Cited by 452Fungal and yeast genetics researchDNA Repair MechanismsBioinformatics and Genomic Networks

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Rewiring of Genetic Networks in Response to DNA Damage

Open access

Bandyopadhyay, Sourav, Mehta, Monika, Kuo, Dwight, Sung, Min-Kyung, Chuang, Ryan, Jaehnig, Eric J., Bodenmiller, Bernd, Licon, Katherine, Copeland, Wilbert, Shales, Michael, Fiedler, Dorothea, Dutkowski, Janusz, Guénolé, Aude, van Attikum, Haico, Shokat, Kevan M., Kolodner, Richard D., Huh, Won-Ki, Aebersold, Ruedi, Keogh, Michael-Christopher, Krogan, Nevan J., Ideker, Trey·2010·Science

DOI 10.1126/science.1195618PMC PMC3006187

Open this paper in CoChat — free account

Abstract

DNA Damage Pathways Revealed: Despite the dynamic nature of cellular responses, the genetic networks that govern these responses have been mapped primarily as static snapshots. Bandyopadhyay et al. (p. 1385 ; see the Perspective by Friedman and Schuldiner ) report a comparison of large genetic interactomes measured among all yeast kinases, phosphatases, and transcription factors, as the cell responded to DNA damage. The interactomes revealed were highly dynamic structures that changed dramatically with changing conditions. These dynamic interactions reveal genetic relationships that can be more effective than classical “static” interactions (for example, synthetic lethals and epistasis maps) in identifying pathways of interest.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1126/science.1195618

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
The Genetic Landscape of a Cell
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science · doi:10.1126/science.1180823 · Cited by 2,162
Multiple Pathways of Recombination Induced by Double-Strand Breaks in Saccharomyces cerevisiae
Pâques, F., & Haber, J. E. (1999) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.63.2.349-404.1999 · Cited by 2,120
Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map
Collins, S. R., Miller, K. M., Maas, N. L., Roguev, A., Fillingham, J., Chu, C. S., Schuldiner, M., Gebbia, M., Recht, J., Shales, M., et al. (2007) · Nature · doi:10.1038/nature05649 · Cited by 869
Self-citation
Exploring genetic interactions and networks with yeast
Boone, C., Bussey, H., & Andrews, B. J. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2085 · Cited by 661
A Snf2 Family ATPase Complex Required for Recruitment of the Histone H2A Variant Htz1
Krogan, N. J., Keogh, M., Datta, N., Sawa, C., Ryan, O. W., Ding, H., Haw, R., Pootoolal, J., Tong, A. H. Y., Canadien, V., et al. (2003) · Molecular Cell · doi:10.1016/s1097-2765(03)00497-0 · Cited by 588
Self-citation
A DNA Integrity Network in the Yeast Saccharomyces cerevisiae
Pan, X., Ye, P., Yuan, D., Wang, X., Bader, J. S., & Boeke, J. D. (2006) · Cell · doi:10.1016/j.cell.2005.12.036 · Cited by 540
Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways
Lehner, B., Crombie, C., Tischler, J., Fortunato, A., & Fraser, A. G. (2006) · Nature Genetics · doi:10.1038/ng1844 · Cited by 502
A genetic study of X-ray sensitive mutants in yeast
Game, J. C., & Mortimer, R. (1974) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/0027-5107(74)90176-6 · Cited by 477
Proteome-wide identification of in vivo targets of DNA damage checkpoint kinases
Smolka, M. B., Albuquerque, C. P., Chen, S.-H., & Zhou, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701622104 · Cited by 404

