OBJECTIVETo develop the methodology of gene chip to analyse genes involved in aflatoxin biosynthesis.

METHODSIn comparing reversed transcriptional PCR with gene chip, the gene chip was used to detect genes involved in aflatoxin biosynthesis.

RESULTSAfter arrayed the slide was incubated in water for 2 hours, exposed to a 650 mJ/cm2 of ultraviolet irradiation in the strata-linker for 30 s, roasted under 80 degrees C for 2 hours in oven, pre-hybridized for 45 minutes and dealt with other procedures. Finally, the slide was hybridized with fluor-derivatized sample at 42 degrees C for 16 hours.

CONCLUSIONWith the reasonable probe design and applicable protocol, the gene chip was prepared effectively for research on genes involved in aflatoxin biosynthesis.

Drug Design ; Genomics ; methods ; Oligonucleotide Array Sequence Analysis ; Protein Array Analysis ; Proteomics ; Technology, Pharmaceutical

OBJECTIVETo investigate HLA genotyping by oligonucleotide arrays.

METHODUnsymmetrical PCR was used to amplify HLA-A gene exon 2, 3. The PCR products were used as templates for hybridization. The oligonucleotide probes were spotted on glass to make microarrays. High signal and specific probes were selected. The effects of the length and location of probes on hybridization signal were studied. A set of computer software was designed for scanning and genotype differentiation.

RESULTThe genotypes of 30 samples analyzed by microarray showed concordance to that by SBT and PCR-SSP.

CONCLUSIONHLA-A genotyping by oligonucleotide array is a good method with advantage of high speed, low cost and high flux.

As the work of sequencing the genome of the human and many model organisms has been partially or fully finished, the "postgenomic era" has begun. Scientists are turning their focus toward identifying gene function from sequencing. Clustering technology, as one of the important tools of analyzing gene expression data and identifying gene function, has been used widely. In this paper we discuss main clustering technology about gene expression data at present, analyze their advantages and disadvantages, present the methods to solve the problems and give new approaches to study gene expression data.
Algorithms ; Cluster Analysis ; Gene Expression Profiling ; methods ; Humans ; Neural Networks (Computer) ; Oligonucleotide Array Sequence Analysis ; methods

OBJECTIVETo develop a method to rapidly quantitate and detect deletion of mitochondrial DNA (mtDNA) by microarray technique as a tool to study its relationship to tumorigenesis.

METHODSA modified PCR was used to amplify full length mtDNA sequence in two samples of normal human blood leukocytes and five samples of gastric cancerous tissues, which were simultaneously labeled with fluorescin. The amplified products were verified by polyacrylamide gel electrophoresis (PAGE) and silver staining. Then, 17 pairs of overlapping primers of mtDNA were designed and their PCR products were used as mitochondrial probes. They were spotted onto amino-slides as microarray and hybridized. Hybridization image was scanned with GeneTAC laser, mtDNA copy number was counted by ScanAnalyzer software.

RESULTSPAGE analysis showed that the designed probes were quite reasonable and strongly specific. The modified PCR method was efficient to amplify the whole mitochondrial genome with high-yield specific bands. The hybridizing spots were distinct, and background was clear. The signals of negative probes were close to those of background, and there was no significant difference between them (P > 0.05). The results were identical to those in the designed experiment. There were no significant differences between the results when the same sample of blood leucocytes or cancer tissues repeatedly examined with the same positive probes (P > 0.05), while there were significant differences when different types of samples were examined (P < 0.01). The hybridizing signals were stable and most of the data distributed in the range of mean +/- 2xSD.

CONCLUSIONThe method here reported can rapidly, correctly and massively determine whether there exist special deletion and/or quantitative changes of mtDNA in patients with tumors. It will be helpful for the study of the relationship between mtDNA alteration and tumor development.

OBJECTIVETo prepare a DNA Microarray that can detect 8 common species of food borne bacterial pathogens in south China.

METHODSAll the 70mer oligo probes were designed on the characteristic genome loci of the 8 species of food borne bacterial pathogens. Eight subarrays corresponding to the 8 food borne bacterial pathogens were spotted onto the slide and integrated into a pan-array on the chip. A number of identified and known bacterial samples from the storage bank were selected for the validation test.

