Gene expression

Semi-quantitative detection of gene expression using bisbenzimide dye

P. Kittimongkolsuk, Tencomnao, T., and Santiyanont, R., Semi-quantitative detection of gene expression using bisbenzimide dye, vol. 10. pp. 3747-3759, 2011.

An electrochemical biosensor, using a disposable electrochemical printed chip aggregation by the bisbenzimide dye (Hoechst 33258), was used for detecting the expression of β-actin and RAGE genes. Using linear sweep voltammetry, the expression of these two genes in HeLa and HepG2 cell lines was determined based on anodic peak current, and the results were compared with conventional agarose gel electrophoresis. Total cellular RNA was reverse transcribed to complementary DNA, and amplification by PCR was carried out.

Transcription factors expressed in soybean roots under drought stress

S. S. Pereira, Guimarães, F. C. M., Carvalho, J. F. C., Stolf-Moreira, R., Oliveira, M. C. N., Rolla, A. A. P., Farias, J. R. B., Neumaier, N., and Nepomuceno, A. L., Transcription factors expressed in soybean roots under drought stress, vol. 10, pp. 3689-3701, 2011.

To gain insight into stress-responsive gene regulation in soybean plants, we identified consensus sequences that could categorize the transcription factors MYBJ7, BZIP50, C2H2, and NAC2 as members of the gene families myb, bzip, c2h2, and nac, respectively. We also investigated the evolutionary relationship of these transcription factors and analyzed their expression levels under drought stress.

An algorithm to infer similarity among cell types and organisms by examining the most expressed sequences

S. A. P. Pinto and Ortega, J. M., An algorithm to infer similarity among cell types and organisms by examining the most expressed sequences, vol. 7, pp. 933-947, 2008.

Following sequence alignment, clustering algorithms are among the most utilized techniques in gene expression data analysis. Clustering gene expression patterns allows researchers to determine which gene expression patterns are alike and most likely to participate in the same biological process being investigated. Gene expression data also allow the clustering of whole samples of data, which makes it possible to find which samples are similar and, consequently, which sampled biological conditions are alike.

Regulation of human alpha-globin gene expression and alpha-thalassemia

D. M. Ribeiro and Sonati, M. F., Regulation of human alpha-globin gene expression and alpha-thalassemia, vol. 7. pp. 1045-1053, 2008.

Hemoglobin and globin genes are important models for studying protein and gene structure, function and regulation. We reviewed the main aspects of regulation of human α-globin synthesis, encoded by two adjacent genes (α2 and α1) clustered on chromosome 16. Their expression is controlled mainly by a regulatory element located 40 kb upstream on the same chromosome, the α-major regulatory element, whose activity is restricted to a core fragment of 350 bp, within which several regulatory protein binding sites have been found.

Identification and characterization of TGFβ-dependent and -independent cis-regulatory modules in the C4ST-1/CHST11 locus

C. M. Willis, Wrana, J. L., and Klüppel, M., Identification and characterization of TGFβ-dependent and -independent cis-regulatory modules in the C4ST-1/CHST11 locus, vol. 8, pp. 1331-1343, 2009.

Chondroitin-4-sulfotransferase-1(C4ST-1)/carbohydrate sul­fotransferase 11 (CHST11) is a Golgi-bound enzyme involved in the biosyn­thesis of the glycosaminoglycan chondroitin sulfate. The sulfation pattern of chondroitin is tightly regulated during development, injury and disease, with the temporal and spatial expression of chondroitin sulfotransferase genes be­lieved to be a crucial determinant of the fine balance of chondroitin sulfation.

Gene selection based on multi-class support vector machines and genetic algorithms

B. Feres de Souza and de Carvalho, A. Ponce de L., Gene selection based on multi-class support vector machines and genetic algorithms, vol. 4, pp. 599-607, 2005.

Microarrays are a new technology that allows biologists to better understand the interactions between diverse pathologic state at the gene level. However, the amount of data generated by these tools becomes problematic, even though data are supposed to be automatically analyzed (e.g., for diagnostic purposes). The issue becomes more complex when the expression data involve multiple states. We present a novel approach to the gene selection problem in multi-class gene expression-based cancer classification, which combines support vector machines and genetic algorithms.

Gene Class Expression: analysis tool of Gene Ontology terms with gene expression data

G. S. P. Pereira, Brandão, R. M., Giuliatti, S., Zago, M. A., and Silva, Jr., W. A., Gene Class Expression: analysis tool of Gene Ontology terms with gene expression data, vol. 5, pp. 108-114, 2006.

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy.

BayBoots: a model-free Bayesian tool to identify class markers from gene expression data

R. Z. N. Vêncio, Patrão, D. F. C., Baptista, C. S., Pereira, C. A. B., and Zingales, B., BayBoots: a model-free Bayesian tool to identify class markers from gene expression data, vol. 5, pp. 138-142, 2006.

One of the goals of gene expression experiments is the identification of differentially expressed genes among populations that could be used as markers. For this purpose, we implemented a model-free Bayesian approach in a user-friendly and freely available web-based tool called BayBoots. In spite of a common misunderstanding that Bayesian and model-free approaches are incompatible, we merged them in the BayBoots implementation using the Kernel density estimator and Rubin’s Bayesian Bootstrap.

Application of MUTIC to the exploration of gene expression data in prostate cancer

L. S. Coelho, Mudado, M. A., Goertzel, B., and Pennachin, C., Application of MUTIC to the exploration of gene expression data in prostate cancer, vol. 6, pp. 890-900, 2007.

We show here an example of the application of a novel method, MUTIC (model utilization-based clustering), used for identifying complex interactions between genes or gene categories based on gene expression data. The method deals with binary categorical data which consist of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple time points.

Optimal clone identifier for genomic shotgun libraries: "OC Identifier tool"

M. E. Cantão, Ferreira, J. E., and Lemos, E. G. M., Optimal clone identifier for genomic shotgun libraries: "OC Identifier tool", vol. 6, pp. 743-755, 2007.

In DNA microarray experiments, the gene fragments that are spotted on the slides are usually obtained by the synthesis of specific oligonucleotides that are able to amplify genes through PCR. Shotgun library sequences are an alternative to synthesis of primers for the study of each gene in the genome. The possibility of putting thousands of gene sequences into a single slide allows the use of shotgun clones in order to proceed with microarray analysis without a completely sequenced genome.

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