Publications by authors named "Terrence F Meehan"

41 Publications

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

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

PLoS Genet 2020 12 28;16(12):e1009190. Epub 2020 Dec 28.

Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.
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http://dx.doi.org/10.1371/journal.pgen.1009190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822523PMC
December 2020

Ten simple rules for annotating sequencing experiments.

PLoS Comput Biol 2020 10 5;16(10):e1008260. Epub 2020 Oct 5.

Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden.

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http://dx.doi.org/10.1371/journal.pcbi.1008260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7535046PMC
October 2020

Human and mouse essentiality screens as a resource for disease gene discovery.

Nat Commun 2020 01 31;11(1):655. Epub 2020 Jan 31.

Institute of Developmental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.
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http://dx.doi.org/10.1038/s41467-020-14284-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994715PMC
January 2020

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

Know Thy PDX Model.

Cancer Res 2019 09;79(17):4324-4325

Mouse Informatics Coordinator, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

Patient-derived tumor xenograft (PDX) models are frequently used to study cancer mechanisms and potential therapeutics, however, differences in tumor evolution between models and patients have called into question their clinical relevance. In this issue, Mer and colleagues describe the Xenograft Visualization and Analysis (Xeva) software tool that empowers pharmacogenomic analysis through integration of PDX model tumor-drug response with genetic data. By performing the largest PDX model meta-analysis of its kind, the authors demonstrate PDX models are robust platforms for cancer treatment studies. With a clear need for more integrative studies, Xeva is well placed to make more important contributions to pharmacogenomic discovery..
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http://dx.doi.org/10.1158/0008-5472.CAN-19-2023DOI Listing
September 2019

Identification of genes required for eye development by high-throughput screening of mouse knockouts.

Commun Biol 2018 21;1:236. Epub 2018 Dec 21.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

Despite advances in next generation sequencing technologies, determining the genetic basis of ocular disease remains a major challenge due to the limited access and prohibitive cost of human forward genetics. Thus, less than 4,000 genes currently have available phenotype information for any organ system. Here we report the ophthalmic findings from the International Mouse Phenotyping Consortium, a large-scale functional genetic screen with the goal of generating and phenotyping a null mutant for every mouse gene. Of 4364 genes evaluated, 347 were identified to influence ocular phenotypes, 75% of which are entirely novel in ocular pathology. This discovery greatly increases the current number of genes known to contribute to ophthalmic disease, and it is likely that many of the genes will subsequently prove to be important in human ocular development and disease.
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http://dx.doi.org/10.1038/s42003-018-0226-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303268PMC
December 2018

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

The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation.

Conserv Genet 2018 19;19(4):995-1005. Epub 2018 May 19.

1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK.

The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied.
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http://dx.doi.org/10.1007/s10592-018-1072-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061128PMC
May 2018

High-throughput mouse phenomics for characterizing mammalian gene function.

Nat Rev Genet 2018 06;19(6):357-370

MRC Harwell Institute, Harwell, UK.

We are entering a new era of mouse phenomics, driven by large-scale and economical generation of mouse mutants coupled with increasingly sophisticated and comprehensive phenotyping. These studies are generating large, multidimensional gene-phenotype data sets, which are shedding new light on the mammalian genome landscape and revealing many hitherto unknown features of mammalian gene function. Moreover, these phenome resources provide a wealth of disease models and can be integrated with human genomics data as a powerful approach for the interpretation of human genetic variation and its relationship to disease. In the future, the development of novel phenotyping platforms allied to improved computational approaches, including machine learning, for the analysis of phenotype data will continue to enhance our ability to develop a comprehensive and powerful model of mammalian gene-phenotype space.
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http://dx.doi.org/10.1038/s41576-018-0005-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582361PMC
June 2018

Unexplored therapeutic opportunities in the human genome.

Nat Rev Drug Discov 2018 05 23;17(5):317-332. Epub 2018 Mar 23.

Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.
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http://dx.doi.org/10.1038/nrd.2018.14DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339563PMC
May 2018

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Nat Commun 2018 01 18;9(1):288. Epub 2018 Jan 18.

Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, Adriano Buzzati-Traverso Campus, Via E. Ramarini 32, Monterotondo Scalo, RM, 00015, Italy.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.
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http://dx.doi.org/10.1038/s41467-017-01995-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773596PMC
January 2018

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Cancer Res 2017 11;77(21):e62-e66

Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-0582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738926PMC
November 2017

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction.

Nat Commun 2017 10 12;8(1):886. Epub 2017 Oct 12.

