PRISE2: software for designing sequence-selective PCR primers and probes.

BACKGROUND: PRISE2 is a new software tool for designing sequence-selective PCR primers and probes. To achieve high level of selectivity, PRISE2 allows the user to specify a collection of target sequences that the primers are supposed to amplify, as well as non-target sequences that should not be amplified. The program emphasizes primer selectivity on the 3' end, which is crucial for selective amplification of conserved sequences such as rRNA genes. In PRISE2, users can specify desired properties of primers, including length, GC content, and others. They can interactively manipulate the list of candidate primers, to choose primer pairs that are best suited for their needs. A similar process is used to add probes to selected primer pairs. More advanced features include, for example, the capability to define a custom mismatch penalty function. PRISE2 is equipped with a graphical, user-friendly interface, and it runs on Windows, Macintosh or Linux machines. RESULTS: PRISE2 has been tested on two very similar strains of the fungus Dactylella oviparasitica, and it was able to create highly selective primers and probes for each of them, demonstrating the ability to create useful sequence-selective assays. CONCLUSIONS: PRISE2 is a user-friendly, interactive software package that can be used to design high-quality selective primers for PCR experiments. In addition to choosing primers, users have an option to add a probe to any selected primer pair, enabling design of Taqman and other primer-probe based assays. PRISE2 can also be used to design probes for FISH and other hybridization-based assays.

PRISE (PRImer SElector): software for designing sequence-selective PCR primers.

This report presents PRImer Selector (PRISE), a new software package that implements several features that improve and streamline the design of sequence-selective PCR primers. The PRISE design process involves two main steps. In the first step, target and non-target DNA sequences are identified. In the second step, primers are designed to amplify target (but not non-target) sequences. One important feature of PRISE is that it automates the task of placing primer-template mismatches at the 3' end of the primers - a property that is crucial for sequence selectivity. Once a list of candidate primers has been produced, sorting tools in PRISE speed up the selection process by allowing a user to sort the primers by properties such as amplicon length, GC content and sequence selectivity. PRISE can be used to design primers with a range of specificities, targeting individual sequences as well as diverse assemblages of genes. PRISE also allows user-defined primers to be analyzed, enabling their properties to be examined in relation to target and non-target sequences. The utility of PRISE was demonstrated by using it to design sequence-selective PCR primers for an rRNA gene from the fungus Pochonia chlamydosporia.

Algorithmic approaches to selecting control clones in DNA array hybridization experiments.

We study the problem of selecting control clones in DNA array hybridization experiments. The problem arises in the OFRG method for analyzing microbial communities. The OFRG method performs classification of rRNA gene clones using binary fingerprints created from a series of hybridization experiments, where each experiment consists of hybridizing a collection of arrayed clones with a single oligonucleotide probe. This experiment produces analog signals, one for each clone, which then need to be classified, that is, converted into binary values 1 and 0 that represent hybridization and non-hybridization events. In addition to the sample rRNA gene clones, the array contains a number of control clones needed to calibrate the classification procedure of the hybridization signals. These control clones must be selected with care to optimize the classification process. We formulate this as a combinatorial optimization problem called Balanced Covering. We prove that the problem is NP-hard, and we show some results on hardness of approximation. We propose approximation algorithms based on randomized rounding, and we show that, with high probability, our algorithms approximate well the optimum solution. The experimental results confirm that the algorithms find high quality control clones. The algorithms have been implemented and are publicly available as part of the software package called CloneTools.

Improved probe selection for DNA arrays using nonparametric kernel density estimation.

Oligonucleotide fingerprinting of rRNA genes (OFRG) is a method for identifying arrayed ribosomal RNA genes (rDNA) through a series of hybridization experiments with short oligonucleotide probes. Due to its low cost and high speed, it is an effective tool for analyzing microbial communities. OFRG relies on probe sets that can discriminate large collections of clones. Although the currently used probe design algorithm produces probe sets whose theoretical accuracy is close to optimum, those probes often do not hybridize in a consistent and predictable manner in actual biological experiments. We assume that these failures occur following an unknown probability distribution. In this paper, a nonparametric kernel density estimation method is proposed to estimate this distribution and to predict probe reliability. These predictions are used to reduce the number of unreliable probes chosen by the probe design algorithms. Our preliminary results show that the application of this method leads to a significant decrease in the number of unreliable probes.

Analysis of bacterial community composition by oligonucleotide fingerprinting of rRNA genes.

One of the first steps in characterizing an ecosystem is to describe the organisms inhabiting it. For microbial studies, experimental limitations have hindered the ability to depict diverse communities. Here we describe oligonucleotide fingerprinting of rRNA genes (OFRG), a method that permits identification of arrayed rRNA genes (rDNA) through a series of hybridization experiments using small DNA probes. To demonstrate this strategy, we examined the bacteria inhabiting two different soils. Analysis of 1,536 rDNA clones revealed 766 clusters grouped into five major taxa: Bacillus, Actinobacteria, Proteobacteria, and two undefined assemblages. Soil-specific taxa were identified and then independently confirmed through cluster-specific PCR of the original soil DNA. Near-species-level resolution was obtained by this analysis as clones with average sequence identities of 97% were grouped in the same cluster. A comparison of these OFRG results with the results obtained in a denaturing gradient gel electrophoresis analysis of the same two soils demonstrated the significance of this methodological advance. OFRG provides a cost-effective means to extensively analyze microbial communities and should have applications in medicine, biotechnology, and ecosystem studies.