ACDBio: The Biological Data Computational Analysis group at ICMC/USP, IFSP, and Barretos Cancer Hospital

Authors

  • Adenilso Simao University of São Paulo
  • Adriane Feijó Evangelista Barretos Cancer Hospital and Oswaldo Cruz Foundation
  • Alfredo Guilherme Souza University of São Paulo
  • Cynthia de Oliveira Lage Ferreira University of São Paulo
  • Jorge Francisco Cutigi Federal Institute of São Paulo
  • Paulo Henrique Ribeiro Federal Institute of São Paulo
  • Rodrigo Henrique Ramos Federal Institute of São Paulo

DOI:

https://doi.org/10.5753/jidm.2024.2622

Keywords:

Bioinformatics, Biological Data Analysis, Computer Science

Abstract

Recent advances in biological and health technology have resulted in vast digital data. However, the major challenge is interpreting such data to find valuable knowledge. For this, using computing is essential and mandatory since quick data processing and analysis, allied with knowledge extraction techniques, enable working effectively with large biological datasets. In this context, the ACDBio group works with the computational analysis of biological data from different sources, aiming to find new information and knowledge in data or answer questions that are not yet known. So far, the group has worked on several challenging topics, such as identifying significant genes for cancer topological analysis of genes in interaction networks, among others. The group uses computational techniques such as complex networks and their algorithms, machine learning, and topological data analysis. This article aims to present the ACDBio group, and the main research topics worked on by its members. We also present the main results and future work expected by the group.

Downloads

Download data is not yet available.

References

Bailey, M. H. et al. (2018). Comprehensive characterization of cancer driver genes and mutations. Cell, 173(2):371 – 385.e18. DOI: https://doi.org/10.1016/j.cell.2018.02.060.

Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46(2):255–308.

Chazal, F. (2016). High-dimensional topological data analysis.

Ciriello, G., Gatza, M. L., Beck, A. H., Wilkerson, M. D., Rhie, S. K., Pastore, A., Zhang, H., McLellan, M., Yau, C., Kandoth, C., et al. (2015). Comprehensive molecular portraits of invasive lobular breast cancer. Cell, 163(2):506–519.

Cisowski, J. and Bergo, M. O. (2017). What makes oncogenes mutually exclusive? Small GTPases, 8(3):187–192.

Cutigi, J. F., Evangelista, A. F., Reis, R. M., and Simao, A. (2021). A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks. Scientific reports, 11(1):1–10.

Cutigi, J. F., Evangelista, A. F., and Simao, A. (2019a). GeNWeMME: A network-based computational method for prioritizing groups of significant related genes in cancer. In Advances in Bioinformatics and Computational Biology, pages 29–40. Springer.

Cutigi, J. F., Evangelista, A. F., and Simao, A. (2019b). A proposal of a graph-based computational method for ranking significant set of related genes in cancer. In Anais Principais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 300–305, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbcas.2019.6266.

Cutigi, J. F., Evangelista, A. F., and Simao, A. (2020a). Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective. Journal of Bioinformatics and Computational Biology, 18(03):2050016. PMID: 32698724. DOI: 10.1142/S021972002050016X.

Cutigi, J. F., Evangelista, R. F., Ramos, R. H., Ferreira, C. d. O. L., Evangelista, A. F., de Carvalho, A. C., and Simao, A. (2020b). Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. In Brazilian Symposium on Bioinformatics, pages 81–92. Springer.

Das, J. and Yu, H. (2012). Hint: High-quality protein interactomes and their applications in understanding human disease. BMC systems biology, 6:92. DOI: 10.1186/1752-0509-6-92.

Deng, Y., Luo, S., Deng, C., Luo, T., Yin, W., Zhang, H., Zhang, Y., Zhang, X., Lan, Y., Ping, Y., et al. (2019). Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability. Briefings in Bioinformatics, 20(1):254–266.

Dentro, S. C., Wedge, D. C., and Van Loo, P. (2017). Principles of reconstructing the subclonal architecture of cancers. Cold Spring Harbor Perspectives in Medicine. DOI: 10.1101/cshperspect.a026625.

Dujon, A., Aktipis, C., Alix-Panabières, C., Amend, S., Boddy, A., Brown, J., Capp, J., DeGregori, J., Ewald, P., Gatenby, R., Gerlinger, M., Giraudeau, M., Hamede, R., Hansen, E., Kareva, I., Maley, C., Marusyk, A., McGranahan, N., Metzger, M., and Ujvari, B. (2021). Identifying key questions in the ecology and evolution of cancer. Evolutionary Applications, 14:877–892. DOI: 10.1111/eva.13190.

Edelsbrunner, H., Letscher, D., and Zomorodian, A. (2000). Topological persistence and simplification. In Proceedings 41st Annual Symposium on Foundations of Computer Science, pages 454–463. DOI: 10.1109/SFCS.2000.892133.

Greaves, M. and Carlo, M. (2012). Clonal evolution in cancer. Nature, 481(7381):306–313. DOI: 10.1038/nature10762.

