Search results for: Gene expression profiling

Authors: Chintanu K. Sarmah, Sandhya Samarasinghe, Don Kulasiri, Daniel Catchpoole

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Gene expression profiling is rapidly evolving into a powerful technique for investigating tumor malignancies. The researchers are overwhelmed with the microarray-based platforms and methods that confer them the freedom to conduct large-scale gene expression profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data.

Keywords: Gene expression profiling, microarray, cDNA, Affymetrix, childhood leukaemia.

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Authors: Khlopova N.S., Glazko V.I., Glazko T.T.

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Using DNA microarrays the comparative analysis of a gene expression profiles is carried out in a liver and kidneys of pigs. The hypothesis of a cross hybridization of one probe with different cDNA sites of the same gene or different genes is checked up, and it is shown, that cross hybridization can be a source of essential errors at revealing of a key genes in organ-specific transcriptome. It is reveald that distinctions in profiles of a gene expression are well coordinated with function, morphology, biochemistry and histology of these organs.

Keywords: Microarray, gene expression profiles, key genes.

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Authors: Masaki Yamaguchi, Akira Ito, Noriaki Okamoto, Yoshinori Kawabe, Masamichi Kamihira

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Heat-inducible gene expression vectors are useful for hyperthermia-induced cancer gene therapy, because the combination of hyperthermia and gene therapy can considerably improve the therapeutic effects. In the present study, we developed an enhanced heat-inducible transgene expression system in which a heat-shock protein (HSP) promoter and tetracycline-responsive transactivator were combined. When the transactivator plasmid containing the tetracycline-responsive transactivator gene was co-transfected with the reporter gene expression plasmid, a high level of heat-induced gene expression was observed compared with that using the HSP promoter without the transactivator. In vitro evaluation of the therapeutic effect using HeLa cells showed that heat-induced therapeutic gene expression caused cell death in a high percentage of these cells, indicating that this strategy is promising for cancer gene therapy.

Keywords: Inducible gene expression, Gene therapy, Hyperthermia, Heat shock protein, Tetracycline transactivator.

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Authors: Carla Layana Luis Diambra

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Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.

Keywords: circadian rhythms, clustering, gene expression, PCA.

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Authors: M. Anandhavalli Gauthaman

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DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.

Keywords: DNA microarray, gene expression, association rule mining.

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Authors: Hiba Hasan, Khalid Raza

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Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to reconstruct genetic regulatory network from time series gene expression data. Supercoiled data set from Escherichia coli has been taken to demonstrate the proposed method.

Keywords: Gene expression, gene regulatory networks (GRNs), clustering, data preprocessing, network visualization.

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Authors: T. Chandrasekhar, K. Thangavel, E. N. Sathishkumar

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Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.

Keywords: Clustering, Feature selection, Gene expression data, Quick reduct.

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Authors: Seo Young Kim, Toshimitsu Hamasaki

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Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this paper, we express and compare the performance of several clustering methods based on data preprocessing including strategies of normalization or noise clearness. We also evaluate each of these clustering methods with validation measures for both simulated data and real gene expression data. Consequently, clustering methods which are common used in microarray data analysis are affected by normalization and degree of noise and clearness for datasets.

Keywords: Gene expression, clustering, data preprocessing.

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Authors: Farahnaz Amini, Woo Yun Kin, Lazwani Kolandaiveloo

