6 results on '"Inga Jarmoskaite"'
Search Results
2. Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis
- Author
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Xin Liu, Tao Sun, Anna Shcherbina, Qin Li, Inga Jarmoskaite, Kalli Kappel, Gokul Ramaswami, Rhiju Das, Anshul Kundaje, and Jin Billy Li
- Subjects
Science - Abstract
The RNA sequence and secondary structure regulate RNA editing by ADAR. Here the authors employ a CRISPR/Cas9-mediated saturation mutagenesis and machine learning to predict RNA editing efficiency of specific substrates.
- Published
- 2021
- Full Text
- View/download PDF
3. How to measure and evaluate binding affinities
- Author
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Inga Jarmoskaite, Ishraq AlSadhan, Pavanapuresan P Vaidyanathan, and Daniel Herschlag
- Subjects
protein‐ligand interaction ,binding affinity ,thermodynamics ,kinetics ,dissociation constant ,RNA binding protein ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
Quantitative measurements of biomolecule associations are central to biological understanding and are needed to build and test predictive and mechanistic models. Given the advances in high-throughput technologies and the projected increase in the availability of binding data, we found it especially timely to evaluate the current standards for performing and reporting binding measurements. A review of 100 studies revealed that in most cases essential controls for establishing the appropriate incubation time and concentration regime were not documented, making it impossible to determine measurement reliability. Moreover, several reported affinities could be concluded to be incorrect, thereby impacting biological interpretations. Given these challenges, we provide a framework for a broad range of researchers to evaluate, teach about, perform, and clearly document high-quality equilibrium binding measurements. We apply this framework and explain underlying fundamental concepts through experimental examples with the RNA-binding protein Puf4.
- Published
- 2020
- Full Text
- View/download PDF
4. Science Educational Outreach Programs That Benefit Students and Scientists.
- Author
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Greg Clark, Josh Russell, Peter Enyeart, Brant Gracia, Aimee Wessel, Inga Jarmoskaite, Damon Polioudakis, Yoel Stuart, Tony Gonzalez, Al MacKrell, Stacia Rodenbusch, Gwendolyn M Stovall, Josh T Beckham, Michael Montgomery, Tania Tasneem, Jack Jones, Sarah Simmons, and Stanley Roux
- Subjects
Biology (General) ,QH301-705.5 - Abstract
Both scientists and the public would benefit from improved communication of basic scientific research and from integrating scientists into education outreach, but opportunities to support these efforts are limited. We have developed two low-cost programs--"Present Your PhD Thesis to a 12-Year-Old" and "Shadow a Scientist"--that combine training in science communication with outreach to area middle schools. We assessed the outcomes of these programs and found a 2-fold benefit: scientists improve their communication skills by explaining basic science research to a general audience, and students' enthusiasm for science and their scientific knowledge are increased. Here we present details about both programs, along with our assessment of them, and discuss the feasibility of exporting these programs to other universities.
- Published
- 2016
- Full Text
- View/download PDF
5. Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding
- Author
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Sarah K. Denny, Inga Jarmoskaite, Daniel Herschlag, Pavanapuresan P. Vaidyanathan, William J. Greenleaf, Rhiju Das, Kalli Kappel, and Winston R. Becker
- Subjects
0303 health sciences ,RNA ,Experimental data ,Non-coding RNA ,Measure (mathematics) ,Nucleic acid secondary structure ,03 medical and health sciences ,Range (mathematics) ,0302 clinical medicine ,Nucleic acid structure ,Algorithm ,Protein secondary structure ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.
- Published
- 2019
- Full Text
- View/download PDF
6. Lessons from Enzyme Kinetics Reveal Specificity Principles for RNA-guided nucleases in RNA Interference and CRISPR-based Genome Editing
- Author
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Namita Bisaria, Daniel Herschlag, and Inga Jarmoskaite
- Subjects
0301 basic medicine ,Small RNA ,Histology ,CRISPR-Associated Proteins ,Article ,Pathology and Forensic Medicine ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Ribonucleases ,Genome editing ,RNA interference ,CRISPR ,Humans ,Clustered Regularly Interspaced Short Palindromic Repeats ,Regulation of gene expression ,Genetics ,Gene Editing ,Nuclease ,biology ,RNA ,Reproducibility of Results ,Cell Biology ,Endonucleases ,Enzymes ,Kinetics ,030104 developmental biology ,chemistry ,biology.protein ,RNA Interference ,CRISPR-Cas Systems ,Genetic Engineering ,030217 neurology & neurosurgery ,DNA ,RNA, Guide, Kinetoplastida - Abstract
RNA-guided nucleases (RGNs) provide sequence-specific gene regulation through base-pairing interactions between a small RNA guide and target RNA or DNA. RGN systems, which include CRISPR-Cas9 and RNA interference (RNAi), hold tremendous promise as programmable tools for engineering and therapeutic purposes. However, pervasive targeting of sequences that closely resemble the intended target has remained a major challenge, limiting the reliability and interpretation of RGN activity and the range of possible applications. Efforts to reduce off-target activity and enhance RGN specificity have led to a collection of empirically derived rules, which often paradoxically include decreased binding affinity of the RNA-guided nuclease to its target. We consider the kinetics of these reactions and show that basic kinetic properties can explain the specificities observed in the literature and the changes in these specificities in engineered systems. The kinetic models described provide a foundation for understanding RGN targeting and a necessary conceptual framework for their rational engineering.
- Published
- 2017
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