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).     

A model of molecular interactions on short oligonucleotide microarrays

High-density short oligonucleotide microarrays have become a widely used tool for measuring gene expression on a large scale. However, details of the mechanism of binding on microarrays remain unclear. Short oligonucleotide probes currently synthesized on microarrays are often ineffective as a result of limited sequence specificity or low sensitivity. Here, we describe a model of binding interactions on microarrays that reveals how probe signals depend on probe sequences and why certain probes are ineffective. The model indicates that the amount of nonspecific binding can be estimated from a simple rule. Using this model, we have developed an improved measure of gene expression for use in data analysis.     

Empirical Evaluation of Oligonucleotide Probe Selection for DNA Microarrays

DNA-based microarrays are increasingly central to biomedical research. Selecting oligonucleotide sequences that will behave consistently across experiments is essential to the design, production and performance of DNA microarrays. Here our aim was to improve on probe design parameters by empirically and systematically evaluating probe performance in a multivariate context. We used experimental data from 19 array CGH hybridizations to assess the probe performance of 385,474 probes tiled in the Duchenne muscular dystrophy (DMD) region of the X chromosome. Our results demonstrate that probe melting temperature, single nucleotide polymorphisms (SNPs), and homocytosine motifs all have a strong effect on probe behavior. These findings, when incorporated into future microarray probe selection algorithms, may improve microarray performance for a wide variety of applications.     

ROSO: optimizing oligonucleotide probes for microarrays

ROSO is software to design optimal oligonucleotide probe sets for microarrays. Selected probes show no significant cross-hybridization, no stable secondary structures and their Tm are chosen to minimize the Tm variability of the probe set. AVAILABILITY: The program is available on the internet. Sources are freely available, for non-profit use, on request to the authors. Supplementary information: http://pbil.univ-lyon1.fr/roso     

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.     

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.     

Empirical Establishment of Oligonucleotide Probe Design Criteria

Criteria for the design of gene-specific and group-specific oligonucleotide probes were established experimentally via an oligonucleotide array that contained perfect match (PM) and mismatch probes (50-mers and 70-mers) based upon four genes. The effects of probe-target identity, continuous stretch, mismatch position, and hybridization free energy on specificity were tested. Little hybridization was observed at a probe-target identity of ≤85% for both 50-mer and 70-mer probes. PM signal intensities (33 to 48%) were detected at a probe-target identity of 94% for 50-mer oligonucleotides and 43 to 55% for 70-mer probes at a probe-target identity of 96%. When the effects of sequence identity and continuous stretch were considered independently, a stretch probe (>15 bases) contributed an additional 9% of the PM signal intensity compared to a nonstretch probe (≤15 bases) at the same identity level. Cross-hybridization increased as the length of continuous stretch increased. A 35-base stretch for 50-mer probes or a 50-base stretch for 70-mer probes had approximately 55% of the PM signal. Little cross-hybridization was observed for probes with a minimal binding free energy greater than −30 kcal/mol for 50-mer probes or −40 kcal/mol for 70-mer probes. Based on the experimental results, a set of criteria are suggested for the design of gene-specific and group-specific oligonucleotide probes, and the experimentally established criteria should provide valuable information for new software and algorithms for microarray-based studies.     

Empirical establishment of oligonucleotide probe design criteria

Criteria for the design of gene-specific and group-specific oligonucleotide probes were established experimentally via an oligonucleotide array that contained perfect match (PM) and mismatch probes (50-mers and 70-mers) based upon four genes. The effects of probe-target identity, continuous stretch, mismatch position, and hybridization free energy on specificity were tested. Little hybridization was observed at a probe-target identity of < or =85% for both 50-mer and 70-mer probes. PM signal intensities (33 to 48%) were detected at a probe-target identity of 94% for 50-mer oligonucleotides and 43 to 55% for 70-mer probes at a probe-target identity of 96%. When the effects of sequence identity and continuous stretch were considered independently, a stretch probe (>15 bases) contributed an additional 9% of the PM signal intensity compared to a nonstretch probe (< or =15 bases) at the same identity level. Cross-hybridization increased as the length of continuous stretch increased. A 35-base stretch for 50-mer probes or a 50-base stretch for 70-mer probes had approximately 55% of the PM signal. Little cross-hybridization was observed for probes with a minimal binding free energy greater than -30 kcal/mol for 50-mer probes or -40 kcal/mol for 70-mer probes. Based on the experimental results, a set of criteria are suggested for the design of gene-specific and group-specific oligonucleotide probes, and the experimentally established criteria should provide valuable information for new software and algorithms for microarray-based studies.     

