1
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Tan Y, Neto FBL, Neto UB. PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1807-1816. [PMID: 33170782 DOI: 10.1109/tcbb.2020.3037090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).
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2
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Lundt S, Zhang N, Li JL, Zhang Z, Zhang L, Wang X, Bao R, Cai F, Sun W, Ge WP, Ding S. Metabolomic and transcriptional profiling reveals bioenergetic stress and activation of cell death and inflammatory pathways in vivo after neuronal deletion of NAMPT. J Cereb Blood Flow Metab 2021; 41:2116-2131. [PMID: 33563078 PMCID: PMC8327099 DOI: 10.1177/0271678x21992625] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/19/2022]
Abstract
Nicotinamide phosphoribosyltransferase (NAMPT) is the rate-limiting enzyme in the NAD+ salvage pathway. Our previous study demonstrated that deletion of NAMPT gene in projection neurons using Thy1-NAMPT-/- conditional knockout (cKO) mice causes neuronal degeneration, muscle atrophy, neuromuscular junction abnormalities, paralysis and eventually death. Here we conducted a combined metabolomic and transcriptional profiling study in vivo in an attempt to further investigate the mechanism of neuronal degeneration at metabolite and mRNA levels after NAMPT deletion. Here using steady-state metabolomics, we demonstrate that deletion of NAMPT causes a significant decrease of NAD+ metabolome and bioenergetics, a buildup of metabolic intermediates upstream of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) in glycolysis, and an increase of oxidative stress. RNA-seq shows that NAMPT deletion leads to the increase of mRNA levels of enzymes in NAD metabolism, in particular PARP family of NAD+ consumption enzymes, as well as glycolytic genes Glut1, Hk2 and PFBFK3 before GAPDH. GO, KEGG and GSEA analyses show the activations of apoptosis, inflammation and immune responsive pathways and the inhibition of neuronal/synaptic function in the cKO mice. The current study suggests that increased oxidative stress, apoptosis and neuroinflammation contribute to neurodegeneration and mouse death as a direct consequence of bioenergetic stress after NAMPT deletion.
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Affiliation(s)
- Samuel Lundt
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
- Interdisciplinary Neuroscience Program, University of Missouri-Columbia, MO, USA
| | - Nannan Zhang
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
| | - Jun-Liszt Li
- Academy for Advanced Interdisciplinary Studies (AAIS), Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Zhe Zhang
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
- Department of Biomedical, Biological and Chemical Engineering, University of Missouri-Columbia, MO, USA
| | - Li Zhang
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
- Interdisciplinary Neuroscience Program, University of Missouri-Columbia, MO, USA
| | - Xiaowan Wang
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
- Department of Biomedical, Biological and Chemical Engineering, University of Missouri-Columbia, MO, USA
| | - Ruisi Bao
- Interdisciplinary Neuroscience Program, University of Missouri-Columbia, MO, USA
| | - Feng Cai
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Wenzhi Sun
- Chinese Institute for Brain Research, Beijing, China
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Woo-Ping Ge
- Chinese Institute for Brain Research, Beijing, China
| | - Shinghua Ding
- Dalton Cardiovascular Research Center, University of Missouri-Columbia, MO, USA
- Interdisciplinary Neuroscience Program, University of Missouri-Columbia, MO, USA
- Department of Biomedical, Biological and Chemical Engineering, University of Missouri-Columbia, MO, USA
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3
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Schilf P, Künstner A, Olbrich M, Waschina S, Fuchs B, Galuska CE, Braun A, Neuschütz K, Seutter M, Bieber K, Hellberg L, Sina C, Laskay T, Rupp J, Ludwig RJ, Zillikens D, Busch H, Sadik CD, Hirose M, Ibrahim SM. A Mitochondrial Polymorphism Alters Immune Cell Metabolism and Protects Mice from Skin Inflammation. Int J Mol Sci 2021; 22:ijms22031006. [PMID: 33498298 PMCID: PMC7863969 DOI: 10.3390/ijms22031006] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/14/2021] [Accepted: 01/18/2021] [Indexed: 12/12/2022] Open
Abstract
Several genetic variants in the mitochondrial genome (mtDNA), including ancient polymorphisms, are associated with chronic inflammatory conditions, but investigating the functional consequences of such mtDNA polymorphisms in humans is challenging due to the influence of many other polymorphisms in both mtDNA and the nuclear genome (nDNA). Here, using the conplastic mouse strain B6-mtFVB, we show that in mice, a maternally inherited natural mutation (m.7778G > T) in the mitochondrially encoded gene ATP synthase 8 (mt-Atp8) of complex V impacts on the cellular metabolic profile and effector functions of CD4+ T cells and induces mild changes in oxidative phosphorylation (OXPHOS) complex activities. These changes culminated in significantly lower disease susceptibility in two models of inflammatory skin disease. Our findings provide experimental evidence that a natural variation in mtDNA influences chronic inflammatory conditions through alterations in cellular metabolism and the systemic metabolic profile without causing major dysfunction in the OXPHOS system.
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Affiliation(s)
- Paul Schilf
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
| | - Axel Künstner
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
- Institute of Cardiogenetics, University of Luebeck, 23562 Luebeck, Germany
| | - Michael Olbrich
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
| | - Silvio Waschina
- Institute of Human Nutrition and Food Science, Christian-Albrechts-University of Kiel, 24098 Kiel, Germany;
| | - Beate Fuchs
- Leibniz-Institute for Farm Animal Biology (FBN), Core Facility Metabolomics, 18196 Dummerstorf, Germany; (B.F.); (C.E.G.)
| | - Christina E. Galuska
- Leibniz-Institute for Farm Animal Biology (FBN), Core Facility Metabolomics, 18196 Dummerstorf, Germany; (B.F.); (C.E.G.)
| | - Anne Braun
- Department of Dermatology, University of Luebeck, 23562 Luebeck, Germany; (A.B.); (M.S.); (D.Z.); (C.D.S.)