Flagged first, then by citations

Show all 23 references

Sorted by citations

Items 101–200 of 452

Interactome disassembly during apoptosis occurs independent of caspase cleavage
Scott, N. E., Rogers, L. D., Prudova, A., Brown, N. F., Fortelny, N., Overall, C. M., & Foster, L. J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167067 · Cited by 52
Pathways and Mechanisms that Prevent Genome Instability in Saccharomyces cerevisiae
Putnam, C. D., & Kolodner, R. D. (2017) · Genetics · doi:10.1534/genetics.112.145805 · Cited by 56
Self-citation
Increased signaling entropy in cancer requires the scale-free property of proteininteraction networks
Teschendorff, A. E., Banerji, C. R. S., Simone Severini, B., Kuehn, R., & Peter Sollich, A. (2015) · Scientific Reports · doi:10.1038/srep09646 · Cited by 53
Genome-wide survey of yeast mutations leading to activation of the yeast cell integrity MAPK pathway: Novel insights into diverse MAPK outcomes
Arias, P., Díez-Muñiz, S., García, R., Nombela, C., Rodríguez-Peña, J. M., & Arroyo, J. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-390 · Cited by 52
Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli
Kumar, A., Beloglazova, N. G., Bundalovic-Torma, C., Phanse, S., Deineko, V., Gagarinova, A. G., Musso, G. A., Vlasblom, J., Lemak, S., Hooshyar, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.060 · Cited by 51
Systematic Genetic Screens Reveal the Dynamic Global Functional Organization of the Bacterial Translation Machinery
Gagarinova, A. G., Stewart, G., Samanfar, B., Phanse, S., White, C. A., Aoki, H., Deineko, V., Beloglazova, N. G., Yakunin, A. F., Golshani, A., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.040 · Cited by 48
Functional Analysis and Characterization of Differential Coexpression Networks
Hsu, C., Juan, H., & Huang, H. (2015) · Scientific Reports · doi:10.1038/srep13295 · Cited by 50
Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks
Ma, X., Gao, L., Karamanlidis, G., Gao, P., Lee, C. F., Garcia‐Menendez, L., Tian, R., & Tan, K. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004332 · Cited by 49
Differential genetic interactions of yeast stress response MAPK pathways
Martı́n, H., Shales, M., Fernández-Piñar, P., Wei, P., Molina, M., Fiedler, D., Shokat, K. M., Beltrão, P., Lim, W. A., & Krogan, N. J. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145606 · Cited by 52
Self-citation
Systems biology analysis reveals role of MDM2 in diabetic nephropathy
Saito, R., Arjó, A. R., You, Y.-H., Darshi, M., Espen, B. V., Miyamoto, S., Pham, J., Pu, M., Romoli, S., Natarajan, L., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.87877 · Cited by 49
Self-citation
FIREWORKS: a bottom-up approach to integrative coessentiality network analysis
Amici, D. R., Jackson, J. M., Truica, M. I., Smith, R. S., Abdulkadir, S. A., & Mendillo, M. L. (2020) · Life Science Alliance · doi:10.26508/lsa.202000882 · Cited by 47
Toward personalized cancer nanomedicine – past, present, and future
Stegh, A. H. (2012) · Integrative Biology · doi:10.1039/c2ib20104f · Cited by 51
Reverse‐engineering human regulatory networks
Lefèbvre, C., Rieckhof, G. E., & Califano, A. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1159 · Cited by 50
From Systems to Structure: Bridging Networks and Mechanism
Fraser, J. S., Gross, J. D., & Krogan, N. J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.01.003 · Cited by 47
Self-citation
Interactions of NADP-Reducing Enzymes Across Varying Environmental Conditions: A Model of Biological Complexity
Rzezniczak, T. Z., & Merritt, T. J. S. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.003715 · Cited by 46
The Chromone Alkaloid, Rohitukine, Affords Anti-Cancer Activity via Modulating Apoptosis Pathways in A549 Cell Line and Yeast Mitogen Activated Protein Kinase (MAPK) Pathway
Safia, Kamil, M., Jadiya, P., Sheikh, S., Haque, E., Nazir, A., Lakshmi, V., & Mir, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137991 · Cited by 46
Extracting Stage-Specific and Dynamic Modules Through Analyzing Multiple Networks Associated with Cancer Progression
Ma, X., Tang, W., Wang, P., Guo, X., & Gao, L. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2625791 · Cited by 46
Identifying Network-Based Biomarkers of Complex Diseases from High-Throughput Data
Liu, Z.-P. (2016) · Biomarkers in Medicine · doi:10.2217/bmm-2015-0035 · Cited by 46
Bayesian Graphical Regression
Ni, Y., Stingo, F. C., & Baladandayuthapani, V. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1389739 · Cited by 45
