Abstract
Classically, scientific research has been driven by hypotheses based on personal inspiration and intuition against the background of personal knowledge. In contrast, scientists have recently proposed that scientific research should basically be driven by data, meaning big data yielded by preliminary omic analyses in this context. A genuine hypothesis-driven strategy is usually exciting but occasionally ends up with negative conclusions, whereas a data-driven approach is less exciting and cost-consuming but produces significant outcomes in most cases. Here, we should be aware that a number of bioscientific resources provide a variety of big data free of charge. Therefore, one of the most effective research strategies is to construct a research question based on comprehensive knowledge derived not only from inside information, but also from the analysis of data available to everybody. However, a classical scientist without a sufficient bioinformatic background may hesitate in dealing with information supplied through the Internet. This chapter is aimed at CCN family researchers who do not possess specific bioinformatic knowledge and/or huge grants-in-aid, in order to assist them in develo** their research by taking advantage of the scientific treasury open to the public.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41(Database issue):D991–D995
ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, Kaul R, Halow J, Van Nostrand EL, Freese P, Gorkin DU, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, Williams BA, Mortazavi A, Keller CA, Zhang XO, Elhajjajy SI, Huey J, Dickel DE, Snetkova V, Wei X, Wang X, Rivera-Mulia JC, Rozowsky J, Zhang J, Chhetri SB, Zhang J, Victorsen A, White KP, Visel A, Yeo GW, Burge CB, Lécuyer E, Gilbert DM, Dekker J, Rinn J, Mendenhall EM, Ecker JR, Kellis M, Klein RJ, Noble WS, Kundaje A, Guigó R, Farnham PJ, Cherry JM, Myers RM, Ren B, Graveley BR, Gerstein MB, Pennacchio LA, Snyder MP, Bernstein BE, Wold B, Hardison RC, Gingeras TR, Stamatoyannopoulos JA, Weng Z (2020) Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583:699–710
Oughtred R, Rust J, Chang C, Breitkreutz BJ, Stark C, Willems A, Boucher L, Leung G, Kolas N, Zhang F, Dolma S, Coulombe-Huntington J, Chatr-Aryamontri A, Dolinski K, Tyers M (2021) The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30:187–200
Glažar P, Papavasileiou P, Rajewsky N (2014) circBase: a database for circular RNAs. RNA 20:1666–1670
Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, Habuka M, Tahmasebpoor S, Danielsson A, Edlund K, Asplund A, Sjöstedt E, Lundberg E, Szigyarto CA, Skogs M, Takanen JO, Berling H, Tegel H, Mulder J, Nilsson P, Schwenk JM, Lindskog C, Danielsson F, Mardinoglu A, Sivertsson A, von Feilitzen K, Forsberg M, Zwahlen M, Olsson I, Navani S, Huss M, Nielsen J, Ponten F, Uhlén M (2014) Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics 13:397–406
Kocialkowski S, Yeger H, Kingdom J, Perbal B, Schofield PN (2001) Expression of the human NOV gene in first trimester fetal tissues. Anat Embryol 203:417–427
Akashi S, Nishida T, Mizukawa T, Kawata K, Takigawa M, Iida S, Kubota S (2020) Regulation of cellular communication network factor 2 (CCN2) in breast cancer cells via the cell-type dependent interplay between CCN2 and glycolysis. J Oral Biosci 62:280–288
Zhou Y, Wang Z, Chen X, Zhang J, Yang L, Liu S, Liu Y (2020) Identification of differentially expressed miRNAs and mRNAs in synovial of osteoarthritis via RNA-sequencing. BMC Med Genet 21:46
Elseoudi A, Nishida T, Mizukawa T, Hattori T, Kawata K, Taha EA, Takigawa M, Kubota S (2021) Bipartite regulation of cellular communication network factor 2 and fibroblast growth factor 1 genes by fibroblast growth factor 1 through histone deacetylase 1 and fork head box protein A1. J Cell Commun Signal. 15:81–91
Huttlin EL, Bruckner RJ, Navarrete-Perea J, Cannon JR, Baltier K, Gebreab F, Gygi MP, Thornock A, Zarraga G, Tam S, Szpyt J, Gassaway BM, Panov A, Parzen H, Fu S, Golbazi A, Maenpaa E, Stricker K, Guha Thakurta S, Zhang T, Rad R, Pan J, Nusinow DP, Paulo JA, Schweppe DK, Vaites LP, Harper JW, Gygi SP (2021) Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.Cell 184:3022-3040
Kubota S, Kawaki H, Perbal B, Kawata K, Hattori T, Nishida T (2021) Cellular communication network factor 3 in cartilage development and maintenance. J Cell Commun Signal. https://doi.org/10.1007/s12079-021-00629-z
Perbal B (1999) Nuclear localisation of NOVH protein: a potential role for NOV in the regulation of gene expression. Mol Pathol 52:84–91
Salzman J, Chen RE, Olsen MN, Wang PL, Brown PO (2013) Cell-type specific features of circular RNA expression. PLoS Genet 9:e1003777
Perbal B, Tweedie S, Bruford E (2018) The official unified nomenclature adopted by the HGNC calls for the use of the acronyms, CCN1-6, and discontinuation in the use of CYR61, CTGF, NOV and WISP 1-3 respectively. J Cell Commun Signal. 12:625–629
Acknowledgments
We gratefully thank Ms. Yoshiko Miyake for secretary assistance.
Funding
This study was supported by JSPS KAKENHI Grant Numbers 21H03105 and 21K19603.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Kubota, S. (2023). Utilizing Public Molecular Biological Databases for CCN Family Research. In: Takigawa, M. (eds) CCN Proteins. Methods in Molecular Biology, vol 2582. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2744-0_12
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2744-0_12
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2743-3
Online ISBN: 978-1-0716-2744-0
eBook Packages: Springer Protocols