MouseCyc: a curated biochemical pathways database for the laboratory mouse
© Evsikov et al,; licensee BioMed Central Ltd. 2009
Received: 22 May 2009
Accepted: 14 August 2009
Published: 14 August 2009
Linking biochemical genetic data to the reference genome for the laboratory mouse is important for comparative physiology and for developing mouse models of human biology and disease. We describe here a new database of curated metabolic pathways for the laboratory mouse called MouseCyc http://mousecyc.jax.org. MouseCyc has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human.
The availability of the nearly complete genome sequence for the laboratory mouse provides a powerful platform for predicting genes and other genome features and for exploring the biological significance of genome organization . However, building a catalog of genome annotations is just the first step in the 'post-genome' biology [2, 3]. Deriving new insights into complex biological processes using complete genomes and related genome-scale data will require understanding how individual biological units that comprise the genome (for example, genes and other genome features) relate to one another in pathways and networks . Identifying components within networks can be achieved through genome-wide assays of an organism's proteome or transcriptome using high-throughput technologies such as microarrays; however, it is the association of experimental data with well-curated biological knowledge that provides meaningful context to the vast amount of information produced in such experiments. Ultimately, researchers seek to understand how perturbations of these networks, presumably through study of dysregulated components, contribute to disease processes.
Biochemical interactions and transformations among organic molecules are arguably the foundation and core distinguishing feature of all organic life. Most of these transformations are understood as sequential interactions among molecules. Thus, biochemical pathways, rather than individual reactions and molecules, are often the most useful 'units' of investigation for biomedical experimentalists by providing conceptual reduction of biological system complexity. Biochemical pathways in mammalian systems historically have been characterized and defined with little or no genetic information, making the present day task of connecting metabolism and genomics a challenging enterprise.
The Kyoto Encyclopedia of Genes and Genomes (KEGG) was one of the first projects that addressed the integration of small molecule biochemical reaction networks with genes, and it includes graphical representations of these reactions [5, 6]. KEGG pathways are based primarily on Enzyme Commission (EC) classifications of enzymes . For individual species, the known (and predicted) EC enzymes are depicted relative to KEGG reference networks for visualization of the sequential small molecule transformations that exist for a given organism.
Another resource that seeks to integrate pathway and genomic data is Reactome [8, 9]. Reactome is a manually curated database of human pathways, networks and processes, including metabolism, signaling pathways, cell-cell interactions, and infection response. Data in Reactome are cross-referenced to numerous external widely used genome informatics resources. The curated human pathway data in Reactome are used to infer orthologous pathways in over 20 other organisms that have complete, or nearly complete, genome sequences and comprehensive protein annotations. The non-human pathway data in Reactome are not manually curated in a systematic fashion.
Another popular platform for integration of genetic and biochemical knowledge is Pathway Tools, a software environment for curation, analysis, and visualization of integrated genomic and pathway data [4, 10]. The PathoLogic component of Pathway Tools predicts complete and partial metabolic pathways for an organism by comparing user-supplied genome annotations (for example, gene names, EC numbers) to a reference database (MetaCyc) of manually curated, experimentally defined metabolic pathways [11, 12]. The output of PathoLogic analysis is an organism-specific pathway genome database (PGDB)  that contains predicted enzymatic reactions, compounds, enzymes, transporters, and pathways. Pathway Tools has been used to implement curated PGDBs for a number of model eukaryotic organisms, for example, budding yeast, Saccharomyces cerevisiae (Saccharomyces Genome Database ), green alga, Chlamydomonas reinhardtii (ChlamyCyc ), thale cress, Arabidopsis thaliana (AraCyc ), rice, Oryza sativa (RiceCyc ), plants of the Solanaceae family (SolCyc ), human, Homo sapiens (HumanCyc ) and, very recently, the bovine, Bos taurus (CattleCyc ), as well as for hundreds of microorganisms . All databases implemented using Pathway Tools share a common web-based user interface while also providing support for users of the software to display organism-specific details and information for genes and pathways.
