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Design and analysis of mismatch probes for long oligonucleotide microarrays
Ye Deng, Zhili He, Joy D Van Nostrand, Jizhong Zhou
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-491
Abstract: Here, an oligonucleotide microarray was constructed to evaluate and optimize parameters for 50-mer mismatch probe design. A perfect match (PM) and 28 mismatch (MM) probes were designed for each of ten target genes selected from three microorganisms. The microarrays were hybridized with synthesized complementary oligonucleotide targets at different temperatures (e.g., 42, 45 and 50°C). In general, the probes with evenly distributed mismatches were more distinguishable than those with randomly distributed mismatches. MM probes with 3, 4 and 5 mismatched nucleotides were differentiated for 50-mer oligonucleotide probes hybridized at 50, 45 and 42°C, respectively. Based on the experimental data generated from this study, a modified positional dependent nearest neighbor (MPDNN) model was constructed to adjust the thermodynamic parameters of matched and mismatched dimer nucleotides in the microarray environment. The MM probes with four flexible positional mismatches were designed using the newly established MPDNN model and the experimental results demonstrated that the redesigned MM probes could yield more consistent hybridizations.This study provides guidance on the design of MM probes for long oligonucleotides (e.g., 50 mers). The novel MPDNN model has improved the consistency for long MM probes, and this modeling method can potentially be used for the prediction of oligonucleotide microarray hybridizations.DNA microarray technology has been widely used in gene expression analysis, disease characterization, environmental monitoring and many other biological processes. PCR amplicons [1], oligonucleotides [2] and genomic DNA [3] have all been used as microarray probes. Currently, the use of oligonucleotide probes has become popular due to better specificity, easier construction, and less cost [4,5] compared to other probe types. In addition, many studies [4,6,7] have demonstrated that longer oligonucleotide probes (e.g., 50 mers or longer) yield better sensitivity than sh
Allelotyping of pooled DNA with 250 K SNP microarrays
Stefan Wilkening, Bowang Chen, Michael Wirtenberger, Barbara Burwinkel, Asta F?rsti, Kari Hemminki, Federico Canzian
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-77
Abstract: We could confirm that the polynomial based probe specific correction (PPC) was the most accurate method for allele frequency estimation. However, a simple k-correction, using the relative allele signal (RAS) of heterozygous individuals, performed only slightly worse and provided results for more SNPs. Using four replicates of the 250 K array and the k-correction using heterozygous RAS values, we obtained results for 104.141 SNPs. The correlation between estimated and real allele frequency was 0.983 and the average error was 0.046, which was comparable to the results obtained with the 10 K array. Furthermore, we could show how the estimation accuracy depended on the SNP type (average error for A/T SNPs: 0.043 and for G/C SNPs: 0.052).The combination of DNA pooling and analysis of single nucleotide polymorphisms (SNPs) on high density microarrays is a promising tool for whole genome association studies.To find new susceptibility loci for complex diseases on the human genome, a high number of case and control samples is required. An old approach with new perspective is the pooling of cases and controls. The larger the number of analyzed SNPs, the more striking are the advantages of a pooling study. With advanced microarray technology it is now possible to analyze SNPs throughout the whole genome. With the Human Mapping 500 K array set from Affymetrix and the BeadChips from Illumina, over 500,000 SNPs can be genotyped on two arrays. Different groups have tested the reliability of Affymetrix microarrays for pooling studies with either the 10 K array [1-6] or the 50 K array [7,8]. On these arrays, each SNP is interrogated by 40 probes (20 for the plus and 20 on the minus strand). On the 250 K arrays over 90% of the SNPs are represented by only 24 probes (some SNPs are only on the plus or the minus strand). This reduction of probes, as well as the reduction of the feature size from 18 μm (10 K), and 8 μm (50 K) to 5 μm (250 K) could have a negative influence on the outcome
Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays
Shin Lin, Benilton Carvalho, David J Cutler, Dan E Arking, Aravinda Chakravarti, Rafael A Irizarry
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-4-r63