How networks change with time
Park, Y., & Bader, J. S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts211 · Cited by 45
Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures
Cai, T. (2016) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-053754 · Cited by 45
An Integrative Framework of Heterogeneous Genomic Data for Cancer Dynamic Modules Based on Matrix Decomposition
Ma, X., Sun, P., & Gong, M. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3004808 · Cited by 43
Comparative Chemogenomics To Examine the Mechanism of Action of DNA-Targeted Platinum-Acridine Anticancer Agents
Cheung-Ong, K., Song, K., Ma, Z., Shabtai, D., Lee, A. Y., Gallo, D., Heisler, L. E., Brown, G. W., Bierbach, U., Giaever, G. N., et al. (2012) · ACS Chemical Biology · doi:10.1021/cb300320d · Cited by 43
Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections
Jiang, Z., Dong, X., & Zhang, Z. (2016) · Scientific Reports · doi:10.1038/srep19149 · Cited by 41
Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets
Lu, X., Kensche, P. R., Huynen, M. A., & Notebaart, R. A. (2013) · Nature Communications · doi:10.1038/ncomms3124 · Cited by 42
Analysing Network Models to Make Discoveries about Biological Mechanisms
Bechtel, W. (2017) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axx051 · Cited by 45
Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles
Chang, L.-Y., Lee, M.-Z., Wu, Y., Lee, W.-K., Ma, C.-L., Chang, J.-M., Chen, C.-W., Huang, T.-C., Lee, C., Lee, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1187 · Cited by 42
A Novel Framework for the Comparative Analysis of Biological Networks
Pache, R. A., & Aloy, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031220 · Cited by 42
Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling
Zeng, T., Wang, D. C., Wang, X., Xu, F., & Chen, L. (2014) · Drug Resistance Updates · doi:10.1016/j.drup.2014.08.002 · Cited by 42
Widespread Rewiring of Genetic Networks upon Cancer Signaling Pathway Activation
Billmann, M., Chaudhary, V., ElMaghraby, M. F., Fischer, B., & Boutros, M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.015 · Cited by 44
Niche adaptation by expansion and reprogramming of general transcription factors
Turkarslan, S., Reiss, D. J., Gibbins, G., Su, W. L., Pan, M., Bare, J. C., Plaisier, C. L., & Baliga, N. S. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.87 · Cited by 42
Genome-wide single-cell-level screen for protein abundance and localization changes in response to DNA damage in S. cerevisiae
Mazumder, A., Pesudo, L. Q., McRee, S. K., Bathe, M., & Samson, L. D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt715 · Cited by 41
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
Teschendorff, A., Banerji, C., Simone Severini, B., Kuehn, R., & Peter Sollich, A. (2015) · UCL Discovery (University College London) · Cited by 41
Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression
Savino, A., Provero, P., & Poli, V. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21249461 · Cited by 40
Chemical Genetics of Rapamycin-Insensitive TORC2 in S. cerevisiae
Kliegman, J. I., Fiedler, D., Ryan, C. J., Xu, Y.-F., Su, X., Thomas, D. M., Caccese, M., Cheng, A., Shales, M., Rabinowitz, J. D., et al. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.11.040 · Cited by 41
Self-citation
Improved discovery of genetic interactions using CRISPRiSeq across multiple environmentsPreprint
Jaffe, M., Dziulko, A., Smith, J., St.Onge, R. P., Levy, S. F., & Sherlock, G. (2019) · Genome Research · doi:10.1101/gr.246603.118 · Cited by 39
Changes in gene expression predictably shift and switch genetic interactions
Li, X., Lalić, J., Baeza-Centurión, P., Dhar, R., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11735-3 · Cited by 38
Dynamic rewiring of the human interactome by interferon signaling
Kerr, C. H., Skinnider, M. A., Madero, A. M., Andrews, D. D. T., Stacey, R. G., Chan, Q. W. T., Stoynov, N., Jan, E., & Foster, L. J. (2020) · Genome biology · doi:10.1186/s13059-020-02050-y · Cited by 38
A multi-scale map of protein assemblies in the DNA damage response
Kratz, A., Kim, M., Kelly, M. R., Zheng, F., Koczor, C. A., Li, J., Ono, K., Qin, Y., Churas, C., Chen, J., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.04.007 · Cited by 36
Self-citation
Histone variant Htz1 promotes histone H3 acetylation to enhance nucleotide excision repair in Htz1 nucleosomes
Yu, Y., Deng, Y., Reed, S. H., Millar, C. B., & Waters, R. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt688 · Cited by 37