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Availability of different genetic tests after completion of Human Genome Project increases the physicians’ responsibility to keep themselves update on the potential implementation of these genetic tests in their daily practice. However, due to numbers of barriers, still many of physicians are not either aware of these tests or are not willing to offer or refer their patients for genetic tests. This study was conducted an anonymous, cross-sectional, mailed-based survey to develop a primary data of Malaysian physicians’ level of knowledge and perception of gene profiling. Questionnaire had 29 questions. Total scores on selected questions were used to assess the level of knowledge. The highest possible score was 11. Descriptive statistics, one way ANOVA and chi-squared test was used for statistical analysis. Sixty three completed questionnaires were returned by 27 general practitioners (GPs) and 36 medical specialists. Responders’ age ranges from 24 to 55 years old (mean 30.2 ± 6.4). About 40% of the participants rated themselves as having poor level of knowledge in genetics in general whilst 60% believed that they have fair level of knowledge; however, almost half (46%) of the respondents felt that they were not knowledgeable about available genetic tests. A majority (94%) of the responders were not aware of any lab or company which is offering gene profiling services in Malaysia. Only 4% of participants were aware of using gene profiling for detection of dosage of some drugs. Respondents perceived greater utility of gene profiling for breast cancer (38%) compared to the colorectal familial cancer (3%). The score of knowledge ranged from 2 to 8 (mean 4.38 ± 1.67). Non- significant differences between score of knowledge of GPs and specialists were observed, with score of 4.19 and 4.58 respectively. There was no significant association between any demographic factors and level of knowledge. However, those who graduated between years 2001 to 2005 had higher level of knowledge. Overall, 83% of participants showed relatively high level of perception on value of gene profiling to detect patient’s risk of disease. However, low perception was observed for both statements of using gene profiling for general population in order to alter their lifestyle (25%) as well as having the full sequence of a patient genome for the purpose of determining a patient’s best match for treatment (18%). The lack of clinical guidelines, limited provider knowledge and awareness, lack of time and resources to educate patients, lack of evidence-based clinical information and cost of tests were the most barriers of ordering gene profiling mentioned by physicians. In conclusion Malaysian physicians who participate in this study had mediocre level of knowledge and awareness in gene profiling. The low exposure to the genetic questions and problems might be a key predictor of lack of awareness and knowledge on available genetic tests. Educational and training workshop might be useful in helping Malaysian physicians incorporate genetic profiling into practice for eligible patients.

Keywords: Gene Profiling, Knowledge, Malaysia, Physician.

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Authors: Ankit Agrawal, Ankush Mittal

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A gene network gives the knowledge of the regulatory relationships among the genes. Each gene has its activators and inhibitors that regulate its expression positively and negatively respectively. Genes themselves are believed to act as activators and inhibitors of other genes. They can even activate one set of genes and inhibit another set. Identifying gene networks is one of the most crucial and challenging problems in Bioinformatics. Most work done so far either assumes that there is no time delay in gene regulation or there is a constant time delay. We here propose a Dynamic Time- Lagged Correlation Based Method (DTCBM) to learn the gene networks, which uses time-lagged correlation to find the potential gene interactions, and then uses a post-processing stage to remove false gene interactions to common parents, and finally uses dynamic correlation thresholds for each gene to construct the gene network. DTCBM finds correlation between gene expression signals shifted in time, and therefore takes into consideration the multi time delay relationships among the genes. The implementation of our method is done in MATLAB and experimental results on Saccharomyces cerevisiae gene expression data and comparison with other methods indicate that it has a better performance.

Keywords: Activators, correlation, dynamic time-lagged correlation based method, inhibitors, multi-time delay gene network.

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Authors: R. Balamurugan, A. M. Natarajan, K. Premalatha

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Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

Keywords: Particle swarm optimization, Shuffled frog leaping, Cuckoo search, biclustering, gene expression data.

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Authors: R. Mallika, V. Saravanan

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This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.

Keywords: Support vector machines-one against all, cancerclassification, Linear Discriminant analysis, K nearest neighbour, microarray gene expression, gene pair ranking.

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Authors: M. Pandi, K. Premalatha

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A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.

Keywords: DNA, Microarray, genomics, Cuckoo Search, Differential Evolution, Gene expression data, Clustering.

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Authors: Seo Young Kim, Jae Won Lee, Jong Sung Bae

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Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms.

Keywords: Clustering, microarray experiment, temporal pattern of gene expression data.

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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Development of a method to estimate gene functions is an important task in bioinformatics. One of the approaches for the annotation is the identification of the metabolic pathway that genes are involved in. Since gene expression data reflect various intracellular phenomena, those data are considered to be related with genes’ functions. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: Metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning.

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Authors: Chunming Xu

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Sparse representation which can represent high dimensional data effectively has been successfully used in computer vision and pattern recognition problems. However, it doesn-t consider the label information of data samples. To overcome this limitation, we develop a novel dimensionality reduction algorithm namely dscriminatively regularized sparse subspace learning(DR-SSL) in this paper. The proposed DR-SSL algorithm can not only make use of the sparse representation to model the data, but also can effective employ the label information to guide the procedure of dimensionality reduction. In addition,the presented algorithm can effectively deal with the out-of-sample problem.The experiments on gene-expression data sets show that the proposed algorithm is an effective tool for dimensionality reduction and gene-expression data classification.

