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RISS 인기검색어
- 검색키워드
- 저자 : Kladwang, Wipapat (검색결과 3 건)
- 무료
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- 유료
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Standardization of RNA Chemical Mapping Experiments
Kladwang, Wipapat,Mann, Thomas H.,Becka, Alex,Tian, Siqi,Kim, Hanjoo,Yoon, Sungroh,Das, Rhiju American Chemical Society 2014 Biochemistry Vol.53 No.19
<P/><P>Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated <I>in vivo</I>) illustrates the utility and necessity of standardization for quantitative RNA mapping.</P>
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RNA design rules from a massive open laboratory
Lee, Jeehyung,Kladwang, Wipapat,Lee, Minjae,Cantu, Daniel,Azizyan, Martin,Kim, Hanjoo,Limpaecher, Alex,Yoon, Sungroh,Treuille, Adrien,Das, Rhiju National Academy of Sciences 2014 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.111 No.6
<P>Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models—even at the secondary structure level—hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies—including several previously unrecognized negative design rules—were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.</P>
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HiTRACE: high-throughput robust analysis for capillary electrophoresis.
Yoon, Sungroh,Kim, Jinkyu,Hum, Justine,Kim, Hanjoo,Park, Seunghyun,Kladwang, Wipapat,Das, Rhiju Oxford University Press 2011 Bioinformatics Vol.27 No.13
<P>Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment.</P>
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