Perfect Match Probes Research Articles

Genetic analysis of Ximenia americana based on the cross-species DNA-DNA hybridization technique

Abstract. Ekandjo A, Naomand E, Kahaka G. 2018. Genetic analysis of Ximenia americana based on DNA-DNA based on the cross-species hybridization technique. Cell Biol Dev 2: 1-7. Genetic analysis of species without the availability of complete genome arrays can be studied using the cross-species hybridization technique. The preliminary microarray studies have created DNA-DNA hybridization of X. americana to the GeneChip of Arabidopsis thaliana (ATH1). Based on the perfect-match (PM) probe signal, the probe pairs of A. thaliana that hybridized with the genomic DNA of X. americana were selected and analyzed using a cel file parser script. This selection was to generate new high-density probe mask files. The X. americana DNA-DNA hybridization data were effectively represented by this file. In addition, this study carried out gene ontology analysis of hybridization data of X. americana/A. thaliana. This gene ontology analysis showed that genes for abiotic stress response were over-expressed in respective comparison to model species of A. thaliana under natural conditions. This analysis was confirmed independently with PCR amplification of the orthologous genes using genomic DNA of X. americana, such as AT4G15910.1, SAD2, HXK1, ACC, and ERF/AP2. Due to the lack of genomic sequence data in X. americana, the primers for genomic amplification were designed using a genomic sequence of A. thaliana. The primers were designed to produce a genomic PCR product of 100 bp. Each selected gene was successfully amplified. Therefore, it gave evidence of homology within primer binding sites. However, the genomic amplification of these crucial abiotic factors in X. americana confirms the response type, which supports the adaptation of X. americana under natural conditions with stress associated (with heat and drought stress). Although transcript levels of these unique abiotic response factors could not be observed in absolute or relative terms, the study showed the inherent existence of such genes at the genomic level of X. americana. Further studies are needed to confirm that some modification of the factors of these abiotic responses or other abiotic responses within the genome of X. americana gives the key basis for its adaptation to the comparatively dry and hot climatic conditions.

Gene expression profiling with principal component analysis depicts the biological continuum from essential thrombocythemia over polycythemia vera to myelofibrosis

The recent discovery of the Janus activating kinase 2 V617F mutation in most patients with polycythemia vera (PV) and half of those with essential thrombocythemia (ET) and primary myelofibrosis (PMF) has favored the hypothesis of a biological continuum from ET over PV to PMF. We performed gene expression profiling of whole blood from control subjects (n = 21) and patients with ET (n = 19), PV (n = 41), and PMF (n = 9) using DNA microarrays. Applying an unsupervised method, principal component analysis, to search for patterns in the data, we demonstrated a separation of the four groups with biological relevant overlaps between the different entities. Moreover, the analysis separates Janus activating kinase 2-negative ET patients from Janus activating kinase 2-positive ET patients. Functional annotation analysis demonstrates that clusters of gene ontology terms related to inflammation, immune system, apoptosis, RNA metabolism, and secretory system were the most significantly deregulated terms in the three different disease groups. Our results yield further support for the hypothesis of a biological continuum originating from ET over PV to PMF. Functional analysis suggests an important implication of these gene ontology clusters in the pathogenesis of these neoplasms and in disease evolution from ET over PV to PMF.

Enzymatic on-Chip Enhancement for High Resolution Genotyping DNA Microarrays

Antibiotic resistance among pathogenic microorganisms is emerging as a major human healthcare concern. While there are a variety of resistance mechanisms, many can be related to single nucleotide polymorphisms and for which DNA microarrays have been widely deployed in bacterial genotyping. However, genotyping by means of allele-specific hybridization can suffer from the drawback that oligonucleotide probes with different nucleotide composition have varying thermodynamic parameters. This results in unpredictable hybridization behavior of mismatch probes. Consequently, the degree of discrimination between perfect match and mismatch probes is insufficient in some cases. We report here an on-chip enzymatic procedure to improve this discrimination in which false-positive hybrids are selectively digested. We find that the application of CEL1 Surveyor nuclease, a mismatch-specific endonuclease, significantly enhances the discrimination fidelity, as demonstrated here on a microarray for the identification of variants of carbapenem resistant Klebsiella pneumoniae carbapenemases and monitored by end point detection of fluorescence intensity. Further fundamental investigations applying total internal reflection fluorescence detection for kinetic real-time measurements confirmed the enzymatic enhancement for SNP discrimination.