Keywords: sparse representation, dimensionality reduction, labelinformation, sparse subspace learning, gene-expression data classification.

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Authors: Rio G. L. D'Souza, K. Chandra Sekaran, A. Kandasamy

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The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.

Keywords: Evolutionary computing, gene expression analysis, gene networks, microarray data analysis, phenomic algorithms.

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Authors: M. Marjaneh, M. Y. Narazah, H. Shahrul

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Array-based gene expression analysis is a powerful tool to profile expression of genes and to generate information on therapeutic effects of new anti-cancer compounds. Anti-apoptotic effect of thymoquinone was studied in MCF7 breast cancer cell line using gene expression profiling with cDNA microarray. The purity and yield of RNA samples were determined using RNeasyPlus Mini kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity of the RNA samples. AffinityScript RT oligo-dT promoter primer was used to generate cDNA strands. T7 RNA polymerase was used to convert cDNA to cRNA. The cRNA samples and human universal reference RNA were labelled with Cy-3-CTP and Cy-5-CTP, respectively. Feature Extraction and GeneSpring softwares analysed the data. The single experiment analysis revealed involvement of 64 pathways with up-regulated genes and 78 pathways with downregulated genes. The MAPK and p38-MAPK pathways were inhibited due to the up-regulation of PTPRR gene. The inhibition of p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of p38-MAPK caused up-regulation of TP53 and down-regulation of Bcl2 genes indicating involvement of intrinsic apoptotic pathway. Down-regulation of CARD16 gene as an adaptor molecule regulated CASP1 and suggested necrosis-like programmed cell death and involvement of caspase in apoptosis. Furthermore, down-regulation of GPCR, EGF-EGFR signalling pathways suggested reduction of ER. Involvement of AhR pathway which control cytochrome P450 and glucuronidation pathways showed metabolism of Thymoquinone. The findings showed differential expression of several genes in apoptosis pathways with thymoquinone treatment in estrogen receptor-positive breast cancer cells.

Keywords: CARD16, CASP10, cDNA microarray, PTPRR, Thymoquinone.

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Authors: W. Loongyai, N. Saengsawang, W. Danvilai, C. Kridtayopas, P. Sopannarath, C. Bunchasak

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Betong chicken is a slow growing and a lean strain of chicken, while the rapid growth of broiler is accompanied by increased fat. We investigated the growth performance, fat accumulations, lipid serum biochemical levels and lipoprotein lipase (LPL) gene expression of female Betong (KU line) at the age of 4 and 6 weeks. A total of 80 female Betong chickens (KU line) and 80 female broiler chickens were reared under open system (each group had 4 replicates of 20 chicks per pen). The results showed that feed intake and average daily gain (ADG) of broiler chicken were significantly higher than Betong (KU line) (P < 0.01), while feed conversion ratio (FCR) of Betong (KU line) at week 6 were significantly lower than broiler chicken (P < 0.01) at 6 weeks. At 4 and 6 weeks, two birds per replicate were randomly selected and slaughtered. Carcass weight did not significantly differ between treatments; the percentage of abdominal fat and subcutaneous fat yield was higher in the broiler (P < 0.01) at 4 and 6 week. Total cholesterol and LDL level of broiler were higher than Betong (KU line) at 4 and 6 weeks (P < 0.05). Abdominal fat samples were collected for total RNA extraction. The cDNA was amplified using primers specific for LPL gene expression and analysed using real-time PCR. The results showed that the expression of LPL gene was not different when compared between Betong (KU line) and broiler chickens at the age of 4 and 6 weeks (P > 0.05). Our results indicated that broiler chickens had high growth rate and fat accumulation when compared with Betong (KU line) chickens, whereas LPL gene expression did not differ between breeds.

Keywords: Lipoprotein lipase gene, Betong (KU line), broiler, abdominal fat, gene expression.

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Authors: M. Anidha, K. Premalatha

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Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.

Keywords: Gene selection, mutual information, Fisher score, classification, SVM.

