Functional ANnoTation Of The Mammalian Genome Research Articles

Pan-cancer analysis of non-coding transcripts reveals the prognostic onco-lncRNA HOXA10-AS in gliomas.

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer and powerful biomarkers; however, most remain uncharacterized. Here, we analyze 5,592 prognostic lncRNAs in 9,446 cancers of 30 types using machine learning. We identify 166 lncRNAs whose expression correlates with survival and improves the accuracy of common clinical variables, molecular features, and cancer subtypes. Prognostic lncRNAs are often characterized by switch-like expression patterns. In low-grade gliomas, HOXA10-AS activation is a robust marker of poor prognosis that complements IDH1/2 mutations, as validated in another retrospective cohort, and correlates with developmental pathways in tumor transcriptomes. Loss- and gain-of-function studies in patient-derived glioma cells, organoids, and xenograft models identify HOXA10-AS as a potent onco-lncRNA that regulates cell proliferation, contact inhibition, invasion, Hippo signaling, and mitotic and neuro-developmental pathways. Our study underscores the pan-cancer potential of the non-coding transcriptome for identifying biomarkers and regulators of cancer progression.

Low Quantity Single Strand CAGE (LQ-ssCAGE) Maps Regulatory Enhancers and Promoters.

The Cap Analysis of Gene Expression (CAGE) is a powerful method to identify Transcription Start Sites (TSSs) of capped RNAs while simultaneously measuring transcripts expression level. CAGE allows mapping at single nucleotide resolution at all active promoters and enhancers. Large CAGE datasets have been produced over the years from individual laboratories and consortia, including the Encyclopedia of DNA Elements (ENCODE) and Functional Annotation of the Mammalian Genome (FANTOM) consortia. These datasets constitute open resource for TSS annotations and gene expression analysis. Here, we provide an experimental protocol for the most recent CAGE method called Low Quantity (LQ) single strand (ss) CAGE "LQ-ssCAGE", which enables cost-effective profiling of low quantity RNA samples. LQ-ssCAGE is especially useful for samples derived from cells cultured in small volumes, cellular compartments such as nuclear RNAs or for samples from developmental stages. We demonstrate the reproducibility and effectiveness of the method by constructing 240 LQ-ssCAGE libraries from 50ng of THP-1 cell extracted RNAs and discover lowly expressed novel enhancer and promoter-derived lncRNAs.

FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs.

The Functional ANnoTation Of the Mammalian genome (FANTOM) Consortium has continued to provide extensive resources in the pursuit of understanding the transcriptome, and transcriptional regulation, of mammalian genomes for the last 20 years. To share these resources with the research community, the FANTOM web-interfaces and databases are being regularly updated, enhanced and expanded with new data types. In recent years, the FANTOM Consortium's efforts have been mainly focused on creating new non-coding RNA datasets and resources. The existing FANTOM5 human and mouse miRNA atlas was supplemented with rat, dog, and chicken datasets. The sixth (latest) edition of the FANTOM project was launched to assess the function of human long non-coding RNAs (lncRNAs). From its creation until 2020, FANTOM6 has contributed to the research community a large dataset generated from the knock-down of 285 lncRNAs in human dermal fibroblasts; this is followed with extensive expression profiling and cellular phenotyping. Other updates to the FANTOM resource includes the reprocessing of the miRNA and promoter atlases of human, mouse and chicken with the latest reference genome assemblies. To facilitate the use and accessibility of all above resources we further enhanced FANTOM data viewers and web interfaces. The updated FANTOM web resource is publicly available at https://fantom.gsc.riken.jp/.

Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response.