Simulation and visualization of flow pattern in microarrays for liquid phase oligonucleotide and peptide synthesis

Microfluidic microarrays have been developed for economical and rapid parallel synthesis of oligonucleotide and peptide libraries. For a synthesis system to be reproducible and uniform, it is crucial to have a uniform reagent delivery throughout the system. Computational fluid dynamics (CFD) is used to model and simulate the microfluidic microarrays to study geometrical effects on flow patterns. By proper design geometry, flow uniformity could be obtained in every microreactor in the microarrays.     

Strategy for the design of custom cDNA microarrays

DNA microarrays are valuable but expensive tools for expression profiling of cells, tissues, and organs. The design of custom microarrays leads to cost reduction without necessarily compromising their biological value. Here we present a strategy for designing custom cDNA microarrays and constructed a microarray for mouse immunology research (ImmunoChip). The strategy used interrogates expressed sequence tag databases available in the public domain but overcomes many of the problems encountered. Immunologically relevant clusters were selected based on the expression of expressed sequence tags in relevant libraries. Selected clusters were organized in modules, and the best representative clones were identified. When tested, this microarray was found to have minimal clone identity errors or phage contamination and identified molecular signatures of lymphoid cell lines. Our proposed design of custom microarrays avoids probe redundancy, allows the organization of the chip to optimize chip production, and reduces microarray production costs. The strategy described is also useful for the design of oligonucleotide microarrays.     

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.     

Comprehensive viral oligonucleotide probe design using conserved protein regions

Oligonucleotide microarrays have been applied to microbial surveillance and discovery where highly multiplexed assays are required to address a wide range of genetic targets. Although printing density continues to increase, the design of comprehensive microbial probe sets remains a daunting challenge, particularly in virology where rapid sequence evolution and database expansion confound static solutions. Here, we present a strategy for probe design based on protein sequences that is responsive to the unique problems posed in virus detection and discovery. The method uses the Protein Families database (Pfam) and motif finding algorithms to identify oligonucleotide probes in conserved amino acid regions and untranslated sequences. In silico testing using an experimentally derived thermodynamic model indicated near complete coverage of the viral sequence database.     

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/     

Tests of rRNA hybridization to microarrays suggest that hybridization characteristics of oligonucleotide probes for species discrimination cannot be predicted

Hybridization of rRNAs to microarrays is a promising approach for prokaryotic and eukaryotic species identification. Typically, the amount of bound target is measured by fluorescent intensity and it is assumed that the signal intensity is directly related to the target concentration. Using thirteen different eukaryotic LSU rRNA target sequences and 7693 short perfect match oligonucleotide probes, we have assessed current approaches for predicting signal intensities by comparing Gibbs free energy (ΔG°) calculations to experimental results. Our evaluation revealed a poor statistical relationship between predicted and actual intensities. Although signal intensities for a given target varied up to 70-fold, none of the predictors were able to fully explain this variation. Also, no combination of different free energy terms, as assessed by principal component and neural network analyses, provided a reliable predictor of hybridization efficiency. We also examined the effects of single-base pair mismatch (MM) (all possible types and positions) on signal intensities of duplexes. We found that the MM effects differ from those that were predicted from solution-based hybridizations. These results recommend against the application of probe design software tools that use thermodynamic parameters to assess probe quality for species identification. Our results imply that the thermodynamic properties of oligonucleotide hybridization are by far not yet understood.     

Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (T_m), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal T_m interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request.     

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.