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Authors: Ngo Thanh Xuan, Mai Thi Hang, Vu Nguyen Thanh

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A. niger XP isolated from Vietnam produces very low amount of acidic phytase with optimal pH at 2.5 and 5.5. The phytase production of this strain was successfully improved through gene cloning and expression. A 1.4 - kb DNA fragment containing the coding region of the phyA gene was amplified by PCR and inserted into the expression vector pPICZαA with a signal peptide α- factor, under the control of AOX1 promoter. The recombined plasmid was transformed into the host strain P. pastoris KM71H and X33 by electroporation. Both host strains could efficiently express and secret phytase. The multicopy strains were screened for over expression of phytase. All the selected multicopy strains of P. pastoris X33 were examined for phytase activity, the maximum phytase yield of 1329 IU/ml was obtained after 4 days of incubation in medium BMM. The recombinant protein with MW of 97.4 KW showed to be the only one protein secreted in the culture broth. Multicopy transformant P. pastoris X33 supposed to be potential candidate for producing the commercial preparation of phytase.

Keywords: Aspergillus niger XP, cloning, over expression, Pichia pastoris, phyA , phytase.

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

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Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: Tiancheng Lan

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E10A is a kind of replication-defective adenovirus which carries the human endostatin gene to inhibit the growth of tumors. Kringle 5(K5) has almost the same function as angiostatin to also inhibit the growth of tumors since they are all the byproduct of the proteolytic cleavage of plasminogen. Tumor size increasing can be suppressed because both of the endostatin and K5 can restrain the angiogenesis process. Therefore, in order to improve the treatment effect on tumor, 2A peptide is used to construct a fusion gene carrying both E10A and K5. Using 2A peptide is an ideal strategy when a fusion gene is expressed because it can avoid many problems during the expression of more than one kind of protein. The overlap extension PCR is also used to connect 2A peptide with E10A and K5. The final construction of fusion gene E10A-2A-K5 can provide a possible new method of the anti-angiogenesis treatment with a better expression performance.

Keywords: E10A, Kringle 5, 2A peptide, overlap extension PCR.

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Authors: Cj Gletus Matthews Cn Jacobs, Kogilah Narayanasamy

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This article explores the sociological perspectives on social problems and the role of the media which has a delicate role to tread in balancing its duty to the public and the victim Whilst social problems have objective conditions, it is the subjective definition of such problems that ensure which social problem comes to the fore and which doesn-t. Further it explores the roles and functions of policymakers when addressing social problems and the impact of the inception of media profiling as well as the advantages and disadvantages of media profiling towards social problems. It focuses on the inception of media profiling due to its length and a follow up article will explore how current media profiling towards social problems have evolved since its inception.

Keywords: Media Profiling, Policy Response, Social Problems

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Authors: M. Pandi, K. Premalatha

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The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.

Keywords: Spread out, simplex, multi-minima, fitness function, optimization, search area, monocyte, solution, genomes.

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Authors: S. Banerjee, A. Apte Deshpande, N. Mandi, S. Padmanabhan

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We report a novel fusion tag for expressing recombinant proteins in E. coli. The fusion tag is the C-terminus part of the human GMCSF gene comprising 45 amino acids, which aid in over expression of otherwise non expressible genes. Expression of hIFN a2b with this fusion tag also escapes the requirement of rare codons for expression. This is also a first report of a small fusion tag of human origin having affinity to heparin sepharose column facilitating the purification of fusion protein.

Keywords: fusion tag, bacterial expression, rare codons, human GMCSF

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Authors: Maria E. Manioudaki, Panayiota Poirazi

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Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.

Keywords: gene modules, artificial neural networks, yeast, stress

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Authors: Liping Jing, Michael K. Ng, Tieyong Zeng

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Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model.

Keywords: Gene Selection, Cancer Prediction, Lasso, Clustering, Classification.

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Authors: I. Nassiri, B. Goliaei, M. Tavassoli

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The ability to distinguish missense nucleotide substitutions that contribute to harmful effect from those that do not is a difficult problem usually accomplished through functional in vivo analyses. In this study, instead current biochemical methods, the effects of missense mutations upon protein structure and function were assayed by means of computational methods and information from the databases. For this order, the effects of new missense mutations in exon 5 of PTEN gene upon protein structure and function were examined. The gene coding for PTEN was identified and localized on chromosome region 10q23.3 as the tumor suppressor gene. The utilization of these methods were shown that c.319G>A and c.341T>G missense mutations that were recognized in patients with breast cancer and Cowden disease, could be pathogenic. This method could be use for analysis of missense mutation in others genes.

Keywords: Bioinformatics, missense mutations, PTEN tumorsuppressor gene.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

Abstract:

Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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