| | - Kerstin Neuschütz
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
| | - Malte Seutter
- Department of Dermatology, University of Luebeck, 23562 Luebeck, Germany; (A.B.); (M.S.); (D.Z.); (C.D.S.)
| | - Katja Bieber
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
| | - Lars Hellberg
- Department of Infectious Diseases and Microbiology, University of Luebeck, 23562 Luebeck, Germany; (L.H.); (T.L.); (J.R.)
| | - Christian Sina
- Institute of Nutritional Medicine, University of Luebeck, 23562 Luebeck, Germany;
| | - Tamás Laskay
- Department of Infectious Diseases and Microbiology, University of Luebeck, 23562 Luebeck, Germany; (L.H.); (T.L.); (J.R.)
| | - Jan Rupp
- Department of Infectious Diseases and Microbiology, University of Luebeck, 23562 Luebeck, Germany; (L.H.); (T.L.); (J.R.)
| | - Ralf J. Ludwig
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
| | - Detlef Zillikens
- Department of Dermatology, University of Luebeck, 23562 Luebeck, Germany; (A.B.); (M.S.); (D.Z.); (C.D.S.)
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
| | - Hauke Busch
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
- Institute of Cardiogenetics, University of Luebeck, 23562 Luebeck, Germany
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
| | - Christian D. Sadik
- Department of Dermatology, University of Luebeck, 23562 Luebeck, Germany; (A.B.); (M.S.); (D.Z.); (C.D.S.)
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
| | - Misa Hirose
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
- Correspondence: (M.H.); (S.M.I.)
| | - Saleh M. Ibrahim
- Luebeck Institute of Experimental Dermatology, University of Luebeck, 23562 Luebeck, Germany; (P.S.); (A.K.); (M.O.); (K.N.); (K.B.); (R.J.L.); (H.B.)
- Center for Research on Inflammation of the Skin (CRIS), University of Luebeck, 23562 Luebeck, Germany
- College of Medicine and Sharjah Institute for Medical Research, University of Sharjah, 27272 Sharjah, UAE
- Correspondence: (M.H.); (S.M.I.)
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Caspi R, Billington R, Keseler IM, Kothari A, Krummenacker M, Midford PE, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes - a 2019 update. Nucleic Acids Res 2020; 48:D445-D453. [PMID: 31586394 PMCID: PMC6943030 DOI: 10.1093/nar/gkz862] [Citation(s) in RCA: 485] [Impact Index Per Article: 121.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 09/19/2019] [Accepted: 10/01/2019] [Indexed: 11/18/2022] Open
Abstract
MetaCyc (MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains 2749 pathways derived from more than 60 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc are evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in BioCyc.org and other genomic portals. This article provides an update on the developments in MetaCyc during September 2017 to August 2019, up to version 23.1. Some of the topics that received intensive curation during this period include cobamides biosynthesis, sterol metabolism, fatty acid biosynthesis, lipid metabolism, carotenoid metabolism, protein glycosylation, antibiotics and cytotoxins biosynthesis, siderophore biosynthesis, bioluminescence, vitamin K metabolism, brominated compound metabolism, plant secondary metabolism and human metabolism. Other additions include modifications to the GlycanBuilder software that enable displaying glycans using symbolic representation, improved graphics and fonts for web displays, improvements in the PathoLogic component of Pathway Tools, and the optional addition of regulatory information to pathway diagrams.
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Affiliation(s)
- Ron Caspi
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | | | - Ingrid M Keseler
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | - Anamika Kothari
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | | | - Peter E Midford
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | - Wai Kit Ong
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | - Suzanne Paley
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
| | | | - Peter D Karp
- SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA
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5
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Rahmati S, Abovsky M, Pastrello C, Kotlyar M, Lu R, Cumbaa CA, Rahman P, Chandran V, Jurisica I. pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species. Nucleic Acids Res 2020; 48:D479-D488. [PMID: 31733064 PMCID: PMC7145646 DOI: 10.1093/nar/gkz989] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/11/2019] [Accepted: 11/13/2019] [Indexed: 12/14/2022] Open
Abstract
PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.
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Affiliation(s)
- Sara Rahmati
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON M5T 2S8, Canada
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
| | - Mark Abovsky
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Richard Lu
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Christian A Cumbaa
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Proton Rahman
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
| | - Vinod Chandran
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON M5T 2S8, Canada
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
- Department of Medicine, Division of Rheumatology, University of Toronto, Toronto, ON M5G 2C4, Canada
- Department of Laboratory Medicine and Pathobiology (LMP), Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medical Biophysics, University of Toronto, ON M 5G 1L7, Canada
- Department of Computer Science, University of Toronto, ON M5S 1A4, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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6
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Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Midford PE, Ong Q, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 2019; 46:D633-D639. [PMID: 29059334 PMCID: PMC5753197 DOI: 10.1093/nar/gkx935] [Citation(s) in RCA: 495] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 10/02/2017] [Indexed: 01/27/2023] Open
Abstract
MetaCyc (https://MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains more than 2570 pathways derived from >54 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc is strictly evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in the BioCyc (https://BioCyc.org) and other PGDB collections. This article provides an update on the developments in MetaCyc during the past two years, including the expansion of data and addition of new features.