RIKEN BioResource Center, Tsukuba, Ibaraki, 305-0074, Japan.

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function.The full extent of the genetic basis for hearing impairment is unknown. Here, as part of the International Mouse Phenotyping Consortium, the authors perform a hearing loss screen in 3006 mouse knockout strains and identify 52 new candidate genes for genetic hearing loss.
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http://dx.doi.org/10.1038/s41467-017-00595-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638796PMC
October 2017

Prevalence of sexual dimorphism in mammalian phenotypic traits.

Nat Commun 2017 06 26;8:15475. Epub 2017 Jun 26.

Mouse Genetics Project, The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans.
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http://dx.doi.org/10.1038/ncomms15475DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490203PMC
June 2017

Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium.

Nat Genet 2017 Aug 26;49(8):1231-1238. Epub 2017 Jun 26.

CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), Illkirch-Graffenstaden, France.

Although next-generation sequencing has revolutionized the ability to associate variants with human diseases, diagnostic rates and development of new therapies are still limited by a lack of knowledge of the functions and pathobiological mechanisms of most genes. To address this challenge, the International Mouse Phenotyping Consortium is creating a genome- and phenome-wide catalog of gene function by characterizing new knockout-mouse strains across diverse biological systems through a broad set of standardized phenotyping tests. All mice will be readily available to the biomedical community. Analyzing the first 3,328 genes identified models for 360 diseases, including the first models, to our knowledge, for type C Bernard-Soulier, Bardet-Biedl-5 and Gordon Holmes syndromes. 90% of our phenotype annotations were novel, providing functional evidence for 1,092 genes and candidates in genetically uncharacterized diseases including arrhythmogenic right ventricular dysplasia 3. Finally, we describe our role in variant functional validation with The 100,000 Genomes Project and others.
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http://dx.doi.org/10.1038/ng.3901DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5546242PMC
August 2017

High-throughput discovery of novel developmental phenotypes.

Nature 2016 09 14;537(7621):508-514. Epub 2016 Sep 14.

Department of Molecular Physiology and Biophysics, Houston, Texas 77030, USA.

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.
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http://dx.doi.org/10.1038/nature19356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295821PMC
September 2016

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability.

J Biomed Semantics 2016 07 4;7(1):44. Epub 2016 Jul 4.

Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.

Background: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies.

Construction And Content: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning.

Utility And Discussion: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs.

Conclusions: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.
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http://dx.doi.org/10.1186/s13326-016-0088-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932724PMC
July 2016

Reporting phenotypes in mouse models when considering body size as a potential confounder.

J Biomed Semantics 2016 9;7. Epub 2016 Feb 9.

Mouse Informatics Group, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire UK.

Genotype-phenotype studies aim to identify causative relationships between genes and phenotypes. The International Mouse Phenotyping Consortium is a high throughput phenotyping program whose goal is to collect phenotype data for a knockout mouse strain of every protein coding gene. The scale of the project requires an automatic analysis pipeline to detect abnormal phenotypes, and disseminate the resulting gene-phenotype annotation data into public resources. A body weight phenotype is a common result of knockout studies. As body weight correlates with many other biological traits, this challenges the interpretation of related gene-phenotype associations. Co-correlation can lead to gene-phenotype associations that are potentially misleading. Here we use statistical modelling to account for body weight as a potential confounder to assess the impact. We find that there is a considerable impact on previously established gene-phenotype associations due to an increase in sensitivity as well as the confounding effect. We investigated the existing ontologies to represent this phenotypic information and we explored ways to ontologically represent the results of the influence of confounders on gene-phenotype associations. With the scale of data being disseminated within the high throughput programs and the range of downstream studies that utilise these data, it is critical to consider how we improve the quality of the disseminated data and provide a robust ontological representation.
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http://dx.doi.org/10.1186/s13326-016-0050-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748495PMC
October 2016

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

Gateways to the FANTOM5 promoter level mammalian expression atlas.

Genome Biol 2015 Jan 5;16:22. Epub 2015 Jan 5.

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.
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http://dx.doi.org/10.1186/s13059-014-0560-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4310165PMC
January 2015

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.

Science 2015 Feb 12;347(6225):1010-4. Epub 2015 Feb 12.

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.
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http://dx.doi.org/10.1126/science.1259418DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681433PMC
February 2015

A promoter-level mammalian expression atlas.

Nature 2014 Mar;507(7493):462-70

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research.
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http://dx.doi.org/10.1038/nature13182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529748PMC
March 2014