Efficient oligonucleotide probe selection for pan-genomic tiling arrays

Background  

Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.     

Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls

Background  

Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization.     

Comprehensive quality control utilizing the prehybridization third-dye image leads to accurate gene expression measurements by cDNA microarrays

Background  

Gene expression profiling using microarrays has become an important genetic tool. Spotted arrays prepared in academic labs have the advantage of low cost and high design and content flexibility, but are often limited by their susceptibility to quality control (QC) issues. Previously, we have reported a novel 3-color microarray technology that enabled array fabrication QC. In this report we further investigated its advantage in spot-level data QC.     

Pre-processing Agilent microarray data

Background  

Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.     

Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application

Background

A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of accuracy. Here we investigate the stability of the probe-sensitivity index across different tissue types, the reproducibility of results in replicate experiments, and the use of MBEI in perfect match (PM)-only arrays.

Results

Probe-sensitivity indexes are stable across tissue types. The target gene's presence in many arrays of an array set allows the probe-sensitivity index to be estimated accurately. We extended the model to obtain expression values for PM-only arrays, and found that the 20-probe PM-only model is comparable to the 10-probe PM/MM difference model, in terms of the expression correlations with the original 20-probe PM/MM difference model. MBEI method is able to extend the reliable detection limit of expression to a lower mRNA concentration. The standard errors of MBEI can be used to construct confidence intervals of fold changes, and the lower confidence bound of fold change is a better ranking statistic for filtering genes. We can assign reliability indexes for genes in a specific cluster of interest in hierarchical clustering by resampling clustering trees. A software dChip implementing many of these analysis methods is made available.

Conclusions

The model-based approach reduces the variability of low expression estimates, and provides a natural method of calculating expression values for PM-only arrays. The standard errors attached to expression values can be used to assess the reliability of downstream analysis.     

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

Background

As an alternative to the frequently used "reference design" for two-channel microarrays, other designs have been proposed. These designs have been shown to be more profitable from a theoretical point of view (more replicates of the conditions of interest for the same number of arrays). However, the interpretation of the measurements is less straightforward and a reconstruction method is needed to convert the observed ratios into the genuine profile of interest (e.g. a time profile). The potential advantages of using these alternative designs thus largely depend on the success of the profile reconstruction. Therefore, we compared to what extent different linear models agree with each other in reconstructing expression ratios and corresponding time profiles from a complex design.

Results

On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.

Conclusion

Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.     

Bitpacking techniques for indexing genomes: II. Enhanced suffix arrays

Background

Suffix arrays and their variants are used widely for representing genomes in search applications. Enhanced suffix arrays (ESAs) provide fast search speed, but require large auxiliary data structures for storing longest common prefix and child interval information. We explore techniques for compressing ESAs to accelerate genomic search and reduce memory requirements.

Results

We evaluate various bitpacking techniques that store integers in fewer than 32 bits each, as well as bytecoding methods that reserve a single byte per integer whenever possible. Our results on the fly, chicken, and human genomes show that bytecoding with an exception guide array is the fastest method for retrieving auxiliary information. Genomic searching can be further accelerated using a data structure called a discriminating character array, which reduces memory accesses to the suffix array and the genome string. Finally, integrating storage of the auxiliary and discriminating character arrays further speeds up genomic search.

Conclusions

The combination of exception guide arrays, a discriminating character array, and integrated data storage provide a 2- to 3-fold increase in speed for genomic searching compared with using bytecoding alone, and is 20 % faster and 40 % more space-efficient than an uncompressed ESA.

     

Microneedle array design determines the induction of protective memory CD8+ T cell responses induced by a recombinant live malaria vaccine in mice

Background

Vaccine delivery into the skin has received renewed interest due to ease of access to the immune system and microvasculature, however the stratum corneum (SC), must be breached for successful vaccination. This has been achieved by removing the SC by abrasion or scarification or by delivering the vaccine intradermally (ID) with traditional needle-and-syringes or with long microneedle devices. Microneedle patch-based transdermal vaccine studies have predominantly focused on antibody induction by inactivated or subunit vaccines. Here, our principal aim is to determine if the design of a microneedle patch affects the CD8⁺ T cell responses to a malaria antigen induced by a live vaccine.