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Affiliation(s)
- Ron Caspi
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | | | - Carol A Fulcher
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | | | - Anamika Kothari
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | | | | | - Peter E Midford
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | - Quang Ong
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | - Wai Kit Ong
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | - Suzanne Paley
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
| | | | - Peter D Karp
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
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7
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Wang TT, Lee CY, Lai LC, Tsai MH, Lu TP, Chuang EY. anamiR: integrated analysis of MicroRNA and gene expression profiling. BMC Bioinformatics 2019; 20:239. [PMID: 31088348 PMCID: PMC6518761 DOI: 10.1186/s12859-019-2870-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/02/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND With advancements in high-throughput technologies, the cost of obtaining expression profiles of both mRNA and microRNA in the same individual has substantially decreased. Integrated analysis of these profiles can help to elucidate the functional effects of RNA expression in complex diseases, such as cancer. However, fundamental discrepancies are observed in the results from microRNA-mRNA target gene prediction algorithms, and few packages can be used to analyze microRNA and mRNA expression levels simultaneously. RESULTS To address these issues, an R package, anamiR, was developed. A total of 10 experimental/prediction databases were integrated. Two analytical functions are provided in anamiR, including the single marker test and functional gene set enrichment analysis, and several parameters can be changed by users. Here we demonstrate the potential application of the anamiR package to 2 publicly available microarray datasets. CONCLUSION The anamiR package is effective for an integrated analysis of both RNA and microRNA profiles. By characterizing biological functions and signaling pathways, this package helps identify dysregulated genes/miRNAs from biological and medical experiments. The source code and manual of the anamiR package are freely available at https://bioconductor.org/packages/release/bioc/html/anamiR.html .
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Affiliation(s)
- Ti-Tai Wang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan
| | - Chien-Yueh Lee
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan
| | - Liang-Chuan Lai
- Graduate Institute of Physiology, National Taiwan University, Taipei, 10051, Taiwan
| | - Mong-Hsun Tsai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.,Institute of Biotechnology, National Taiwan University, Taipei, 10672, Taiwan.,Center for Biotechnology, National Taiwan University, Taipei, 10672, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 10055, Taiwan.
| | - Eric Y Chuang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan. .,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.
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Marín de Evsikova C, Raplee ID, Lockhart J, Jaimes G, Evsikov AV. The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis. J Pers Med 2019; 9:E21. [PMID: 31032818 PMCID: PMC6617151 DOI: 10.3390/jpm9020021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 04/20/2019] [Accepted: 04/25/2019] [Indexed: 11/16/2022] Open
Abstract
As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.
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Affiliation(s)
- Caralina Marín de Evsikova
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
- Epigenetics & Functional Genomics Laboratories, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA.
| | - Isaac D Raplee
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - John Lockhart
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Gilberto Jaimes
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Alexei V Evsikov
- Epigenetics & Functional Genomics Laboratories, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA.
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9
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Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects. Mol Psychiatry 2018; 23:708-712. [PMID: 28555076 PMCID: PMC5709242 DOI: 10.1038/mp.2017.111] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 03/08/2017] [Accepted: 04/12/2017] [Indexed: 01/14/2023]
Abstract
Genome-wide association studies (GWAS) for schizophrenia have identified over 100 loci encoding >500 genes. It is unclear whether any of these genes, other than dopamine receptor D2, are immediately relevant to antipsychotic effects or represent novel antipsychotic targets. We applied an in vivo molecular approach to this question by performing RNA sequencing of brain tissue from mice chronically treated with the antipsychotic haloperidol or vehicle. We observed significant enrichments of haloperidol-regulated genes in schizophrenia GWAS loci and in schizophrenia-associated biological pathways. Our findings provide empirical support for overlap between genetic variation underlying the pathophysiology of schizophrenia and the molecular effects of a prototypical antipsychotic.
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10
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Arends D, Hesse D, Brockmann GA. Invited review: Genetic and genomic mouse models for livestock research. Arch Anim Breed 2018. [DOI: 10.5194/aab-61-87-2018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract. Knowledge about the function and functioning of single or
multiple interacting genes is of the utmost significance for understanding the
organism as a whole and for accurate livestock improvement through genomic
selection. This includes, but is not limited to, understanding the
ontogenetic and environmentally driven regulation of gene action
contributing to simple and complex traits. Genetically modified mice, in
which
the functions of single genes are annotated; mice with reduced genetic
complexity; and simplified structured populations are tools to gain
fundamental knowledge of inheritance patterns and whole system genetics and
genomics. In this review, we briefly describe existing mouse resources and
discuss their value for fundamental and applied research in livestock.
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11
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Bryant WA, Stentz R, Le Gall G, Sternberg MJE, Carding SR, Wilhelm T. In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host. Front Microbiol 2017; 8:2440. [PMID: 29276507 PMCID: PMC5727896 DOI: 10.3389/fmicb.2017.02440] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/24/2017] [Indexed: 11/15/2022] Open
Abstract
The interactions between the gut microbiota and its host are of central importance to the health of the host. Outer membrane vesicles (OMVs) are produced ubiquitously by Gram-negative bacteria including the gut commensal Bacteroides thetaiotaomicron. These vesicles can interact with the host in various ways but until now their complement of small molecules has not been investigated in this context. Using an untargeted high-coverage metabolomic approach we have measured the small molecule content of these vesicles in contrasting in vitro conditions to establish what role these metabolites could perform when packed into these vesicles. B. thetaiotaomicron packs OMVs with a highly conserved core set of small molecules which are strikingly enriched with mouse-digestible metabolites and with metabolites previously shown to be associated with colonization of the murine GIT. By use of an expanded genome-scale metabolic model of B. thetaiotaomicron and a potential host (the mouse) we have established many possible metabolic pathways between the two organisms that were previously unknown, and have found several putative novel metabolic functions for mouse that are supported by gene annotations, but that do not currently appear in existing mouse metabolic networks. The lipidome of these OMVs bears no relation to the mouse lipidome, so the purpose of this particular composition of lipids remains unclear. We conclude from this analysis that through intimate symbiotic evolution OMVs produced by B. thetaiotaomicron are likely to have been adopted as a conduit for small molecules bound for the mammalian host in vivo.
