Search results for: conserved genes

Authors: Rehana Naz, D. P. Mason, Fazal Mahomed

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A systematic way to derive the conserved quantities for the axisymmetric liquid jet, free jet and wall jet using conservation laws is presented. The flow in axisymmetric jets is governed by Prandtl-s momentum boundary layer equation and the continuity equation. The multiplier approach is used to construct a basis of conserved vectors for the system of two partial differential equations for the two velocity components. The basis consists of two conserved vectors. By integrating the corresponding conservation laws across the jet and imposing the boundary conditions, conserved quantities are derived for the axisymmetric liquid and free jet. The multiplier approach applied to the third-order partial differential equation for the stream function yields two local conserved vectors one of which is a non-local conserved vector for the system. One of the conserved vectors gives the conserved quantity for the axisymmetric free jet but the conserved quantity for the wall jet is not obtained from the second conserved vector. The conserved quantity for the axisymmetric wall jet is derived from a non-local conserved vector of the third-order partial differential equation for the stream function. This non-local conserved vector for the third-order partial differential equation for the stream function is obtained by using the stream function as multiplier.

Keywords: Axisymmetric jet, liquid jet, free jet, wall jet, conservation laws, conserved quantity.

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Authors: Tom Snir, Eitan Rubin

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Mendelian Disease Genes represent a collection of single points of failure for the various systems they constitute. Such genes have been shown, on average, to encode longer proteins than 'non-disease' proteins. Existing models suggest that this results from the increased likeli-hood of longer genes undergoing mutations. Here, we show that in saturated mutagenesis experiments performed on model organisms, where the likelihood of each gene mutating is one, a similar relationship between length and the probability of a gene being lethal was observed. We thus suggest an extended model demonstrating that the likelihood of a mutated gene to produce a severe phenotype is length-dependent. Using the occurrence of conserved domains, we bring evidence that this dependency results from a correlation between protein length and the number of functions it performs. We propose that protein length thus serves as a proxy for protein cardinality in different networks required for the organism's survival and well-being. We use this example to argue that the collection of Mendelian Disease Genes can, and should, be used to study the rules governing systems vulnerability in living organisms.

Keywords: Systems Biology, Protein Length

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Authors: Efendi Zaenudin, Chien-Hung Huang, Ka-Lok Ng

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Essential genes play an important role in the survival of an organism. It has been shown that cancer-associated essential genes are genes necessary for cancer cell proliferation, where these genes are potential therapeutic targets. Also, it was demonstrated that mutations of the cancer-associated essential genes give rise to the resistance of immunotherapy for patients with tumors. In the present study, we focus on studying the biological effects of the essential genes from a network perspective. We hypothesize that one can analyze a biological molecular network by decomposing it into both three-node and four-node digraphs (subgraphs). These network subgraphs encode the regulatory interaction information among the network’s genetic elements. In this study, the frequency of occurrence of the subgraph-associated essential genes in a molecular network was quantified by using the statistical parameter, odds ratio. Biological effects of subgraph-associated essential genes are discussed. In summary, the subgraph approach provides a systematic method for analyzing molecular networks and it can capture useful biological information for biomedical research.

Keywords: Biological molecular networks, essential genes, graph theory, network subgraphs.

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Authors: U. Kairov, T. Karpenyuk, E. Ramanculov, A. Zinovyev

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The most common result of analysis of highthroughput data in molecular biology represents a global list of genes, ranked accordingly to a certain score. The score can be a measure of differential expression. Recent work proposed a new method for selecting a number of genes in a ranked gene list from microarray gene expression data such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. Here we present calculation results of relative connectivity of genes from META-OFTEN network and tentative biological interpretation of the most reproducible signal. The relative connectivity and inbetweenness values of genes from META-OFTEN network were estimated.

Keywords: Microarray, META-OFTEN, gene network.