Target concentration dependence of DNA melting temperature on oligonucleotide microarrays

The design of microarrays is currently based on studies focusing on DNA hybridization reaction in bulk solution. However, the presence of a surface to which the probe strand is attached can make the solution-based approximations invalid, resulting in sub-optimum hybridization conditions. To determine the effect of surfaces on DNA duplex formation, the authors studied the dependence of DNA melting temperature (T(m)) on target concentration. An automated system was developed to capture the melting profiles of a 25-mer perfect-match probe-target pair initially hybridized at 23°C. Target concentrations ranged from 0.0165 to 15 nM with different probe amounts (0.03-0.82 pmol on a surface area of 10(18) Å(2)), a constant probe density (5 × 10(12) molecules/cm(2)) and spacer length (15 dT). The authors found that T(m) for duplexes anchored to a surface is lower than in-solution, and this difference increases with increasing target concentration. In a representative set, a target concentration increase from 0.5 to 15 nM with 0.82 pmol of probe on the surface resulted in a T(m) decrease of 6°C when compared with a 4°C increase in solution. At very low target concentrations, a multi-melting process was observed in low temperature domains of the curves. This was attributed to the presence of truncated or mismatch probes.

Probe Design for DNA Chips

DNA chips are a promising fundamental technology for the post-genome research [1]. A large number of DNA oligonucleotide probes integrated in a small area of the chip surface facilitate speedy simultaneous detection of many target sequences. So, expression profile analysis of thousands of genes can be finished in a few days by using DNA chip technology although it takes years to finish the analysis by using conventional methods such as northern hybridization and SAGE. High-density DNA chips are made by light-directed combinatorial oligonucleotide synthesis technology, which is based on photolithography technology for semiconductor micro-fabrication [4, 5]. Recently, high-density DNA oligonucleotide chips have been commercially available [2]. Those chips can detect thousands of mRNA transcripts simultaneously. However, as many as 20 pairs of perfect match and center-mismatch oligonucleotide probes are needed to identify each transcript. This is due to low specificity of probe sequences designed by unsatisfactory method [3]. The necessity of many probes not only decreases the detection capacity of DNA chips but also makes DNA chip development difficult and expensive. Here, we describe a more rational method for designing oligonucleotide probe sequences of DNA chips for gene expression profile analysis, which can reduce the number of DNA probes needed for identification of target sequences. Each transcript is identified with a pair of two oligonucleotide probes, which can hybridize to target transcript specifically under the same hybridization condition. In addition, they can be used as a PCR primer pair, which helps experiments to examine the specificity of designed probe sequences.

Extensive increase of microarray signals in cancers calls for novel normalization assumptions

When using microarray data for studying a complex disease such as cancer, it is a common practice to normalize data to force all arrays to have the same distribution of probe intensities regardless of the biological groups of samples. The assumption underlying such normalization is that in a disease the majority of genes are not differentially expressed genes (DE genes) and the numbers of up- and down-regulated genes are roughly equal. However, accumulated evidences suggest gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalization assumption. Here, we analyzed 7 large Affymetrix datasets of pair-matched normal and cancer samples for cancers collected in the NCBI GEO database. We showed that in 6 of these 7 datasets, the medians of perfect match (PM) probe intensities increased in cancer state and the increases were significant in three datasets, suggesting the assumption that all arrays have the same median probe intensities regardless of the biological groups of samples might be misleading. Then, we evaluated the effects of three currently most widely used normalization algorithms (RMA, MAS5.0 and dChip) on the selection of DE genes by comparing them with LVS which relies less on the above-mentioned assumption. The results showed using RMA, MAS5.0 and dChip may produce lots of false results of down-regulated DE genes while missing many up-regulated DE genes. At least for cancer study, normalizing all arrays to have the same distribution of probe intensities regardless of the biological groups of samples might be misleading. Thus, most current normalizations based on unreliable assumptions may distort biological differences between normal and cancer samples. The LVS algorithm might perform relatively well due to that it relies less on the above-mentioned assumption. Also, our results indicate that genes may be widely up-regulated in most human cancer.