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Affiliation(s)
- William A. Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Régis Stentz
- Gut Health and Food Safety Programme, Quadram Institute Bioscience, Norwich, United Kingdom
| | - Gwenaelle Le Gall
- Metabolomics Unit, Quadram Institute Bioscience, Norwich, United Kingdom
| | - Michael J. E. Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Simon R. Carding
- Gut Health and Food Safety Programme, Quadram Institute Bioscience, Norwich, United Kingdom
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Thomas Wilhelm
- Theoretical Systems Biology Lab, Quadram Institute Bioscience, Norwich, United Kingdom
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12
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Labena AA, Gao YZ, Dong C, Hua HL, Guo FB. Metabolic pathway databases and model repositories. QUANTITATIVE BIOLOGY 2017. [DOI: 10.1007/s40484-017-0108-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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13
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Kelly SA, Zhao L, Jung KC, Hua K, Threadgill DW, Kim Y, de Villena FPM, Pomp D. Prevention of tumorigenesis in mice by exercise is dependent on strain background and timing relative to carcinogen exposure. Sci Rep 2017; 7:43086. [PMID: 28225043 PMCID: PMC5320535 DOI: 10.1038/srep43086] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 01/19/2017] [Indexed: 12/15/2022] Open
Abstract
Among cancer diagnoses, colorectal cancer (CRC) is prevalent, with a lifetime risk of developing CRC being approximately 5%. Population variation surrounding the mean risk of developing CRCs has been associated with both inter-individual differences in genomic architecture and environmental exposures. Decreased risk of CRC has been associated with physical activity, but protective responses are variable. Here, we utilized a series of experiments to examine the effects of genetic background (strain), voluntary exercise (wheel running), and their interaction on azoxymethane (AOM)-induced intestinal tumor number and size in mice. Additionally, we investigated how the timing of exercise relative to AOM exposure, and amount of exercise, affected tumor number and size. Our results indicated that voluntary exercise significantly reduced tumor number in a strain dependent manner. Additionally, among strains where exercise reduced tumor number (A/J, CC0001/Unc) the timing of voluntary exercise relative to AOM exposure was crucial. Voluntary exercise prior to or during AOM treatment resulted in a significant reduction in tumor number, but exercise following AOM exposure had no effect. The results indicate that voluntary exercise should be used as a preventative measure to reduce risk for environmentally induced CRC with the realization that the extent of protection may depend on genetic background.
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Affiliation(s)
- Scott A Kelly
- Department of Zoology, Ohio Wesleyan University, Delaware, Ohio 43015, USA
| | - Liyang Zhao
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Kuo-Chen Jung
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Kunjie Hua
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - David W Threadgill
- Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas 77843, USA.,Department of Molecular and Cellular Medicine, College of Medicine, Texas A&M University, College Station, Texas 77843, USA
| | - Yunjung Kim
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | | | - Daniel Pomp
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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14
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Blake JA, Eppig JT, Kadin JA, Richardson JE, Smith CL, Bult CJ. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res 2016; 45:D723-D729. [PMID: 27899570 PMCID: PMC5210536 DOI: 10.1093/nar/gkw1040] [Citation(s) in RCA: 230] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 10/28/2016] [Indexed: 11/30/2022] Open
Abstract
The Mouse Genome Database (MGD: http://www.informatics.jax.org) is the primary community data resource for the laboratory mouse. It provides a highly integrated and highly curated system offering a comprehensive view of current knowledge about mouse genes, genetic markers and genomic features as well as the associations of those features with sequence, phenotypes, functional and comparative information, and their relationships to human diseases. MGD continues to enhance access to these data, to extend the scope of data content and visualizations, and to provide infrastructure and user support that ensures effective and efficient use of MGD in the advancement of scientific knowledge. Here, we report on recent enhancements made to the resource and new features.
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Affiliation(s)
- Judith A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Janan T Eppig
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | | | - Cynthia L Smith
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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15
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Hill DP, D'Eustachio P, Berardini TZ, Mungall CJ, Renedo N, Blake JA. Modeling biochemical pathways in the gene ontology. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw126. [PMID: 27589964 PMCID: PMC5009323 DOI: 10.1093/database/baw126] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 08/10/2016] [Indexed: 12/05/2022]
Abstract
The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.
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Affiliation(s)
- David P Hill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Peter D'Eustachio
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Tanya Z Berardini
- Arabidopsis Information Resource, Phoenix Bioinformatics, Redwood City, CA 94063, USA
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16
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Bult CJ, Eppig JT, Blake JA, Kadin JA, Richardson JE. Mouse genome database 2016. Nucleic Acids Res 2015; 44:D840-7. [PMID: 26578600 PMCID: PMC4702860 DOI: 10.1093/nar/gkv1211] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 10/23/2015] [Indexed: 01/09/2023] Open
Abstract
The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the primary community model organism database for the laboratory mouse and serves as the source for key biological reference data related to mouse genes, gene functions, phenotypes and disease models with a strong emphasis on the relationship of these data to human biology and disease. As the cost of genome-scale sequencing continues to decrease and new technologies for genome editing become widely adopted, the laboratory mouse is more important than ever as a model system for understanding the biological significance of human genetic variation and for advancing the basic research needed to support the emergence of genome-guided precision medicine. Recent enhancements to MGD include new graphical summaries of biological annotations for mouse genes, support for mobile access to the database, tools to support the annotation and analysis of sets of genes, and expanded support for comparative biology through the expansion of homology data.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Janan T Eppig
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Judith A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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17
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Eppig JT, Richardson JE, Kadin JA, Ringwald M, Blake JA, Bult CJ. Mouse Genome Informatics (MGI): reflecting on 25 years. Mamm Genome 2015; 26:272-84. [PMID: 26238262 PMCID: PMC4534491 DOI: 10.1007/s00335-015-9589-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 07/20/2015] [Indexed: 12/02/2022]
Abstract
From its inception in 1989, the mission of the Mouse Genome Informatics (MGI) resource remains to integrate genetic, genomic, and biological data about the laboratory mouse to facilitate the study of human health and disease. This mission is ever more feasible as the revolution in genetics knowledge, the ability to sequence genomes, and the ability to specifically manipulate mammalian genomes are now at our fingertips. Through major paradigm shifts in biological research and computer technologies, MGI has adapted and evolved to become an integral part of the larger global bioinformatics infrastructure and honed its ability to provide authoritative reference datasets used and incorporated by many other established bioinformatics resources. Here, we review some of the major changes in research approaches over that last quarter century, how these changes are reflected in the MGI resource you use today, and what may be around the next corner.