Here, we describe the implementation and curation of the MouseCyc database  using the Pathway Tools platform. MouseCyc now joins the existing biochemical pathway resources for major biomedically relevant model organisms, providing ease of use through implementation of the Pathway Tools web interface, and integration with other Mouse Genome Informatics (MGI) resources . MouseCyc contains information on central, intermediary, and small-molecule metabolism in the laboratory mouse and serves as a resource for analyzing the mouse genome using the functional framework of biochemical pathways. MouseCyc facilitates the use of the laboratory mouse as a model system for understanding human biology and disease processes in three ways. First, the database provides a means by which the available wealth of biological knowledge about mouse genes can be organized in the context of biochemical pathways. Second, the query and analysis tools for the database serve as a means for researchers to view and analyze genome scale experiments by overlaying these data onto global views of the curated mouse metabolome. Finally, MouseCyc supports direct comparisons of metabolic processes and pathways between mouse and human; comparisons that may be critical to understanding both the power and the biological limitations of using mouse models of human disease.
Initial PathoLogic analysis, manual curation, and PathoLogic incremental updates
The initial implementation of the MouseCyc pathway genome database using the PathoLogic prediction software with Pathway Tools resulted in the prediction of 304 pathways, 1,832 enzymatic reactions, and 5 transport reactions. Following the automated build of MouseCyc, the predicted reactions and pathways were evaluated and refined manually. The initial manual curation effort focused on identifying pathways and reactions, predicted by PathoLogic, that were not relevant to mammalian biochemistry (for example, biosynthesis of essential amino acids). The manual curation process resulted in the elimination of 135 non-mammalian pathways (45% of the pathways predicted for mouse by PathoLogic) from the database. The high percentage of predicted pathways in MouseCyc that required manual re-assignment was not surprising given that, for historic reasons, the MetaCyc reference database [11, 12] used by PathoLogic is somewhat biased toward prokaryotic and plant biochemistry. Finally, PathoLogic's Transport Inference Parser (TIP) utility was used to identify putative transport reactions. For the mouse genome, TIP predicted 80 transport reactions and 542 transporters.
Because of the limited amount of data on vertebrate organisms within the reference database that PathoLogic relies on for its predictions of metabolic potential (that is, the MetaCyc database), a number of important pathways were missing from the initial build of MouseCyc. Examples of curated biochemical pathways for the mouse that have been also submitted for inclusion in the MetaCyc reference database include biosynthesis of androgens, biosynthesis of corticosteroids, biosynthesis of estrogens, biosynthesis of prostaglandins, biosynthesis of serotonin and melatonin, ceramide biosynthesis, cyclic AMP biosynthesis, cyclic GMP biosynthesis, Leloir pathway, sphingomyelin metabolism, sphingosine and sphingosine-1-phosphate metabolism, and L-ascorbate biosynthesis VI (Additional data file 1). Thus, one of the major ongoing manual curation processes for MouseCyc is the creation of records for biochemical pathways that are specific to mammalian systems or the laboratory mouse that were not predicted by PathoLogic.
Comparison of mouse and human biochemical pathway databases
One of the primary benefits of using Pathway Tools for building PGDBs is that the software supports comparative metabolomics by allowing users to display the same pathway from different PGDBs simultaneously. In addition to side-by-side evaluation of individual pathways (Figure 2c), MouseCyc also provides access to global overviews of similarities and differences among several selected PGDBs for other organisms . There are a number of biochemical pathways that differ among mammalian species, usually due to the absence of a critical functional enzyme in a pathway. For example, vitamin C biosynthesis (L-ascorbate biosynthesis VI pathway) is disrupted in humans and great apes as a result of ancestral nonsense mutations in the gulonolactone oxidase (GULO) gene . Melatonin biosynthesis pathway is disrupted in a number of inbred mouse strains due to the lack of cetylserotonin O-methyltransferase (Asmt) gene . Purine degradation pathways in mouse and human differ in their final metabolite that is secreted with urine. In humans, absence of urate oxidase gene makes ureic acid the 'end product' of this pathway, while in mice, activity of Uox (EC 18.104.22.168) and Urah (EC 22.214.171.124) leads to formation of allantoin, a much more soluble and less toxic compound .
Integration of MouseCyc with Mouse Genome Informatics
MouseCyc and the OmicsViewer
Testing MouseCyc as a hypothesis generation tool
Documenting the similarities and differences of biochemistry and metabolism between mice and humans is particularly important for investigators seeking to use the laboratory mouse in animal studies related to drug therapies, toxicology, and human disease. In our curation of MouseCyc to date we have documented, and formally represented, differences in metabolic potential among mammals that are due to the absence of critical enzymes or to functional divergence of putative orthologs. Connecting mouse genes and pathways to human diseases in MouseCyc highlights differences in biochemistry that cannot yet be clearly associated with specific genes and proteins. For example, the Leloir pathway (Figure 3b) is the major route for galactose utilization in both mice and humans. However, humans have galactosemias, while mice do not, presumably due to yet unknown pathways of galactose breakdown in the mouse. As proteomic and metabolomic research uncovers new biochemical pathways in the mouse, they will be incorporated into MouseCyc to further enhance the utility of this resource in facilitating the use of the laboratory mouse as a model organism for understanding human biology and disease.