Methodology and Findings

Recombinant modified vaccinia virus Ankara (MVA) expressing a malaria antigen was percutaneously administered to mice using a range of silicon microneedle patches, termed ImmuPatch, that differed in microneedle height, density, patch area and total pore volume. We demonstrate that microneedle arrays that have small total pore volumes induce a significantly greater proportion of central memory T cells that vigorously expand to secondary immunization. Microneedle-mediated vaccine priming induced significantly greater T cell immunity post-boost and equivalent protection against malaria challenge compared to ID vaccination. Notably, unlike ID administration, ImmuPatch-mediated vaccination did not induce inflammatory responses at the site of immunization or in draining lymph nodes.

Conclusions/Significance

This study demonstrates that the design of microneedle patches significantly influences the magnitude and memory of vaccine-induced CD8⁺ T cell responses and can be optimised for the induction of desired immune responses. Furthermore, ImmuPatch-mediated delivery may be of benefit to reducing unwanted vaccine reactogenicity. In addition to the advantages of low cost and lack of pain, the development of optimised microneedle array designs for the induction of T cell responses by live vaccines aids the development of solutions to current obstacles of immunization programmes.     

DNA repeat arrays in chicken and human genomes and the adaptive evolution of avian genome size

Background

Birds have smaller average genome sizes than other tetrapod classes, and it has been proposed that a relatively low frequency of repeating DNA is one factor in reduction of avian genome sizes.

Results

DNA repeat arrays in the sequenced portion of the chicken (Gallus gallus) autosomes were quantified and compared with those in human autosomes. In the chicken 10.3% of the genome was occupied by DNA repeats, in contrast to 44.9% in human. In the chicken, the percentage of a chromosome occupied by repeats was positively correlated with chromosome length, but even the largest chicken chromosomes had repeat densities much lower than those in human, indicating that avoidance of repeats in the chicken is not confined to minichromosomes. When 294 simple sequence repeat types shared between chicken and human genomes were compared, mean repeat array length and maximum repeat array length were significantly lower in the chicken than in human.

Conclusions

The fact that the chicken simple sequence repeat arrays were consistently smaller than arrays of the same type in human is evidence that the reduction in repeat array length in the chicken has involved numerous independent evolutionary events. This implies that reduction of DNA repeats in birds is the result of adaptive evolution. Reduction of DNA repeats on minichromosomes may be an adaptation to permit chiasma formation and alignment of small chromosomes. However, the fact that repeat array lengths are consistently reduced on the largest chicken chromosomes supports the hypothesis that other selective factors are at work, presumably related to the reduction of cell size and consequent advantages for the energetic demands of flight.     

CGHpower: exploring sample size calculations for chromosomal copy number experiments

Background  

Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH).     

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background

The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms.

Principal Findings

50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods.

Conclusions

Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays.     

Structural comparison of metabolic networks in selected single cell organisms

Background  

There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life.     

On the global CRISPR array behavior in class I systems

Background

Much effort is underway to build and upgrade databases and tools related to occurrence, diversity, and characterization of CRISPR-Cas systems. As microbial communities and their genome complements are unearthed, much emphasis has been placed on details of individual strains and model systems within the CRISPR-Cas classification, and that collection of information as a whole affords the opportunity to analyze CRISPR-Cas systems from a quantitative perspective to gain insight into distribution of CRISPR array sizes across the different classes, types and subtypes. CRISPR diversity, nomenclature, occurrence, and biological functions have generated a plethora of data that created a need to understand the size and distribution of these various systems to appreciate their features and complexity.

Results

By utilizing a statistical framework and visual analytic techniques, we have been able to test several hypotheses about CRISPR loci in bacterial class I systems. Quantitatively, though CRISPR loci can expand to hundreds of spacers, the mean and median sizes are 40 and 25, respectively, reflecting rather modest acquisition and/or retention overall. Histograms uncovered that CRISPR array size displayed a parametric distribution, which was confirmed by a goodness-of fit test. Mapping the frequency of CRISPR loci on a standardized chromosome plot revealed that CRISPRs have a higher probability of occurring at clustered locations along the positive or negative strand. Lastly, when multiple arrays occur in a particular system, the size of a particular CRISPR array varies with its distance from the cas operon, reflecting acquisition and expansion biases.

Conclusions

This study establishes that bacterial Class I CRISPR array size tends to follow a geometric distribution; these CRISPRs are not randomly distributed along the chromosome; and the CRISPR array closest to the cas genes is typically larger than loci in trans. Overall, we provide an analytical framework to understand the features and behavior of CRISPR-Cas systems through a quantitative lens.

Reviewers

This article was reviewed by Eugene Koonin (NIH-NCBI) and Uri Gophna (Tel Aviv University).

     

An adaptive method for cDNA microarray normalization

Background  

Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.