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Affiliation(s)
- Janan T. Eppig
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Joel E. Richardson
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - James A. Kadin
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Martin Ringwald
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Judith A. Blake
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Carol J. Bult
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
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18
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Richardson JE, Bult CJ. Visual annotation display (VLAD): a tool for finding functional themes in lists of genes. Mamm Genome 2015; 26:567-73. [PMID: 26047590 PMCID: PMC4602057 DOI: 10.1007/s00335-015-9570-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 05/19/2015] [Indexed: 12/27/2022]
Abstract
Experiments that employ genome scale technology platforms frequently result in lists of tens to thousands of genes with potential significance to a specific biological process or disease. Searching for biologically relevant connections among the genes or gene products in these lists is a common data analysis task. We have implemented a software application for uncovering functional themes in sets of genes based on their annotations to bio-ontologies, such as the gene ontology and the mammalian phenotype ontology. The application, called VisuaL Annotation Display (VLAD), performs a statistical analysis to test for the enrichment of ontology terms in a set of genes submitted by a researcher. The results for each analysis using VLAD includes a table of ontology terms, sorted in decreasing order of significance. Each row contains the term, statistics such as the number of annotated terms, the p value, etc., and the symbols of annotated genes. An accompanying graphical display shows portions of the ontology hierarchy, where node sizes are scaled based on p values. Although numerous ontology term enrichment programs already exist, VLAD is unique in that it allows users to upload their own annotation files and ontologies for customized term enrichment analyses, supports the analysis of multiple gene sets at once, provides interfaces to customize graphical output, and is tightly integrated with functional and biological details about mouse genes in the Mouse Genome Informatics (MGI) database. VLAD is available as a web-based application from the MGI web site (http://proto.informatics.jax.org/prototypes/vlad/).
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Affiliation(s)
- Joel E Richardson
- Mouse Genome Informatics (MGI) Database Consortium, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA.
| | - Carol J Bult
- Mouse Genome Informatics (MGI) Database Consortium, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA.
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19
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Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res 2014; 43:D726-36. [PMID: 25348401 PMCID: PMC4384027 DOI: 10.1093/nar/gku967] [Citation(s) in RCA: 293] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The Mouse Genome Database (MGD, http://www.informatics.jax.org) serves the international biomedical research community as the central resource for integrated genomic, genetic and biological data on the laboratory mouse. To facilitate use of mouse as a model in translational studies, MGD maintains a core of high-quality curated data and integrates experimentally and computationally generated data sets. MGD maintains a unified catalog of genes and genome features, including functional RNAs, QTL and phenotypic loci. MGD curates and provides functional and phenotype annotations for mouse genes using the Gene Ontology and Mammalian Phenotype Ontology. MGD integrates phenotype data and associates mouse genotypes to human diseases, providing critical mouse–human relationships and access to repositories holding mouse models. MGD is the authoritative source of nomenclature for genes, genome features, alleles and strains following guidelines of the International Committee on Standardized Genetic Nomenclature for Mice. A new addition to MGD, the Human–Mouse: Disease Connection, allows users to explore gene–phenotype–disease relationships between human and mouse. MGD has also updated search paradigms for phenotypic allele attributes, incorporated incidental mutation data, added a module for display and exploration of genes and microRNA interactions and adopted the JBrowse genome browser. MGD resources are freely available to the scientific community.
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Affiliation(s)
- Janan T Eppig
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Judith A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - James A Kadin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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20
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Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res 2013; 42:D810-7. [PMID: 24285300 PMCID: PMC3964950 DOI: 10.1093/nar/gkt1225] [Citation(s) in RCA: 176] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The Mouse Genome Database (MGD) (http://www.informatics.jax.org) is the community model organism database resource for the laboratory mouse, a premier animal model for the study of genetic and genomic systems relevant to human biology and disease. MGD maintains a comprehensive catalog of genes, functional RNAs and other genome features as well as heritable phenotypes and quantitative trait loci. The genome feature catalog is generated by the integration of computational and manual genome annotations generated by NCBI, Ensembl and Vega/HAVANA. MGD curates and maintains the comprehensive listing of functional annotations for mouse genes using the Gene Ontology, and MGD curates and integrates comprehensive phenotype annotations including associations of mouse models with human diseases. Recent improvements include integration of the latest mouse genome build (GRCm38), improved access to comparative and functional annotations for mouse genes with expanded representation of comparative vertebrate genomes and new loads of phenotype data from high-throughput phenotyping projects. All MGD resources are freely available to the research community.
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Affiliation(s)
- Judith A Blake
- Bioinformatics and Computational Biology, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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21
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Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Subhraveti P, Weaver DS, Weerasinghe D, Zhang P, Karp PD. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 2013; 42:D459-71. [PMID: 24225315 PMCID: PMC3964957 DOI: 10.1093/nar/gkt1103] [Citation(s) in RCA: 777] [Impact Index Per Article: 70.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.
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Affiliation(s)
- Ron Caspi
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA, Carnegie Institution, Department of Plant Biology, 260 Panama Street, Stanford, CA 94305, USA and Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, New York 14853 USA
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Zhou H, Jin J, Zhang H, Yi B, Wozniak M, Wong L. IntPath--an integrated pathway gene relationship database for model organisms and important pathogens. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 2:S2. [PMID: 23282057 PMCID: PMC3521174 DOI: 10.1186/1752-0509-6-s2-s2] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. Results In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and relationship errors in KEGG). We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath. Conclusions We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at http://compbio.ddns.comp.nus.edu.sg:8080/IntPath.