Integration of multiple biological contexts reveals principles of synthetic lethality that affect reproducibility
Ku, A. A., Hu, H., Zhao, X., Shah, K. N., Kongara, S., Wu, D., McCormick, F. P., Balmain, A., & Bandyopadhyay, S. (2020) · Nature Communications · doi:10.1038/s41467-020-16078-y · Cited by 38
Self-citation
Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back
Kurz, F. T., Kembro, J. M., Flesia, A. G., Armoundas, A. A., Cortassa, S. C., Aon, M. A., & Lloyd, D. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1352 · Cited by 37
Identifying Condition-Specific Modules by Clustering Multiple Networks
Ma, X., Sun, P., & Qin, G. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2761339 · Cited by 38
Differential C3NET reveals disease networks of direct physical interactions
Altay, G., Asim, M., Markowetz, F. M., & Neal, D. E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-296 · Cited by 36
Bio-soliton model that predicts non-thermal electromagnetic frequency bands, that either stabilize or destabilize living cells
Geesink, J. H., & Meijer, D. K. F. (2017) · Electromagnetic Biology and Medicine · doi:10.1080/15368378.2017.1389752 · Cited by 45
qPCA: a scalable assay to measure the perturbation of protein–protein interactions in living cells
Freschi, L., Torres‐Quiroz, F., Dubé, A. K., & Landry, C. R. (2012) · Molecular BioSystems · doi:10.1039/c2mb25265a · Cited by 36
All or Nothing: Protein Complexes Flip Essentiality between Distantly Related Eukaryotes
Ryan, C. J., Krogan, N. J., Cunningham, P., & Cagney, G. (2013) · Genome Biology and Evolution · doi:10.1093/gbe/evt074 · Cited by 34
Self-citation
Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes
Basha, O., Argov, C. M., Artzy, R., Zoabi, Y., Hekselman, I., Alfandari, L., Caspi, V. C., & Yeger‐Lotem, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa034 · Cited by 33
Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)
Gysi, D. M., Fragoso, T. D. M., Zebardast, F., Bertoli, W., Busskamp, V., Almaas, E., & Nowick, K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240523 · Cited by 36
Altered expression and activities of enzymes involved in thiamine diphosphate biosynthesis in Saccharomyces cerevisiae under oxidative and osmotic stress
Kowalska, E., Kujda, M., Wolak, N., & Kozik, A. (2012) · FEMS Yeast Research · doi:10.1111/j.1567-1364.2012.00804.x · Cited by 37
Podbat: A Novel Genomic Tool Reveals Swr1-Independent H2A.Z Incorporation at Gene Coding Sequences through Epigenetic Meta-Analysis
Sadeghi, L., Bonilla, C., Strålfors, A., Ekwall, K., & Svensson, J. P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002163 · Cited by 34
Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks
Landeghem, S. V., Parys, T. V., Dubois, M., Inzé, D., & de Peer, Y. V. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0863-y · Cited by 34
Protein Moonlighting Revealed by Noncatalytic Phenotypes of Yeast Enzymes
Espinosa‐Cantú, A., Ascencio, D., Herrera-Basurto, M. S., Xu, J., Roguev, A., Krogan, N. J., & DeLuna, A. (2017) · Genetics · doi:10.1534/genetics.117.300377 · Cited by 34
Self-citation
Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure
Lareau, C. A., White, B. C., Oberg, A. L., & McKinney, B. A. (2015) · BioData Mining · doi:10.1186/s13040-015-0040-x · Cited by 34
Systems Biology: Confronting the Complexity of Cancer
Gentles, A. J., & Gallahan, D. (2011) · Cancer Research · doi:10.1158/0008-5472.can-11-1569 · Cited by 35
The INO80 chromatin remodeler sustains metabolic stability by promoting TOR signaling and regulating histone acetylation
Beckwith, S. L., Schwartz, E. K., García-Nieto, P. E., King, D. A., Gowans, G. J., Wong, K. M., Yao, W., Eckley, T. L., Paraschuk, A. P., Peltan, E. L., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007216 · Cited by 34
Gene coexpression measures in large heterogeneous samples using count statistics
Wang, Y. X. R., Waterman, M. S., & Huang, H. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1417128111 · Cited by 33
Development of Ultra-High-Density Screening Tools for Microbial “Omics”
Bean, G. J., Jaeger, P. A., Bahr, S., & Ideker, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085177 · Cited by 32
Self-citation
Inference for high-dimensional differential correlation matrices
Cai, T., & Zhang, A. R. (2015) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2015.08.019 · Cited by 34
Analysis of Protein-Protein Interactions in Cross-talk Pathways Reveals CRKL Protein as a Novel Prognostic Marker in Hepatocellular Carcinoma
