In fact, our HPknotter is not CPU intensive at all because based on our experiments, a great number of the hit sequences produced by RNAMotif were filtered out by the hit filter. Take the experiments with SARS-TW1-30 in Table 4.2 for an example. In the first phase, RNAMotif in total found 2,132 hits that conform to the descriptor of general class. If we directly apply PKNOTS to all of these unfiltered hits to check if they fold into a stable h-pseudoknot, then the program will require about 51 hours to finish the job. However, after running the hit filter, only 43 different hit sequences were remained, which then cost the following PKNOTS only about 5.2 minutes to determine if they are stable pseudoknots. As a result, the third phase of running pseudoknot prediction with PKNOTS left us with only 11 pseudoknot candidates that could fold into stable pseudoknots. Next, only 7 candidates were remained after running the h-pseudoknot filter in the fourth phase. In fact, some of these filtered h-pseudoknots may have an overlap among their ranges in the sequence, which suggests that they can not exist simultaneously in a stable pseudoknotted structure in SARS-TW1-30. Finally, only 2 h-pseudoknots with minimum free energy were selected in the phase of computing the maximum weight independent set. Table 4.4 lists the CPU usage time for PKNOTS, NUPACK, pknotsRG and our HPknotter, where all tests were run on IBM PC with 3.06 GHz processor and 2 GB RAM under Linux system.
Table 4.4: CPU usage time for PKNOTS, NUPACK, pknotsRG and HPknotter, where in our testing computer environment, PKNOTS and NUPACK cannot deal with the sequences of length greater than 220 bp and 180 bp, respectively, due to running out of the memory.
HPknotter (General Class) HPknotter (Specific Class) Length (bp) PKNOTS NUPACK pknotsRG PKNOTS-kernel NUPACK-kernel pknotsRG-kernel PKNOTS-kernel NUPACK-kernel pknotsRG-kernel
84 7.3 min 13.1 sec 0.05 sec 31 sec 27 sec 26 sec 9 sec 7 sec 6 sec 105 35 min 44.7 sec 0.1 sec 2.2 min 35 sec 29 sec 38 sec 10 sec 8 sec
200 72 hr – 0.8 sec 5.2 min 1.8 min 1.5 min 1.6 min 33 sec 30 sec
341 – – 7.4 sec 7.1 min 2.4 min 2.3 min 2.2 min 46 sec 45 sec
946 – – 10.1 min 13.8 min 7.5 min 6.9 min 4.1 min 2.2 min 2.1 min
1340 – – 43.5 min 35.3 min 11.6 min 10.9 min 11.6 min 3.1 min 2.5 min
26
Chapter 5
Conclusion and Future Works
In this thesis, we designed a heuristic approach for efficiently and accurately detecting RNA h-pseudoknots, the ubiquitous pseudoknots in the naturally occurring RNAs.
The currently existing thermodynamic-based programs, like PKNOTS, NUPACK and pknotsRG, are useful for finding stable h-pseudoknots. However, most of them are very time- and memory-consuming, which limits them to predict short sequences of a couple of hundred bases long. Another main weakness of these programs is that they may not be effective to detect the actually existing h-pseudoknots that are contained in a long RNA sequence, as evidenced by our experiments. Based on our heuristic approach mentioned in this thesis, we implemented a novel program, called HPknotter, capable of efficiently and accurately detecting the h-pseudoknots of a given RNA sequence by incorporating four existing programs RNAMotif, PKNOTS, NUPACK and pknotsRG.
In summary, we demonstrated the practicability and effectiveness of our developed HPknotter by testing it on several RNA sequences, most of which have been proven to contain the h-pseudoknotted structures. By several experiments, our HPknotter has shown to be practical for the detection of h-pseudoknots in RNA sequences because it is not computationally expensive and has much better sensitivity and specificity than PKNOTS, NUPACK and pknotsRG.
In the following, we describe a couple of interesting problems for future researches.
First, how to reduce the number of the sequence fragments hit by RNAMotif by con-sidering the GC ratio of the pseudoknot stems, the conserved sequence patterns in
the structural motifs of the h-pseudoknots, etc., or even by designing a new and more efficient algorithm for identifying the sequence fragments. Second, how to develop a more efficient program for detecting the h-pseudoknots of RNA sequences so that it can replace the kernel programs used by our HPknotter, such as PKNOTS, NUPACK and pknotsRG. Finally, how to extend our heuristic approach to detecting more general classes of pseudoknots for a given RNA sequence.
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