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Affiliation(s)
- Hufeng Zhou
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore
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Bult CJ, Eppig JT, Blake JA, Kadin JA, Richardson JE. The mouse genome database: genotypes, phenotypes, and models of human disease. Nucleic Acids Res 2012; 41:D885-91. [PMID: 23175610 PMCID: PMC3531104 DOI: 10.1093/nar/gks1115] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The laboratory mouse is the premier animal model for studying human biology because all life stages can be accessed experimentally, a completely sequenced reference genome is publicly available and there exists a myriad of genomic tools for comparative and experimental research. In the current era of genome scale, data-driven biomedical research, the integration of genetic, genomic and biological data are essential for realizing the full potential of the mouse as an experimental model. The Mouse Genome Database (MGD; http://www.informatics.jax.org), the community model organism database for the laboratory mouse, is designed to facilitate the use of the laboratory mouse as a model system for understanding human biology and disease. To achieve this goal, MGD integrates genetic and genomic data related to the functional and phenotypic characterization of mouse genes and alleles and serves as a comprehensive catalog for mouse models of human disease. Recent enhancements to MGD include the addition of human ortholog details to mouse Gene Detail pages, the inclusion of microRNA knockouts to MGD’s catalog of alleles and phenotypes, the addition of video clips to phenotype images, providing access to genotype and phenotype data associated with quantitative trait loci (QTL) and improvements to the layout and display of Gene Ontology annotations.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609 USA.
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24
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Lee JJ, Jacobsen EA, Ochkur SI, McGarry MP, Condjella RM, Doyle AD, Luo H, Zellner KR, Protheroe CA, Willetts L, Lesuer WE, Colbert DC, Helmers RA, Lacy P, Moqbel R, Lee NA. Human versus mouse eosinophils: "that which we call an eosinophil, by any other name would stain as red". J Allergy Clin Immunol 2012; 130:572-84. [PMID: 22935586 DOI: 10.1016/j.jaci.2012.07.025] [Citation(s) in RCA: 139] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Revised: 07/25/2012] [Accepted: 07/25/2012] [Indexed: 12/20/2022]
Abstract
The respective life histories of human subjects and mice are well defined and describe a unique story of evolutionary conservation extending from sequence identity within the genome to the underpinnings of biochemical, cellular, and physiologic pathways. As a consequence, the hematopoietic lineages of both species are invariantly maintained, each with identifiable eosinophils. This canonical presence nonetheless does not preclude disparities between human and mouse eosinophils, their effector functions, or both. Indeed, many books and reviews dogmatically highlight differences, providing a rationale to discount the use of mouse models of human eosinophilic diseases. We suggest that this perspective is parochial and ignores the wealth of available studies and the consensus of the literature that overwhelming similarities (and not differences) exist between human and mouse eosinophils. The goal of this review is to summarize this literature and in some cases provide experimental details comparing and contrasting eosinophils and eosinophil effector functions in human subjects versus mice. In particular, our review will provide a summation and an easy-to-use reference guide to important studies demonstrating that although differences exist, more often than not, their consequences are unknown and do not necessarily reflect inherent disparities in eosinophil function but instead species-specific variations. The conclusion from this overview is that despite nominal differences, the vast similarities between human and mouse eosinophils provide important insights as to their roles in health and disease and, in turn, demonstrate the unique utility of mouse-based studies with an expectation of valid extrapolation to the understanding and treatment of patients.
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Affiliation(s)
- James J Lee
- Division of Pulmonary Medicine, Department of Biochemistry and Molecular Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
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25
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Drabkin HJ, Blake JA. Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2012; 2012:bas045. [PMID: 23110975 PMCID: PMC3483533 DOI: 10.1093/database/bas045] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications.
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Affiliation(s)
- Harold J Drabkin
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
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Orman MA, Berthiaume F, Androulakis IP, Ierapetritou MG. Advanced stoichiometric analysis of metabolic networks of mammalian systems. Crit Rev Biomed Eng 2012; 39:511-34. [PMID: 22196224 DOI: 10.1615/critrevbiomedeng.v39.i6.30] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.
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Affiliation(s)
- Mehmet A Orman
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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Orman MA, Mattick J, Androulakis IP, Berthiaume F, Ierapetritou MG. Stoichiometry based steady-state hepatic flux analysis: computational and experimental aspects. Metabolites 2012; 2:268-91. [PMID: 24957379 PMCID: PMC3901202 DOI: 10.3390/metabo2010268] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Revised: 03/05/2012] [Accepted: 03/06/2012] [Indexed: 11/16/2022] Open
Abstract
: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.
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Affiliation(s)
- Mehmet A Orman
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - John Mattick
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Francois Berthiaume
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Marianthi G Ierapetritou
- Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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Bordbar A, Palsson BO. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 2012; 271:131-41. [PMID: 22142339 PMCID: PMC3243107 DOI: 10.1111/j.1365-2796.2011.02494.x] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology and pharmacology. There are currently more than 20 publications that utilize Recon 1, including studies of cancer, diabetes, host-pathogen interactions, heritable metabolic disorders and off-target drug binding effects. In this mini-review, we focus on the reconstruction of the global human metabolic network and four classes of its application. We show that computational simulations for numerous pathologies have yielded clinically relevant results, many corroborated by existing or newly generated experimental data.
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Affiliation(s)
- A Bordbar
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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Saunders EC, MacRae JI, Naderer T, Ng M, McConville MJ, Likić VA. LeishCyc: a guide to building a metabolic pathway database and visualization of metabolomic data. Methods Mol Biol 2012; 881:505-529. [PMID: 22639224 DOI: 10.1007/978-1-61779-827-6_17] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The complexity of the metabolic networks in even the simplest organisms has raised new challenges in organizing metabolic information. To address this, specialized computer frameworks have been developed to capture, manage, and visualize metabolic knowledge. The leading databases of metabolic information are those organized under the umbrella of the BioCyc project, which consists of the reference database MetaCyc, and a number of pathway/genome databases (PGDBs) each focussed on a specific organism. A number of PGDBs have been developed for bacterial, fungal, and protozoan pathogens, greatly facilitating dissection of the metabolic potential of these organisms and the identification of new drug targets. Leishmania are protozoan parasites belonging to the family Trypanosomatidae that cause a broad spectrum of diseases in humans. In this work we use the LeishCyc database, the BioCyc database for Leishmania major, to describe how to build a BioCyc database from genomic sequences and associated annotations. By using metabolomic data generated in our group, we show how such databases can be utilized to elucidate specific changes in parasite metabolism.