Abstract: Genome-wide association studies hold great promise in discovering genes underlying complex, heritable disorders for which less powerful study designs have failed in the past [1-3]. Much effort spanning academia and industry and across multiple disciplines has already been invested in making this type of study a reality, with the most recent and largest effort being the Human HapMap Project [4].Single nucleotide polymorphism (SNP) microarrays represent a key technology allowing for the high throughput genotyping necessary to assess genome-wide variation and conduct association studies [5-9]. Over the years, Affymetrix has introduced SNP microarrays of ever increasing density. The GeneChip? Human Mapping 100K and 500K arrays are beginning to be widely used in association studies, and the 6.0 array with >900,000 SNPs has recently been introduced. At these genotype densities, association studies are theoretically well-powered to detect variants of small phenotypic effect in samples involving hundreds to thousands of subjects [10], and indeed, a number of such successes have recently been reported [11-16].Practically though, the use of SNP microarrays in association studies has not been entirely straightforward. Genotyping errors, even at a low rate, are known to produce large numbers of putative disease loci, which upon further investigation are found to be false positives. Work by Mitchell and colleagues [17] suggests a per single SNP rate of 0.5% as a maximal threshold for error, particularly for family-based tests. Arriving short of a dataset with such a low rate of error is not so much a failure of the microarray platform per se but rather the inadequacy of current SNP calling programs to extract the greatest information from the raw data and, more importantly, to quantify SNP quality, so that unreliable SNPs may be eliminated from further analysis.In general, genotyping algorithms make a call (AA, AB, or BB) for a SNP of each sample assuming diploids. Typically, a
Position dependent mismatch discrimination on DNA microarrays – experiments and model
Thomas Naiser, Jona Kayser, Timo Mai, Wolfgang Michel, Albrecht Ott
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-509
Abstract: We observe that mismatch discrimination is mostly determined by the defect position (relative to the duplex ends) as well as by the sequence context. We investigate the thermodynamics of the oligonucleotide duplexes on the basis of double-ended molecular zipper. Theoretical predictions of defect positional influence as well as long range sequence influence agree well with the experimental results.Molecular zipping at thermodynamic equilibrium explains the binding affinity of mismatched DNA duplexes on microarrays well. The position dependent nearest neighbor model (PDNN) can be inferred from it. Quantitative understanding of microarray experiments from first principles is in reach.The well-known double-helix structure of nucleic acids results from sequence-specific binding between complementary single strands. Sequential base pairing between A·T and C·G base pairs along the two complementary strands results in the formation of stable duplexes. This so called hybridization process is fundamental to many biological processes and biotechnologies. Microarrays consist of surface-tethered probe sequences, which act as specific scavengers for their respective complementary target sequence. The molecular recognition enables a highly parallel detection of nucleic acid sequences in complex target mixtures. Hybridization also occurs with single mismatched (MM) base pairs, however, these duplexes are significantly less stable than the corresponding perfect match (PM) [1,2]. The single base pair mismatch-discrimination capability of short (~20 nt) oligonucleotide probes provides an important diagnostic tool for the detection of point-mutations and single nucleotide polymorphisms (SNPs) [3]. DNA duplex stability arises from hydrogen bonding and base stacking interactions (the latter comprise van der Waals interactions, electrostatic and hydrophobic interactions between adjacent base pairs). According to the well-established nearest-neighbor model, thermodynamically a nucleic acid
Detecting imbalanced expression of SNP alleles by minisequencing on microarrays
Ulrika Liljedahl, Mona Fredriksson, Andreas Dahlgren, Ann-Christine Syv?nen
BMC Biotechnology , 2004, DOI: 10.1186/1472-6750-4-24
