Lana Popova
Cell Signaling Technology
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Lana Popova.
Nature Biotechnology | 2012
Shuji Sato; Sean A. Beausoleil; Lana Popova; Jason G Beaudet; Ravi K Ramenani; Xiaowu Zhang; James S Wieler; Sandra M Schieferl; Wan Cheung Cheung; Roberto D. Polakiewicz
1. Trapnell, C. et al. Nat. Biotechnol. 28, 511–515 (2010). 2. Trapnell, C. et al. Nat. Protoc. 7, 562–578 (2012). 3. Nielsen, C.B., Cantor, M., Dubchak, I., Gordon, D. & Wang, T. Nat. Methods 7, S5–S15 (2010). 4. Rutherford, K. et al. Bioinformatics 16, 944–945 (2000). 5. Kent, W.J. Genome Res. 12, 996–1006 (2002). 6. Robinson, J.T. et al. Nat. Biotechnol. 29, 24–26 (2011). 7. Fiume, M., Williams, V., Brook, A. & Brudno, M. Bioinformatics 26, 1938–1944 (2010). 8. Jankun-Kelly, T.J. & Kwan-Liu, M. IEEE Transactions on Visualization and Computer Graphics 7, 275–287 (2001). 9. Pretorius, A.J., Bray, M.A.P., Carpenter, A.E. & Ruddle, R.A. IEEE Transactions on Visualization and Computer Graphics 17, 2402–2411 (2011). 10. Goecks, J., Nekrutenko, A., Taylor, J. & the Galaxy Team. Genome Biology 11, R86 (2010). 11. Blankenberg, D. et al. Curr. Protoc. Mol. Biol. 89, 19.10.1–19.10.21 (2010). 12. Ron, D. & Walter, P. Nat. Rev. Mol. Cell Biol. 8, 519–529 (2007). 13. Walter, P. & Ron, D. Science 334, 1081–1086 (2011). 14. Mori, K. J. Biochem. 146, 743–750 (2009). 15. Calfon, M. et al. Nature 415, 92–96 (2002). 16. Yanagitani, K., Kimata, Y., Kadokura, H. & Kohno, K. Science 331, 586–589 (2011). 17. Guo, H., Ingolia, N.T., Weissman, J.S. & Bartel, D.P. Nature 466, 835–840 (2010). 18. Reid, D.W. & Nicchitta, C.V. J. Biol. Chem. 287, 5518– 5527 (2012). share or publish the new visualization on the web. Trackster’s use of data subsets to reduce analysis computation time is applicable to a wide set of genomic tools. For instance, genomic interval operations (such as intersect and subtract), transcript assembly and quantification, and human variation analysis (such as SNP calling) are compatible with Trackster’s analysis approach. However, tools (such as some peak callers) that use data from many or all genomic regions to build a global model require additional support to work with Trackster. These tools must be run once in full to generate the model, and then the model can be stored in Galaxy and reused in Trackster. Transcript quantification in Cufflinks benefits from a global model, and Trackster makes use of it when it available. Alternatively, a tool (for example, a read mapper) may require all input data because it is not possible to identify, before runtime, a subset of input data needed to produce correct output in a particular genomic region. For such tools, dynamic filtering can be used to simulate running a tool using different parameters. In this approach, a tool’s parameters are relaxed so that many potential outputs are produced and attribute values are attached to output data. Filtering can then be used to observe the data that would be produced for particular parameter values. Visualization and data analysis tools are used in nearly all high-throughput sequencing experiments, yet too often they are not well integrated. Coupling visualization and analysis tools into a visual analysis environment where analysis output can be generated and visually assessed in real time is a powerful approach for computational science. Trackster provides an environment for interactive visual analysis that is widely applicable to many different high-throughput sequencing experiments. General visual analysis techniques that can be performed in Trackster include tool parameter-space visualization and exploration, systematic sweeps of parameter values and dynamic filtering. Trackster makes visual analysis possible for a wide variety of tools by leveraging the Galaxy framework, thereby tapping into the large collection of tools already integrated into Galaxy and providing a simple path for integrating additional tools into Trackster. This approach to tool integration enables popular, production-level tools, such as Cufflinks in our example, to be integrated into Trackster without modification to the tools themselves. In our experiment, Trackster’s visual analysis features made it possible to use interactive visualization to improve Cufflinks’ transcript assemblies via parameter-space exploration and to remove assembly artifacts using dynamic filtering. Trackster also supports collaborative visual analysis via web-based, fully functional shared visualizations that can be modified, extended, re-shared and published.
