A comparative analysis of existing oligonucleotides selection algorithms for microarray technology

In system biology, DNA microarray technology is an indispensable tool for the biological analysis involved at the level of the whole genome. Among the sophisticated analytical problems in microarray technology at the front and back ends, respectively, are the selection of optimal DNA oligonucleotides (henceforth oligos) and computational analysis of the genes expression data. A computational comparative analysis of the methods used to select oligos is important since the design and quality of the microarray probes are of critical importance for the hybridization experiments as well as subsequent analysis of the data. In an attempt to enhance efficient and effective design at the front end, a computational comparative analysis was performed on oligos selection tools using the barley ESTs, as well as the Saccharomyces cerevisiae, Encephalitozoon cuniculi and human genomes. The analysis also shows that a large number of the existing tools are difficult to install and configure. For cross hybridization test, most rely on BLAST and therefore design ill specific oligonucleotides. Furthermore, most are non-intuitive to use and lack important oligo design and software features

A Polymerase-chain-reaction Assay for the Specific Identification of Transcripts Encoded by Individual Carcinoembryonic Antigen (CEA)-gene-family Members

Carcinoembryonic antigen (CEA) is a tumor marker that belongs to a family of closely related molecules with variable expression patterns. We have developed sets of oligonucleotide primers for the specific amplification of transcripts from individual CEA-family members using the reverse transcriptase/ polymerase chain reaction (RT/PCR). Specific primer sets were designed for CEA, non-specific cross-reacting antigen (NCA), biliary glycoprotein (BGP), carcinoembryonic antigen gene-family members 1, 6 and 7 (CGMI, CGM6 and CGM7), and one set for all pregnancy-specific glycoprotein (PSG) transcripts. Primers were first tested for their specificity against individual cDNA clones and product-hybridization with internal, transcript-specific oligonucleotides. Total RNA from 12 brain and 63 gynecological tumors were then tested for expression of CEA-related transcripts. None were found in tumors located in the brain, including various mesenchymal and neuro-epithelial tumors. CEA and NCA transcripts were, however, present in an adenocarcinoma located in the nasal sinuses. In ovarian mucinous adenocarcinomas, we always found co-expression of CEA and NCA transcripts, and occasionally BGP mRNA. CEA-related transcripts were also found in some serous, endometrioid and clear-cell ovarian carcinomas. CEA, NCA and BGP transcripts were present in endometrial carcinomas of the uterus and cervical carcinomas, whereas uterine leiomyomas were completely negative. No transcripts were found from CGM 1, CGM6, CGM7 or from PSG genes in any of the tumors tested. The PCR data were compared with immunohistochemical investigations of ovarian tumors at the protein level using CEA (26/3/13)-, NCA-50/90 (9A6FR) and NCA-95 (80H3)-specific monoclonal antibodies

A novel design of whole-genome microarray probes for Saccharomyces cerevisiae which minimizes cross-hybridization

BACKGROUND: Numerous DNA microarray hybridization experiments have been performed in yeast over the last years using either synthetic oligonucleotides or PCR-amplified coding sequences as probes. The design and quality of the microarray probes are of critical importance for hybridization experiments as well as subsequent analysis of the data. RESULTS: We present here a novel design of Saccharomyces cerevisiae microarrays based on a refined annotation of the genome and with the aim of reducing cross-hybridization between related sequences. An effort was made to design probes of similar lengths, preferably located in the 3'-end of reading frames. The sequence of each gene was compared against the entire yeast genome and optimal sub-segments giving no predicted cross-hybridization were selected. A total of 5660 novel probes (more than 97% of the yeast genes) were designed. For the remaining 143 genes, cross-hybridization was unavoidable. Using a set of 18 deletant strains, we have experimentally validated our cross-hybridization procedure. Sensitivity, reproducibility and dynamic range of these new microarrays have been measured. Based on this experience, we have written a novel program to design long oligonucleotides for microarray hybridizations of complete genome sequences. CONCLUSIONS: A validated procedure to predict cross-hybridization in microarray probe design was defined in this work. Subsequently, a novel Saccharomyces cerevisiae microarray (which minimizes cross-hybridization) was designed and constructed. Arrays are available at Eurogentec S. A. Finally, we propose a novel design program, OliD, which allows automatic oligonucleotide design for microarrays. The OliD program is available from authors

Genome-wide selection of unique and valid oligonucleotides

Functional genomics methods are used to investigate the huge amount of information contained in genomes. Numerous experimental methods rely on the use of oligo- or polynucleotides. Nucleotide strand hybridization forms the underlying principle for these methods. For all these techniques, the probes should be unique for analyzed genes. In addition to being unique for the studied genes, the probes should fulfill a large number of criteria to be usable and valid. The criteria include for example, avoidance of self-annealing, suitable melting temperature and nucleotide composition. We developed a method for searching unique and valid oligonucleotides or probes for genes so that there is not even a similar (approximate) occurrence in any other location of the whole genome. By using probe size 25, we analyzed 17 complete genomes representing a wide range of both prokaryotic and eukaryotic organisms. More than 92% of all the genes in the investigated genomes contained valid oligonucleotides. Extensive statistical tests were performed to characterize the properties of unique and valid oligonucleotides. Unique and valid oligonucleotides were relatively evenly distributed in genes except for the beginning and end, which were somewhat overrepresented. The flanking regions in eukaryotes were clearly underrepresented among suitable oligonucleotides. In addition to distributions within genes, the effects on codon and amino acid usage were also studied