Liu, C.-H., Chen, T.-C., Chau, G., Jan, Y., Chen, C., Hsu, C.-N., Lin, K., Juang, Y., Lu, P., Cheng, H.-C., et al. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o112.020404 · Cited by 33
Extracting high confidence protein interactions from affinity purification data: At the crossroads
Pu, S., Vlasblom, J., Turinsky, A. L., Marcon, E., Phanse, S., Trimble, S. S., Olsen, J. B., Greenblatt, J. F., Emili, A., & Wodak, S. J. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.03.009 · Cited by 33
Constructing module maps for integrated analysis of heterogeneous biological networks
Amar, D., & Ron Shamir, A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku102 · Cited by 33
Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells
Lenoir, W. F., Morgado, M. C., DeWeirdt, P. C., McLaughlin, M. E., Griffith, A. L., Sangree, A. K., Feeley, M. N., Anvar, N. E., Kim, E., Bertolet, L. L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26867-8 · Cited by 31
Synthetic Lethality between Gene Defects Affecting a Single Non-essential Molecular Pathway with Reversible Steps
Zinovyev, A. Y., Kuperstein, I., Barillot, E., & Heyer, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003016 · Cited by 32
Active interaction mapping reveals the hierarchical organization of autophagy
Kramer, M. H., Farré, J.-C., Mitra, K., Yu, M. K., Ono, K., Demchak, B., Licon, K., Flagg, M., Balakrishnan, R., Cherry, J. M., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2016.12.024 · Cited by 31
Self-citation
Integrative network-based approach identifies key genetic elements in breast invasive carcinoma
Hamed, M., Spaniol, C., Zapp, A., & Helms, V. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s5-s2 · Cited by 30
Use of genetic and chemical synthetic lethality as probes of complexity in bacterial cell systems
Klobucar, K., & Brown, E. D. (2017) · FEMS Microbiology Reviews · doi:10.1093/femsre/fux054 · Cited by 31
JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data
Ji, J., He, D., Feng, Y., He, Y., Xue, F., & Xie, L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx360 · Cited by 30
Regulation of alternative polyadenylation in the yeast Saccharomyces cerevisiae by histone H3K4 and H3K36 methyltransferases
Michaels, K. K., Mostafa, S. M., Capella, J. R., & Moore, C. L. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa292 · Cited by 32
Bioinformatic identification of genes suppressing genome instability
Putnam, C. D., Allen-Soltero, S., Martinez, S. L., Chan, J. E., Hayes, T. K., & Kolodner, R. D. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216733109 · Cited by 32
Self-citation
Replication-Independent Endogenous DNA Double-Strand Breaks in Saccharomyces cerevisiae Model
Thongsroy, J., Matangkasombut, O., Thongnak, A., Rattanatanyong, P., Jirawatnotai, S., & Mutirangura, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072706 · Cited by 31
A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction
Chen, H., He, Y., Ji, J., & Shi, Y. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01162 · Cited by 31
What do ’omic technologies have to offer periodontal clinical practice in the future?
Grant, M. M. (2011) · Journal of Periodontal Research · doi:10.1111/j.1600-0765.2011.01387.x · Cited by 31
Set5 and Set1 cooperate to repress gene expression at telomeres and retrotransposons
Martín, G. M., King, D. A., Green, E. M., García-Nieto, P. E., Alexander, R. E., Collins, S. R., Krogan, N. J., Gozani, O., & Morrison, A. J. (2014) · Epigenetics · doi:10.4161/epi.27645 · Cited by 29
Self-citation
Phosphorylation of Histone H4T80 Triggers DNA Damage Checkpoint Recovery
Millán-Zambrano, G., Santos-Rosa, H., Puddu, F., Robson, S. C., Jackson, S. P., & Kouzarides, T. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.09.023 · Cited by 32
Summarizing static and dynamic big graphs
Khan, A., Bhowmick, S. S., & Bonchi, F. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137765.3137825 · Cited by 45
Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls
Shen, J. P., & Ideker, T. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.06.026 · Cited by 27
Self-citation
Pan- and core- network analysis of co-expression genes in a model plant
He, F., & Maslov, S. (2016) · Scientific Reports · doi:10.1038/srep38956 · Cited by 29
Layer-Specific Modules Detection in Cancer Multi-Layer Networks
Ma, X., Zhao, W. Z., & Wu, W. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3176859 · Cited by 28
The effect of network biology on drug toxicology
