Chapter Summary
When whole genome sequences of various organisms were started to be determined, the trend to grasp everything became one mainstream of modern biology, and omic studies are now flourishing on genomes, transcriptomes, proteomes, metabolomes, and phenomes. Because of their massive amount of information, we need their databases for omics studies. We thus discuss various omic worlds and their corresponding databases in this chapter.
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References
NCBI (National Center for Biotechnology Information) Genome Biology. (https://www.ncbi.nlm.nih.gov/genome.
Ensembl Genomes. (http://ensemblgenomes.org).
Ensembl. (http://www.ensembl.org).
UCSC (University of California, Santa Cruz) Genome Bioinformatics. (http://genome.ucsc.edu).
GenomeSync. (http://genomesync.org).
JGI (Joint Genome Institute; http://www.jgi.doe.gov/).
MycoCosm. (https://genome.jgi.doe.gov/programs/fungi/index.jsf).
Phytozome. (https://phytozome.jgi.doe.gov/pz/portal.html).
GOLD (Genomes OnLine Database). (https://gold.jgi.doe.gov/index).
MBGD (Microbial Genome Database). (http://mbgd.nibb.ac.jp).
PlantGDB. (http://www.plantgdb.org).
PGDB (Plant Genome Database Japan). (http://pgdbj.jp).
FungiDB. (http://fungidb.org/fungidb/).
EuPathDB. (https://eupathdb.org/eupathdb/).
KEGG Genome Database. (http://www.genome.jp/kegg/genome.html).
China National Gene Bank. https://www.cngb.org/database.html).
Velculescu, V. E., et al. (1997). Characterization of the yeast transcriptome. Cell, 88, 243–251.
The FANTOM Consortium. (2005). The transcriptional landscape of the mammalian genome. Science, 309, 1559–1563.
Djebali, S., et al. (2012). Landscape of transcription in human cells. Nature, 489, 101–108.
Graveley, B. R., et al. (2011). The developmental transcriptome of Drosophila melanogaster. Nature, 471, 473–479.
Endo, A., et al. (2012). Tissue-specific transcriptome analysis reveals cell wall metabolism, flavonol biosynthesis and defense responses are activated in the endosperm of germinating Arabidopsis thaliana seeds. Plant and Cell Physiology, 53, 16–27.
Diez-Roux, G., et al. (2011). A high-resolution anatomical atlas of the transcriptome in the mouse embryo. PLoS Biology, 9, e1000582.
Eurexpress atlas. (http://www.eurexpress.org).
Athanasiadis, A., Rich, A., & Maas, S. (2004). Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biology, 2, e391.
Kim, D. D. Y., et al. (2004). Widespread RNA editing of embedded Alu elements in the human transcriptome. Genome Research, 14, 1719–1725.
The ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74.
Thurman, R. E., et al. (2012). The accessible chromatin landscape of the human genome. Nature, 489, 75–82.
Neph, S., et al. (2012). An expansive human regulatory lexicon encoded in transcription factor footprints. Nature, 489, 83–90.
Gestein, M. B. (2012). Architecture of the human regulatory network derived from ENCODE data. Nature, 489, 91–100.
O’Farrell, P. H. (1975). High resolution two-dimensional electrophoresis of proteins. Journal of Biological Chemistry, 250, 4007–4021.
Tsugita, A., et al. (2000). Proteome analysis of mouse brain: Two-dimensional electrophoresis profiles of tissue proteins during the course of aging. Electrophoresis, 21, 1853–1871.
Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422, 193–207.
Xie, L., et al. (2005). Genomic and proteomic analysis of mammary tumors arising in transgenic mice. Journal of Proteome Research, 4, 2088–2098.
Gavin, A.-C., et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature, 415, 141–147.
Taniguchi, Y., et al. (2010). Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science, 329, 533–538.
Newman, J. R. S., et al. (2006). Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature, 441, 840–846.
UNIPROT. (http://www.uniprot.org).
Kitano, T., Noda, R., Takenaka, O., & Saitou, N. (2009). Relic of ancient recombinations in gibbon ABO blood group genes deciphered through phylogenetic network analysis. Molecular Phylogenetics and Evolution, 51, 465–471.
Protein Data Bank (PDB). (https://www.rcsb.org).
Tornroth-Horsefield, S., et al. (2006). Structural mechanism of plant aquaporin gating. Nature, 439, 688–694.
PFAM. (http://pfam.xfam.org).
InterPro. (https://www.ebi.ac.uk/interpro/).
Sumiyama, K., Washio-Watanabe, K., Saitou, N., Hayakawa, T., & Ueda, S. (1996). Class III POU genes: Generation of homopolymeric amino acid repeats under GC pressure in mammals. Journal of Molecular Evolution, 43, 170–178.
PRIDE Archive. (https://www.ebi.ac.uk/pride/archive/).
Peptide Atlas. (http://www.peptideatlas.org).
Tweeddale, H., Notley-McRobb, L., & Ferenci, T. (1998). Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool (“metabolome”) analysis. Journal of Bacteriology, 180, 5109–5114.
Fu, X., et al. (2011). Rapid metabolic evolution in human prefrontal cortex. Proceedings of the National Academy of Sciences USA, 108, 6181–6186.
Freimer, N., & Sabatti, C. (2003). The human phenome project. Nature Genetics, 34, 15–21.
