Mice and more

A report on the Mouse Initiatives V meeting 'Genomics of Complex Systems in Biomedical Research', The Jackson Laboratory, Bar Harbor, USA, 30 July-2 August 2003

The alliance of genome resources: transforming comparative genomics.

Comparing genomic and biological characteristics across multiple species is essential to using model systems to investigate the molecular and cellular mechanisms underlying human biology and disease and to translate mechanistic insights from studies in model organisms for clinical applications. Building a scalable knowledge commons platform that supports cross-species comparison of rich, expertly curated knowledge regarding gene function, phenotype, and disease associations available for model organisms and humans is the primary mission of the Alliance of Genome Resources (the Alliance). The Alliance is a consortium of seven model organism knowledgebases (mouse, rat, yeast, nematode, zebrafish, frog, fruit fly) and the Gene Ontology resource. The Alliance uses a common set of gene ortholog assertions as the basis for comparing biological annotations across the organisms represented in the Alliance. The major types of knowledge associated with genes that are represented in the Alliance database currently include gene function, phenotypic alleles and variants, human disease associations, pathways, gene expression, and both protein-protein and genetic interactions. The Alliance has enhanced the ability of researchers to easily compare biological annotations for common data types across model organisms and human through the implementation of shared programmatic access mechanisms, data-specific web pages with a unified look and feel , and interactive user interfaces specifically designed to support comparative biology. The modular infrastructure developed by the Alliance allows the resource to serve as an extensible knowledge commons capable of expanding to accommodate additional model organisms

Multiple genome viewer (MGV): a new tool for visualization and comparison of multiple annotated genomes.

The assembled and annotated genomes for 16 inbred mouse strains (Lilue et al., Nat Genet 50:1574-1583, 2018) and two wild-derived strains (CAROLI/EiJ and PAHARI/EiJ) (Thybert et al., Genome Res 28:448-459, 2018) are valuable resources for mouse genetics and comparative genomics. We developed the multiple genome viewer (MGV; http://www.informatics.jax.org/mgv ) to support visualization, exploration, and comparison of genome annotations within and across these genomes. MGV displays chromosomal regions of user-selected genomes as horizontal tracks. Equivalent features across the genome tracks are highlighted using vertical \u27swim lane\u27 connectors. Navigation across the genomes is synchronized as a researcher uses the scroll and zoom functions. Researchers can generate custom sets of genes and other genome features to be displayed in MGV by entering genome coordinates, function, phenotype, disease, and/or pathway terms. MGV was developed to be genome agnostic and can be used to display homologous features across genomes of different organisms

ACDA: implementation of an augmented drug synergy prediction algorithm.

Motivation: Drug synergy prediction is approached with machine learning techniques using molecular and pharmacological data. The published Cancer Drug Atlas (CDA) predicts a synergy outcome in cell-line models from drug target information, gene mutations and the models’ monotherapy drug sensitivity. We observed low performance of the CDA, 0.339, measured by Pearson correlation of predicted versus measured sensitivity on DrugComb datasets. Results: We augmented the approach CDA by applying a random forest regression and optimization via cross-validation hyper-parameter tuning and named it Augmented CDA (ACDA). We benchmarked the ACDA’s performance, which is 68% higher than that of the CDA when trained and validated on the same dataset spanning 10 tissues. We compared the performance of ACDA to one of the winning methods of the DREAM Drug Combination Prediction Challenge, the performance of which was lower than ACDA in 16 out of 19 cases. We further trained the ACDA on Novartis Institutes for BioMedical Research PDX encyclopedia data and generated sensitivity predictions for PDX models. Finally, we developed a novel approach to visualize synergy-prediction data. Availability and implementation: The source code is available at https://github.com/TheJacksonLaboratory/drug-synergy and the software package at PyPI. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics Advances online

Application of Spatial Concepts to Genome Data

This project will investigate the application of geographic information science concepts and methods to the modeling and analysis of genome data. The primary objective of the research is to develop a data model for genomes that supports the graphical exploration of the higher order spatial arrangement of genome features through spatial queries and spatial data analysis tools. The spatial genome model formalizes topological and order relationships among genome features (before, after, overlap), uses metric properties to refine spatial topologies, and includes representations of features that have uncertain metric properties. The genome spatial model enhances the integrative and comparative potential of genome data by providing the foundation for more powerful spatial reasoning and inferences than can be achieved by data models that incorporate only a small subset of possible temporal-spatial relationships among genome features (e.g. order and distance). The research represents a logical extension from current feature by feature analytical approaches of genome studies to one that allows biologists to ask questions about the contextual and organizational significance of the spatial arrangement of genome features. These functional capabilities should, in turn, aid in the automation of repetitive analytical tasks associated with the mapping of genome features and drive the discovery of biologically significant aspects of genome organization and function