Abstract: The accuracy and sensitivity of quantitative detection of allelic imbalance was assessed for each SNP by constructing regression lines using a dilution series of mixed samples from individuals of different genotype. Accurate quantification of SNP alleles by both assay formats was evidenced for by R2 values > 0.95 for the majority of the regression lines. According to a two sample t-test, we were able to distinguish 1–9% of a minority SNP allele from a homozygous genotype, with larger variation between SNPs than between assay formats. Six of the SNPs, heterozygous in either of the two cell lines, were genotyped in RNA extracted from the endothelial cells. The coefficient of variation between the fluorescent signals from five parallel reactions was similar for cDNA and genomic DNA. The fluorescence signal intensity ratios measured in the cDNA samples were compared to those in genomic DNA to determine the relative expression levels of the two alleles of each SNP. Four of the six SNPs tested displayed a higher than 1.4-fold difference in allelic ratios between cDNA and genomic DNA. The results were verified by allele-specific oligonucleotide hybridisation and minisequencing in a microtiter plate format.We conclude that microarray based minisequencing is an accurate and accessible tool for multiplexed screening for imbalanced allelic expression in multiple samples and tissues in parallel.Single nucleotide polymorphisms (SNPs) are highly abundant in the human genome, appearing on average at 0.1% of the nucleotide positions [1]. Thus, each gene or transcriptional unit will contain multiple SNPs that potentially give rise to sequence variation between individuals and tissues on the level of RNA. Recent studies indicate that differences in the expression levels of the alleles of heterozygous SNPs may occur frequently for human genes [2-6]. Imbalanced allelic expression was detected in foetal liver or kidney tissues for more than half of 602 genes analysed, and one third of t
Optimal Stack Generation for CMOS Analog Modules with Parasitic and Mismatch Constraints
带寄生及匹配约束的CMOS模拟电路模块的STACK生成优化方法(英文)

Zeng Xuan,LI Ming-yuan,ZHAO Wen-qing,TANG Pu-shan,ZHOU Dian,
曾璇
,李明原,赵文庆,唐璞山,周电

半导体学报 , 2001,
Abstract: The performances of analog circuits depend greatly on the layout parasitics and mismatches.Novel techniques are proposed for modeling the distributed parasitic capacitance,parasitic parameter mismatch due to process gradient and the inner stack routing mismatch.Based on the proposed models,an optimal stack generation technique is developed to control the parasitics and mismatches,optimize the stack shape and ensure the generation of an Eulerian graph for a given CMOS analog module.An OPA circuit example is given to demonstrate that the circuit performances such as unit gain bandwidth and phase margin are enhanced by the proposed layout optimization method.
Genes modulated by Ginkgo biloba revealed by DNA microarrays  [cached]
Dan Ferber
Genome Biology , 2001, DOI: 10.1186/gb-spotlight-20010611-01
Abstract: Pete Schultz of The Scripps Research Institute in La Jolla, California, and his colleagues have used DNA microarrays to see how a Ginkgo biloba extract affects brain function on the molecular level (Proc Natl Acad Sci USA 2001, 98:6577-6580.). Although thousands of consumers buy Ginkgo biloba off the shelf to ease symptoms of aging such as short-term memory loss, hearing loss, and lack of attention, definitive clinical-trial data are lacking. "There's no scientific proof to say that it enhances memory," pointed out Coran Watanabe, a post-doctoral researcher in Schultz's laboratory and lead author of the study.To see how the remedy affected the brain on the molecular level, Watanabe and her colleagues fed mice diets supplemented with a standard Ginkgo biloba extract for four weeks. They then dissected out the cortex and the hippocampus; prepared labelled RNA from both tissues, and tested with microarrays for expression of 12,000 genes. Of these genes, only 10 were activated more than three-fold in the ginkgo-fed animals compared with control animals fed otherwise identical mouse chow.Most of the genes are already known to carry out reactions that might help protect brain cells. For example, the only gene activated by ginkgo in the hippocampus makes a protein called transthyretin, which is known to block the aggregation of amyloid beta protein in test-tube experiments, and is reduced in the cerebrospinal fluid of patients with Alzheimer's disease. Raising transthyretin, by implication, might block formation of the plaques that 'gum up' brain cells in Alzheimer's patients. In addition, two of the genes activated in the cortex, tyrosine/threonine phosphatase I and microtubule-associated tau, have been linked to the formation and breakdown of the intracellular tangles that are linked with Alzheimer's disease.Although the results are consistent with a neuromodulatory role for Ginkgo biloba, it is still not clear whether the changes might have therapeutic effects, harmful
KinSNP software for homozygosity mapping of disease genes using SNP microarrays
El-Ad Amir, Ofer Bartal, Efrat Morad, Tal Nagar, Jony Sheynin, Ruti Parvari, Vered Chalifa-Caspi
Human Genomics , 2010, DOI: 10.1186/1479-7364-4-6-394
