Chemical data mining of the NCI human tumor cell line database

The NCI Developmental Therapeutics Program Human Tumor cell line data set is a publicly available database that contains cellular assay screening data for over 40 000 compounds tested in 60 human tumor cell lines. The database also contains microarray assay gene expression data for the cell lines, and so it provides an excellent information resource particularly for testing data mining methods that bridge chemical, biological, and genomic information. In this paper we describe a formal knowledge discovery approach to characterizing and data mining this set and report the results of some of our initial experiments in mining the set from a chemoinformatics perspective.     

Polymer microfluidic chip for online monitoring of microarray hybridizations

A disposable single use polymer microfluidics chip has been developed and manufactured by micro injection molding. The chip has the same outer dimensions as a standard microscope slide (25 x 76 x 1.1 mm) and is designed to be compatible with existing microscope slide handling equipment like microarray scanners. The chip contains an inlet, a 10 microL hybridization chamber capable of holding a 1000 spot array, a waste chamber and a vent to allow air to escape when sample is injected. The hybridization chamber ensures highly homogeneous hybridization conditions across the microarray. We describe the use of this chip in a flexible setup with fluorescence based detection, temperature control and liquid handling by computer controlled syringe pumps. The chip and the setup presented in this article provide a powerful tool for highly parallel studies of kinetics and thermodynamics of duplex formation in DNA microarrays. The experimental setup presented in this article enables the on-chip microarray to be hybridized and monitored at several different stringency conditions during a single assay. The performance of the chip and the setup is demonstrated by on-line measurements of a hybridization of a DNA target solution to a microarray. A presented numerical model indicates that the hybridization process in microfluidic hybridization assays is diffusion limited, due to the low values of the diffusion coefficients D of the DNA and RNA molecules involved.     

Cell microarray chip system for accurate,rapid diagnosis and target treatment of breast cancer cells SK-BR-3

Biometrics probe is a molecule that specifically interacts with a specific target molecule and can be detected by a specific method. Three-dimensional (3D) embedded cell scaffold in the cell array chip can affect culture cancer cells in a 3D environment with continuous medium supplementary and help controlling the diffusion of small molecules drugs. Based on modification of DNA segment, this type of cell micro-array chip is a new biochip technology with convenient focusing and high throughput screening.     

Nanostructured DNA Microarrays for Dual SERS and Electrochemical Read‐out

We present a strategy to fabricate nanostructured microarrays ready to perform a dual read‐out, namely electrochemical (EC) as well as surface‐enhanced Raman spectroscopy (SERS) based detection of DNA hydridization. A polystyrene nanobeads monolayer assembly, obtained by means of a Langmuir Blodgett type technique, followed by electrochemical Au deposition, was employed to construct homogeneous nanostructures in the form of inverse‐opal nanovoids on a 32‐electrode Au microarray chip. Characterization of the obtained nanostructured electrodes of the array by means of cyclic voltammetry demonstrated high reproducibility of the surface modification process. The performance of the obtained array platform was investigated by modifying the microarray electrodes with three different oligonucleotide capture probes using a previously developed potential‐assisted surface modification protocol. Two ferrocene‐labeled target DNA sequences and one target RNA sequence with a Texas red label were detected electrochemically and via SERS, respectively.     

MSDCARB — a database and software for mass spectral data of carbohydrates

A special database has been developed for the mass spectral data of carbohydrates and their conjugates (MSDCARB), together with appropriate software which allows one to collect, store, edit, retrieve, search, compare and display mass spectra in table form or in graphic mode. The database contains over 400 electron impact 70 eV and/or 12 eV low resolution mass spectra obtained at the Institute of Chemistry of the Slovak Academy of Sciences. The database can be extended using re-evaluated mass spectral data from other sources. The software described enables the handling of mass spectral data libraries, the creation of sub-libraries, browsing through the libraries according to various criteria and their combination and the comparison of spectra using a variety of parameters.     

Gene selection from microarray data for cancer classification--a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, nave Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.     