Gautier, L., Taboureau, O., & Audouze, K. (2013) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2013.820704 · Cited by 28
How to train your microbe: methods for dynamically characterizing gene networks
Castillo-Hair, S. M., Igoshin, O. A., & Tabor, J. J. (2015) · Current Opinion in Microbiology · doi:10.1016/j.mib.2015.01.008 · Cited by 27
Time-resolved mapping of genetic interactions to model rewiring of signaling pathways
Heigwer, F., Scheeder, C., Miersch, T., Schmitt, B. A., Blass, C., Pour-Jamnani, M. V., & Boutros, M. (2018) · eLife · doi:10.7554/elife.40174 · Cited by 25
NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities
da Rocha, E. L., Ung, C. Y., McGehee, C. D., Correia, C., & Li, H. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw166 · Cited by 28
An Information Theoretic Approach to Constructing Robust Boolean Gene Regulatory Networks
Vasic, B. V., Ravanmehr, V., & Krishnan, A. R. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.61 · Cited by 27
C16orf72/HAPSTR1 is a molecular rheostat in an integrated network of stress response pathways
Amici, D. R., Ansel, D. J., Metz, K. A., Smith, R. S., Phoumyvong, C. M., Gayatri, S., Chamera, T., Edwards, S. L., O’Hara, B. P., Srivastava, S., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111262119 · Cited by 29
Identifying module biomarkers from gastric cancer by differential correlation network
Liu, X., & Chang, X. (2016) · OncoTargets and Therapy · doi:10.2147/ott.s113281 · Cited by 25
Intermodule Coupling Analysis of Huang-Lian-Jie-Du Decoction on Stroke
Wang, P., Li, D., Zhou, W., Meng, J., Zhang, M., Wu, Y., Huo, H.-R., Xiong, X., & Sui, F. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01288 · Cited by 26
Bayesian Structure Learning in Multilayered Genomic Networks
Ha, M. J., Stingo, F. C., & Baladandayuthapani, V. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1775611 · Cited by 26
Protein kinase C controls activation of the DNA integrity checkpoint
Soriano-Carot, M., Quilis, I., Bañó, M. C., & Igual, J. C. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku373 · Cited by 29
A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions
Sameith, K., Amini, S., Koerkamp, M. J. A. G., van Leenen, D., Brok, M. O., Brabers, N., Lijnzaad, P., van Hooff, S. R., Benschop, J. J., Lenstra, T. L., et al. (2015) · BMC Biology · doi:10.1186/s12915-015-0222-5 · Cited by 25
Mapping genetic interactions in cancer: a road to rational combination therapies
Tutuncuoglu, B., & Krogan, N. J. (2019) · Genome Medicine · doi:10.1186/s13073-019-0680-4 · Cited by 25
Self-citation
iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast
Jaffe, M., Sherlock, G., & Levy, S. F. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.034207 · Cited by 26
Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states
Sun, S., Baryshnikova, A., Brandt, N., & Gresham, D. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199167 · Cited by 25
Computational solutions for modeling and controlling plant response to abiotic stresses: a review with focus on iron deficiency
Tong, H., Madison, I., Long, T. A., & Williams, C. (2020) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2020.05.006 · Cited by 25
Gene Network and Proteomic Analyses of Cardiac Responses to Pathological and Physiological Stress
Drozdov, I. A., Didangelos, A., Yin, X., Zampetaki, A., Abonnenc, M., Murdoch, C. E., Zhang, M., Ouzounis, C., Mayr, M., Tsoka, S., et al. (2013) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000063 · Cited by 24
Personalized Integrated Network Modeling of the Cancer Proteome Atlas
Ha, M. J., Banerjee, S., Akbani, R., Liang, H., Mills, G. B., Do, K.-A., & Baladandayuthapani, V. (2018) · Scientific Reports · doi:10.1038/s41598-018-32682-x · Cited by 24
Metabolic Rewiring in the Face of Genomic Assault: Integrating DNA Damage Response and Cellular Metabolism
Ma, W., & Zhou, S. (2025) · Biomolecules · doi:10.3390/biom15020168 · Cited by 31
Assembling global maps of cellular function through integrative analysis of physical and genetic networks
Srivas, R., Hannum, G., Ruscheinski, J., Ono, K., Wang, P., Smoot, M. E., & Ideker, T. (2011) · Nature Protocols · doi:10.1038/nprot.2011.368 · Cited by 24
Self-citation
Networks in proteomics analysis of cancer
Goh, W. W. B., & Wong, L. (2013) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2013.02.011 · Cited by 23
Stress-induced changes in gene interactions in human cells
Nayak, R. R., Bernal, W. E., Lee, J. W., Kearns, M. J., & Cheung, V. G. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt999 · Cited by 25

