination and prioritization of work within the consortium. Researchers can access up-to-date information on IKMC knockout vectors, ES cells and mice for specific genes, and follow links to the respective repositories from which corresponding IKMC products can be ordered. Researchers can also use the web site to nominate genes for targeting, or to indicate that targeting of a gene should receive high priority. The IKMC database provides data to, and features extensive interconnections with, other community databases.
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http://dx.doi.org/10.1093/nar/gkq879DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013768PMC
January 2011