Identification of Novel Reference Genes Based on MeSH Categories

Cataloged from PDF version of article.Transcriptome experiments are performed to assess protein abundance through mRNA expression analysis. Expression levels of genes vary depending on the experimental conditions and the cell response. Transcriptome data must be diverse and yet comparable in reference to stably expressed genes, even if they are generated from different experiments on the same biological context from various laboratories. In this study, expression patterns of 9090 microarray samples grouped into 381 NCBI-GEO datasets were investigated to identify novel candidate reference genes using randomizations and Receiver Operating Characteristic (ROC) curves. The analysis demonstrated that cell type specific reference gene sets display less variability than a united set for all tissues. Therefore, constitutively and stably expressed, origin specific novel reference gene sets were identified based on their coefficient of variation and percentage of occurrence in all GEO datasets, which were classified using Medical Subject Headings (MeSH). A large number of MeSH grouped reference gene lists are presented as novel tissue specific reference gene lists. The most commonly observed 17 genes in these sets were compared for their expression in 8 hepatocellular, 5 breast and 3 colon carcinoma cells by RT-qPCR to verify tissue specificity. Indeed, commonly used housekeeping genes GAPDH, Actin and EEF2 had tissue specific variations, whereas several ribosomal genes were among the most stably expressed genes in vitro. Our results confirm that two or more reference genes should be used in combination for differential expression analysis of large-scale data obtained from microarray or next generation sequencing studies. Therefore context dependent reference gene sets, as presented in this study, are required for normalization of expression data from diverse technological backgrounds. © 2014 Ersahin et al

Implicit motif distribution based hybrid computational kernel for sequence classification

Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive. Results: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved ∼81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol. © The Author 2004. Published by Oxford University Press. All rights reserved

Special issue on microscopic image processing

Cataloged from PDF version of article

Cancer Cell Cytotoxicities of 1-(4-Substitutedbenzoyl)-4-(4-chlorobenzhydryl) piperazine Derivatives

Cataloged from PDF version of article.A series of novel 1-(4-substitutedbenzoyl)-4-(4-chlorobenzhydryl)piperazine derivatives 5a-g was designed by a nucleophilic substitution reaction of 1-(4-chlorobenzhydryl) piperazine with various benzoyl chlorides and characterized by elemental analyses, IR and H-1 nuclear magnetic resonance spectra. Cytotoxicity of the compounds was demonstrated on cancer cell lines from liver (HUH7, FOCUS, MAHLAVU, HEPG2, HEP3B), breast (MCF7, BT20, T47D, CAMA-1), colon (HCT-116), gastric (KATO-3) and endometrial (MFE-296) cancer cell lines. Time-dependent cytotoxicity analysis of compound 5a indicated the long-term in situ stability of this compound. All compounds showed significant cell growth inhibitory activity on the selected cancer cell lines

A new integrable generalization of the Korteweg - de Vries equation

A new integrable sixth-order nonlinear wave equation is discovered by means of the Painleve analysis, which is equivalent to the Korteweg - de Vries equation with a source. A Lax representation and a Backlund self-transformation are found of the new equation, and its travelling wave solutions and generalized symmetries are studied.Comment: 13 pages, 2 figure

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

Synthesis of Novel 6-(4-Substituted piperazine-1-yl)-9(b-D-ribofuranosyl) purine Derivatives, Which Lead to Senescence-Induced Cell Death in liver Cancer Cells

Cataloged from PDF version of article.Novel purine ribonucleoside analogues (9-13) containing a 4-substituted piperazine in the substituent at N-6 were synthesized and evaluated for their cytotoxicity on Huh7, HepG2, FOCUS, Mahlavu liver, MCF7 breast, and HCT116 colon carcinoma cell lines. The purine nucleoside analogues were analyzed initially by an anticancer drug-screening method based on a sulforhodamine B assay. Two nucleoside derivatives with promising cytotoxic activities (11 and 12) were further analyzed on the hepatoma cells. The N-6-(4-Trifluoromethylphenyl)piperazine analogue 11 displayed the best antitumor activity, with IC50 values between 5.2 and 9.2 mu M. Similar to previously described nucleoside analogues, compound 11 also interferes with cellular ATP reserves, possibly through influencing cellular kinase activities. Furthermore, the novel nucleoside analogue 11 was shown to induce senescence-associated cell death, as demonstrated by the SA beta-gal assay. The senescence-dependent cytotoxic effect of 11 was also confirmed through phosphorylation of the Rb protein by p15(INK4b) overexpression in the presence of this compound

Data and model driven hybrid approach to activity scoring of cyclic pathways

Analysis of large scale -omics data based on a single tool remains inefficient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hybrid approach to evaluate biological activity of cellular processes. Biological pathway models were taken as graphs and gene scores were transferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a specific biological process was computed by owing of converged gene scores until reaching a target process. Biological pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways. © 2011 Springer Science+Business Media B.V

Bi-k-bi clustering: Mining large scale gene expression data using two-level biclustering

Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations. Copyright © 2010 Inderscience Enterprises Ltd