A primary value-added aspect of the MouseCyc project relative to other pathway databases lies in the extent to which pathways in MouseCyc have been integrated with the comprehensive functional and phenotypic knowledge of mouse genes and the associations of mouse genes with human disease phenotypes that are available through the MGI resources. In addition to the reciprocal hypertext links between genes and pathways that are available in MGI and MouseCyc, researchers can rapidly visualize the literature-curated functional and phenotypic annotations of genes and gene products available from MGI in the context of all biochemical pathways known for mouse. As illustrated by the mouse oocyte transcriptome study described in this manuscript (Figure 5), supporting the ability of researchers to navigate easily among global views of the mouse metabolome, specific pathways, and the details of individual genes and proteins allows a systems-based approach for the analysis and interpretation of genetic and genomic data.
The initial implementation of the MouseCyc database required substantial manual refinement to make the presentation of pathway knowledge more representative of mammalian biology. The degree of manual refinement required was due, in part, to the fact that most vigorous biochemical genetics research has been performed using microorganisms such as bacteria and yeast. As a result, the MetaCyc reference database that was used for pathway prediction is somewhat biased toward biology of unicellular microorganisms. The ongoing incorporation of curated data from MouseCyc into MetaCyc, as well as expansion of curatorial efforts for other projects using mammalian systems, specifically HumanCyc  and CattleCyc , will ensure that future applications of the PathwayTools system to metazoan data sets will result in improvement in the predictions of pathways that take into account knowledge about animal, and specifically mammalian, biology.
An important future direction for the MouseCyc resource will be to represent explicitly the cell and tissue-type specificity of particular pathways and their reactions. In the current implementation of the database, all genes encoding enzymes with the same function are assigned to the same biochemical reaction, making it impossible to discern the network of enzymes executing a particular pathway in one tissue versus another. For example, ethanol metabolism (Figure 2) depends on different enzymes in different tissues due to the differences in gene expression for alcohol dehydrogenases, aldehyde dehydrogenases, and short-chain acyl-CoA synthesases. While Pathway Tools was originally developed as software designed for PGDBs of unicellular organisms (for which tissue specificity is irrelevant), implementation of new biochemical databases for higher organisms using this platform, such as MouseCyc, will promote future developments of Pathway Tools to address the subject of representation and visualization for biochemical pathways that are processed by multiple, differentially expressed genes encoding functionally similar enzymes in different tissues.
Installing pathway tools
The Pathway Tools development kit software (version 10.0) was downloaded from Stanford Research Institute and installed on each of two Sun Fire X4100 servers (2.6 Ghz/1 MB processor; 1 Gb memory; 73 Gb hard drive) running SUSE Linux. One of the servers is devoted to development and curation activities; the second server is the dedicated host for the public instance of the MouseCyc database  and HumanCyc .
The Pathway Tools software system has four main components . The PathoLogic component creates a PGDB for an organism based on user-supplied organism-specific genome annotations. The Pathway Tools Ontology defines the schema of the database. The Pathway/Genome Navigator component supports query, visualization and Web-publishing services for PGDBs. Finally, the software includes Pathway/Genome Editing tools permitting curators to edit and update data in the baseline PGDB.
Mouse genome annotation
A catalog of mouse genes and annotations was downloaded from the MGI FTP site (6 November 2007). The gene annotations included gene name and symbol, EC numbers, Gene Ontology annotations, genome coordinates (for NCBI build 36) and accession identifiers for EntrezGene, UniProt, and MGI. RNA genes and pseudogenes were not included in the annotation file.
A total of 47 files were created as input to the PathoLogic algorithm following the format specifications outlined in the Pathway Tools installation guide. Annotation files were created for 19 mouse autosomes, 2 sex chromosomes, the mitochondrial genome, and for genes with unknown chromosome location. For each annotation file, a separate chromosome sequence file was created in FASTA format. Finally, a file (the genetic elements file) to guide the instantiation of the chromosomes and their annotations was also created.