Abstract: We present KinSNP, a user-friendly software tool for homozygosity mapping using SNP arrays. The software searches for stretches of SNPs which are homozygous to the same allele in all ascertained sick individuals. User-specified parameters control the number of allowed genotyping 'errors' within homozygous blocks. Candidate disease regions are then reported in a detailed, coloured Excel file, along with genotypes of family members and healthy controls. An interactive genome browser has been included which shows homozygous blocks, individual genotypes, genes and further annotations along the chromosomes, with zooming and scrolling capabilities. The software has been used to identify the location of a mutated gene causing insensitivity to pain in a large Bedouin family. KinSNP is freely available from http://bioinfo.bgu.ac.il/bsu/software/kinSNP webcite.The availability of high-density mapping microarrays, bearing sufficient probes to analyse 10,000-1,000,000 single nucleotide polymorphisms (SNPs) in one assay, offers an efficient alternative to traditional, microsatellite-based, genome-wide linkage scans [1]. Accordingly, several public linkage software programs have been developed, such as Allegro, EasyLinkage and dChip, which can handle the high number of SNPs in these arrays. All of these have been successfully used for mapping Mendelian disorders [2-4].Genotyping individuals from large, multiply-consanguineous families of isolated populations offers a unique advantage for positional cloning of rare diseases [5,6]. A mutation occurring in a founder may be rapidly inherited by numerous individuals in the population, and the offspring of consanguineous parents will have a high probability of inheriting two copies of the mutated chromosomal segment and thus expressing the disease. Classical linkage analysis of SNP arrays in these studies is highly problematic, however, due to the computational load needed to deal with both the large number of SNPs and the family struc
Mismatch oligonucleotides in human and yeast: guidelines for probe design on tiling microarrays
Michael Seringhaus, Joel Rozowsky, Thomas Royce, Ugrappa Nagalakshmi, Justin Jee, Michael Snyder, Mark Gerstein
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-635
Abstract: We examined all possible nucleotide substitutions at the central position of 36-nucleotide probes, and found that nonspecific binding by MM oligos depends upon the individual nucleotide substitutions they incorporate: C→A, C→G and T→A (yielding purine-purine mispairs) are most disruptive, whereas A→X were least disruptive. We also quantify a marked GC skew effect: substitutions raising probe GC content exhibit higher intensity (and vice versa). This skew is small in highly-expressed regions (± 0.5% of total intensity range) and large (± 2% or more) elsewhere. Multiple mismatches per oligo are largely additive in effect: each MM added in a distributed fashion causes an additional 21% intensity drop relative to PM, three-fold more disruptive than adding adjacent mispairs (7% drop per MM).We investigate several parameters for oligonucleotide design, including the effects of each central nucleotide substitution on array signal intensity and of multiple MM per oligo. To avoid GC skew, individual substitutions should not alter probe GC content. RNA sample mixture complexity may increase the amount of nonspecific hybridization, magnify GC skew and boost the intensity of MM oligos at all levels.Oligonucleotide tiling arrays are a popular tool for detecting transcriptionally active regions on a genomic scale. They comprise short oligomeric probes (generally 25–70 bp) immobilized on a slide surface; a typical custom-built tiling array today contains about 400,000 features. Tiling arrays are distinct from traditional microarrays, which are most often used to measure differential gene expression in multiple biological conditions. As such, different techniques must be employed in their analysis [1].The principle behind microarray analysis is similar to that of traditional hybridization using nitrocellulose membranes [2]: When fluorescently-labeled sample (target) is applied to the array-bound features (probes), complementary regions of probe and target DNA will anneal to form a
Supplementing High-Density SNP Microarrays for Additional Coverage of Disease-Related Genes: Addiction as a Paradigm  [PDF]
Scott F. Saccone, Laura J. Bierut, Elissa J. Chesler, Peter W. Kalivas, Caryn Lerman, Nancy L. Saccone, George R. Uhl, Chuan-Yun Li, Vivek M. Philip, Howard J. Edenberg, Stephen T. Sherry, Michael Feolo, Robert K. Moyzis, Joni L. Rutter
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0005225
Abstract: Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.
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