Physical-chemistry-based analysis of affymetrix microarray data

We analyze publicly available data on Affymetrix microarray spike-in experiments on the human HGU133 chipset in which sequences are added in solution at known concentrations. The spike-in set contains sequences of bacterial, human, and artificial origin. Our analysis is based on a recently introduced molecular-based model (Carlon, E.; Heim, T. Physica A 2006, 362, 433) that takes into account both probe-target hybridization and target-target partial hybridization in solution. The hybridization free energies are obtained from the nearest-neighbor model with experimentally determined parameters. The molecular-based model suggests a rescaling that should result in a "collapse" of the data at different concentrations into a single universal curve. We indeed find such a collapse, with the same parameters as obtained previously for the older HGU95 chip set. The quality of the collapse varies according to the probe set considered. Artificial sequences, chosen by Affymetrix to be as different as possible from any other human genome sequence, generally show a much better collapse and thus a better agreement with the model than all other sequences. This suggests that the observed deviations from the predicted collapse are related to the choice of probes or have a biological origin rather than being a problem with the proposed model.     

Zinc(II)‐Dipicolylamine‐Functionalized Polydiacetylene–Liposome Microarray: A Selective and Sensitive Sensing Platform for Pyrophosphate Ions

A microarray‐chip assay system for the fluorescence detection of phosphate‐containing analytes in aqueous media has been constructed from stimuli‐responsive polymerized poly(diacetylene)‐liposomes for the first time. Proper combination of the liposome components (Zn^II‐dipicolylamine for phosphate binding and an amine‐terminated component for anchoring the liposome onto an aldehyde‐derivatized glass plate), has led to a microarray chip that selectively detects pyrophosphate, an important biomarker, over competing anions, such as phosphate and adenosine triphosphate, with nanomolar sensitivity. The chip‐based assay shows advantages, such as high specificity and sensitivity, over solution‐based assays that use the same liposomes, and over known homogeneous molecular sensing systems.     

Development of a photodiode array biochip using a bipolar semiconductor and its application to detection of human papilloma virus

We describe a DNA microarray system using a bipolar integrated circuit photodiode array (PDA) chip as a new platform for DNA analysis. The PDA chip comprises an 8 × 6 array of photodiodes each with a diameter of 600 μm. Each photodiode element acts both as a support for an immobilizing probe DNA and as a two-dimensional photodetector. The usefulness of the PDA microarray platform is demonstrated by the detection of high-risk subtypes of human papilloma virus (HPV). The polymerase chain reaction (PCR)-amplified biotinylated HPV target DNA was hybridized with the immobilized probe DNA on the photodiode surface, and the chip was incubated in an anti-biotin antibody-conjugated gold nanoparticle solution. The silver enhancement by the gold nanoparticles bound to the biotin of the HPV target DNA precipitates silver metal particles at the chip surfaces, which block light irradiated from above. The resulting drop in output voltage depends on the amount of target DNA present in the sample solution, which allows the specific detection and the quantitative analysis of the complementary target DNA. The PDA chip showed high relative signal ratios of HPV probe DNA hybridized with complementary target DNA, indicating an excellent capability in discriminating HPV subtypes. The detection limit for the HPV target DNA analysis improved from 1.2 nM to 30 pM by changing the silver development time from 5 to 10 min. Moreover, the enhanced silver development promoted by the gold nanoparticles could be applied to a broader range of target DNA concentration by controlling the silver development time. Figure An optical image of the PDA chip and target DNA detection through silver enhancement Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Dimensionality of amino acid space and solvent accessibility prediction with neural networks

Solvent accessibility prediction from amino acid sequences has been pursued by several researchers. Such a prediction typically starts by transforming the amino acid category (or type) information into numerical representations. All twenty amino acids can be completely and uniquely represented by 20-dimensional vectors. Here, we investigate if the amino acid space defined in this way really requires twenty dimensions. We tried to develop corresponding representations in fewer dimensions. A method for searching optimal codification schema in an arbitrary space using neural networks was developed. The method is used to obtain optimal encoding of amino acids at various levels of dimensionality, and applied to optimize the amino acid codifications for the prediction of the solvent accessibility values of the proteins using feed-forward neural networks. The traditional 20-dimensional codification seems to be redundant in solving the solvent accessibility prediction problem, since a 1-dimensional codification is able to achieve almost the same degree of accuracy as the 20-dimensional codification. Optimal coding in much fewer dimensions could be used to make the predictions of accessible surface area with almost the same degree of accuracy as that obtained by a fully unique 20-dimensional coding. The 1-dimensional amino acid codification for solvent accessibility prediction obtained by a purely mathematical way based on neural networks is highly correlated with a physical property of the amino acids, namely their average solvent accessibility. The method developed to find the optimal codification is general, although the codification thus produced is dependent on the type of estimated property.     