Scientific Reports | 2016
Jason J. Lohmueller; Shuji Sato; Lana Popova; Isabel Chu; Meghan Tucker; Roberto Barberena; Gregory Innocenti; Mare Cudic; James D. Ham; Wan Cheung Cheung; Roberto Polakiewicz; Olivera J. Finn
MUC1 is a shared tumor antigen expressed on >80% of human cancers. We completed the first prophylactic cancer vaccine clinical trial based on a non-viral antigen, MUC1, in healthy individuals at-risk for colon cancer. This trial provided a unique source of potentially effective and safe immunotherapeutic drugs, fully-human antibodies affinity-matured in a healthy host to a tumor antigen. We purified, cloned, and characterized 13 IgGs specific for several tumor-associated MUC1 epitopes with a wide range of binding affinities. These antibodies bind hypoglycosylated MUC1 on human cancer cell lines and tumor tissues but show no reactivity against fully-glycosylated MUC1 on normal cells and tissues. We found that several antibodies activate complement-mediated cytotoxicity and that T cells carrying chimeric antigen receptors with the antibody variable regions kill MUC1+ target cells, express activation markers, and produce interferon gamma. Fully-human and tumor-specific, these antibodies are candidates for further testing and development as immunotherapeutic drugs.
Leukemia | 2007
Ting-Lei Gu; Lana Popova; Cynthia Reeves; Julie Nardone; Joan MacNeill; John Rush; Stephen D. Nimer; Roberto D. Polakiewicz
Phosphoproteomic analysis identifies the M0-91 cell line as a cellular model for the study of TEL-TRKC fusion-associated leukemia
Blood | 2007
Ting Lei Gu; Thomas Mercher; Jeffrey W. Tyner; Valerie Goss; Denise K. Walters; Melanie G. Cornejo; Cynthia Reeves; Lana Popova; Kimberly Lee; Michael C. Heinrich; John Rush; Masanori Daibata; Isao Miyoshi; D. Gary Gilliland; Brian J. Druker; Roberto D. Polakiewicz
Journal of Immunological Methods | 2005
Randy Wetzel; Valerie Goss; Brett Norris; Lana Popova; Michael B. Melnick; Bradley L. Smith
Archive | 2016
Ting-Lei Gu; Valerie Goss; Cynthia Reeves; Lana Popova; Julie Nardone; Denise K. Walters; Yi Wang; John Rush; Michael J. Comb; Brian J. Druker; Roberto Polakiewicz
Cancer Research | 2015
Jason J. Lohmueller; Shuji Sato; Wan Cheung Cheung; Isabel Chu; Lana Popova; Christopher A. Manning; Katherine Crosby; Christopher Grange; James D. Ham; Roberto Polakiewicz; Olivera J. Finn
Archive | 2013
Yi Wang; John Rush; Michael J. Comb; Brian J. Druker; Roberto Polakiewicz; Ting-Lei Gu; Valerie Goss; Cynthia Reeves; Lana Popova; Julie Nardone; Joan MacNeill; Denise K
Archive | 2007
Ting-Lei Gu; Thomas Mercher; Jeffrey W. Tyner; Valerie Goss; Denise K. Walters; Melanie G. Cornejo; Cynthia Reeves; Lana Popova; Kimberly Lee; Michael C. Heinrich; John Edward Rush; Masanori Daibata; Isao Miyoshi; D. Gary Gilliland; Brian J. Druker; Roberto D. Polakiewicz