Jassal, B., Matthews, L., Viteri, G., Gong, C., Lorente, P., Fabregat, A., Sidiropoulos, K., Cook, J., Gillespie, M., Haw, R., et al. (2020). The reactome pathway knowledgebase. Nucleic acids research, 48(D1):D498–D503.

Keshava Prasad, T. S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., Balakrishnan, L., Marimuthu, A., Banerjee, S., Somanathan, D. S., Sebastian, A., Rani, S., Ray, S., Harrys Kishore, C. J., Kanth, S., Ahmed, M., Kashyap, M. K., Mohmood, R., Ramachandra, Y. L., Krishna, V., Rahiman, B. A., Mohan, S., Ranganathan, P., Ramabadran, S., Chaerkady, R., and Pandey, A. (2009). Human protein reference database–2009 update. Nucleic acids research, 37(Database issue):D767–72. DOI: 10.1093/nar/gkn892.

Kim, Y., Cho, D., and Przytycka, T. M. (2016). Understanding genotype-phenotype effects in cancer via network approaches. PLoS Computational Biology, 12(3). DOI: 10.1371/journal.pcbi.1004747.

Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., et al. (2020). A reference map of the human binary protein interactome. Nature, pages 1–7.

Nik-Zainal, S., Van Loo, P., Wedge, D. C., Alexandrov, L. B., Greenman, C. D., Lau, K. W., Raine, K., Jones, D., Marshall, J., Ramakrishna, M., Shlien, A., Cooke, S. L., Hinton, J., Menzies, A., Stebbings, L. A., Leroy, C., Jia, M., Rance, R., Mudie, L. J., Gamble, S. J., Stephens, P. J., McLaren, S., Tarpey, P. S., Papaemmanuil, E., Davies, H. R., Varela, I., McBride, D. J., Bignell, G. R., Leung, K., Butler, A. P., Teague, J. W., Martin, S., Jönsson, G., Mariani, O., Boyault, S., Miron, P., Fatima, A., Langerød, A., Aparicio, S. A., Tutt, A., Sieuwerts, A. M., Borg, A., Thomas, G., Salomon, A. V., Richardson, A. L., Børresen-Dale, A.-L., Futreal, P. A., Stratton, M. R., and Campbell, P. J. (2012). The life history of 21 breast cancers. Cell (Cambridge), 149(5):994–1007.

Ozturk, K., Dow, M., Carlin, D. E., Bejar, R., and Carter, H. (2018). The emerging potential for network analysis to inform precision cancer medicine. Journal of molecular biology, 430(18):2875–2899.

Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z., Jonnalagadda, C. K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T. K. B., Gronborg, M., Ibarrola, N., Deshpande, N., Shanker, K., Shivashankar, H. N., Rashmi, B. P., Ramya, M. A., Zhao, Z., Chandrika, K. N.,

Padma, N., Harsha, H. C., Yatish, A. J., Kavitha, M. P., Menezes, M., Choudhury, D. R., Suresh, S., Ghosh, N., Saravana, R., Chandran, S., Krishna, S., Joy, M., Anand, S. K., Madavan, V., Joseph, A., Wong, G. W., Schiemann, W. P., Constantinescu, S. N., Huang, L., Khosravi-Far, R.,

Steen, H., Tewari, M., Ghaffari, S., Blobe, G. C., Dang, C. V., Garcia, J. G. N., Pevsner, J., Jensen, O. N., Roepstorff, P., Deshpande, K. S., Chinnaiyan, A. M., Hamosh, A., Chakravarti, A., and Pandey, A. (2003). Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome research, 13(10):2363–71.

Rabadan, R. and Blumberg, A. J. (2019). Topological Data Analysis for Genomics and Evolution: Topology in Biology. Cambridge University Press.

Ramos, R. H., Cutigi, J. F., de Oliveira Lage Ferreira, C., Evangelista, A. F., and Simao, A. (2020). Analyzing different cancer mutation data sets from breast invasive carcinoma (brca), lung adenocarcinoma (luad), and prostate adenocarcinoma (prad). In Anais Principais do XX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 37–48, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbcas.2020.11500.

Ramos, R. H., Cutigi, J. F., Oliveira Lage Ferreira, C. d., and Simao, A. (2021). Topological characterization of cancer driver genes using reactome super pathways networks. In Brazilian Symposium on Bioinformatics, pages 26–37. Springer.

Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., and Kinzler, K. W. (2013). Cancer genome landscapes. Science, 339(6127):1546–1558. DOI: 10.1126/science.1235122.

Downloads

Published

2024-02-17

How to Cite

Simao, A., Feijó Evangelista, A., Guilherme Souza, A., de Oliveira Lage Ferreira, C., Francisco Cutigi, J., Henrique Ribeiro, P., & Henrique Ramos, R. (2024). ACDBio: The Biological Data Computational Analysis group at ICMC/USP, IFSP, and Barretos Cancer Hospital. Journal of Information and Data Management, 15(1), 61–68. https://doi.org/10.5753/jidm.2024.2622

Issue

Section

Brazilian Bioinformatics Research Groups