基于微流控芯片的微阵列分析

微阵列芯片具有高通量、微量化和自动化等特点,已经在很多领域得到广泛应用。但是微阵列芯片仍然具有不足之处,如所需设备昂贵、分析时间较长、灵敏度不高、多样品平行分析能力不足等。微流控芯片微米级的通道具有相对较大的比表面积和较短的扩散距离,能够显著加快分析速度、提高检测效率、增强分析性能,并且能够加工大量的平行通道用于多样品分析。目前已经有大量文献报道将微流控芯片和微阵列芯片相结合,发展了独特的杂交方式并在实验和理论上分别证明了两者相结合的优势,本文综述了将微流控芯片技术应用于微阵列分析的研究进展,着重介绍了在微流控芯片上进行微阵列分析时的杂交方式、促进杂交的措施以及杂交过程的数学建模,同时也介绍了其他分析步骤方面的进展。最后分析了目前微流控芯片技术在进行微阵列杂交应用方面的不足及其原因,并指出这两项技术相结合的优势和未来。     

Fullerene data mining using bibliometrics and database tomography

Database tomography (DT) is a textual database analysis system consisting of two major components: (1) algorithms for extracting multiword phrase frequencies and phrase proximities (physical closeness of the multiword technical phrases) from any type of large textual database, to augment (2) interpretative capabilities of the expert human analyst. DT was used to derive technical intelligence from a fullerenes database derived from the Science Citation Index and the Engineering Compendex. Phrase frequency analysis by the technical domain experts provided the pervasive technical themes of the fullerenes database, and phrase proximity analysis provided the relationships among the pervasive technical themes. Bibliometric analysis of the fullerenes literature supplemented the DT results with author/journal/institution publication and citation data. Comparisons of fullerenes results with past analyses of similarly structured near-earth space, chemistry, hypersonic/supersonic flow, aircraft, and ship hydrodynamics databases are made. One important finding is that many of the normalized bibliometric distribution functions are extremely consistent across these diverse technical domains and could reasonably be expected to apply to broader chemical topics than fullerenes that span multiple structural classes. Finally, lessons learned about integrating the technical domain experts with the data mining tools are presented.     

Electrochemical Evaluation of Binding Affinity for Aptamer Selection Using the Microarray Chip

Simultaneous evaluation of the binding affinity of a series of aptamers toward a target molecule was investigated using an electrochemical microarray chip. The chip was modified by immobilizing seven aptamers obtained from the SELEX process and a control sequence onto gold nanoparticle‐comprised conducting polymer‐coated microarray electrodes. The chip was then incubated with the target molecule, kanamycin. The electrochemical response of the captured kanamycin was studied to evaluate the binding affinity of the aptamers, which showed the same trend with the fluorescence spectroscopic results. The lowest dissociation constant between a selected aptamer and kanamycin was determined to be 38.06±0.73 nM.     

Controlling DNA/Surface Interactions for Potential Pulse‐Assisted Preparation of Multi‐Probe DNA Microarrays

The possibility of selectively modifying microarray electrodes with different DNA sequences in a controlled way without the need for local positioning of solutions or local modification of array surfaces is demonstrated. Potential pulse sequences are employed to perform sequential surface modification of a 32‐gold‐electrode array with two different thiolated DNA capture sequences, surface passivation and regeneration of selected microarray electrodes, all by adjusting the potential intensities of the same potential pulse‐assisted method. We achieve reproducible and controlled DNA immobilization together with minimization of false signals originating from unspecific adsorption or undesired co‐immobilization. This methodology is not limited to DNA chips and it is potentially suitable for a wide range of applications employing Au?S chemistry. It can be employed in laboratory conditions for localizing different reactive chemistries onto predefined electrodes of an array without the need for complex and expensive apparatus and special conditions.     