Preprocessing of gene expression data by optimally robust estimators

BackgroundThe preprocessing of gene expression data obtained from several platforms routinely includes the aggregation of multiple raw signal intensities to one expression value. Examples are the computation of a single expression measure based on the perfect match (PM) and mismatch (MM) probes for the Affymetrix technology, the summarization of bead level values to bead summary values for the Illumina technology or the aggregation of replicated measurements in the case of other technologies including real-time quantitative polymerase chain reaction (RT-qPCR) platforms. The summarization of technical replicates is also performed in other "-omics" disciplines like proteomics or metabolomics.Preprocessing methods like MAS 5.0, Illumina's default summarization method, RMA, or VSN show that the use of robust estimators is widely accepted in gene expression analysis. However, the selection of robust methods seems to be mainly driven by their high breakdown point and not by efficiency.ResultsWe describe how optimally robust radius-minimax (rmx) estimators, i.e. estimators that minimize an asymptotic maximum risk on shrinking neighborhoods about an ideal model, can be used for the aggregation of multiple raw signal intensities to one expression value for Affymetrix and Illumina data. With regard to the Affymetrix data, we have implemented an algorithm which is a variant of MAS 5.0.Using datasets from the literature and Monte-Carlo simulations we provide some reasoning for assuming approximate log-normal distributions of the raw signal intensities by means of the Kolmogorov distance, at least for the discussed datasets, and compare the results of our preprocessing algorithms with the results of Affymetrix's MAS 5.0 and Illumina's default method.The numerical results indicate that when using rmx estimators an accuracy improvement of about 10-20% is obtained compared to Affymetrix's MAS 5.0 and about 1-5% compared to Illumina's default method. The improvement is also visible in the analysis of technical replicates where the reproducibility of the values (in terms of Pearson and Spearman correlation) is increased for all Affymetrix and almost all Illumina examples considered. Our algorithms are implemented in the R package named RobLoxBioC which is publicly available via CRAN, The Comprehensive R Archive Network (http://cran.r-project.org/web/packages/RobLoxBioC/).ConclusionsOptimally robust rmx estimators have a high breakdown point and are computationally feasible. They can lead to a considerable gain in efficiency for well-established bioinformatics procedures and thus, can increase the reproducibility and power of subsequent statistical analysis.

Washing scaling of GeneChip microarray expression.

BackgroundPost-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.ResultsWe conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.ConclusionsWashing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.

A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays

BackgroundHigh-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < ~300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (~400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool.ResultsWe compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip® Rice Genome Array, using the rice cultivars with full genome sequence, japonica cultivar Nipponbare and indica cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated.ConclusionsThe optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of Oryza sativa) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.