Mouse Phenome Database (MPD)

The Mouse Phenome Project was launched a decade ago to complement mouse genome sequencing efforts by promoting new phenotyping initiatives under standardized conditions and collecting the data in a central public database, the Mouse Phenome Database (MPD; http://phenome.jax.org). MPD houses a wealth of strain characteristics data to facilitate the use of the laboratory mouse in translational research for human health and disease, helping alleviate problems involving experimentation in humans that cannot be done practically or ethically. Data sets are voluntarily contributed by researchers from a variety of institutions and settings, or in some cases, retrieved by MPD staff from public sources. MPD maintains a growing collection of standardized reference data that assists investigators in selecting mouse strains for research applications; houses treatment/control data for drug studies and other interventions; offers a standardized platform for discovering genotype–phenotype relationships; and provides tools for hypothesis testing. MPD improvements and updates since our last NAR report are presented, including the addition of new tools and features to facilitate navigation and data mining as well as the acquisition of new data (phenotypic, genotypic and gene expression)

Cancer Biology Data Curation at the Mouse Tumor Biology Database (MTB)

Many advances in the field of cancer biology have been made using mouse models of human cancer. The Mouse Tumor Biology (MTB, "http://tumor.informatics.jax.org":http://tumor.informatics.jax.org) database provides web-based access to data on spontaneous and induced tumors from genetically defined mice (inbred, hybrid, mutant, and genetically engineered strains of mice). These data include standardized tumor names and classifications, pathology reports and images, mouse genetics, genomic and cytogenetic changes occurring in the tumor, strain names, tumor frequency and latency, and literature citations.

Although primary source for the data represented in MTB is peer-reviewed scientific literature an increasing amount of data is derived from disparate sources. MTB includes annotated histopathology images and cytogenetic assay images for mouse tumors where these data are available from The Jackson Laboratory’s mouse colonies and from outside contributors. MTB encourages direct submission of mouse tumor data and images from the cancer research community and provides investigators with a web-accessible tool for image submission and annotation. 

Integrated searches of the data in MTB are facilitated by the use of several controlled vocabularies and by adherence to standard nomenclature. MTB also provides links to other related online resources such as the Mouse Genome Database, Mouse Phenome Database, the Biology of the Mammary Gland Web Site, Festing's Listing of Inbred Strains of Mice, the JAX® Mice Web Site, and the Mouse Models of Human Cancers Consortium's Mouse Repository. 

MTB provides access to data on mouse models of cancer via the internet and has been designed to facilitate the selection of experimental models for cancer research, the evaluation of mouse genetic models of human cancer, the review of patterns of mutations in specific cancers, and the identification of genes that are commonly mutated across a spectrum of cancers.

MTB is supported by NCI grant CA089713

Factors that influence response classifications in chemotherapy treated patient-derived xenografts (PDX).

In this study, we investigated the impact of initial tumor volume, rate of tumor growth, cohort size, study duration, and data analysis method on chemotherapy treatment response classifications in patient-derived xenografts (PDXs). The analyses were conducted on cisplatin treatment response data for 70 PDX models representing ten cancer types with up to 28-day study duration and cohort sizes of 3-10 tumor-bearing mice. The results demonstrated that a 21-day dosing study using a cohort size of eight was necessary to reliably detect responsive models (i.e., tumor volume ratio of treated animals to control between 0.1 and 0.42)-independent of analysis method. A cohort of three tumor-bearing animals led to a reliable classification of models that were both highly responsive and highly nonresponsive to cisplatin (i.e., tumor volume ratio of treated animals to control animals less than 0.10). In our set of PDXs, we found that tumor growth rate in the control group impacted treatment response classification more than initial tumor volume. We repeated the study design factors using docetaxel treated PDXs with consistent results. Our results highlight the importance of defining endpoints for PDX dosing studies when deciding the size of cohorts to use in dosing studies and illustrate that response classifications for a study do not differ significantly across the commonly used analysis methods that are based on tumor volume changes in treatment versus control groups

MouseCyc: a curated biochemical pathways database for the laboratory mouse

MouseCyc is a database of curated metabolic pathways for the laboratory mouse

The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology

The Mouse Genome Database (MGD) forms the core of the Mouse Genome Informatics (MGI) system (http://www.informatics.jax.org), a model organism database resource for the laboratory mouse. MGD provides essential integration of experimental knowledge for the mouse system with information annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genotype (sequence) through phenotype information, including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships among genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse and to build and implement the data and semantic standards that are essential for comparative genome analysis. Recent improvements in MGD discussed here include the enhancement of phenotype resources, the re-development of the International Mouse Strain Resource, IMSR, the update of mammalian orthology datasets and the electronic publication of classic books in mouse genetics