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Affiliation(s)
- Eleanor C Saunders
- Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, VIC, Australia
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Sugimoto M, Ikeda S, Niigata K, Tomita M, Sato H, Soga T. MMMDB: Mouse Multiple Tissue Metabolome Database. Nucleic Acids Res 2011; 40:D809-14. [PMID: 22139941 PMCID: PMC3245187 DOI: 10.1093/nar/gkr1170] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The Mouse Multiple Tissue Metabolome Database (MMMDB) provides comprehensive and quantitative metabolomic information for multiple tissues from single mice. Manually curated databases that integrate literature-based individual metabolite information have been available so far. However, data sets on the absolute concentration of a single metabolite integrated from multiple resources are often difficult to be used when different metabolomic studies are compared because the relative balance of the multiple metabolite concentrations in the metabolic pathways as a snapshot of a dynamic system is more important than the absolute concentration of a single metabolite. We developed MMMDB by performing non-targeted analyses of cerebra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis and plasma using capillary electrophoresis time-of-flight mass spectrometry and detected 428 non-redundant features from which 219 metabolites were successfully identified. Quantified concentrations of the individual metabolites and the corresponding processed raw data; for example, the electropherograms and mass spectra with their annotations, such as isotope and fragment information, are stored in the database. MMMDB is designed to normalize users’ data, which can be submitted online and used to visualize overlaid electropherograms. Thus, MMMDB allows newly measured data to be compared with the other data in the database. MMMDB is available at: http://mmmdb.iab.keio.ac.jp.
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Affiliation(s)
- Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.
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Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Pujar A, Shearer AG, Travers M, Weerasinghe D, Zhang P, Karp PD. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2011; 40:D742-53. [PMID: 22102576 PMCID: PMC3245006 DOI: 10.1093/nar/gkr1014] [Citation(s) in RCA: 430] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.
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Affiliation(s)
- Ron Caspi
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA, USA
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Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE. The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse. Nucleic Acids Res 2011; 40:D881-6. [PMID: 22075990 PMCID: PMC3245042 DOI: 10.1093/nar/gkr974] [Citation(s) in RCA: 216] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Mouse Genome Database (MGD, http://www.informatics.jax.org) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. Data in MGD are obtained through loads from major data providers and experimental consortia, electronic submissions from laboratories and from the biomedical literature. MGD maintains a comprehensive, unified, non-redundant catalog of mouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA. MGD serves as the authoritative source for the nomenclature of mouse genes, mutations, alleles and strains. MGD is the primary source for evidence-supported functional annotations for mouse genes and gene products using the Gene Ontology (GO). MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from the Online Mendelian Inheritance in Man (OMIM) resource. MGD is freely accessible online through our website, where users can browse and search interactively, access data in bulk using Batch Query or BioMart, download data files or use our web services Application Programming Interface (API). Improvements to MGD include expanded genome feature classifications, inclusion of new mutant allele sets and phenotype associations and extensions of GO to include new relationships and a new stream of annotations via phylogenetic-based approaches.
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Bult CJ, Drabkin HJ, Evsikov A, Natale D, Arighi C, Roberts N, Ruttenberg A, D'Eustachio P, Smith B, Blake JA, Wu C. The representation of protein complexes in the Protein Ontology (PRO). BMC Bioinformatics 2011; 12:371. [PMID: 21929785 PMCID: PMC3189193 DOI: 10.1186/1471-2105-12-371] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Accepted: 09/19/2011] [Indexed: 11/26/2022] Open
Abstract
Background Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes. Description We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/. Conclusion PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME 04609, USA.
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Web-based metabolic network visualization with a zooming user interface. BMC Bioinformatics 2011; 12:176. [PMID: 21595965 PMCID: PMC3113945 DOI: 10.1186/1471-2105-12-176] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Accepted: 05/19/2011] [Indexed: 12/26/2022] Open
Abstract
Background Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging. Description We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Cellular Overview. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the Omics Viewer. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams. Conclusions This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including Biocyc.org: The Cellular Overview is accessible under the Tools menu.
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Karp PD, Caspi R. A survey of metabolic databases emphasizing the MetaCyc family. Arch Toxicol 2011; 85:1015-33. [PMID: 21523460 DOI: 10.1007/s00204-011-0705-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Accepted: 04/07/2011] [Indexed: 12/21/2022]
Abstract
Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli, Saccharomyces cerevisiae, Mus musculus, and Arabidopsis thaliana. These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA.
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Dinasarapu AR, Saunders B, Ozerlat I, Azam K, Subramaniam S. Signaling gateway molecule pages--a data model perspective. ACTA ACUST UNITED AC 2011; 27:1736-8. [PMID: 21505029 DOI: 10.1093/bioinformatics/btr190] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
SUMMARY The Signaling Gateway Molecule Pages (SGMP) database provides highly structured data on proteins which exist in different functional states participating in signal transduction pathways. A molecule page starts with astate of a native protein, without any modification and/or interactions. New states are formed with every post-translational modification or interaction with one or more proteins, small molecules or class molecules and with each change in cellular location. State transitions are caused by a combination of one or more modifications, interactions and translocations which then might be associated with one or more biological processes. In a characterized biological state, a molecule can function as one of several entities or their combinations, including channel, receptor, enzyme, transcription factor and transporter. We have also exported SGMP data to the Biological Pathway Exchange (BioPAX) and Systems Biology Markup Language (SBML) as well as in our custom XML. AVAILABILITY SGMP is available at www.signaling-gateway.org/molecule.