G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration

BackgroundThe brightness of the probe spots on expression microarrays intends to measure the abundance of specific mRNA targets. Probes with runs of at least three guanines (G) in their sequence show abnormal high intensities which reflect rather probe effects than target concentrations. This G-bias requires correction prior to downstream expression analysis.ResultsLonger runs of three or more consecutive G along the probe sequence and in particular triple degenerated G at its solution end ((GGG)1-effect) are associated with exceptionally large probe intensities on GeneChip expression arrays. This intensity bias is related to non-specific hybridization and affects both perfect match and mismatch probes. The (GGG)1-effect tends to increase gradually for microarrays of later GeneChip generations. It was found for DNA/RNA as well as for DNA/DNA probe/target-hybridization chemistries. Amplification of sample RNA using T7-primers is associated with strong positive amplitudes of the G-bias whereas alternative amplification protocols using random primers give rise to much smaller and partly even negative amplitudes.We applied positional dependent sensitivity models to analyze the specifics of probe intensities in the context of all possible short sequence motifs of one to four adjacent nucleotides along the 25meric probe sequence. Most of the longer motifs are adequately described using a nearest-neighbor (NN) model. In contrast, runs of degenerated guanines require explicit consideration of next nearest neighbors (GGG terms). Preprocessing methods such as vsn, RMA, dChip, MAS5 and gcRMA only insufficiently remove the G-bias from data.ConclusionsPositional and motif dependent sensitivity models accounts for sequence effects of oligonucleotide probe intensities. We propose a positional dependent NN+GGG hybrid model to correct the intensity bias associated with probes containing poly-G motifs. It is implemented as a single-chip based calibration algorithm for GeneChips which can be applied in a pre-correction step prior to standard preprocessing.

Mapping Affymetrix Microarray Probes to the Rat Genome via a Persistent Index

A probe mapping technique using a novel implementation of a persistent q-gram index was developed. It guarantees to find all matches that meet certain definitions. These include exact matching of the central 19 bases of 25 base probes, matching the central 19 bases with at most one or three mismatches and exact matching of any 16 bases. In comparison with BLAST and BLAT, the new methods were either significantly faster or identified matches missed by the heuristics. The 16 bp method was used to map the 342,410 perfect match probes from the Affymetrix GeneChip Rat Genome 230 2.0 Array to the genome. When compared with the mapping from Ensembl, the new mapping included over seven million novel matches, providing additional evidence for researchers wishing to further investigate the sources of signals measured in microarray experiments. The results demonstrate the practicality of the index, which could support other q-gram based algorithms.

AGRONOMICS1: A New Resource for Arabidopsis Transcriptome Profiling

Transcriptome profiling has become a routine tool in biology. For Arabidopsis (Arabidopsis thaliana), the Affymetrix ATH1 expression array is most commonly used, but it lacks about one-third of all annotated genes present in the reference strain. An alternative are tiling arrays, but previous designs have not allowed the simultaneous analysis of both strands on a single array. We introduce AGRONOMICS1, a new Affymetrix Arabidopsis microarray that contains the complete paths of both genome strands, with on average one 25mer probe per 35-bp genome sequence window. In addition, the new AGRONOMICS1 array contains all perfect match probes from the original ATH1 array, allowing for seamless integration of the very large existing ATH1 knowledge base. The AGRONOMICS1 array can be used for diverse functional genomics applications such as reliable expression profiling of more than 30,000 genes, detection of alternative splicing, and chromatin immunoprecipitation coupled to microarrays (ChIP-chip). Here, we describe the design of the array and compare its performance with that of the ATH1 array. We find results from both microarrays to be of similar quality, but AGRONOMICS1 arrays yield robust expression information for many more genes, as expected. Analysis of the ATH1 probes on AGRONOMICS1 arrays produces results that closely mirror those of ATH1 arrays. Finally, the AGRONOMICS1 array is shown to be useful for ChIP-chip experiments. We show that heterochromatic H3K9me2 is strongly confined to the gene body of target genes in euchromatic chromosome regions, suggesting that spreading of heterochromatin is limited outside of pericentromeric regions.

