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6. N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity

Author

Varland, Sylvia, Silva, Rui Duarte, Kjosås, Ine, Faustino, Alexandra, Bogaert, Annelies, Billmann, Maximilian, Boukhatmi, Hadi, Kellen, Barbara, Costanzo, Michael, Drazic, Adrian, Osberg, Camilla, Chan, Katherine, Zhang, Xiang, Tong, Amy Hin Yan, Andreazza, Simonetta, Lee, Juliette J., Nedyalkova, Lyudmila, Ušaj, Matej, Whitworth, Alexander J., Andrews, Brenda J., Moffat, Jason, Myers, Chad L., Gevaert, Kris, Boone, Charles, Martinho, Rui Gonçalo, and Arnesen, Thomas

Published
2023
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8. Identification of Candidate Susceptibility Genes to Puccinia graminis f. sp. tritici in Wheat

Author

Henningsen, Eva C, Omidvar, Vahid, Della Coletta, Rafael, Michno, Jean-Michel, Gilbert, Erin, Li, Feng, Miller, Marisa E, Myers, Chad L, Gordon, Sean P, Vogel, John P, Steffenson, Brian J, Kianian, Shahryar F, Hirsch, Cory D, and Figueroa, Melania

Subjects
Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Infection, susceptibility, rust, wheat, transcription, co-expression, non-host, Plant Biology
Abstract

Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.

Published
2021

15. Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Author

Fu, Ci, Zhang, Xiang, Veri, Amanda O., Iyer, Kali R., Lash, Emma, Xue, Alice, Yan, Huijuan, Revie, Nicole M., Wong, Cassandra, Lin, Zhen-Yuan, Polvi, Elizabeth J., Liston, Sean D., VanderSluis, Benjamin, Hou, Jing, Yashiroda, Yoko, Gingras, Anne-Claude, Boone, Charles, O’Meara, Teresa R., O’Meara, Matthew J., Noble, Suzanne, Robbins, Nicole, Myers, Chad L., and Cowen, Leah E.

Published
2021
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16. Systematic analysis of bypass suppression of essential genes

Author

van Leeuwen, Jolanda, Pons, Carles, Tan, Guihong, Wang, Jason Zi, Hou, Jing, Weile, Jochen, Gebbia, Marinella, Liang, Wendy, Shuteriqi, Ermira, Li, Zhijian, Lopes, Maykel, Ušaj, Matej, Dos Santos Lopes, Andreia, van Lieshout, Natascha, Myers, Chad L, Roth, Frederick P, Aloy, Patrick, Andrews, Brenda J, and Boone, Charles

Published
2020
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17. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism

Author

Aregger, Michael, Lawson, Keith A., Billmann, Maximillian, Costanzo, Michael, Tong, Amy H. Y., Chan, Katherine, Rahman, Mahfuzur, Brown, Kevin R., Ross, Catherine, Usaj, Matej, Nedyalkova, Lucy, Sizova, Olga, Habsid, Andrea, Pawling, Judy, Lin, Zhen-Yuan, Abdouni, Hala, Wong, Cassandra J., Weiss, Alexander, Mero, Patricia, Dennis, James W., Gingras, Anne-Claude, Myers, Chad L., Andrews, Brenda J., Boone, Charles, and Moffat, Jason

Published
2020
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19. Integration of Differential Gene-combination Search and Gene Set Enrichment Analysis: A General Approach

Author

Fang, Gang, Steinbach, Michael, Myers, Chad L., and Kumar, Vipin

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Molecular Networks
Abstract

Gene Set Enrichment Analysis (GSEA) and its variations aim to discover collections of genes that show moderate but coordinated differences in expression. However, such techniques may be ineffective if many individual genes in a phenotype-related gene set have weak discriminative power. A potential solution is to search for combinations of genes that are highly differentiating even when individual genes are not. Although such techniques have been developed, these approaches have not been used with GSEA to any significant degree because of the large number of potential gene combinations and the heterogeneity of measures that assess the differentiation provided by gene groups of different sizes. To integrate the search for differentiating gene combinations and GSEA, we propose a general framework with two key components: (A) a procedure that reduces the number of scores to be handled by GSEA to the number of genes by summarizing the scores of the gene combinations involving a particular gene in a single score, and (B) a procedure to integrate the heterogeneous scores from combinations of different sizes and from different gene combination measures by mapping the scores to p-values. Experiments on four gene expression data sets demonstrate that the integration of GSEA and gene combination search can enhance the power of traditional GSEA by discovering gene sets that include genes with weak individual differentiation but strong joint discriminative power. Also, gene sets discovered by the integrative framework share several common biological processes and improve the consistency of the results among three lung cancer data sets.