Following the automated build of MouseCyc, the data-editing tools built into the Pathway Tools software system were used for manual refinement and annotation of pathways and reactions.
Display of mouse gene phenotype annotations using OmicsViewer
Pre-compiled OmicsViewer files for phenotype annotations of mouse genes from the MGI database are available via FTP . These files can be uploaded directly into the OmicsViewer  to display phenotype annotations in the context of the curated mouse metabolome.
Software and data updates
Updates to the Pathway Tools software are implemented as they become available. MouseCyc currently runs on Pathway Tools version 13.0.
The MouseCyc database is updated bi-monthly with new and revised manually curated pathways. Updates to mouse genome annotations (gene names, symbols, and so on) are propagated to MouseCyc using the PathoLogic incremental update utilities. With each genome annotation update, potential new pathways and reactions are generated automatically and reviewed manually. Information on the current content and history of updates to MouseCyc can be found by following the 'History of updates to this database' link on the MouseCyc home page.
Additional data files
The following additional data are included with this article: a table listing biochemical pathways created by MouseCyc group (Additional data file 1).
Enzyme Commission (Nomenclature Committee of the International Union of Biochemistry and Molecular Biology)
Kyoto Encyclopedia of Genes and Genomes
Mouse Genome Informatics
pathway genome database
Transport Inference Parser.
The authors thank Drs Judy Blake, Matthew Hibbs, and Carrie Marín de Evsikova for a critical reading of this manuscript. The MouseCyc database project is funded by NIH NHGRI grant HG003622 to CJB.
- Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, et al: Initial sequencing and comparative analysis of the mouse genome. Nature. 2002, 420: 520-562. 10.1038/nature01262.PubMedView ArticleGoogle Scholar
- Kanehisa M, Bork P: Bioinformatics in the post-sequence era. Nat Genet. 2003, 33 (Suppl): 305-310. 10.1038/ng1109.PubMedView ArticleGoogle Scholar
- Baldarelli RM, Hill DP, Blake JA, Adachi J, Furuno M, Bradt D, Corbani LE, Cousins S, Frazer KS, Qi D, Yang L, Ramachandran S, Reed D, Zhu Y, Kasukawa T, Ringwald M, King BL, Maltais LJ, McKenzie LM, Schriml LM, Maglott D, Church DM, Pruitt K, Eppig JT, Richardson JE, Kadin JA, Bult CJ: Connecting sequence and biology in the laboratory mouse. Genome Res. 2003, 13: 1505-1519. 10.1101/gr.991003.PubMedPubMed CentralView ArticleGoogle Scholar
- Karp PD, Krummenacker M, Paley S, Wagg J: Integrated pathway-genome databases and their role in drug discovery. Trends Biotechnol. 1999, 17: 275-281. 10.1016/S0167-7799(99)01316-5.PubMedView ArticleGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36: D480-484. 10.1093/nar/gkm882.PubMedPubMed CentralView ArticleGoogle Scholar
- Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P, Goto S, Kanehisa M: KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 2008, 36: W423-426. 10.1093/nar/gkn282.PubMedPubMed CentralView ArticleGoogle Scholar
- Enzyme Nomenclature. [http://www.chem.qmul.ac.uk/iubmb/enzyme/]
- Vastrik I, D'Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L: Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007, 8: R39-10.1186/gb-2007-8-3-r39.PubMedPubMed CentralView ArticleGoogle Scholar
- Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33: D428-432. 10.1093/nar/gki072.PubMedPubMed CentralView ArticleGoogle Scholar
- Karp PD, Paley S, Romero P: The Pathway Tools software. Bioinformatics. 2002, 18: S225-232.PubMedView ArticleGoogle Scholar
- Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD: The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2008, 36: D623-631. 10.1093/nar/gkm900.PubMedPubMed CentralView ArticleGoogle Scholar
- Karp PD, Riley M, Saier M, Paulsen IT, Paley SM, Pellegrini-Toole A: The EcoCyc and MetaCyc databases. Nucleic Acids Res. 2000, 28: 56-59. 10.1093/nar/28.1.56.PubMedPubMed CentralView ArticleGoogle Scholar
- Karp PD: Pathway databases: a case study in computational symbolic theories. Science. 2001, 293: 2040-2044. 10.1126/science.1064621.PubMedView ArticleGoogle Scholar
- Christie KR, Weng S, Balakrishnan R, Costanzo MC, Dolinski K, Dwight SS, Engel SR, Feierbach B, Fisk DG, Hirschman JE, Hong EL, Issel-Tarver L, Nash R, Sethuraman A, Starr B, Theesfeld CL, Andrada R, Binkley G, Dong Q, Lane C, Schroeder M, Botstein D, Cherry JM: Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 2004, 32: D311-314. 10.1093/nar/gkh033.PubMedPubMed CentralView ArticleGoogle Scholar
- May P, Christian JO, Kempa S, Walther D: ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii. BMC Genomics. 2009, 10: 209-10.1186/1471-2164-10-209.PubMedPubMed CentralView ArticleGoogle Scholar
- Mueller LA, Zhang P, Rhee SY: AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol. 2003, 132: 453-460. 10.1104/pp.102.017236.PubMedPubMed CentralView ArticleGoogle Scholar
- Jaiswal P, Ni J, Yap I, Ware D, Spooner W, Youens-Clark K, Ren L, Liang C, Zhao W, Ratnapu K, Faga B, Canaran P, Fogleman M, Hebbard C, Avraham S, Schmidt S, Casstevens TM, Buckler ES, Stein L, McCouch S: Gramene: a bird's eye view of cereal genomes. Nucleic Acids Res. 2006, 34: D717-723. 10.1093/nar/gkj154.PubMedPubMed CentralView ArticleGoogle Scholar
- Mazourek M, Pujar A, Borovsky Y, Paran I, Mueller L, Jahn MM: A dynamic interface for capsaicinoid systems biology. Plant Physiol. 2009, 150: 1806-1821. 10.1104/pp.109.136549.PubMedPubMed CentralView ArticleGoogle Scholar
- Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD: Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005, 6: R2-10.1186/gb-2004-6-1-r2.PubMedPubMed CentralView ArticleGoogle Scholar
- Seo S, Lewin HA: Reconstruction of metabolic pathways for the cattle genome. BMC Syst Biol. 2009, 3: 33-10.1186/1752-0509-3-33.PubMedPubMed CentralView ArticleGoogle Scholar
- BioCyc Pathway/Genome Databases. [http://biocyc.org/]
- MouseCyc Database. [http://mousecyc.jax.org]
- Mouse Genome Informatics. [http://www.informatics.jax.org]
- Studer RA, Robinson-Rechavi M: How confident can we be that orthologs are similar, but paralogs differ?. Trends Genet. 2009, 25: 210-216. 10.1016/j.tig.2009.03.004.PubMedView ArticleGoogle Scholar
- Horyn O, Luhovyy B, Lazarow A, Daikhin Y, Nissim I, Yudkoff M, Nissim I: Biosynthesis of agmatine in isolated mitochondria and perfused rat liver: studies with 15N-labelled arginine. Biochem J. 2005, 388: 419-425. 10.1042/BJ20041260.PubMedPubMed CentralView ArticleGoogle Scholar
- Li G, Regunathan S, Barrow CJ, Eshraghi J, Cooper R, Reis DJ: Agmatine: an endogenous clonidine-displacing substance in the brain. Science. 1994, 263: 966-969. 10.1126/science.7906055.PubMedView ArticleGoogle Scholar
- Morris SM: Arginine metabolism: boundaries of our knowledge. J Nutr. 2007, 137: 1602S-1609S.PubMedGoogle Scholar
- Halaris A, Plietz J: Agmatine: metabolic pathway and spectrum of activity in brain. CNS Drugs. 2007, 21: 885-900. 10.2165/00023210-200721110-00002.PubMedView ArticleGoogle Scholar
- Zhu MY, Iyo A, Piletz JE, Regunathan S: Expression of human arginine decarboxylase, the biosynthetic enzyme for agmatine. Biochim Biophys Acta. 2004, 1670: 156-164.PubMedPubMed CentralView ArticleGoogle Scholar