Immune checkpoint genes (ICGs) play critical roles in circumventing self-reactivity and represent a novel target to develop treatments for cancers. However, a comprehensive analysis for the expression profile of ICGs at a pan-cancer level and their correlation with patient response to immune checkpoint blockade (ICB) based therapy is still lacking. In this study, we defined three expression patterns of ICGs using a comprehensive survey of RNA-seq data of tumor and immune cells from the functional annotation of the mammalian genome (FANTOM5) project. The correlation between the expression patterns of ICGs and patients survival and response to ICB therapy was investigated. The expression patterns of ICGs were robust across cancers, and upregulation of ICGs was positively correlated with high lymphocyte infiltration and good prognosis. Furthermore, we built a model (ICGe) to predict the response of patients to ICB therapy using five features of ICG expression. A validation scenario of six independent datasets containing data of 261 patients with CTLA-4 and PD-1 blockade immunotherapies demonstrated that ICGe achieved area under the curves of 0.64-0.82 and showed a robust performance and outperformed other mRNA-based predictors. In conclusion, this work revealed expression patterns of ICGs and underlying correlations between ICGs and response to ICB, which helps to understand the mechanisms of ICGs in ICB signal pathways and other anticancer treatments.

The intersectional genetics landscape for humans.

ABSTRACTBackgroundThe human body is made up of hundreds—perhaps thousands—of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an “AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output.ResultsHere, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank “AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential “versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq–derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented.ConclusionsOur work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.

Multiomics Evaluation of Gastrointestinal and Other Clinical Characteristics of COVID-19

Since December 2019, coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced a worldwide panic. Beyond the principal human-to-human transmission method by droplet and contact, there is still limited knowledge about possible alternate transmission methods to guide clinical care. Recent clinical studies have observed digestive symptoms in patients with COVID-19,1 possibly because of the enrichment and infection of SARS-CoV-2 in the gastrointestinal tract, mediated by virus receptor of angiotensin converting enzyme 2 (ACE2),2 which suggests the potential for a fecal-oral route of SARS-CoV-2 transmission.3,4 However, there is still a large gap in the biological knowledge of COVID-19. In this study, via a bulk-to-cell strategy focusing on ACE2, we performed an integrated omics analysis at the genome, transcriptome, and proteome levels in bulk tissues and single cells across species to decipher the potential routes for SARS-CoV-2 infection in depth.

VarGen: an R package for disease-associated variant discovery and annotation.

Over the past decade, there has been an exponential increase in the amount of disease-related genomic data available in public databases. However, this high-quality information is spread across independent sources and researchers often need to access these separately. Hence, there is a growing need for tools that gather and compile this information in an easy and automated manner. Here, we present 'VarGen', an easy-to-use, customizable R package that fetches, annotates and rank variants related to diseases and genetic disorders, using a collection public databases (viz. Online Mendelian Inheritance in Man, the Functional Annotation of the Mammalian genome 5, the Genotype-Tissue Expression and the Genome Wide Association Studies catalog). This package is also capable of annotating these variants to identify the most impactful ones. We expect that this tool will benefit the research of variant-disease relationships. VarGen is open-source and freely available via GitHub: https://github.com/MCorentin/VarGen. The software is implemented as an R package and is supported on Linux, MacOS and Windows. Supplementary data are available at Bioinformatics online.

Abstract 4172: Identification of familial Hodgkin lymphoma predisposing genes by whole genome sequencing

