Abstract
Despite great interest in solving RNA secondary structures due to their impact on function, it remains an open problem to determine structure from sequence. Among experimental approaches, a promising candidate is the "chemical modification strategy", which involves application of chemicals to RNA that are sensitive to structure and that result in modifications that can be assayed via sequencing technologies. One approach that can reveal paired nucleotides via chemical modification followed by sequencing is SHAPE, and it has been used in conjunction with capillary electrophoresis (SHAPE-CE) and high-throughput sequencing (SHAPE-Seq). The solution of mathematical inverse problems is needed to relate the sequence data to the modified sites, and a number of approaches have been previously suggested for SHAPE-CE, and separately for SHAPE-Seq analysis. Here we introduce a new model for inference of chemical modification experiments, whose formulation results in closed-form maximum likelihood estimates that can be easily applied to data. The model can be specialized to both SHAPE-CE and SHAPE-Seq, and therefore allows for a direct comparison of the two technologies. We then show that the extra information obtained with SHAPE-Seq but not with SHAPE-CE is valuable with respect to ML estimation.
| Original language | English (US) |
|---|---|
| Title of host publication | 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 |
| Pages | 1743-1750 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2011 |
| Event | 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 - Monticello, IL, United States Duration: Sep 28 2011 → Sep 30 2011 |
Other
| Other | 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 |
|---|---|
| Country | United States |
| City | Monticello, IL |
| Period | 9/28/11 → 9/30/11 |
Fingerprint
ASJC Scopus subject areas
- Computer Networks and Communications
- Control and Systems Engineering
Cite this
RNA structure characterization from chemical mapping experiments. / Aviran, Sharon; Lucks, Julius B.; Pachter, Lior.
2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011. 2011. p. 1743-1750 6120379.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - RNA structure characterization from chemical mapping experiments
AU - Aviran, Sharon
AU - Lucks, Julius B.
AU - Pachter, Lior
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Despite great interest in solving RNA secondary structures due to their impact on function, it remains an open problem to determine structure from sequence. Among experimental approaches, a promising candidate is the "chemical modification strategy", which involves application of chemicals to RNA that are sensitive to structure and that result in modifications that can be assayed via sequencing technologies. One approach that can reveal paired nucleotides via chemical modification followed by sequencing is SHAPE, and it has been used in conjunction with capillary electrophoresis (SHAPE-CE) and high-throughput sequencing (SHAPE-Seq). The solution of mathematical inverse problems is needed to relate the sequence data to the modified sites, and a number of approaches have been previously suggested for SHAPE-CE, and separately for SHAPE-Seq analysis. Here we introduce a new model for inference of chemical modification experiments, whose formulation results in closed-form maximum likelihood estimates that can be easily applied to data. The model can be specialized to both SHAPE-CE and SHAPE-Seq, and therefore allows for a direct comparison of the two technologies. We then show that the extra information obtained with SHAPE-Seq but not with SHAPE-CE is valuable with respect to ML estimation.
AB - Despite great interest in solving RNA secondary structures due to their impact on function, it remains an open problem to determine structure from sequence. Among experimental approaches, a promising candidate is the "chemical modification strategy", which involves application of chemicals to RNA that are sensitive to structure and that result in modifications that can be assayed via sequencing technologies. One approach that can reveal paired nucleotides via chemical modification followed by sequencing is SHAPE, and it has been used in conjunction with capillary electrophoresis (SHAPE-CE) and high-throughput sequencing (SHAPE-Seq). The solution of mathematical inverse problems is needed to relate the sequence data to the modified sites, and a number of approaches have been previously suggested for SHAPE-CE, and separately for SHAPE-Seq analysis. Here we introduce a new model for inference of chemical modification experiments, whose formulation results in closed-form maximum likelihood estimates that can be easily applied to data. The model can be specialized to both SHAPE-CE and SHAPE-Seq, and therefore allows for a direct comparison of the two technologies. We then show that the extra information obtained with SHAPE-Seq but not with SHAPE-CE is valuable with respect to ML estimation.
UR - http://www.scopus.com/inward/record.url?scp=84856092623&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856092623&partnerID=8YFLogxK
U2 - 10.1109/Allerton.2011.6120379
DO - 10.1109/Allerton.2011.6120379
M3 - Conference contribution
AN - SCOPUS:84856092623
SN - 9781457718168
SP - 1743
EP - 1750
BT - 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
ER -