Physico-chemical modelling of target depletion during hybridization on oligonulceotide microarrays

The effect of target molecule depletion from the supernatant solution is incorporated into a physico-chemical model of hybridization on oligonucleotide microarrays. Two possible regimes are identified: local depletion, in which depletion by a given probe feature only affects that particular probe, and global depletion, in which all features responding to a given target species are affected. Examples are given of two existing spike-in data sets experiencing measurable effects of target depletion. The first of these, from an experiment by Suzuki et al using custom built arrays with a broad range of probe lengths and mismatch positions, is verified to exhibit local and not global depletion. The second data set, the well-known Affymetrix HGU133a latin square experiment, is shown to be very well explained by a global depletion model. It is shown that microarray calibrations relying on Langmuir isotherm models which ignore depletion effects will significantly underestimate specific target concentrations. It is also shown that a combined analysis of perfect match and mismatch probe signals in terms of a simple graphical summary, namely the hook curve method, can discriminate between cases of local and global depletion.

Rapid discrimination of single‐nucleotide mismatches based on reciprocating flow on a compact disc microfluidic device

In this study, a compact disc (CD) microfluidic device based on reciprocating flow is developed for the rapid discrimination of single-nucleotide mismatch (SNM). This device consists of a PDMS CD slab containing eight SNM assay units and a glass substrate immobilized with DNA probe array. The rapid SNM assay is realized by two-step controllable reciprocating flow in the microchannel, resulting from centrifugal force and capillary action as well. A perfect-match probe and three mismatched probes containing a SNM at various positions were designed to investigate the ability of this device to discriminate SNM. Based on the difference between the dissociation kinetics of perfect-match and mismatched duplexes, this CD device was able to discriminate SNM within 400 s. Moreover, only sub-microliter volume DNA samples (350 nL) and washing buffers (350 nL) were required per assay unit. The results indicate that this device has the potential to be used for fast, accurate gene diagnostics, especially for the detection of SNPs.

Assessment of the swine protein‐annotated oligonucleotide microarray

The specificity and utility of the swine protein-annotated oligonucleotide microarray, or Pigoligoarray (http://www.pigoligoarray.org), has been evaluated by profiling the expression of transcripts from four porcine tissues. Tools for comparative analyses of expression on the Pigoligoarray were developed including HGNC identities and comparative mapping alignments with human orthologs. Hybridization results based on the Pigoligoarray's sets of control, perfect match (PM) and deliberate mismatch (MM) probes provide an important means of assessing non-specific hybridization. Simple descriptive diagnostic analyses of PM/MM probe sets are introduced in this paper as useful tools for detecting non-specific hybridization. Samples of RNA from liver, brain stem, longissimus dorsi muscle and uterine endothelium from four pigs were prepared and hybridized to the arrays. Of the total 20,400 oligonucleotides on the Pigoligoarray, 12,429 transcripts were putatively differentially expressed (DE). Analyses for tissue-specific expression [over-expressed in one tissue with respect to all the remaining three tissues (q < 0.01)] identified 958 DE transcripts in liver, 726 in muscle, 286 in uterine endothelium and 1027 in brain stem. These hybridization results were confirmed by quantitative PCR (QPCR) expression patterns for a subset of genes after affirming that cDNA and amplified antisense RNA (aRNA) exhibited similar QPCR results. Comparison to human ortholog expression confirmed the value of this array for experiments of both agricultural importance and for tests using pigs as a biomedical model for human disease.

High-throughput Biology in the Postgenomic Era

High-throughput Biology in the Postgenomic Era

Using high-density exon arrays to profile gene expression in closely related species

Global comparisons of gene expression profiles between species provide significant insight into gene regulation, evolutionary processes and disease mechanisms. In this work, we describe a flexible and intuitive approach for global expression profiling of closely related species, using high-density exon arrays designed for a single reference genome. The high-density probe coverage of exon arrays allows us to select identical sets of perfect-match probes to measure expression levels of orthologous genes. This eliminates a serious confounding factor in probe affinity effects of species-specific microarray probes, and enables direct comparisons of estimated expression indexes across species. Using a newly designed Affymetrix exon array, with eight probes per exon for approximately 315 000 exons in the human genome, we conducted expression profiling in corresponding tissues from humans, chimpanzees and rhesus macaques. Quantitative real-time PCR analysis of differentially expressed candidate genes is highly concordant with microarray data, yielding a validation rate of 21/22 for human versus chimpanzee differences, and 11/11 for human versus rhesus differences. This method has the potential to greatly facilitate biomedical and evolutionary studies of gene expression in nonhuman primates and can be easily extended to expression array design and comparative analysis of other animals and plants.