- Lopez-Contreras AJ, Lopez-Garcia C, Jimenez-Cervantes C, Cremades A, Penafiel R: Mouse ornithine decarboxylase-like gene encodes an antizyme inhibitor devoid of ornithine and arginine decarboxylating activity. J Biol Chem. 2006, 281: 30896-30906. 10.1074/jbc.M602840200.PubMedView ArticleGoogle Scholar
- Kanerva K, Makitie LT, Pelander A, Heiskala M, Andersson LC: Human ornithine decarboxylase paralogue (ODCp) is an antizyme inhibitor but not an arginine decarboxylase. Biochem J. 2008, 409: 187-192. 10.1042/BJ20071004.PubMedView ArticleGoogle Scholar
- Gonzalez-Duarte R, Albalat R: Merging protein, gene and genomic data: the evolution of the MDR-ADH family. Heredity. 2005, 95: 184-197. 10.1038/sj.hdy.6800723.PubMedView ArticleGoogle Scholar
- Simard J, Ricketts ML, Gingras S, Soucy P, Feltus FA, Melner MH: Molecular biology of the 3beta-hydroxysteroid dehydrogenase/delta5-delta4 isomerase gene family. Endocr Rev. 2005, 26: 525-582. 10.1210/er.2002-0050.PubMedView ArticleGoogle Scholar
- MouseCyc Comparative Analysis and Statistics. [http://mousecyc.jax.org/comp-genomics]
- Nishikimi M, Fukuyama R, Minoshima S, Shimizu N, Yagi K: Cloning and chromosomal mapping of the human nonfunctional gene for L-gulono-gamma-lactone oxidase, the enzyme for L-ascorbic acid biosynthesis missing in man. J Biol Chem. 1994, 269: 13685-13688.PubMedGoogle Scholar
- Ebihara S, Marks T, Hudson DJ, Menaker M: Genetic control of melatonin synthesis in the pineal gland of the mouse. Science. 1986, 231: 491-493. 10.1126/science.3941912.PubMedView ArticleGoogle Scholar
- Ramazzina I, Folli C, Secchi A, Berni R, Percudani R: Completing the uric acid degradation pathway through phylogenetic comparison of whole genomes. Nat Chem Biol. 2006, 2: 144-148. 10.1038/nchembio768.PubMedView ArticleGoogle Scholar
- Tyfield L, Reichardt J, Fridovich-Keil J, Croke DT, Elsas LJ, Strobl W, Kozak L, Coskun T, Novelli G, Okano Y, Zekanowski C, Shin Y, Boleda MD: Classical galactosemia and mutations at the galactose-1-phosphate uridyl transferase (GALT) gene. Hum Mutat. 1999, 13: 417-430. 10.1002/(SICI)1098-1004(1999)13:6<417::AID-HUMU1>3.0.CO;2-0.PubMedView ArticleGoogle Scholar
- Leslie ND, Yager KL, McNamara PD, Segal S: A mouse model of galactose-1-phosphate uridyl transferase deficiency. Biochem Mol Med. 1996, 59: 7-12. 10.1006/bmme.1996.0057.PubMedView ArticleGoogle Scholar
- Online Mendelian Inheritance in Man. [http://www.ncbi.nlm.nih.gov/omim/]
- MouseCyc Omics Viewer. [http://mousecyc.jax.org/expression.html]
- Mouse Genes Having Knockout or Gene Trap Alleles. [ftp://ftp.informatics.jax.org/pub/curatorwork/MouseCyc/FilesOmics/komp_and_genetrap.txt]
- Evsikov AV, Graber JH, Brockman JM, Hampl A, Holbrook AE, Singh P, Eppig JJ, Solter D, Knowles BB: Cracking the egg: molecular dynamics and evolutionary aspects of the transition from the fully grown oocyte to embryo. Genes Dev. 2006, 20: 2713-2727. 10.1101/gad.1471006.PubMedPubMed CentralView ArticleGoogle Scholar
- Esposito G, Vitale AM, Leijten FP, Strik AM, Koonen-Reemst AM, Yurttas P, Robben TJ, Coonrod S, Gossen JA: Peptidylarginine deiminase (PAD) 6 is essential for oocyte cytoskeletal sheet formation and female fertility. Mol Cell Endocrinol. 2007, 273: 25-31. 10.1016/j.mce.2007.05.005.PubMedView ArticleGoogle Scholar
- Summers MC, Biggers JD: Chemically defined media and the culture of mammalian preimplantation embryos: historical perspective and current issues. Hum Reprod Update. 2003, 9: 557-582. 10.1093/humupd/dmg039.PubMedView ArticleGoogle Scholar
- MouseCyc Files. [ftp://ftp.informatics.jax.org/pub/curatorwork/MouseCyc/FilesOmics/index.html]
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.