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Our previous work applied neural network techniques to the problem of discriminating open reading frame (ORF) sequences taken from introns versus exons. The method counted the codon frequencies in an ORF of a specified length, and then used this codon frequency representation of DNA fragments to train a neural net (essentially a Perceptron with a sigmoidal, or "soft step function", output) to perform this discrimination. After training, the network was then applied to a disjoint "predict" set of data to assess accuracy. The resulting accuracy in our previous work was 98.4%, exceeding accuracies reported in the literature at that time for other algorithms. Here, we report even higher accuracies stemming from calculations of mutual information (a correlation measure) of spatially separated codons in exons, and in introns. Significant mutual information exists in exons, but not in introns, between adjacent codons. This suggests that dicodon frequencies of adjacent codons are important for intron/exon discrimination. We report that accuracies obtained using a neural net trained on the frequency of dicodons is significantly higher at smaller fragment lengths than even our original results using codon frequencies, which were already higher than simple statistical methods that also used codon frequencies. We also report accuracies obtained from including codon and dicodon statistics in all six reading frames, i.e. the three frames on the original and complement strand. Inclusion of six-frame statistics increases the accuracy still further. We also compare these neural net results to a Bayesian statistical prediction method that assumes independent codon frequencies in each position. The performance of the Bayesian scheme is poorer than any of the neural based schemes, however many methods reported in the literature either explicitly, or implicitly, use this method. Specifically, Bayesian prediction schemes based on codon frequencies achieve 90.9% accuracy on 90 codon ORFs, while our best neural net scheme reaches 99.4% accuracy on 60 codon ORFs. "Accuracy" is defined as the average of the exon and intron sensitivities. Achievement of sufficiently high accuracies on short fragment lengths can be useful in providing a computational means of finding coding regions in unannotated DNA sequences such as those arising from the mega-base sequencing efforts of the Human Genome Project. We caution that the high accuracies reported here do not represent a complete solution to the problem of identifying exons in "raw" base sequences. The accuracies are considerably lower from exons of small length, although still higher than accuracies reported in the literature for other methods. Short exon lengths are not uncommon.(ABSTRACT TRUNCATED AT 400 WORDS)

A technique to quantitate small volumes of dilute solutions of different-sized DNA fragments has been developed. The detection limit was 0.7 micrograms/ml and the technique could be used even in the presence of diffusable substances, including those such as sodium dodecyl sulfate which affect surface tension and also exhibit fluorescence when stained with ethidium bromide and excited by ultraviolet light. The DNA was mixed with low-melting-point agarose and pipetted into preformed wells in an agarose plate, where it solidified. After diffusion of small molecules, the amount of DNA was estimated by comparing ethidium bromide-mediated fluorescence of samples with that of standards.

The limitations of current mutagenesis techniques are analyzed in terms of the number and kinds of codon changes they make and in terms of the population size needed to produce all single or multiple amino acid variants. It is shown how a technique that can alter a single codon of a gene, producing all possible variant codons without affecting the rest of the gene, has certain advantages, if it can be used at each place in the gene in one experiment. Such a technique has advantages when the goals are to understand: (1) how specific structural alterations in a mutant protein cause it to function in a different but specific way, (2) how to predict which amino acids in a protein contact or interact with each other, and (3) why a protein is more or less sensitive to mutational disruption, depending upon the specific mutation. This is because it would generate the maximum number of (1) mutant proteins with different functions, (2) intracistronic suppressor for any starting mutation, and (3) random amino acid substitutions at random places. Furthermore, such a technique could produce useful variants more quickly and on a smaller scale than either evolution or current methods.