Transcript-Specific Expression Profiles Derived from Sequence-Based Analysis of Standard Microarrays

BackgroundAlternative mRNA processing mechanisms lead to multiple transcripts (i.e. splice isoforms) of a given gene which may have distinct biological functions. Microarrays like Affymetrix GeneChips measure mRNA expression of genes using sets of nucleotide probes. Until recently probe sets were not designed for transcript specificity. Nevertheless, the re-analysis of established microarray data using newly defined transcript-specific probe sets may provide information about expression levels of specific transcripts.Methodology/Principal FindingsIn the present study alignment of probe sequences of the Affymetrix microarray HG-U133A with Ensembl transcript sequences was performed to define transcript-specific probe sets. Out of a total of 247,965 perfect match probes, 95,008 were designated “transcript-specific”, i.e. showing complete sequence alignment, no cross-hybridization, and transcript-, not only gene-specificity. These probes were grouped into 7,941 transcript-specific probe sets and 15,619 gene-specific probe sets, respectively. The former were used to differentiate 445 alternative transcripts of 215 genes. For selected transcripts, predicted by this analysis to be differentially expressed in the human kidney, confirmatory real-time RT-PCR experiments were performed. First, the expression of two specific transcripts of the genes PPM1A (PP2CA_HUMAN and P35813) and PLG (PLMN_HUMAN and Q5TEH5) in human kidneys was determined by the transcript-specific array analysis and confirmed by real-time RT-PCR. Secondly, disease-specific differential expression of single transcripts of PLG and ABCA1 (ABCA1_HUMAN and Q5VYS0_HUMAN) was computed from the available array data sets and confirmed by transcript-specific real-time RT-PCR.ConclusionsTranscript-specific analysis of microarray experiments can be employed to study gene-regulation on the transcript level using conventional microarray data. In this study, predictions based on sufficient probe set size and fold-change are confirmed by independent means.

Thermodynamic scaling behavior in genechips

BackgroundAffymetrix Genechips are characterized by probe pairs, a perfect match (PM) and a mismatch (MM) probe differing by a single nucleotide. Most of the data preprocessing algorithms neglect MM signals, as it was shown that MMs cannot be used as estimators of the non-specific hybridization as originally proposed by Affymetrix. The aim of this paper is to study in detail on a large number of experiments the behavior of the average PM/MM ratio. This is taken as an indicator of the quality of the hybridization and, when compared between different chip series, of the quality of the chip design.ResultsAbout 250 different GeneChip hybridizations performed at the VIB Microarray Facility for Homo sapiens, Drosophila melanogaster, and Arabidopsis thaliana were analyzed. The investigation of such a large set of data from the same source minimizes systematic experimental variations that may arise from differences in protocols or from different laboratories. The PM/MM ratios are derived theoretically from thermodynamic laws and a link is made with the sequence of PM and MM probe, more specifically with their central nucleotide triplets.ConclusionThe PM/MM ratios subdivided according to the different central nucleotides triplets follow qualitatively those deduced from the hybridization free energies in solution. It is shown also that the PM and MM histograms are related by a simple scale transformation, in agreement with what is to be expected from hybridization thermodynamics. Different quantitative behavior is observed on the different chip organisms analyzed, suggesting that some organism chips have superior probe design compared to others.