Normalization of cDNA microarray data using wavelet regressions

Normalization is an essential step in microarray data mining and analysis. For cDNA microarray data, the primary purpose of normalization is removing the intensity-dependent bias across different slides within an experimental group or between multiple groups. The locally weighted regression (lowess) procedure has been widely used for this purpose but can be comparatively time consuming when the dataset becomes relatively large. In this study, we applied wavelet regressions, a new smoothing method for recovering a regression function from data that is supposed to outperform other methods in many cases, such as spline or local polynomial fitting, to normalize two cDNA microarray datasets. Relative to the lowess procedure, we found that wavelet regressions not only produced reliable normalization results but also ran much faster. The computing speed represents one of the most important advantages over other algorithms, especially when one is interested in analyzing a large microarray experiment involving hundreds of slides.     

Identifying novel prostate cancer associated pathways based on integrative microarray data analysis

The development and diverse application of microarray and next generation sequencing technologies has made the meta-analysis widely used in expression data analysis. Although it is commonly accepted that pathway, network and systemic level approaches are more reproducible than reductionism analyses, the meta-analysis of prostate cancer associated molecular signatures at the pathway level remains unexplored. In this article, we performed a meta-analysis of 10 prostate cancer microarray expression datasets to identify the common signatures at both the gene and pathway levels. As the enrichment analysis result of GeneGo's database and KEGG database, 97.8% and 66.7% of the signatures show higher similarity at pathway level than that at gene level, respectively. Analysis by using gene set enrichment analysis (GSEA) method also supported the hypothesis. Further analysis of PubMed citations verified that 207 out of 490 (42%) pathways from GeneGo and 48 out of 74 (65%) pathways from KEGG were related to prostate cancer. An overlap of 15 enriched pathways was observed in at least eight datasets. Eight of these pathways were first described as being associated with prostate cancer. In particular, endothelin-1/EDNRA transactivation of the EGFR pathway was found to be overlapped in nine datasets. The putative novel prostate cancer related pathways identified in this paper were indirectly supported by PubMed citations and would provide essential information for further development of network biomarkers and individualized therapy strategy for prostate cancer.     

Replication of a DNA microarray

A mechanical method for efficient replication of DNA microarrays is described. The approach consists of three steps. First, a master DNA microarray consisting of single-stranded DNA elements is exposed to a solution containing the biotin-functionalized complement of each array element. Following hybridization, a replica surface modified with streptavidin is brought into contact with the master. This results in linking of the biotin-functionalized complement with the replica surface. Next, the replica is separated from the master, and the complementary strands are transferred to the replica surface. The resulting complementary DNA microarray contains position-coded sequences that mirror the information contained on the master DNA microarray. Multiple replicas can be prepared from a single master, the replicas efficiently hybridize only their complement, and DNA not labeled with biotin is not transferred to the replica surface.     

Practical approach to archival and retrieval of analytical data in the laboratory

Today's analytical laboratory uses a large number of different instruments that are connected in networks. Together with increasing automation, data are produced at a rate that can easily reach gigabytes per month, which generates the problem of systematic archival. In addition, working under Good Laboratory Practice requires that archival of raw data be performed in such a way that they can be readily retrieved upon request, even years later. While systematic archival of data is already performed in most laboratories, it is the retrieval of saved information that is often far from straightforward. This paper describes a simple but systematic approach for both archival and retrieval of data files and related electronic documents. It consists of an unambiguous scheme for the naming of electronic files, an efficient backup strategy, a simple database holding information about any data acquired, and a convenient interface to this database that can be accessed from any workplace while assuring restricted access. The system is capable of handling several databases concurrently and is used in our facility to archive data from several workgroups. The use of freely available software such as the Linux operating system made it possible to implement a fast and stable solution at exceptionally low cost.     

On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data

It has been shown that the generalized F-statistics can give satisfactory performances in identifying differentially expressed genes with microarray data. However, for some complex diseases, it is still possible to identify a high proportion of false positives because of the modest differential expressions of disease related genes and the systematic noises of microarrays. The main purpose of this study is to develop statistical methods for Affymetrix microarray gene expression data so that the impact on false positives from non-expressed genes can be reduced. I proposed two novel generalized F-statistics for identifying differentially expressed genes and a novel approach for estimating adjusting factors. The proposed statistical methods systematically combine filtering of non-expressed genes and identification of differentially expressed genes. For comparison, the discussed statistical methods were applied to an experimental data set for a type 2 diabetes study. In both two- and three-sample analyses, the proposed statistics showed improvement on the control of false positives.