Mprobe 2.0: computer-aided probe design for oligonucleotide microarray

DNA chips have proven to be effective tools in detecting gene expression levels. Compared with DNA chips using complementary DNA as probes, oligonucleotide microarrays using oligonucleotides as probes have attracted great attention because of their well known advantages. The design of gene-specific probes for each target is essential to the development of oligonucleotide microarrays. We have previously reported the development of a probe design software termed Mprobe 1.0. Here, we present a new version of this software, termed Mprobe 2.0. Several new features are included in Mprobe 2.0. Firstly, a paradox-based sequence database management system has been developed and integrated into the software, which consequently allows interoperability with sequences in GenBank, EMBL, and FASTA formats. Secondly, in contrast to setting a fixed threshold for the secondary structure of probes in Mprobe 1.0 and other related software, Mprobe 2.0 employs a different method. After parameters such as GC type, probe melting temperature and GC contents have been evaluated, candidate probes are sorted by the free energy from high to low value, followed by specificity analysis. Thirdly, Mprobe 2.0 provides users with substantial parameter options in the visual mode. Mprobe 2.0 possesses an easier interface for users to manage sequences annotated in different formats and design the optimal probes for oligonucleotide microarrays and other applications. AVAILABILITY: The program is free for non-commercial users and can be downloaded from the web page http://www.biosun.org.cn/mprobe/ CONTACT: Wuju Li (wujuli@yahoo.com or liwj@nic.bmi.ac.cn).     

Position of the fluorescent label is a crucial factor determining signal intensity in microarray hybridizations

A key issue in applications of short oligonucleotide-based microarrays is how to design specific probes with high sensitivity. Some details of the factors affecting microarray hybridization remain unclear, hampering a reliable quantification of target nucleic acids. We have evaluated the effect of the position of the fluorescent label [position of label (POL)] relative to the probe-target duplex on the signal output of oligonucleotide microarrays. End-labelled single-stranded DNA targets of different lengths were used for hybridization with perfect-match oligonucleotide probe sets targeting different positions of the same molecule. Hybridization results illustrated that probes targeting the labelled terminus of the target showed significantly higher signals than probes targeting other regions. This effect was independent of the target gene, the fluorophore and the slide surface chemistry. Comparison of microarray signal patterns of fluorescently end-labelled, fluorescently internally random-labelled and radioactively end-labelled target-DNAs with the same set of oligonucleotide probes identified POL as a critical factor affecting signal intensity rather than binding efficiency. Our observations define a novel determinant for large differences of signal intensities. Application of the POL effect may contribute to better probe design and data interpretation in microarray applications.     

MProbe: computer aided probe design for oligonucleotide microarrays

The present work describes a complete probe design software system for oligonucleotide microarrays based on Kane's research on probe sensitivity and specificity (Kane's rule). Combining Kane's rule and traditional criteria for probe design we constructed MProbe, the software system for oligonucleotide microarrays using Java. The general criteria for probe design are: (1) probes may have different lengths that range from 20 to 100 bases; (2) they should have a similar melting temperature (Tm) or GC content; (3) they should not contain stable secondary structures; and (4) they abide by Kane's rule.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.     

Specific and nonspecific hybridization of oligonucleotide probes on microarrays

Gene expression analysis by means of microarrays is based on the sequence-specific binding of RNA to DNA oligonucleotide probes and its measurement using fluorescent labels. The binding of RNA fragments involving sequences other than the intended target is problematic because it adds a chemical background to the signal, which is not related to the expression degree of the target gene. The article presents a molecular signature of specific and nonspecific hybridization with potential consequences for gene expression analysis. We analyzed the signal intensities of perfect match (PM) and mismatch (MM) probes of GeneChip microarrays to specify the effect of specific and nonspecific hybridization. We found that these events give rise to different relations between the PM and MM intensities as function of the middle base of the PM, namely a triplet-like (C > G approximately T > A > 0) and a duplet-like (C approximately T > 0 > G approximately A) pattern of the PM-MM log-intensity difference upon binding of specific and nonspecific RNA fragments, respectively. The systematic behavior of the intensity difference can be rationalized on the level of basepairings of DNA/RNA oligonucleotide duplexes in the middle of the probe sequence. Nonspecific binding is characterized by the reversal of the central Watson-Crick (WC) pairing for each PM/MM probe pair, whereas specific binding refers to the combination of a WC and a self-complementary (SC) pairing in PM and MM probes, respectively. The Gibbs free energy contribution of WC pairs to duplex stability is asymmetric for purines and pyrimidines of the PM and decreases according to C > G approximately T > A. SC pairings on the average only weakly contribute to duplex stability. The intensity of complementary MM introduces a systematic source of variation which decreases the precision of expression measures based on the MM intensities.     

Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays

Oligonucleotide microarrays are based on the hybridization of labeled mRNA molecules to short length oligonucleotide probes on a glass surface. Two effects have been shown to affect the raw data: the sequence dependence of the probe hybridization properties and the chemical saturation resulting from surface adsorption processes. We address both issues simultaneously using a physically motivated hybridization model. Based on publicly available calibration data sets, we show that Langmuir adsorption accurately describes GeneChip hybridization, with model parameters that we predict from the sequence composition of the probes. Because these parameters have physical units, we are able to estimate absolute mRNA concentrations in picomolar. Additionally, by accounting for chemical saturation, we substantially reduce the compressive bias of differential expression estimates that normally occurs toward high concentrations.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression [J. Mol. Biol. 317 (2002) 225]. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.     