Application Forum: Mutant Allele Amplification Bias Using Rapid Cycle-Real Time PCR and Hi-Res Melting ® with LunaProbes™ on the LightScanner ® 32

BioTechniquesVol. 45, No. 5 Application Forum - Sponsored PaperOpen AccessMutant Allele Amplification Bias Using Rapid Cycle-Real Time PCR and Hi-Res Melting® with LunaProbes™ on the LightScanner® 32Idaho Technology Inc.Idaho Technology Inc.Idaho Technology Inc., 390 Wakara Way, Salt Lake City, UT, USASearch for more papers by this authorPublished Online:16 May 2018https://doi.org/10.2144/000113021AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail IntroductionHi-Res Melting was introduced as a homogeneous method of scanning PCR amplicons for heterogzygous sequence variants. Based on the use of dsDNA saturating dyes, Hi-Res Melting is capable of detecting SNP's and In/Del's in amplicons up to 400 bp at a sensitivity >99%. Since its introduction in 2003, additional applications for Hi-Res Melting have been developed, including genotyping for known sequence variants using small amplicons or unlabeled probes (LunaProbes). LunaProbe's are blocked on the 3′ end to prevent extension during PCR and use the dsDNA saturation dye LCGreen Plus to discriminate the genotype of the allele based on probe melting temperature (Tm). The probe sequence can be designed to match either allele and is based on maximizing the ΔTm between the perfect match and mismatched probe.MethodsWe investigated the use of LunaProbes to discriminate the mutant allele at ≤5% in a background of the wild-type allele. Because LunaProbes are inherently large (25–30 bp), in order to generate sufficient fluorescent signal, they present a unique opportunity to preferentially bias amplification of the mismatch allele. Mutant allele amplification bias (MAAB) is achieved by setting the annealing temperature of PCR such that it is approximately half way between the Tm of the perfectly matched and mismatched probe. At this mid-Tm annealing temperature, the perfect match probe (wild-type allele) is bound to its target and is stable enough to partially retard amplification. Rapid cycle PCR performed on the LightScanner 32 (LS32) was required to maintain the stringency of the target annealing temperature and hinder amplification of the wild-type allele. In contrast, the slower temperature transition rates of a conventional thermal block cycling instrument were not practical for MAAB.ResultsDetection sensitivity of the mutant allele without MAAB was determined to be 5%. Based on the observed Tm's of the LunaProbe (62° and 54°C for the wild-type and mutant alleles, respectively), an annealing temperature of 58°C was used to induce MAAB in a dilution series of mixed samples. MAAB was not observed when the same approach to setting the annealing temperature of PCR was performed on a standard thermal block cycler, presumably due to the slower transition rates between annealing and denaturation temperatures.Figure 1. Normalized derivative peaks from LunaProbe. Wild-type allele (blue) = 62°C. Mutant allele (red) = 54°C. An annealing temperature of 58°C was used to bias the amplification of the mutant allele in the 50-50 mixed samples (green). This amplification bias allows greater resolution of the mutant allele by a factor of ∼10× and sensitivity down to 0.7–1.5%.ConclusionsRapid cycle PCR was critical for inducing MAAB over the wild-type allele in samples that were mixed at 50% of each allele. A MAAB factor of approximately 10× allowed discrimination of the mutant allele down to 0.7–1.5%. Applications of this method include detection/confirmation of common somatic mutations (p53, EGFR, BRAF) and early identification of mutant bacterial infections (malaria) where standard therapies are contraindicated. The ability to use a combination of real time PCR data and Hi-Res Melting on the LS32 instrument, coupled with the rapid cycle PCR approach appear to be critical to performing MAAB using LunaProbes.For further information visit our web site at www.idahotech.com or contact us at 1-800-735-6544: Jason T. McKinney, Life Science Projects Manager, Idaho Technology Inc.FiguresReferencesRelatedDetailsCited BySomatic mutation detection efficiency in EGFR: a comparison between high resolution melting analysis and Sanger sequencing22 September 2020 | BMC Cancer, Vol. 20, No. 1 Vol. 45, No. 5 Follow us on social media for the latest updates Metrics Downloaded 262 times History Published online 16 May 2018 Published in print November 2008 Information© 2008 Future Science LtdPDF download