Abstract

This study examines mRNAs of less than 5000 base pairs in size, to determine the effects of base composition on folding free energy. Statistical analysis between the native mRNA and its randomized sequences was conducted, and when comparing mRNAs in human, chimp, chicken, mouse, and several other transcriptomes, we found that the native mRNAs were more stable . It has been found that when length and base composition are conserved, native mRNA sequences are more stable than random mRNA sequences. More stable folding conformations have greater negative free energy values. This negative bias in free energies can be statistically measured as a Z-score which normalizes for sequence length. In an effort to determine if sequence patterns correlate with secondary structure, a neural network was trained using three training sets separately to compare the effect of neural network learning from the folding characteristics of the gene sequences. The training sets were typically allowed to run for up to 100,000 generations, and the resulting sum square errors were periodically saved. We found that the negative Z-score training set gives lower neural network sum square errors than the positive Z-score training set, and the Z-scores near zero have the highest training error. This indicates that there are more detectable sequence patterns in genes with more secondary structure than in genes exhibiting more positive Z-scores.