Serial analysis of gene expression (SAGE) is an effective method of determining gene expression profiles of tissues and organs under different conditions. In this paper, the detail protocol of SAGE was introduced and some modified procedure of SAGE was reviewed.
Gene Expression Profiling ; methods

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.

It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
Algorithms ; Alzheimer Disease ; genetics ; Gene Expression Profiling ; methods ; Humans

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.
Biomedical Research ; methods ; Gene Expression Profiling ; Genomics ; Metabolomics ; Pharmacology ; Proteomics

To provide a theoretical basis for controlling the spread of rust disease, cultivating disease-resistant varieties and reducing yield losses, we investigated the transcriptome differences between Gymnosporangium yamadae and Gymnosporangium asiaticum at the haustorial stage and revealed a specialized selection mechanism for Gymnosporangium species to infect host plants. We sequenced the transcriptomes of the haustoria in rust-infected leaves when basidiospores of G. yamadae and G. asiaticum infected their hosts, and obtained 21 213 and 13 015 unigenes, respectively. Real-time fluorescence quantitative PCR validation of five genes selected from G. yamadae and G. asiaticum, respectively, showed that their expression profiles were generally consistent with the results of transcriptome analysis, demonstrating the reliability of the transcriptome data. We used seven databases such as Nr, GO, KEGG, and KOG to perform gene function annotation and enrichment analysis, and found that the genes from both rusts were mainly enriched in cellular processes, translation, and metabolism-related pathways. Moreover, we used SignalP, TMHMM online website and other software such as dbCAN, BLSAT, HMMER to show that there were 343 (2.51%) and 175 (2.79%) candidate effector proteins containing 14 and 5 proteases and 10 and 3 lipases in the haustoria of G. yamadae and G. asiaticum, respectively. Furthermore, we used OrthoFinder, BLAST and KaKs Calculator software to analyze the evolutionary relationship of the two fungi. Among one-to-one homologous genes, gene pairs with > 82% alignment were considered to be under conservative selection, and 12.37% under positive selection. Five effectors of G. asiaticum were under positive selection, and one of which was a lipase. No significant differences were found in the enrichment of expressed genes between G. yamadae and G. asiaticum, indicating the biological processes involved in haustoria were relatively conserved, despite the typical host selectivity between species. The low protein similarity between the two species suggested that they were under greater host selective pressure and there was significant evolutionary divergence, which might be related to the host-specific selection mechanism. In the haustorial, the main purpose of the effectors might be to regulate physiological processes in the plants rather than attacking the host directly, and G. yamadae and G. asiaticum might use plant lipids as energy sources.
Transcriptome ; Reproducibility of Results ; Plant Diseases/microbiology* ; Gene Expression Profiling/methods*

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.
Biomarkers ; Breast Neoplasms* ; Breast* ; Classification* ; Gene Expression ; Gene Expression Profiling ; Genome ; Humans ; Methods ; Transcriptome

We revealed the feature pathways by computing the classification error rates of out-of-bag (OOB) by random forests combined with pathway analysis. At each feature pathway, the relativity of gene expression was studied and the co-regulated gene patterns under different experiment conditions were analyzed by MAP (Mining attribute profile) algorithm. The discovered patterns were also clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same pathway was similar. The co-regulated patterns were found in two feature pathways of which one contained 108 patterns and the other contained 1 pattern. The results of clusters showed that the smallest Pearson coefficient of the clusters was more than 0.623, indicating that the co-regulated patterns in different experiment conditions were more similar at the same KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway. The methods can provide biological insight into the study of microarray data.
Algorithms ; Cluster Analysis ; Gene Expression Profiling ; methods ; Gene Expression Regulation ; Humans ; Metabolic Networks and Pathways ; genetics ; Oligonucleotide Array Sequence Analysis ; methods

When projecting microarray data of yeast time series into principal component space based on time-points (arrays), we can not only ascribe biologically meaningful explanations to the first few principal components, but also discover sensible gene expression patterns and the according genes with periodic fluctuation this helps the subsequent research of gene periodic expression and gene regulatory network.
Algorithms ; Gene Expression Profiling ; methods ; Gene Expression Regulation ; Oligonucleotide Array Sequence Analysis ; methods ; Principal Component Analysis ; Yeasts ; genetics

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

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.