Authors: Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M.-K., Chuang, R., Jaehnig, E. J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., Fiedler, D., Dutkowski, J., Guénolé, A., van Attikum, H., Shokat, K. M., Kolodner, R. D., Huh, W.-K., Aebersold, R., Keogh, M.-C., Krogan, N. J., & Ideker, T.
Affiliations: University of California San Diego (US)
Albert Einstein College of Medicine (US)
Seoul National University (KR)
University of Zurich (CH)
University of California, San Francisco (US)
Howard Hughes Medical Institute (US)
Leiden University Medical Center (NL)
Department of Toxicogenetics, Leiden University Medical Center, Leiden, Netherlands.
Corresponding author: Keogh, Michael-Christopher
Krogan, Nevan J.
Ideker, Trey
ORCID: Bandyopadhyay, Sourav
Mehta, Monika
Kuo, Dwight
Sung, Min-Kyung
Chuang, Ryan
Jaehnig, Eric J.
Bodenmiller, Bernd
Copeland, Wilbert
Fiedler, Dorothea
Shokat, Kevan M.
Kolodner, Richard D.
Huh, Won-Ki
Aebersold, Ruedi
Keogh, Michael-Christopher
Krogan, Nevan J.
Ideker, Trey
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2010-12-03
Volume: 330
Issue: 6009
Pages: 1385-1389
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1195618
PMC: PMC3006187
OpenAlex: W1988263223
Cited by: 447
References: 23
Citations by year: 2026: 7 · 2025: 5 · 2024: 12 · 2023: 10 · 2022: 17 · 2021: 18 · 2020: 31 · 2019: 40 · 2018: 22 · 2017: 42 · 2016: 28 · 2015: 37 · 2014: 55 · 2013: 51 · 2012: 41
FWCI: 15.91
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Science (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
Funders: Howard Hughes Medical Institute
National Cancer Institute (P30CA013330)
National Institute of General Medical Sciences (R01GM084448, P50GM081879, R01GM084279, R37GM026017, R01GM026017)
National Institute of Environmental Health Sciences (R01ES014811)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA Repair Mechanisms
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: DNA damage response, genetic interaction networks, phosphatase, transcription factors, DNA repair pathways, protein complex stability, network rewiring
MeSH terms: Chromatin, DNA Damage, DNA Repair, DNA, Fungal, Epistasis, Genetic, Genes, Fungal, Histones, Methyl Methanesulfonate, Mutagens, Mutation, Phosphoprotein Phosphatases, Saccharomyces cerevisiae, Transcription Factors, Signal Transduction, Protein Serine-Threonine Kinases, Mitogen-Activated Protein Kinases, Protein Interaction Mapping, Saccharomyces cerevisiae Proteins, Gene Regulatory Networks

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Rewiring of Genetic Networks in Response to DNA Damage

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks