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10 results on '"Banfai, Balazs"'

1. Inducers of the endothelial cell barrier identified through chemogenomic screening in genome-edited hPSC-endothelial cells

Author

Roudnicky, Filip, Kim, Bo Kyoung, Pandya, Nikhil J., Lan, Yanjun, Sach-Peltason, Lisa, Ragelle, Heloise, Strassburger, Pamela, Gruener, Sabine, Lazendic, Mirjana, Uhles, Sabine, Revelant, Franco, Eidam, Oliv, Sturm, Gregor, Kueppers, Verena, Christensen, Klaus, Goldstein, Leonard D., Tzouros, Manuel, Banfai, Balazs, Modrusan, Zora, Graf, Martin, Patsch, Christoph, Burcin, Mark, Meyer, Claas A., Westenskow, Peter D., and Cowan, Chad A.

Published
2020

2. Secreted retrovirus-like GAG-domain-containing protein PEG10 is regulated by UBE3A and is involved in Angelman syndrome pathophysiology

Author

Pandya, Nikhil J., Wang, Congwei, Costa, Veronica, Lopatta, Paul, Meier, Sonja, Zampeta, F. Isabella, Punt, A. Mattijs, Mientjes, Edwin, Grossen, Philip, Distler, Tania, Tzouros, Manuel, Martí, Yasmina, Banfai, Balazs, Patsch, Christoph, Rasmussen, Soren, Hoener, Marius, Berrera, Marco, Kremer, Thomas, Dunkley, Tom, Ebeling, Martin, Distel, Ben, Elgersma, Ype, and Jagasia, Ravi

Published
2021
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3. An integrated encyclopedia of DNA elements in the human genome

Author

Dunham, Ian, Kundaje, Anshul, Aldred, Shelley F., Collins, Patrick J., Davis, Carrie A., Doyle, Francis, Epstein, Charles B., Frietze, Seth, Harrow, Jennifer, Kaul, Rajinder, Khatun, Jainab, Lajoie, Bryan R., Landt, Stephen G., Lee, Bum-Kyu, Pauli, Florencia, Rosenbloom, Kate R., Sabo, Peter, Safi, Alexias, Sanyal, Amartya, Shoresh, Noam, Simon, Jeremy M., Song, Lingyun, Trinklein, Nathan D., Altshuler, Robert C., Birney, Ewan, Brown, James B., Cheng, Chao, Djebali, Sarah, Dong, Xianjun, Ernst, Jason, Furey, Terrence S., Gerstein, Mark, Giardine, Belinda, Greven, Melissa, Hardison, Ross C., Harris, Robert S., Herrero, Javier, Hoffman, Michael M., Iyer, Sowmya, Kellis, Manolis, Kheradpour, Pouya, Lassmann, Timo, Li, Qunhua, Lin, Xinying, Marinov, Georgi K., Merkel, Angelika, Mortazavi, Ali, Parker, Stephen C. J., Reddy, Timothy E., Rozowsky, Joel, Schlesinger, Felix, Thurman, Robert E., Wang, Jie, Ward, Lucas D., Whitfield, Troy W., Wilder, Steven P., Wu, Weisheng, Xi, Hualin S., Yip, Kevin Y., Zhuang, Jiali, Bernstein, Bradley E., Green, Eric D., Gunter, Chris, Snyder, Michael, Pazin, Michael J., Lowdon, Rebecca F., Dillon, Laura A. L., Adams, Leslie B., Kelly, Caroline J., Zhang, Julia, Wexler, Judith R., Good, Peter J., Feingold, Elise A., Crawford, Gregory E., Dekker, Job, Elnitski, Laura, Farnham, Peggy J., Giddings, Morgan C., Gingeras, Thomas R., Guigo, Roderic, Hubbard, Timothy J., Kent, W. James, Lieb, Jason D., Margulies, Elliott H., Myers, Richard M., Stamatoyannopoulos, John A., Tenenbaum, Scott A., Weng, Zhiping, White, Kevin P., Wold, Barbara, Yu, Yanbao, Wrobel, John, Risk, Brian A., Gunawardena, Harsha P., Kuiper, Heather C., Maier, Christopher W., Xie, Ling, Chen, Xian, Mikkelsen, Tarjei S., Gillespie, Shawn, Goren, Alon, Ram, Oren, Zhang, Xiaolan, Wang, Li, Issner, Robbyn, Coyne, Michael J., Durham, Timothy, Ku, Manching, Truong, Thanh, Eaton, Matthew L., Dobin, Alex, Tanzer, Andrea, Lagarde, Julien, Lin, Wei, Xue, Chenghai, Williams, Brian A., Zaleski, Chris, Roder, Maik, Kokocinski, Felix, Abdelhamid, Rehab F., Alioto, Tyler, Antoshechkin, Igor, Baer, Michael T., Batut, Philippe, Bell, Ian, Bell, Kimberly, Chakrabortty, Sudipto, Chrast, Jacqueline, Curado, Joao, Derrien, Thomas, Drenkow, Jorg, Dumais, Erica, Dumais, Jackie, Duttagupta, Radha, Fastuca, Megan, Fejes-Toth, Kata, Ferreira, Pedro, Foissac, Sylvain, Fullwood, Melissa J., Gao, Hui, Gonzalez, David, Gordon, Assaf, Howald, Cedric, Jha, Sonali, Johnson, Rory, Kapranov, Philipp, King, Brandon, Kingswood, Colin, Li, Guoliang, Luo, Oscar J., Park, Eddie, Preall, Jonathan B., Presaud, Kimberly, Ribeca, Paolo, Robyr, Daniel, Ruan, Xiaoan, Sammeth, Michael, Sandhu, Kuljeet Singh, Schaeffer, Lorain, See, Lei-Hoon, Shahab, Atif, Skancke, Jorgen, Suzuki, Ana Maria, Takahashi, Hazuki, Tilgner, Hagen, Trout, Diane, Walters, Nathalie, Wang, Huaien, Hayashizaki, Yoshihide, Reymond, Alexandre, Antonarakis, Stylianos E., Hannon, Gregory J., Ruan, Yijun, Carninci, Piero, Sloan, Cricket A., Learned, Katrina, Malladi, Venkat S., Wong, Matthew C., Barber, Galt P., Cline, Melissa S., Dreszer, Timothy R., Heitner, Steven G., Karolchik, Donna, Kirkup, Vanessa M., Meyer, Laurence R., Long, Jeffrey C., Maddren, Morgan, Raney, Brian J., Grasfeder, Linda L., Giresi, Paul G., Battenhouse, Anna, Sheffield, Nathan C., Showers, Kimberly A., London, Darin, Bhinge, Akshay A., Shestak, Christopher, Schaner, Matthew R., Ki Kim, Seul, Zhang, Zhuzhu Z., Mieczkowski, Piotr A., Mieczkowska, Joanna O., Liu, Zheng, McDaniell, Ryan M., Ni, Yunyun, Rashid, Naim U., Kim, Min Jae, Adar, Sheera, Zhang, Zhancheng, Wang, Tianyuan, Winter, Deborah, Keefe, Damian, Iyer, Vishwanath R., Zheng, Meizhen, Wang, Ping, Gertz, Jason, Vielmetter, Jost, Partridge, E., Varley, Katherine E., Gasper, Clarke, Bansal, Anita, Pepke, Shirley, Jain, Preti, Amrhein, Henry, Bowling, Kevin M., Anaya, Michael, Cross, Marie K., Muratet, Michael A., Newberry, Kimberly M., McCue, Kenneth, Nesmith, Amy S., Fisher-Aylor, Katherine I., Pusey, Barbara, DeSalvo, Gilberto, Parker, Stephanie L., Balasubramanian, Sreeram, Davis, Nicholas S., Meadows, Sarah K., Eggleston, Tracy, Newberry, J. Scott, Levy, Shawn E., Absher, Devin M., Wong, Wing H., Blow, Matthew J., Visel, Axel, Pennachio, Len A., Petrykowska, Hanna M., Abyzov, Alexej, Aken, Bronwen, Barrell, Daniel, Barson, Gemma, Berry, Andrew, Bignell, Alexandra, Boychenko, Veronika, Bussotti, Giovanni, Davidson, Claire, Despacio-Reyes, Gloria, Diekhans, Mark, Ezkurdia, Iakes, Frankish, Adam, Gilbert, James, Gonzalez, Jose Manuel, Griffiths, Ed, Harte, Rachel, Hendrix, David A., Hunt, Toby, Jungreis, Irwin, Kay, Mike, Khurana, Ekta, Leng, Jing, Lin, Michael F., Loveland, Jane, Lu, Zhi, Manthravadi, Deepa, Mariotti, Marco, Mudge, Jonathan, Mukherjee, Gaurab, Notredame, Cedric, Pei, Baikang, Rodriguez, Jose Manuel, Saunders, Gary, Sboner, Andrea, Searle, Stephen, Sisu, Cristina, Snow, Catherine, Steward, Charlie, Tapanari, Electra, Tress, Michael L., van Baren, Marijke J., Washietl, Stefan, Wilming, Laurens, Zadissa, Amonida, Zhang, Zhengdong, Brent, Michael, Haussler, David, Valencia, Alfonso, Addleman, Nick, Alexander, Roger P., Auerbach, Raymond K., Balasubramanian, Suganthi, Bettinger, Keith, Bhardwaj, Nitin, Boyle, Alan P., Cao, Alina R., Cayting, Philip, Charos, Alexandra, Cheng, Yong, Eastman, Catharine, Euskirchen, Ghia, Fleming, Joseph D., Grubert, Fabian, Habegger, Lukas, Hariharan, Manoj, Harmanci, Arif, Iyengar, Sushma, Jin, Victor X., Karczewski, Konrad J., Kasowski, Maya, Lacroute, Phil, Lam, Hugo, Lamarre-Vincent, Nathan, Lian, Jin, Lindahl-Allen, Marianne, Min, Renqiang, Miotto, Benoit, Monahan, Hannah, Moqtaderi, Zarmik, Mu, Xinmeng J., Ouyang, Zhengqing, Patacsil, Dorrelyn, Raha, Debasish, Ramirez, Lucia, Reed, Brian, Shi, Minyi, Slifer, Teri, Witt, Heather, Wu, Linfeng, Xu, Xiaoqin, Yan, Koon-Kiu, Yang, Xinqiong, Struhl, Kevin, Weissman, Sherman M., Penalva, Luiz O., Karmakar, Subhradip, Bhanvadia, Raj R., Choudhury, Alina, Domanus, Marc, Ma, Lijia, Moran, Jennifer, Victorsen, Alec, Auer, Thomas, Centanin, Lazaro, Eichenlaub, Michael, Gruhl, Franziska, Heermann, Stephan, Hoeckendorf, Burkhard, Inoue, Daigo, Kellner, Tanja, Kirchmaier, Stephan, Mueller, Claudia, Reinhardt, Robert, Schertel, Lea, Schneider, Stephanie, Sinn, Rebecca, Wittbrodt, Beate, Wittbrodt, Jochen, Partridge, E. Christopher, Jain, Gaurav, Balasundaram, Gayathri, Bates, Daniel L., Byron, Rachel, Canfield, Theresa K., Diegel, Morgan J., Dunn, Douglas, Ebersol, Abigail K., Frum, Tristan, Garg, Kavita, Gist, Erica, Hansen, R. Scott, Boatman, Lisa, Haugen, Eric, Humbert, Richard, Johnson, Audra K., Johnson, Ericka M., Kutyavin, Tattyana V., Lee, Kristen, Lotakis, Dimitra, Maurano, Matthew T., Neph, Shane J., Neri, Fiedencio V., Nguyen, Eric D., Qu, Hongzhu, Reynolds, Alex P., Roach, Vaughn, Rynes, Eric, Sanchez, Minerva E., Sandstrom, Richard S., Shafer, Anthony O., Stergachis, Andrew B., Thomas, Sean, Vernot, Benjamin, Vierstra, Jeff, Vong, Shinny, Weaver, Molly A., Yan, Yongqi, Zhang, Miaohua, Akey, Joshua M., Bender, Michael, Dorschner, Michael O., Groudine, Mark, MacCoss, Michael J., Navas, Patrick, Stamatoyannopoulos, George, Beal, Kathryn, Brazma, Alvis, Flicek, Paul, Johnson, Nathan, Lukk, Margus, Luscombe, Nicholas M., Sobral, Daniel, Vaquerizas, Juan M., Batzoglou, Serafim, Sidow, Arend, Hussami, Nadine, Kyriazopoulou-Panagiotopoulou, Sofia, Libbrecht, Max W., Schaub, Marc A., Miller, Webb, Bickel, Peter J., Banfai, Balazs, Boley, Nathan P., Huang, Haiyan, Li, Jingyi Jessica, Noble, William Stafford, Bilmes, Jeffrey A., Buske, Orion J., Sahu, Avinash D., Kharchenko, Peter V., Park, Peter J., Baker, Dannon, Taylor, James, and Lochovsky, Lucas

Subjects
Genetic research, Human genome -- Research, Genetic transcription -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research., Author(s): The ENCODE Project Consortium; Overall coordination (data analysis coordination); Ian Dunham [2]; Anshul Kundaje [3, 82]; Data production leads (data production); Shelley F. Aldred [4]; Patrick J. Collins [4]; [...]

Published
2012
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5. An Introduction to Machine Learning.

Author

Badillo, Solveig, Banfai, Balazs, Birzele, Fabian, Davydov, Iakov I., Hutchinson, Lucy, Kam‐Thong, Tony, Siebourg‐Polster, Juliane, Steiert, Bernhard, and Zhang, Jitao David

Subjects
CLINICAL pharmacology, ARTIFICIAL intelligence, MOLECULAR biology
Abstract

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever‐increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective. [ABSTRACT FROM AUTHOR]

Published
2020
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6. Binding to SMN2 pre-mRNA-protein complex elicits specificity for small molecule splicing modifiers.

Author

Sivaramakrishnan, Manaswini, McCarthy, Kathleen D., Campagne, Sébastien, Huber, Sylwia, Meier, Sonja, Augustin, Angélique, Heckel, Tobias, Meistermann, Hélène, Hug, Melanie N., Birrer, Pascale, Moursy, Ahmed, Khawaja, Sarah, Schmucki, Roland, Berntenis, Nikos, Giroud, Nicolas, Golling, Sabrina, Tzouros, Manuel, Banfai, Balazs, Duran-Pacheco, Gonzalo, and Lamerz, Jens

Subjects
SMALL molecules, SPLICEOSOMES, NUCLEOPROTEINS, SPINAL muscular atrophy, RNA splicing, QUATERNARY structure, MOTOR neurons
Abstract

Small molecule splicing modifiers have been previously described that target the general splicing machinery and thus have low specificity for individual genes. Several potent molecules correcting the splicing deficit of the SMN2 (survival of motor neuron 2) gene have been identified and these molecules are moving towards a potential therapy for spinal muscular atrophy (SMA). Here by using a combination of RNA splicing, transcription, and protein chemistry techniques, we show that these molecules directly bind to two distinct sites of the SMN2 pre-mRNA, thereby stabilizing a yet unidentified ribonucleoprotein (RNP) complex that is critical to the specificity of these small molecules for SMN2 over other genes. In addition to the therapeutic potential of these molecules for treatment of SMA, our work has wide-ranging implications in understanding how small molecules can interact with specific quaternary RNA structures. [ABSTRACT FROM AUTHOR]

Published
2017
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7. Magnetic Field Effects on CRT Computer Monitors.

Author

Banfai, Balazs and Karady, George G.

Subjects
*CATHODE ray tubes, *MAGNETIC fields, *OPTICAL computer equipment
Abstract

Discusses the effect of external low frequency magnetic field interference on cathode ray tube computer monitors. Monitor principles; Human response to monitor jitter; Jitter pattern; Jitter magnitude.

Published
2000
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8. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.

Author

Huang T, Choi M, Tzouros M, Golling S, Pandya NJ, Banfai B, Dunkley T, and Vitek O

Subjects
Humans, Proteomics, Isotope Labeling, Proteome metabolism, Statistics as Topic, Tandem Mass Spectrometry
Abstract

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT , a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures., Competing Interests: Conflict of interest—Authors declare no competing interests., (© 2020 Huang et al.)

Published
2020
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9. Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

Author

Tsai TH, Choi M, Banfai B, Liu Y, MacLean BX, Dunkley T, and Vitek O

Subjects
Databases, Protein, Protein Processing, Post-Translational, Reproducibility of Results, Sensitivity and Specificity, Software, Mass Spectrometry methods, Proteins analysis, Proteomics methods
Abstract

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein ( e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats., (© 2020 Tsai et al.)

Published
2020
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10. HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication.

Author

Cabrera AC, Melo E, Roth D, Topp A, Delobel F, Stucki C, Chen CY, Jakob P, Banfai B, Dunkley T, Schilling O, Huber S, Iacone R, and Petrone P

Subjects
Allosteric Regulation, Amyloid beta-Peptides chemistry, Amyloid beta-Peptides genetics, Amyloid beta-Peptides metabolism, High-Temperature Requirement A Serine Peptidase 1 genetics, High-Temperature Requirement A Serine Peptidase 1 metabolism, Humans, Protein Domains, Structure-Activity Relationship, Tubulin chemistry, Tubulin genetics, Tubulin metabolism, tau Proteins chemistry, tau Proteins genetics, tau Proteins metabolism, High-Temperature Requirement A Serine Peptidase 1 chemistry, Protein Multimerization, Proteolysis
Abstract

The human protease family HtrA is responsible for preventing protein misfolding and mislocalization, and a key player in several cellular processes. Among these, HtrA1 is implicated in several cancers, cerebrovascular disease and age-related macular degeneration. Currently, HtrA1 activation is not fully characterized and relevant for drug-targeting this protease. Our work provides a mechanistic step-by-step description of HtrA1 activation and regulation. We report that the HtrA1 trimer is regulated by an allosteric mechanism by which monomers relay the activation signal to each other, in a PDZ-domain independent fashion. Notably, we show that inhibitor binding is precluded if HtrA1 monomers cannot communicate with each other. Our study establishes how HtrA1 trimerization plays a fundamental role in proteolytic activity. Moreover, it offers a structural explanation for HtrA1-defective pathologies as well as mechanistic insights into the degradation of complex extracellular fibrils such as tubulin, amyloid beta and tau that belong to the repertoire of HtrA1.

Published
2017
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