Improving signal intensities for genes with low-expression on oligonucleotide microarrays

Background  

DNA microarrays using long oligonucleotide probes are widely used to evaluate gene expression in biological samples. These oligonucleotides are pre-synthesized and sequence-optimized to represent specific genes with minimal cross-hybridization to homologous genes. Probe length and concentration are critical factors for signal sensitivity, particularly when genes with various expression levels are being tested. We evaluated the effects of oligonucleotide probe length and concentration on signal intensity measurements of the expression levels of genes in a target sample.     

On-chip hybridization kinetics for optimization of gene expression experiments

DNA microarray technology is a powerful tool for getting an overview of gene expression in biological samples. Although the successful use of microarray-based expression analysis was demonstrated in a number of applications, the main problem with this approach is the fact that expression levels deduced from hybridization experiments do not necessarily correlate with RNA concentrations. Moreover oligonucleotide probes corresponding to the same gene can give different hybridization signals. Apart from cross-hybridizations and differential splicing, this could be due to secondary structures of probes or targets. In addition, for low-copy genes, hybridization equilibrium may be reached after hybridization times much longer than the one commonly used (overnight, i.e., 15 h). Thus, hybridization signals could depend on kinetic properties of the probe, which may vary between different oligonucleotide probes immobilized on the same microarray. To validate this hypothesis, on-chip hybridization kinetics and duplex thermostability analysis were performed using oligonucleotide microarrays containing 50-mer probes corresponding to 10 mouse genes. We demonstrate that differences in hybridization kinetics between the probes exist and can influence the interpretation of expression data. In addition, we show that using on-chip hybridization kinetics, quantification of targets is feasible using calibration curves.     

Design of long oligonucleotide probes for functional gene detection in a microbial community

MOTIVATION: Analysis of the functions of microorganisms and their dynamics in the environment is essential for understanding microbial ecology. For analysis of highly similar sequences of a functional gene family using microarrays, the previous long oligonucleotide probe design strategies have not been useful in generating probes. RESULTS: We developed a Hierarchical Probe Design (HPD) program that designs both sequence-specific probes and hierarchical cluster-specific probes from sequences of a conserved functional gene based on the clustering tree of the genes, specifically for analyses of functional gene diversity in environmental samples. HPD was tested on datasets for the nirS and pmoA genes. Our results showed that HPD generated more sequence-specific probes than several popular oligonucleotide design programs. With a combination of sequence-specific and cluster-specific probes, HPD generated a probe set covering all the sequences of each test set. AVAILABILITY: http://brcapp.kribb.re.kr/HPD/     

Probe selection algorithms with applications in the analysis of microbial communities

We propose two efficient heuristics for minimizing the number of oligonucleotide probes needed for analyzing populations of ribosomal RNA gene (rDNA) clones by hybridization experiments on DNA microarrays. Such analyses have applications in the study of microbial communities. Unlike in the classical SBH (sequencing by hybridization) procedure, where multiple probes are on a DNA chip, in our applications we perform a series of experiments, each one consisting of applying a single probe to a DNA microarray containing a large sample of rDNA sequences from the studied population. The overall cost of the analysis is thus roughly proportional to the number of experiments, underscoring the need for minimizing the number of probes. Our algorithms are based on two well-known optimization techniques, i.e. simulated annealing and Lagrangian relaxation, and our preliminary tests demonstrate that both algorithms are able to find satisfactory probe sets for real rDNA data.     

Towards quality control for DNA microarrays.

We present a framework for detecting probes in oligonucleotide microarrays that may add significant error to measurements in hybridization experiments. Four types of so-called degenerate probe behavior are considered: secondary structure formation, self-dimerization, cross-hybridization, and dimerization. The framework uses a well-established model for computing the free energy of nucleic acid sequence hybridization and a novel method for the detection of patterns in hybridization experiment data. Our primary result is the identification of unique patterns in hybridization experiment data that are shown to correlate with each type of degenerate probe behavior. A support function for identifying degenerate probes from a large set of hybridization experiments is given and some preliminary experimental results are given for the Affymetrix HuGeneFL GeneChip. Finally, we show a strong relationship between the Affymetrix discrimination measure for a probe and the free-energy estimate from theoretical models of hybridization. In particular, probes on the HuGeneFL GeneChip with high free-energy estimates (weak hybridization) have almost always approximately zero discrimination. The framework can be applied to any Affymetrix oligonucleotide array, and the software is made freely available to the community.     

A method for cross-species gene expression analysis with high-density oligonucleotide arrays

DNA microarrays have been widely used in gene expression analysis of biological processes. Due to a lack of sequence information, the applications have been largely restricted to humans and a few model organisms. Presented within this study are results of the cross-species hybridization with Affymetrix human high-density oligonucleotide arrays or GeneChip® using distantly related mammalian species; cattle, pig and dog. Based on the unique feature of the Affymetrix GeneChip® where every gene is represented by multiple probes, we hypothesized that sequence conservation within mammals is high enough to generate sufficient signals from some of the probes for expression analysis. We demonstrated that while overall hybridization signals are low for cross-species hybridization, a few probes of most genes still generated signals equivalent to the same-species hybridization. By masking the poorly hybridized probes electronically, the remaining probes provided reliable data for gene expression analysis. We developed an algorithm to select the reliable probes for analysis utilizing the match/mismatch feature of GeneChip®. When comparing gene expression between two tissues using the selected probes, we found a linear correlation between the cross-species and same-species hybridization. In addition, we validated cross-species hybridization results by quantitative PCR using randomly selected genes. The method shown herein could be applied to both plant and animal research.