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Authors: Jafar Ahmadi, Nafiseh Noormohammadi, Sedigheh Fabriki Ourang

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GRF, Growth regulating factor, genes encode a novel class of plant-specific transcription factors. The GRF proteins play a role in the regulation of cell numbers in young and growing tissues and may act as transcription activations in growth and development of plants. Identification of GRF genes and their expression are important in plants to performance of the growth and development of various organs. In this study, to better understanding the structural and functional differences of GRFs family, 45 GRF proteins sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H. vulgare and S. bicolor, have been collected and analyzed through bioinformatics data mining. As a result, in secondary structure of GRFs, the number of alpha helices was more than beta sheets and in all of them QLQ domains were completely in the biggest alpha helix. In all GRFs, QLQ and WRC domains were completely protected except in AtGRF9. These proteins have no trans-membrane domain and due to have nuclear localization signals act in nuclear and they are component of unstable proteins in the test tube.

Keywords: Domain, Gene Family, GRF, Motif.

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Authors: R. K. Agrawal, Rajni Bala

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Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.

Keywords: Gene selection, genetic algorithm, microarray datasets, multi-class SVM.

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Authors: Sh. A. Yousif, M. Bhave

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There is strong evidence that water channel proteins 'aquaporins (AQPs)' are central components in plant-water relations as well as a number of other physiological parameters. We had previously reported the isolation of 24 plasma membrane intrinsic protein (PIP) type AQPs. However, the gene numbers in rice and the polyploid nature of bread wheat indicated a high probability of further genes in the latter. The present work focused on identification of further AQP isoforms in bread wheat. With the use of altered primer design, we identified five genes homologous, designated PIP1;5b, PIP2;9b, TaPIP2;2, TaPIP2;2a, TaPIP2;2b. Sequence alignments indicate PIP1;5b, PIP2;9b are likely to be homeologues of two previously reported genes while the other three are new genes and could be homeologs of each other. The results indicate further AQP diversity in wheat and the sequence data will enable physical mapping of these genes to identify their genomes as well as genetic to determine their association with any quantitative trait loci (QTLs) associated with plant-water relation such as salinity or drought tolerance.

Keywords: Aquaporins, homeologues, PIP, wheat

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

Abstract:

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: R. Haddad, K. Morris, V. Buchanan-Wollaston

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Biochemical and molecular analysis of some antioxidant enzyme genes revealed different level of gene expression on oilseed (Brassica napus). For molecular and biochemical analysis, leaf tissues were harvested from plants at eight different developmental stages, from young to senescence. The levels of total protein and chlorophyll were increased during maturity stages of plant, while these were decreased during the last stages of plant growth. Structural analysis (nucleotide and deduced amino acid sequence, and phylogenic tree) of a complementary DNA revealed a high level of similarity for a family of Catalase genes. The expression of the gene encoded by different Catalase isoforms was assessed during different plant growth phase. No significant difference between samples was observed, when Catalase activity was statistically analyzed at different developmental stages. EST analysis exhibited different transcripts levels for a number of other relevant antioxidant genes (different isoforms of SOD and glutathione). The high level of transcription of these genes at senescence stages was indicated that these genes are senescenceinduced genes.

Keywords: Biochemical analysis, Oilseed, Expression pattern, Growth phases

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Authors: Yuhong Lu

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Graves’ Disease (GD), an autoimmune health condition caused by the over reactiveness of the thyroid, affects about 1 in 200 people worldwide. GD is not caused by one specific single nucleotide polymorphism (SNP) or gene mutation, but rather determined by multiple factors, each differing from each other. Malfunction of the genes in Human Leukocyte Antigen (HLA) family tend to play a major role in autoimmune diseases, but other genes, such as LOC101929163, have functions that still remain ambiguous. Currently, little studies were done to study GD, resulting in inconclusive results. This study serves not only to introduce background knowledge about GD, but also to organize and pinpoint the major SNPs and genes that are potentially related to the occurrence of GD in humans. Collected from multiple sources from genome-wide association studies (GWAS) Central, the potential SNPs related to the causes of GD are included in this study. This study has located the genes that are related to those SNPs and closely examines a selected sample. Using the data from this study, scientists will then be able to focus on the most expressed genes in GD patients and develop a treatment for GD.

Keywords: CTLA4, Graves’ Disease, HLA, single nucleotide polymorphism, SNP.