RESULTSBased on the PPR ranking, for LM sub-array, the PPR of the 3 Listeria bacteria LM, Lin and Liv was 68.8%, 51.8% and 59.6%, respectively, while that of the non-Listeria bacterial samples was all below 43%. For VC sub-array, the PPR of VC sample was 54.1% and that of the non-VC bacterial samples was lower than 17.2%. For VP sub-array, the PPR was 66.7% for VP sample and below 24.2% for non-VP bacterial samples. For Sal sub-array, the PPR was 55.9% for Sal sample and below 50.5% for non-Sal bacterial samples. For Shi sub-array, the PPR of Shi sample and the non-Shi bacterial samples was 53.8% and below 36.6%, respectively. For SA sub-array, the PPR of SA sample and non-SA bacterial samples was 65.2% and below 22.7%, respectively. For CJ sub-array, the PPR of the 2 Campylobacter bacteria CJ and CC were 88.2% and 58.8%, respectively, and that of the non-Campylobacter bacterial samples was lower than 35.3%. For EC sub-array, the PPR of EC sample was 47.9%, and that of the non-EC bacterial samples was lower than 41.6%. Evaluation of the Biosafood-8 chip developed in this study by 18 biological samples from different origins demonstrated its good specificity and accuracy in the identification of the pathogens.

CONCLUSIONThe chip we developed can clearly differentiate the target food borne pathogenic bacteria and non-target bacteria and allows specific and accurate identification of the species of the tested bacteria isolates.

OBJECTIVETo validate and supplement the libraries of serial analysis of gene expression (SAGE) by the application of the real-time quantitative polymerase chain reaction PCR).

METHODSThe primers were designed based on the full sequences of the genes. Nine single matched tags, 6 multiple matched tags, 1 non-matched tag due to the update of the National Center for Biotechnology Information (NCBI) database, and 2 non-matched tags were selected to fulfill the validation of real-time PCR.

RESULTSThe genes were all specifically amplified by the primers pairs. The expressions of the single matched tags were identical to those of the SAGE libraries; however, the expressions of only 3 genes of the 6 multi-matched tags were identical to those of the SAGE libraries. The PCR data of the non-matched tag due to the update of the NCBI database were opposite to those of the SAGE libraries. The data did not support the significant difference of the non-matched gene of the SAGE libraries.

CONCLUSIONSReal-time PCR is a reliable tool for the validation of high through-put data such as SAGE. The reliability of data depends on the match of the tags of the SAGE libraries.

We developed an R function named "microarray outlier filter" (MOF) to assist in the identification of failed arrays. In sorting a group of similar arrays by the likelihood of failure, two statistical indices were employed: the correlation coefficient and the percentage of outlier spots. MOF can be used to monitor the quality of microarray data for both trouble shooting, and to eliminate bad datasets from downstream analysis. The function is freely avaliable at http://www.wriwindber.org/applications/mof/.
Algorithms ; Computational Biology ; methods ; Internet ; Oligonucleotide Array Sequence Analysis ; methods ; statistics & numerical data ; Reproducibility of Results

Fluorescent Linear Labelling technique is an effective, single-tube and linear amplification method. The sample cDNA was synthesized from total RNA, and was labelled antisense cRNA from double-stranded cDNA with T7RNA polymerase; it can be used for hybridization to oligonucleotide biochip. Fluorescent Linear Labelling Method can result in 50- to 100-fold higher degrees of amplification, and has been shown to retain information on transcript abundance, thus making it an efficient, robust technique for fluorescent labelling on biochip.
Fluorescent Dyes ; chemistry ; Humans ; Oligonucleotide Array Sequence Analysis ; methods ; Staining and Labeling ; methods

BACKGROUNDThe major difficulty in the research of DNA microarray data is the large number of genes compared with the relatively small number of samples as well as the complex data structure. Random forest has received much attention recently; its primary characteristic is that it can form a classification model from the data with high dimensionality. However, optimal results can not be obtained for gene selection since it is still affected by undifferentiated genes. We proposed recursive random forest analysis and applied it to gene selection.

METHODSRecursive random forest, which is an improvement of random forest, obtains optimal differentiated genes after step by step dropping of genes which, according to a certain algorithm, have no effects on classification. The method has the advantage of random forest and provides a gene importance scale as well. The value of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, which synthesizes the information of sensitivity and specificity, is adopted as the key standard for evaluating the performance of this method. The focus of the paper is to validate the effectiveness of gene selection using recursive random forest through the analysis of five microarray datasets; colon, prostate, leukemia, breast and skin data.

RESULTSFive microarray datasets were analyzed and better classification results have been attained using only a few genes after gene selection. The biological information of the selected genes from breast and skin data was confirmed according to the National Center for Biotechnology Information (NCBI). The results prove that the genes associated with diseases can be effectively retained by recursive random forest.

CONCLUSIONSRecursive random forest can be effectively applied to microarray data analysis and gene selection. The retained genes in the optimal model provide important information for clinical diagnoses and research of the biological mechanism of diseases.