Saitou, N., Kimura, R., Fukase, H., Yogi, A., Murayama, S., & Ishida, H. (2011). Advanced CT images reveal nonmetric cranial variations in living humans. Anthropological Science, 119, 231–237.
Saitou, N. (2013). Introduction to evolutionary genomics. Heidelberg: Springer.
Venter, C. G., et al. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science, 304, 66–74.
KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database. (http://www.genome.jp/kegg/pathway.html).
Gene Ontology Consortium. (http://www.geneontology.org).
Takahashi, M., & Saitou, N. (2012). Identification and characterization of lineage-specific highly conserved noncoding sequences in mammalian genomes. Genome Biology and Evolution, 4, 641–657.
Matsunami, M., & Saitou, N. (2013). Vertebrate paralogous conserved noncoding sequences may be related to gene expressions in brain. Genome Biology and Evolution, 5, 140–150.
Babarinde, I., & Saitou, N. (2013). Heterogeneous tempo and mode of conserved noncoding sequence evolution among four mammalian orders. Genome Biology and Evolution, 5, 2330–2343.
Hettiarachchi, N., Kryukov, K., Sumiyama, K., & Saitou, N. (2014). Lineage-specific conserved noncoding sequences of plant genomes: Their possible role in nucleosome positioning. Genome Biology and Evolution, 6, 2527–2542.
Hettiarachchi, N., & Saitou, N. (2016). GC content heterogeneity transition of conserved noncoding sequences occurred at the emergence of vertebrates. Genome Biology and Evolution, 8, 3377–3392.
Saber, M. M., Babarinde, I. A., Hettiarachchi, N., & Saitou, N. (2016). Emergence and evolution of Hominidae-specific coding and noncoding genomic sequences. Genome Biology and Evolution, 8, 2076–2092.
Saber, M. M., & Saitou, N. (2017). Silencing effect of hominoid highly conserved non-coding sequences on embryonic brain development. Genome Biology and Evolution, 9, 2037–2048.
UK BioBank. (http://www.ukbiobank.ac.uk/).
Matrix of Comparative Anthropogeny (MOCA). (https://carta.anthropogeny.org/moca/).
GenBank. (https://www.ncbi.nlm.nih.gov/genbank/).
European Nucleotide Archive (ENA). (https://www.ebi.ac.uk/ena).
DNA Data Bank of Japan (DDBJ). (https://www.ddbj.nig.ac.jp/).
International Nucleotide Sequence Database Collaboration. (http://www.insdc.org).
Kitano, T., Sumiyama, K., Shiroishi, T., & Saitou, N. (1998). Conserved evolution of the Rh50 gene compared to its homologous Rh blood group gene. Biochemical and Biophysical Research Communications, 249, 78–85.
PubMed. (https://www.ncbi.nlm.nih.gov/pubmed/).
Google Scholar. (https://scholar.google.com).
Zuckerkandl, E., & Pauling, L. (1965). Evolutionary divergence and convergence in proteins. In V. Bryson & H. J. Vogel (Eds.), Evolving genes and proteins (pp. 97–166). New York: Academic Press.
Saitou, N., & Nei, M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4, 406–425.
Online Mendelian Inheritance in Man (OMIM). (https://www.omim.org).
Online Mendelian Inheritance in Animals (OMIA). (http://omia.org/home/).
MITOMAP. (https://www.mitomap.org/MITOMAP).
dbSNP. (https://www.ncbi.nlm.nih.gov/snp).
Darwin, C. (1859). On the origin of species. London: John Murray.
Tree of Life. (http://tolweb.org/tree/).
Maddison, D. R., Schulz, K.-S., & Maddison, W. P. (2007). The tree of life web project. Zootaxa, 1668, 19–40.
NCBI Taxonomy. (https://www.ncbi.nlm.nih.gov/taxonomy).
Time Tree. (http://www.timetree.org).
Dayhoff, M. O, Eck, R. V, Chang, M. A., & Sochard, M. R. (1965). Atlas of protein sequence and structure. Silver Spring: National Biomedical Research Foundation.
McKusick, V. A. (1966). Mendelian inheritance in man: Catalogs of autosomal dominant, autosomal recessives, and X-linked phenotypes. London: William Heinemann Medical Books.
History of EMBL-EBI. (https://www.ebi.ac.uk/history).
Tateno, Y., & Gojobori, T. (1997). DNA Data Bank of Japan in the age of information biology. Nucleic Acid Research, 25, 14–17.
Fumoto, M., Miyazaki, S., & Sugawara, H. (2002). Genome Information Broker (GIB): Data retrieval and comparative analysis system for completed microbial genomes and more. Nucleic Acid Research, 30, 66–68.
Fukuchi, S., Homma, K., Sakamoto, S., Sugawara, H., Tateno, Y., Gojobori, T., et al. (2009). The GTOP database in 2009: Updated content and novel features to expand and deepen insights into protein structures and functions. Nucleic Acid Research, 37, D333–D337.
Misu, S., Iizuka, T., Kawanishi, Y., Fukami, K., & Saitou, N. (2001). CAMUS DB for amino acid sequence data. Genome Informatics, 2001, 502–503.
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Saitou, N. (2018). Omic Worlds and Their Databases. In: Introduction to Evolutionary Genomics. Computational Biology, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-92642-1_14
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DOI: https://doi.org/10.1007/978-3-319-92642-1_14
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