Published
2011

20. Construction and Functional Analysis of Human Genetic Interaction Networks with Genome-wide Association Data

Author

Fang, Gang, Wang, Wen, Paunic, Vanja, Oately, Benjamin, Haznadar, Majda, Steinbach, Michael, Van Ness, Brian, Myers, Chad L., and Kumar, Vipin

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Genomics
Abstract

Genetic interaction measures how different genes collectively contribute to a phenotype, and can reveal functional compensation and buffering between pathways under genetic perturbations. Recently, genome-wide screening for genetic interactions has revealed genetic interaction networks that provide novel insights either when analyzed by themselves or when integrated with other functional genomic datasets. For higher eukaryotes such as human, the above reverse-genetics approaches are not straightforward since the phenotypes of interest for higher eukaryotes are difficult to study in a cell based assay. We propose a general framework for constructing and analyzing human genetic interaction networks from genome-wide single nucleotide polymorphism (SNP) data used for case-control studies on complex diseases. Specifically, the approach contains three major steps: (1) estimating SNP-SNP genetic interactions, (2) identifying linkage disequilibrium (LD) blocks and mapping SNP-SNP interactions to block-block interactions, and (3) functional mapping for LD blocks. We performed two sets of functional analyses for each of the six datasets used in the paper, and demonstrated that (i) the constructed genetic interaction networks are supported by functional evidence from independent biological databases, and (ii) the network can be used to discover pairs of compensatory gene modules (between-pathway models) in their joint association with a disease phenotype. The proposed framework should provide novel insights beyond existing approaches that either ignore interactions between SNPs or model different SNP-SNP pairs with genetic interactions separately. Furthermore, our study provides evidence that some of the core properties of genetic interaction networks based on reverse genetics in model organisms like yeast are also present in genetic interactions revealed by natural variation in human populations.

Published
2011

21. Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics

Author

Brown, Jessica CS, Nelson, Justin, VanderSluis, Benjamin, Deshpande, Raamesh, Butts, Arielle, Kagan, Sarah, Polacheck, Itzhack, Krysan, Damian J, Myers, Chad L, and Madhani, Hiten D

Subjects
Microbiology, Biological Sciences, Genetics, Vaccine Related, Biotechnology, HIV/AIDS, Infectious Diseases, Aetiology, Development of treatments and therapeutic interventions, 2.1 Biological and endogenous factors, 2.2 Factors relating to the physical environment, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, AIDS-Related Opportunistic Infections, Algorithms, Animals, Antifungal Agents, Cryptococcus neoformans, Drug Discovery, Gene Knockout Techniques, Microbial Sensitivity Tests, Saccharomyces cerevisiae, Virulence Factors, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths.

Published
2014

23. Systematic analysis of complex genetic interactions

Author

Kuzmin, Elena, VanderSluis, Benjamin, Wang, Wen, Tan, Guihong, Deshpande, Raamesh, Chen, Yiqun, Usaj, Matej, Balint, Attila, Usaj, Mojca Mattiazzi, van Leeuwen, Jolanda, Koch, Elizabeth N., Pons, Carles, Dagilis, Andrius J., Pryszlak, Michael, Wang, Jason Zi Yang, Hanchard, Julia, Riggi, Margot, Xu, Kaicong, Heydari, Hamed, San Luis, Bryan-Joseph, Shuteriqi, Ermira, Zhu, Hongwei, Van Dyk, Nydia, Sharifpoor, Sara, Costanzo, Michael, Loewith, Robbie, Caudy, Amy, Bolnick, Daniel, Brown, Grant W., Andrews, Brenda J., Boone, Charles, and Myers, Chad L.