Abstract Hodgkin lymphoma (HL) originates from germinal center B-cells and accounts for about 10% of newly diagnosed lymphomas and 1% of all de-novo neoplasms worldwide. Though familial risk for HL is among the highest of all cancers, not many genetic risk factors have been identified besides associations with the human leukocyte antigen (HLA) complex and few germline variants in familial classical Hodgkin lymphoma and nodular lymphocyte predominant Hodgkin lymphoma. With the aim of identifying novel predisposing germline variants in familial Hodgkin lymphoma, we performed whole genome sequencing (WGS) on seven affected and nine unaffected family members from three HL-prone families (families I, II and III). WGS identified a total of 98564, 170551 and 113654 variants in families I, II and III, respectively at ≤0.1% minor allele frequency. Variants were prioritized using the Familial Cancer Variant Prioritization Pipeline version 2 (FCVPPv2) developed by us. Pedigree-based filtering reduced the number of variants to 18158, 496 and 26851. Non-synonymous SNVs were the most represented exonic variants. Application of FCVPPv2 criteria resulted in prioritization of 19, 6 and 13 potentially causative exonic variants in families I, II and III, respectively. Variants affecting promoters, enhancers, super-enhancers, transcription factor binding sites and microRNA seed sequences were identified using FANTOM5 (Functional Annotation of the Mammalian Genome 5), SEA (Super-Enhancer Archive), SNPnexus and TargetScan tools. Pathway analysis of the involved genes using Ingenuity Pathway Analysis showed enrichment of B-cell receptor signaling, PI3K signaling in B lymphocytes and protein kinase A signaling pathways. Implementation of FCVPPv2 on data from family I resulted in confirmation and functional validation of a novel heterozygous missense variant (c.T5133G: p.I1711M) in the tumor suppressor gene DICER1 as potential HL predisposition factor. In order to identify the causal variants in families II and III, the variants short listed based on our pipeline are being investigated for segregation, frequency in large cohorts and functional consequences. In conclusion, WGS data analysis of three HL-prone families allowed us to prioritize coding and non-coding variants and identify enrichment of B-cell activating pathways. We aim to select and validate novel cancer predisposing variants that can facilitate genetic counseling and personalized therapy in these families as well as screening of other individuals at risk of developing Hodgkin lymphoma. Citation Format: Sara Giangiobbe, Aayushi Srivastava, Abhishek Kumar, Nagarajan Paramasivam, Dagmara Dymerska, Wolfgang Benisch, Mathias Witzens-Harig, Mathias Schlessner, Jan Lubinski, Kari Hemminki, Asta Försti, Obul R. Bandapalli. Identification of familial Hodgkin lymphoma predisposing genes by whole genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4172.

Characterization of Glucose Transporter 6 in Lipopolysaccharide-Induced Bone Marrow-Derived Macrophage Function.

The polarization processes for M1 versus M2 macrophages are quite distinct in the context of changes in cellular metabolism. M1 macrophages are highly glycolytic, whereas M2 macrophages require a more oxidative nutrient metabolism. An important part of M1 polarization involves upregulation of the glucose transporter (GLUT) GLUT1 to facilitate increased glucose uptake and glycolytic metabolism; however, the role of other glucose transporters in this process is largely unknown. In surveying the Functional Annotation of the Mammalian Genome and Gene Expression Omnibus Profiles databases, we discovered that the glucose transporter GLUT6 is highly upregulated in LPS-activated macrophages. In our previous work, we have not detected mouse GLUT6 protein expression in any noncancerous tissue; therefore, in this study, we investigated the expression and significance of GLUT6 in bone marrow-derived macrophages from wild-type and GLUT6 knockout C57BL/6 mice. We show that LPS-induced M1 polarization markedly upregulated GLUT6 protein, whereas naive macrophages and IL-4-induced M2 macrophages do not express GLUT6 protein. However, despite strong upregulation of GLUT6 in M1 macrophages, the absence of GLUT6 did not alter M1 polarization in the context of glucose uptake, glycolytic metabolism, or cytokine production. Collectively, these data show that GLUT6 is dispensable for LPS-induced M1 polarization and function. These findings are important because GLUT6 is an anticancer drug target, and this study suggests that inhibition of GLUT6 may not impart detrimental side effects on macrophage function to interfere with their antitumor properties.

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods

BackgroundIn the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide.ResultsBased on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome).ConclusionThe predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

GeneHancer: genome-wide integration of enhancers and target genes in GeneCards.

