Papers to read #379
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Ohse S, Boerries M, Busch H. 2019. Blind normalization of public high-throughput databases. PeerJ Computer Science 5:e231 https://doi.org/10.7717/peerj-cs.231 |
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COMBOS: Web App for Finding |
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Yu, R., & Nielsen, J. (2019). Big data in yeast systems biology. FEMS yeast research, 19(7), foz070. https://doi.org/10.1093/femsyr/foz070 |
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Monteiro, P. T., Pedreira, T., Galocha, M., Teixeira, M. C., & Chaouiya, C. (2020). Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae. Scientific Reports, 10(1), 1-11. https://www.nature.com/articles/s41598-020-74043-7 |
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Lupo, O., Krieger, G., Jonas, F., & Barkai, N. (2021). Accumulation of cis-and trans-regulatory variations is associated with phenotypic divergence of a complex trait between yeast species. G3, 11(2), jkab016. https://doi.org/10.1093/g3journal/jkab016 |
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Rossi MJ, Kuntala PK, Lai WKM et al. A high-resolution protein architecture of the budding yeast genome. Nature doi:10.1038/s41586-021-03314-8 (2021) (Epub ahead of print). https://www.nature.com/articles/s41586-021-03314-8 |
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Zhou, Z., Tang, H., Wang, W., Zhang, L., Su, F., Wu, Y., ... & Xu, P. (2021). A cold shock protein promotes high-temperature microbial growth through binding to diverse RNA species. |
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Decoding disease: from genomes to networks to phenotypes. |
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Patel, Z. M., & Hughes, T. R. (2021). Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms. Genome biology, 22(1), 1-19. https://link.springer.com/article/10.1186/s13059-021-02503-y |
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Castro, D. M., De Veaux, N. R., Miraldi, E. R., & Bonneau, R. (2019). Multi-study inference of regulatory networks for more accurate models of gene regulation. PLoS computational biology, 15(1), e1006591. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006591 |
I came across this paper in a Web of Science alert that may be helpful in writing our grant:
Shojaie, A., & Sedaghat, N. (2017). How Different Are Estimated Genetic Networks of Cancer Subtypes?. In Big and Complex Data Analysis (pp. 159-192). Springer International Publishing.
It's a book chapter in a book that LMU can access electronically, link to library catalog: http://linus.lmu.edu/record=b3188858~S1
Abstract:
Genetic networks provide compact representations of interactions between genes, and offer a systems perspective into biological processes and cellular functions. Many algorithms have been developed to estimate such networks based on steady-state gene expression profiles. However, the estimated networks using different methods are often very different from each other. On the other hand, it is not clear whether differences observed between estimated networks in two different biological conditions are truly meaningful, or due to variability in estimation procedures. In this paper, we aim to answer these questions by conducting a comprehensive empirical study to compare networks obtained from different estimation methods and for different subtypes of cancer. We evaluate various network descriptors to assess complex properties of estimated networks, beyond their local structures, and propose a simple permutation test for comparing estimated networks. The results provide new insight into properties of estimated networks using different reconstruction methods, as well as differences in estimated networks in different biological conditions.
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