Dynamic probe selection for studying microbial transcriptome with high-density genomic tiling microarrays

<p>Abstract</p> <p>Background</p> <p>Current commercial high-density oligonucleotide microarrays can hold millions of probe spots on a single microscopic glass slide and are ideal for studying the transcriptome of microbial genomes using a tiling probe design. This paper describes a comprehensive computational pipeline implemented specifically for designing tiling probe sets to study microbial transcriptome profiles.</p> <p>Results</p> <p>The pipeline identifies every possible probe sequence from both forward and reverse-complement strands of all DNA sequences in the target genome including circular or linear chromosomes and plasmids. Final probe sequence lengths are adjusted based on the maximal oligonucleotide synthesis cycles and best isothermality allowed. Optimal probes are then selected in two stages - sequential and gap-filling. In the sequential stage, probes are selected from sequence windows tiled alongside the genome. In the gap-filling stage, additional probes are selected from the largest gaps between adjacent probes that have already been selected, until a predefined number of probes is reached. Selection of the highest quality probe within each window and gap is based on five criteria: sequence uniqueness, probe self-annealing, melting temperature, oligonucleotide length, and probe position.</p> <p>Conclusions</p> <p>The probe selection pipeline evaluates global and local probe sequence properties and selects a set of probes dynamically and evenly distributed along the target genome. Unique to other similar methods, an exact number of non-redundant probes can be designed to utilize all the available probe spots on any chosen microarray platform. The pipeline can be applied to microbial genomes when designing high-density tiling arrays for comparative genomics, ChIP chip, gene expression and comprehensive transcriptome studies.</p

A comparative analysis of existing oligonucleotides selection algorithms for microarray technology

In system biology, DNA microarray technology is an indispensable tool for the biological analysis involved at the level of the whole genome. Among the sophisticated analytical problems in microarraytechnology at the front and back ends, respectively, are the selection of optimal DNA oligonucleotides (henceforth oligos) and computational analysis of the genes expression data. A computational comparative analysis of the methods used to select oligos is important since the design and quality of the microarray probes are of critical importance for the hybridization experiments as well as subsequent analysis of the data. In an attempt to enhance efficient and effective design at the front end, a computational comparative analysis was performed on oligos selection tools using the barley ESTs, as well as the Saccharomyces cerevisiae, Encephalitozoon cuniculi and human genomes. The analysis also shows that a large number of the existing tools are difficult to install and configure. For cross hybridization test, most rely on BLAST and therefore design ill specific oligonucleotides. Furthermore,most are non-intuitive to use and lack important oligo design and software features

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

MPrime: efficient large scale multiple primer and oligonucleotide design for customized gene microarrays

BACKGROUND: Enhancements in sequencing technology have recently yielded assemblies of large genomes including rat, mouse, human, fruit fly, and zebrafish. The availability of large-scale genomic and genic sequence data coupled with advances in microarray technology have made it possible to study the expression of large numbers of sequence products under several different conditions in days where traditional molecular biology techniques might have taken months, or even years. Therefore, to efficiently study a number of gene products associated with a disease, pathway, or other biological process, it is necessary to be able to design primer pairs or oligonucleotides en masse rather than using a time consuming and laborious gene-by-gene method. RESULTS: We have developed an integrated system, MPrime, in order to efficiently calculate primer pairs or specific oligonucleotides for multiple genic regions based on a keyword, gene name, accession number, or sequence fasta format within the rat, mouse, human, fruit fly, and zebrafish genomes. A set of products created for mouse housekeeping genes from MPrime-designed primer pairs has been validated using both PCR-amplification and DNA sequencing. CONCLUSION: These results indicate MPrime accurately incorporates standard PCR primer design characteristics to produce high scoring primer pairs for genes of interest. In addition, sequence similarity for a set of oligonucleotides constructed for the same set of genes indicates high specificity in oligo design

Model-based probe set optimization for high-performance microarrays

A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions. (iii) A fast calibrated sequence-similarity-based heuristic for cross-hybridization prediction supporting large-scale designs. (iv) A novel compound score formulation for the integrated assessment of multiple probe design objectives. In contrast to a greedy search for probes meeting parameter thresholds, this approach permits an optimization at the probe set level and facilitates the selection of highly specific probe candidates while maintaining probe set uniformity. (v) Lastly, a flexible target grouping structure allows easy adaptation of the pipeline to a variety of microarray application scenarios. The algorithm and features are discussed and demonstrated on actual design runs. Source code is available on request