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Affiliation(s)
- Ashok Reddy Dinasarapu
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
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Finger JH, Smith CM, Hayamizu TF, McCright IJ, Eppig JT, Kadin JA, Richardson JE, Ringwald M. The mouse Gene Expression Database (GXD): 2011 update. Nucleic Acids Res 2010; 39:D835-41. [PMID: 21062809 PMCID: PMC3013713 DOI: 10.1093/nar/gkq1132] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The Gene Expression Database (GXD) is a community resource of mouse developmental expression information. GXD integrates different types of expression data at the transcript and protein level and captures expression information from many different mouse strains and mutants. GXD places these data in the larger biological context through integration with other Mouse Genome Informatics (MGI) resources and interconnections with many other databases. Web-based query forms support simple or complex searches that take advantage of all these integrated data. The data in GXD are obtained from the literature, from individual laboratories, and from large-scale data providers. All data are annotated and reviewed by GXD curators. Since the last report, the GXD data content has increased significantly, the interface and data displays have been improved, new querying capabilities were implemented, and links to other expression resources were added. GXD is available through the MGI web site (www.informatics.jax.org), or directly at www.informatics.jax.org/expression.shtml.
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Blake JA, Bult CJ, Kadin JA, Richardson JE, Eppig JT. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Res 2010; 39:D842-8. [PMID: 21051359 PMCID: PMC3013640 DOI: 10.1093/nar/gkq1008] [Citation(s) in RCA: 197] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The Mouse Genome Database (MGD) is the community model organism database for the laboratory mouse and the authoritative source for phenotype and functional annotations of mouse genes. MGD includes a complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) resource. MGD contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. Major improvements to the Mouse Genome Database include comprehensive update of genetic maps, implementation of new classification terms for genome features, development of a recombinase (cre) portal and inclusion of all alleles generated by the International Knockout Mouse Consortium (IKMC).
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Sigurdsson MI, Jamshidi N, Steingrimsson E, Thiele I, Palsson BØ. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC SYSTEMS BIOLOGY 2010; 4:140. [PMID: 20959003 PMCID: PMC2978158 DOI: 10.1186/1752-0509-4-140] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2010] [Accepted: 10/19/2010] [Indexed: 12/16/2022]
Abstract
BACKGROUND Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms. RESULTS We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated. CONCLUSIONS We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening.
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Affiliation(s)
- Martin I Sigurdsson
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
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Natale DA, Arighi CN, Barker WC, Blake JA, Bult CJ, Caudy M, Drabkin HJ, D'Eustachio P, Evsikov AV, Huang H, Nchoutmboube J, Roberts NV, Smith B, Zhang J, Wu CH. The Protein Ontology: a structured representation of protein forms and complexes. Nucleic Acids Res 2010; 39:D539-45. [PMID: 20935045 PMCID: PMC3013777 DOI: 10.1093/nar/gkq907] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.
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Affiliation(s)
- Darren A Natale
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.
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Latendresse M, Karp PD. An advanced web query interface for biological databases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2010; 2010:baq006. [PMID: 20624715 PMCID: PMC2911841 DOI: 10.1093/database/baq006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Although most web-based biological databases (DBs) offer some type of web-based form to allow users to author DB queries, these query forms are quite restricted in the complexity of DB queries that they can formulate. They can typically query only one DB, and can query only a single type of object at a time (e.g. genes) with no possible interaction between the objects--that is, in SQL parlance, no joins are allowed between DB objects. Writing precise queries against biological DBs is usually left to a programmer skillful enough in complex DB query languages like SQL. We present a web interface for building precise queries for biological DBs that can construct much more precise queries than most web-based query forms, yet that is user friendly enough to be used by biologists. It supports queries containing multiple conditions, and connecting multiple object types without using the join concept, which is unintuitive to biologists. This interactive web interface is called the Structured Advanced Query Page (SAQP). Users interactively build up a wide range of query constructs. Interactive documentation within the SAQP describes the schema of the queried DBs. The SAQP is based on BioVelo, a query language based on list comprehension. The SAQP is part of the Pathway Tools software and is available as part of several bioinformatics web sites powered by Pathway Tools, including the BioCyc.org site that contains more than 500 Pathway/Genome DBs.
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Affiliation(s)
- Mario Latendresse
- Bioinformatics Research Group, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.
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Cottret L, Wildridge D, Vinson F, Barrett MP, Charles H, Sagot MF, Jourdan F. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res 2010; 38:W132-7. [PMID: 20444866 PMCID: PMC2896158 DOI: 10.1093/nar/gkq312] [Citation(s) in RCA: 106] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.
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Bult CJ, Kadin JA, Richardson JE, Blake JA, Eppig JT. The Mouse Genome Database: enhancements and updates. Nucleic Acids Res 2009; 38:D586-92. [PMID: 19864252 PMCID: PMC2808942 DOI: 10.1093/nar/gkp880] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Mouse Genome Database (MGD) is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) database resource and serves as the primary community model organism database for the laboratory mouse. MGD is the authoritative source for mouse gene, allele and strain nomenclature and for phenotype and functional annotations of mouse genes. MGD contains comprehensive data and information related to mouse genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data for MGD are obtained from diverse sources including manual curation of the biomedical literature and direct contributions from individual investigator's laboratories and major informatics resource centers, such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology and the Mammalian Phenotype Ontology. Recent improvements in MGD described here includes integration of mouse gene trap allele and sequence data, integration of gene targeting information from the International Knockout Mouse Consortium, deployment of an MGI Biomart, and enhancements to our batch query capability for customized data access and retrieval.
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Affiliation(s)
- Carol J Bult
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609 USA.
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Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P, Karthikeyan AS, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Paley S, Popescu L, Pujar A, Shearer AG, Zhang P, Karp PD. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2009; 38:D473-9. [PMID: 19850718 PMCID: PMC2808959 DOI: 10.1093/nar/gkp875] [Citation(s) in RCA: 328] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.
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Affiliation(s)
- Ron Caspi
- SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA
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