Name
Title | ||
|---|---|---|
Secondary structure predictions for long RNA sequences based on inversion excursions: preliminary results |
Abstract | ||
|---|---|---|
The tremendous demand on computer memory and computing time for prediction of complex secondary structures limits the applicability of most RNA secondary structure prediction programs available to short RNA sequences. We propose to approach this problem by segmenting a long RNA sequence into shorter non-overlapping chunks, predicting the secondary structures of each chunk individually, and then assembling the prediction results to give the structure of the original sequence. The selection of cutting points is a crucial component of the approach. Noting that stem-loops and pseudoknots always contain an inversion, we developed two cutting methods, the centered and optimized methods, for segmenting long RNA sequences based on inversion excursions. For the majority of the sequences in a dataset of 50 RNAs from the RFAM database, the prediction algorithm PKnotsRG used with these cutting methods produces more accurate secondary structures than those predicted for the whole sequence without segmentation. Both the centered and optimized cutting methods outperform the naïve regular segmentation. These results support our claim that cutting is a promising approach for the prediction of long RNA sequences, and choosing the cutting points intelligently by considering sequence features such as inversion excursions can further enhance prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2382936.2383016 | BCB |
Keywords | Field | DocType |
long rna sequence,rna secondary structure prediction,prediction accuracy,inversion excursion,preliminary result,prediction result,long rna,cutting method,short rna sequence,prediction algorithm,accurate secondary structure,inversion | RNA,Rfam,RNA Sequence,Inversion (meteorology),Segmentation,Computer science,Algorithm,Rna secondary structure prediction,Artificial intelligence,Protein secondary structure,Computer memory,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.37 | 3 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel T. Yehdego | 1 | 1 | 1.05 |
| boyu zhang | 2 | 71 | 17.54 |
| michela taufer | 3 | 352 | 53.04 |
| Vikram Kumar Reddy Kodimala | 4 | 1 | 0.37 |
| Rahulsimham Vegesna | 5 | 5 | 0.90 |
| Sameera Viswakula | 6 | 1 | 0.37 |
| Kyle L Johnson | 7 | 3 | 1.82 |
| Ming-Ying Leung | 8 | 111 | 17.78 |