A major challenge in understanding gene regulation is the unequivocal identification of enhancer elements and uncovering their connections to genes. We present GeneHancer, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards. First, we integrated a total of 434 000 reported enhancers from four different genome-wide databases: the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project and the VISTA Enhancer Browser. Employing an integration algorithm that aims to remove redundancy, GeneHancer portrays 285 000 integrated candidate enhancers (covering 12.4% of the genome), 94 000 of which are derived from more than one source, and each assigned an annotation-derived confidence score. GeneHancer subsequently links enhancers to genes, using: tissue co-expression correlation between genes and enhancer RNAs, as well as enhancer-targeted transcription factor genes; expression quantitative trait loci for variants within enhancers; and capture Hi-C, a promoter-specific genome conformation assay. The individual scores based on each of these four methods, along with gene–enhancer genomic distances, form the basis for GeneHancer’s combinatorial likelihood-based scores for enhancer–gene pairing. Finally, we define ‘elite’ enhancer–gene relations reflecting both a high-likelihood enhancer definition and a strong enhancer–gene association.GeneHancer predictions are fully integrated in the widely used GeneCards Suite, whereby candidate enhancers and their annotations are displayed on every relevant GeneCard. This assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant–phenotype interpretation of whole-genome sequences in health and disease. Database URL: http://www.genecards.org/

The FANTOM5 Computation Ecosystem: Genomic Information Hub for Promoters and Active Enhancers.

The Functional Annotation of the Mammalian Genome 5 (FANTOM5) project conducted transcriptome analysis of various mammalian cell types and provided a comprehensive resource to understand transcriptome and transcriptional regulation in individual cellular states encoded in the genome.FANTOM5 used cap analysis of gene expression (CAGE) with single-molecule sequencing to map transcription start sites (TSS) and measured their expression in a diverse range of samples. The main results from FANTOM5 were published as a promoter-level mammalian expression atlas and an atlas of active enhancers across human cell types. The FANTOM5 dataset is composed of raw experimental data and the results of bioinformatics analyses. In this chapter, we give a detailed description of the content of the FANTOM5 dataset and elaborate on different computing applications developed to publish the data and enable reproducibility and discovery of new findings. We present use cases in which the FANTOM5 dataset has been reused, leading to new findings.

A promoter-level mammalian expression atlas.

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.

In Vivo Functional Requirement of the Mouse Ifitm1 Gene for Germ Cell Development, Interferon Mediated Immune Response and Somitogenesis

The mammalian Interferon induced transmembrane protein 1 (Ifitm1) gene was originally identified as a member of a gene family highly inducible by type I and type II interferons. Based on expression analyses, it was suggested to be required for normal primordial germ cell migration. The knockdown of Ifitm1 in mouse embryos provided evidence for a role in somitogenesis. We generated the first targeted knockin allele of the Ifitm1 gene to systematically reassess all inferred functions. Sperm motility and the fertility of male and female mutant mice are as in wild type littermates. Embryonic somites and the adult vertebral column appear normal in homozygous Ifitm1 knockout mice, demonstrating that Ifitm1 is not essential for normal segmentation of the paraxial mesoderm. Proportions of leucocyte subsets, including granulocytes, monocytes, B-cells, T-cells, NK-cells, and NKT-cells, are unchanged in mutant mice. Based on a normal immune response to Listeria monocytogenes infection, there is no evidence for a dysfunction in downstream IFNγ signaling in Ifitm1 mutant mice. Expression from the Ifitm1 locus from E8.5 to E14.5 is highly dynamic. In contrast, in adult mice, Ifitm1 expression is highly restricted and strong in the bronchial epithelium. Intriguingly, IFITM1 is highly overexpressed in tumor epithelia cells of human squamous cell carcinomas and in adenocarcinomas of NSCLC patients. These analyses underline the general importance of targeted in vivo studies for the functional annotation of the mammalian genome. The first comprehensive description of the Ifitm1 expression pattern provides a rational basis for the further examination of Ifitm1 gene functions. Based on our data, the fact that IFITM1 can function as a negative regulator of cell proliferation, and because the gene maps to chromosome band 11p15.5, previously associated with NSCLC, it is likely that IFITM1 in man has a key role in tumor formation.

Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project.