Published
2018

27. A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Author

Peña-Castillo, Lourdes, Tasan, Murat, Myers, Chad L, Lee, Hyunju, Joshi, Trupti, Zhang, Chao, Guan, Yuanfang, Leone, Michele, Pagnani, Andrea, Kim, Wan Kyu, Krumpelman, Chase, Tian, Weidong, Obozinski, Guillaume, Qi, Yanjun, Mostafavi, Sara, Lin, Guan Ning, Berriz, Gabriel F, Gibbons, Francis D, Lanckriet, Gert, Qiu, Jian, Grant, Charles, Barutcuoglu, Zafer, Hill, David P, Warde-Farley, David, Grouios, Chris, Ray, Debajyoti, Blake, Judith A, Deng, Minghua, Jordan, Michael I, Noble, William S, Morris, Quaid, Klein-Seetharaman, Judith, Bar-Joseph, Ziv, Chen, Ting, Sun, Fengzhu, Troyanskaya, Olga G, Marcotte, Edward M, Xu, Dong, Hughes, Timothy R, and Roth, Frederick P

Subjects
Genetics, Biotechnology, Networking and Information Technology R&D (NITRD), Human Genome, Generic health relevance, Algorithms, Animals, Mice, Proteins, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundSeveral years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.ResultsIn this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.ConclusionWe performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Published
2008

28. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

Author

Peña-Castillo, Lourdes, Tasan, Murat, Myers, Chad L, Lee, Hyunju, Joshi, Trupti, Zhang, Chao, Guan, Yuanfang, Leone, Michele, Pagnani, Andrea, Kim, Wan Kyu, Krumpelman, Chase, Tian, Weidong, Obozinski, Guillaume, Qi, Yanjun, Mostafavi, Sara, Lin, Guan Ning, Berriz, Gabriel F, Gibbons, Francis D, Lanckriet, Gert, Qiu, Jian, Grant, Charles, Barutcuoglu, Zafer, Hill, David P, Warde-Farley, David, Grouios, Chris, Ray, Debajyoti, Blake, Judith A, Deng, Minghua, Jordan, Michael I, Noble, William S, Morris, Quaid, Klein-Seetharaman, Judith, Bar-Joseph, Ziv, Chen, Ting, Sun, Fengzhu, Troyanskaya, Olga G, Marcotte, Edward M, Xu, Dong, Hughes, Timothy R, and Roth, Frederick P

Subjects
Animals, Mice, Proteins, Algorithms, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundSeveral years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.ResultsIn this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.ConclusionWe performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Published
2008

33. Supplementary Figure 3 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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34. Supplementary Figure 4 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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35. Supplementary Methods from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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36. Supplementary Table 2 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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37. Supplementary Figure 1 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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38. Supplementary Figure 2 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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39. Supplementary Table 4 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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40. Data from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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41. Supplementary Table 1 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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42. Supplementary Table 3 from Profiling Bortezomib Resistance Identifies Secondary Therapies in a Mouse Myeloma Model

Author

Stessman, Holly A.F., primary, Baughn, Linda B., primary, Sarver, Aaron, primary, Xia, Tian, primary, Deshpande, Raamesh, primary, Mansoor, Aatif, primary, Walsh, Susan A., primary, Sunderland, John J., primary, Dolloff, Nathan G., primary, Linden, Michael A., primary, Zhan, Fenghuang, primary, Janz, Siegfried, primary, Myers, Chad L., primary, and Van Ness, Brian G., primary

Published
2023
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43. Supplementary Data from Single-Cell Gene Expression Analyses Reveal Distinct Self-Renewing and Proliferating Subsets in the Leukemia Stem Cell Compartment in Acute Myeloid Leukemia

Author

Sachs, Karen, primary, Sarver, Aaron L., primary, Noble-Orcutt, Klara E., primary, LaRue, Rebecca S., primary, Antony, Marie Lue, primary, Chang, Daniel, primary, Lee, Yoonkyu, primary, Navis, Connor M., primary, Hillesheim, Alexandria L., primary, Nykaza, Ian R., primary, Ha, Ngoc A., primary, Hansen, Conner J., primary, Karadag, Fatma K., primary, Bergerson, Rachel J., primary, Verneris, Michael R., primary, Meredith, Matthew M., primary, Schomaker, Matthew L., primary, Linden, Michael A., primary, Myers, Chad L., primary, Largaespada, David A., primary, and Sachs, Zohar, primary

Published
2023
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44. Tables 1-7 from Single-Cell Gene Expression Analyses Reveal Distinct Self-Renewing and Proliferating Subsets in the Leukemia Stem Cell Compartment in Acute Myeloid Leukemia

