Parallel overlap assembly for the construction of computational DNA libraries

Hybridization-Ligation Versus Parallel Overlap Assembly: An Experimental Comparison of Initial Pool Generation for Direct-Proportional Length-Based DNA Computing

Previously, direct-proportional length-based DNA computing (DPLB-DNAC) for solving weighted graph problems has been reported. The proposed DPLB-DNAC has been successfully applied to solve the shortest path problem, which is an instance of weighted graph problems. The design and development of DPLB-DNAC is important in order to extend the capability of DNA computing for solving numerical optimization problem. According to DPLB-DNAC, after the initial pool generation, the initial solution is subjected to amplification by polymerase chain reaction and, finally, the output of the computation is visualized by gel electrophoresis. In this paper, however, we give more attention to the initial pool generation of DPLB-DNAC. For this purpose, two kinds of initial pool generation methods, which are generally used for solving weighted graph problems, are evaluated. Those methods are hybridization-ligation and parallel overlap assembly (POA). It is found that for DPLB-DNAC, POA is better than that of the hybridization-ligation method, in terms of population size, generation time, material usage, and efficiency, as supported by the results of actual experiments.

Signal and noise in bridging PCR

BACKGROUND

In a variant of the standard PCR reaction termed bridging, or jumping, PCR the primer-bound sequences are originally on separate template molecules. Bridging can occur if, and only if, the templates contain a region of sequence similarity. A 3' end of synthesis in one round of synthesis that terminates in this region of similarity can prime on the other. In principle, Bridging PCR (BPCR) can detect a subpopulation of one template that terminates synthesis in the region of sequence shared by the other template. This study considers the sensitivity and noise of BPCR as a quantitative assay for backbone interruptions. Bridging synthesis is also important to some methods for computing with DNA.

RESULTS

In this study, BPCR was tested over a 328 base pair segment of the E. coli lac operon and a signal to noise ratio (S/N) of approximately 10 was obtained under normal PCR conditions with Taq polymerase. With special precautions in the case of Taq or by using the Stoffel fragment the S/N was improved to 100, i.e. 1 part of cut input DNA yielded the same output as 100 parts of intact input DNA.

CONCLUSIONS

In the E. coli lac operator region studied here, depending on details of protocol, between 3 and 30% per kilobase of final PCR product resulted from bridging. Other systems are expected to differ in the proportion of product that is bridged consequent to PCR protocol and the sequence analyzed. In many cases physical bridging during PCR will have no informational consequence because the bridged templates are of identical sequence, but in a number of special cases bridging creates, or, destroys, information.

Ligation errors in DNA computing

DNA computing is a novel method of computing proposed by Adleman (1994), in which the data is encoded in the sequences of oligonucleotides. Massively parallel reactions between oligonucleotides are expected to make it possible to solve huge problems. In this study, reliability of the ligation process employed in the DNA computing is tested by estimating the error rate at which wrong oligonucleotides are ligated. Ligation of wrong oligonucleotides would result in a wrong answer in the DNA computing. The dependence of the error rate on the number of mismatches between oligonucleotides and on the combination of bases is investigated.

DNA solution of the maximal clique problem

The maximal clique problem has been solved by means of molecular biology techniques. A pool of DNA molecules corresponding to the total ensemble of six-vertex cliques was built, followed by a series of selection processes. The algorithm is highly parallel and has satisfactory fidelity. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.