Two large-scale phenotyping efforts, the European Mouse Disease Clinic (EUMODIC) and the Wellcome Trust Sanger Institute Mouse Genetics Project (SANGER-MGP), started during the late 2000s with the aim to deliver a comprehensive assessment of phenotypes or to screen for robust indicators of diseases in mouse mutants. They both took advantage of available mouse mutant lines but predominantly of the embryonic stem (ES) cells resources derived from the European Conditional Mouse Mutagenesis programme (EUCOMM) and the Knockout Mouse Project (KOMP) to produce and study 799 mouse models that were systematically analysed with a comprehensive set of physiological and behavioural paradigms. They captured more than 400 variables and an additional panel of metadata describing the conditions of the tests. All the data are now available through EuroPhenome database (www.europhenome.org) and the WTSI mouse portal (http://www.sanger.ac.uk/mouseportal/), and the corresponding mouse lines are available through the European Mouse Mutant Archive (EMMA), the International Knockout Mouse Consortium (IKMC), or the Knockout Mouse Project (KOMP) Repository. Overall conclusions from both studies converged, with at least one phenotype scored in at least 80 % of the mutant lines. In addition, 57 % of the lines were viable, 13 % subviable, 30 % embryonic lethal, and 7 % displayed fertility impairments. These efforts provide an important underpinning for a future global programme that will undertake the complete functional annotation of the mammalian genome in the mouse model.

MYC Protein Inhibits Transcription of the MicroRNA Cluster MC-let-7a-1∼let-7d via Noncanonical E-box

The human microRNA cluster MC-let-7a-1∼let-7d, with three members let-7a-1, let-7f-1, and let-7d, is an important cluster of the let-7 family. These microRNAs play critical roles in regulating development and carcinogenesis. Therefore, precise control of MC-let-7a-1∼let-7d level is critical for cellular functions. In this study, we first showed that the expression of these three members was significantly reduced in human hepatocellular carcinoma HepG2 cells as compared with the immortalized human liver L02 cells. We demonstrated that the MC-let-7a-1∼let-7d cluster was encoded by a single polycistronic transcript driven by a 10-kb upstream promoter, with two MYC-binding sites. Importantly, MYC inhibited MC-let-7a-1∼let-7d promoter activity via binding to the noncanonical E-box 3 downstream of the transcription start sites, whereas it enhanced promoter activity by binding to the canonical E-box 2 upstream of the transcription start sites. We found that although the binding affinity of MYC to E-box 2 was stronger than E-box 3, the binding quantum of MYC to E-box 3 was significantly higher in cancerous HepG2 cells as compared with the noncancerous L02 cells. In addition, forced expression of let-7 could reverse the MYC-mediated cell proliferation. These findings suggested that in L02 cells with a low level of MYC, MYC binds mainly to E-box 2 to enhance MC-let-7a-1∼let-7d expression. However, in HepG2 cells with an elevated MYC, the extra MYC could bind to E-box 3 to suppress the transcription of MC-let-7a-1∼let-7d and thus enable HepG2 cells to maintain a high level of MYC and a low level of let-7 microRNAs simultaneously.

The Sanger Mouse Genetics Programme: High Throughput Characterisation of Knockout Mice

AbstractPurpose The Sanger Mouse Genetics Project (MGP) is committed to making a significant contribution to the functional annotation of the mammalian genome by generating, characterising and archiving in the order of 200 lines of knockout mice per year, including 125 lines which have been processed as part of the EUMODIC consortium. Phenotypic data on a spectrum of disease conditions are obtained for each mouse line without the need for any prior assumptions about function by performing a standardised battery of phenotyping screens. The data generated will help to further the understanding of the interplay of genes and disease and provide an insight into the various underlying biological pathways. All phenotyping data and biological resources generated by the project are openly available to the scientific community.Methods Eye morphology is routinely assessed using a standard parameter assessment list in conjunction with the Slit Lamp and Ophthalmoscope and images are collected when abnormalities are identified. Expression profiling via the lacZ reporter gene is performed for each mutant line in adults and at E14.5. We collaborate with University of Iowa who are performing a pathology review of H&E stained paraffin sections and also sectioning of lacZ positive eyes.Results To date, the eye screen has been completed on over 310 mutant lines. Here we report examples of novel eye‐related abnormalities identified by the eye morphology, embryonic lethality and/or expression screens performed by the Sanger MGP. We will present how to identify a potentially interesting mouse mutant on our database and discuss the impact our knockout mouse models might have on your research.

Optimising experimental design for high-throughput phenotyping in mice: a case study.

To further the functional annotation of the mammalian genome, the Sanger Mouse Genetics Programme aims to generate and characterise knockout mice in a high-throughput manner. Annually, approximately 200 lines of knockout mice will be characterised using a standardised battery of phenotyping tests covering key disease indications ranging from obesity to sensory acuity. From these findings secondary centres will select putative mutants of interest for more in-depth, confirmatory experiments. Optimising experimental design and data analysis is essential to maximise output using the resources with greatest efficiency, thereby attaining our biological objective of understanding the role of genes in normal development and disease. This study uses the example of the noninvasive blood pressure test to demonstrate how statistical investigation is important for generating meaningful, reliable results and assessing the design for the defined research objectives. The analysis adjusts for the multiple-testing problem by applying the false discovery rate, which controls the number of false calls within those highlighted as significant. A variance analysis finds that the variation between mice dominates this assay. These variance measures were used to examine the interplay between days, readings, and number of mice on power, the ability to detect change. If an experiment is underpowered, we cannot conclude whether failure to detect a biological difference arises from low power or lack of a distinct phenotype, hence the mice are subjected to testing without gain. Consequently, in confirmatory studies, a power analysis along with the 3Rs can provide justification to increase the number of mice used.Electronic supplementary materialThe online version of this article (doi:10.1007/s00335-010-9279-1) contains supplementary material, which is available to authorized users.

The Functional Annotation of Mammalian Genomes: The Challenge of Phenotyping

The mouse is central to the goal of establishing a comprehensive functional annotation of the mammalian genome that will help elucidate various human disease genes and pathways. The mouse offers a unique combination of attributes, including an extensive genetic toolkit that underpins the creation and analysis of models of human disease. An international effort to generate mutations for every gene in the mouse genome is a first and essential step in this endeavor. However, the greater challenge will be the determination of the phenotype of every mutant. Large-scale phenotyping for genome-wide functional annotation presents numerous scientific, infrastructural, logistical, and informatics challenges. These include the use of standardized approaches to phenotyping procedures for the population of unified databases with comparable data sets. The ultimate goal is a comprehensive database of molecular interventions that allows us to create a framework for biological systems analysis in the mouse on which human biology and disease networks can be revealed.

The Sanger Institute mouse genetics programme: progress on the high throughput characterisation of knockout mice

Abstract Purpose Anna‐Karin Gerdin The Wellcome Trust Mouse Genetics Programme Group Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, England, UK. Email: mousegenetics@sanger.ac.uk The Sanger Institute Mouse Genetics Programme is committed to making a significant contribution to the functional annotation of the mammalian genome. We are utilising the growing knockout first conditional ready targeted ES cell resource currently being created by the EUCOMM and KOMP initiatives to generate, characterise and archive 250 lines of knockout mice per year, including 40 lines for the EUMODIC consortium. In addition to studying the role of each gene in normal development and function, the breadth and depth of our phenotyping platform ensures that phenotype information on a wide spectrum of disease conditions is obtained for each individual mouse line without the need for any prior assumptions about function. All phenotype data and biological resources generated by the programme are openly available to the scientific community. Methods Eye morphology is routinely assessed using the Slit Lamp and Ophthalmoscope. Details are documented using a standardised checklist of observable characteristics. When abnormalities are identified, images are collected directly from the Slit Lamp or, for the retina, by using a topical endoscope (TEFI). Furthermore, we are in the process of assessing the Optokinetic Drum and its suitability in our high throughput phenotyping pipeline to measure visual perception and acuity. Here we report the progress on the Eye and Vision screens. Results We present a summary of the phenotyping data available to date and examples of novel findings from a subset of interesting mutant lines.