Author

Sachs, Karen, primary, Sarver, Aaron L., primary, Noble-Orcutt, Klara E., primary, LaRue, Rebecca S., primary, Antony, Marie Lue, primary, Chang, Daniel, primary, Lee, Yoonkyu, primary, Navis, Connor M., primary, Hillesheim, Alexandria L., primary, Nykaza, Ian R., primary, Ha, Ngoc A., primary, Hansen, Conner J., primary, Karadag, Fatma K., primary, Bergerson, Rachel J., primary, Verneris, Michael R., primary, Meredith, Matthew M., primary, Schomaker, Matthew L., primary, Linden, Michael A., primary, Myers, Chad L., primary, Largaespada, David A., primary, and Sachs, Zohar, primary

Published
2023
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45. Data from Single-Cell Gene Expression Analyses Reveal Distinct Self-Renewing and Proliferating Subsets in the Leukemia Stem Cell Compartment in Acute Myeloid Leukemia

Author

Sachs, Karen, primary, Sarver, Aaron L., primary, Noble-Orcutt, Klara E., primary, LaRue, Rebecca S., primary, Antony, Marie Lue, primary, Chang, Daniel, primary, Lee, Yoonkyu, primary, Navis, Connor M., primary, Hillesheim, Alexandria L., primary, Nykaza, Ian R., primary, Ha, Ngoc A., primary, Hansen, Conner J., primary, Karadag, Fatma K., primary, Bergerson, Rachel J., primary, Verneris, Michael R., primary, Meredith, Matthew M., primary, Schomaker, Matthew L., primary, Linden, Michael A., primary, Myers, Chad L., primary, Largaespada, David A., primary, and Sachs, Zohar, primary

Published
2023
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46. Supplementary Figure from A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Author

Deshpande, Raamesh, primary, Asiedu, Michael K., primary, Klebig, Mitchell, primary, Sutor, Shari, primary, Kuzmin, Elena, primary, Nelson, Justin, primary, Piotrowski, Jeff, primary, Ho Shin, Seung, primary, Yoshida, Minoru, primary, Costanzo, Michael, primary, Boone, Charles, primary, Wigle, Dennis A., primary, and Myers, Chad L., primary

Published
2023
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47. Supplementary Table 2 from A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Author

Deshpande, Raamesh, primary, Asiedu, Michael K., primary, Klebig, Mitchell, primary, Sutor, Shari, primary, Kuzmin, Elena, primary, Nelson, Justin, primary, Piotrowski, Jeff, primary, Ho Shin, Seung, primary, Yoshida, Minoru, primary, Costanzo, Michael, primary, Boone, Charles, primary, Wigle, Dennis A., primary, and Myers, Chad L., primary

Published
2023
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48. Supplementary Table 4 from A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Author

Deshpande, Raamesh, primary, Asiedu, Michael K., primary, Klebig, Mitchell, primary, Sutor, Shari, primary, Kuzmin, Elena, primary, Nelson, Justin, primary, Piotrowski, Jeff, primary, Ho Shin, Seung, primary, Yoshida, Minoru, primary, Costanzo, Michael, primary, Boone, Charles, primary, Wigle, Dennis A., primary, and Myers, Chad L., primary

Published
2023
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49. Data from A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Author

Deshpande, Raamesh, primary, Asiedu, Michael K., primary, Klebig, Mitchell, primary, Sutor, Shari, primary, Kuzmin, Elena, primary, Nelson, Justin, primary, Piotrowski, Jeff, primary, Ho Shin, Seung, primary, Yoshida, Minoru, primary, Costanzo, Michael, primary, Boone, Charles, primary, Wigle, Dennis A., primary, and Myers, Chad L., primary

Published
2023
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50. Supplementary Table and Figure Legends from A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Author

Deshpande, Raamesh, primary, Asiedu, Michael K., primary, Klebig, Mitchell, primary, Sutor, Shari, primary, Kuzmin, Elena, primary, Nelson, Justin, primary, Piotrowski, Jeff, primary, Ho Shin, Seung, primary, Yoshida, Minoru, primary, Costanzo, Michael, primary, Boone, Charles, primary, Wigle, Dennis A., primary, and Myers, Chad L., primary

Published
2023
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