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Authors: Fereshteh Shacheraghi, Mohammad Reza Shakibaie, Hanieh Noveiri

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Fourty one strains of ESBL producing P.aeruginosa which were previously isolated from burn patients in Kerman University general hospital, Iran were subjected to PCR, RFLP and sequencing in order to determine the type of extended spectrum β- lactamases (ESBL), the restriction digestion pattern and possibility of mutation among detected genes. DNA extraction was carried out by phenol chloroform method. PCR for detection of bla genes was performed using specific primer for each gene. Restriction Fragment Length Polymorphism (RFLP) for ESBL genes was carried out using EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The PCR products were subjected to direct sequencing of both the strands for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1, blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the (n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29) 70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates respectively. The RFLP analysis showed that each ESBL gene has identical pattern of digestion among the isolated strains. Sequencing of the ESBL genes confirmed the genuinety of PCR products and revealed no mutation in the restriction sites of the above genes. From results of the present investigation it can be concluded that blaVEB-1 and blaCTX-M were the most and the least frequently isolated ESBL genes among the P.aeruginosa strains isolated from burn patients. The RFLP and sequencing analysis revealed that same clone of the bla genes were indeed existed among the antibiotic resistant strains.

Keywords: ESBL genes, PCR, RFLP, Sequencing, P.aeruginosa

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Authors: JiEun Jeong, Se-Won Kang, Hee-Ju Hwang, Sung-Hwa Chae, Sang-Haeng Choi, Hong-SeogPark, YeonSoo Han, Bok-Reul Lee, Dae-Hyun Seog, Yong Seok Lee

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Tofurther advance research on immune-related genes from T. molitor, we constructed acDNA library and analyzed expressed sequence taq (EST) sequences from 1,056 clones. After removing vector sequence and quality checkingthrough thePhred program (trim_alt 0.05 (P-score>20), 1039 sequences were generated. The average length of insert was 792 bp. In addition, we identified 162 clusters, 167 contigs and 391 contigs after clustering and assembling process using a TGICL package. EST sequences were searchedagainst NCBI nr database by local BLAST (blastx, EKeywords: EST, Innate immunity, Tenebriomolitor

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Authors: C. Gunavathi, K. Premalatha

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Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.

Keywords: Microarray, T-Statistics, Signal-to-Noise Ratio, FStatistics, Particle Swarm Optimization, Shuffled Frog Leaping, Naïve Bayes Classifier.

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Authors: E. Al Daoud

Abstract:

The purpose of computing the similarity and the diversity in the species is to trace the process of evolution and to find the relationship between the species and discover the unique, the special, the common and the universal proteins. The proteins of the whole genome of 40 species are compared with the cronobacter genome which is used as reference genome. More than 3 billion pairwise alignments are performed using blastp. Several findings are introduced in this study, for example, we found 172 proteins in cronobacter genome which have insignificant hits in other species, 116 significant proteins in the all tested species with very high score value and 129 common proteins in the plants but have insignificant hits in mammals, birds, fishes, and insects.

Keywords: Genome, species, blastp, conserved genes, cronobacter.

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Authors: Nadezhda M. Usmanova, Nikolai V. Tomilin

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Mammalian genomes contain large number of retroelements (SINEs, LINEs and LTRs) which could affect expression of protein coding genes through associated transcription factor binding sites (TFBS). Activity of the retroelement-associated TFBS in many genes is confirmed experimentally but their global functional impact remains unclear. Human SINEs (Alu repeats) and mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich gene rich genome segments consistent with the view that they can contribute to regulation of gene expression. We have shown earlier that Alu are involved in formation of cis-regulatory modules (clusters of TFBS) in human promoters, and other authors reported that Alu located near promoter CpG islands have an increased frequency of CpG dinucleotides suggesting that these Alu are undermethylated. Human Alu and mouse B1/B2 elements have an internal bipartite promoter for RNA polymerase III containing conserved sequence motif called B-box which can bind basal transcription complex TFIIIC. It has been recently shown that TFIIIC binding to B-box leads to formation of a boundary which limits spread of repressive chromatin modifications in S. pombe. SINEassociated B-boxes may have similar function but conservation of TFIIIC binding sites in SINEs located near mammalian promoters has not been studied earlier. Here we analysed abundance and distribution of retroelements (SINEs, LINEs and LTRs) in annotated sequences of the Database of mammalian transcription start sites (DBTSS). Fractions of SINEs in human and mouse promoters are slightly lower than in all genome but >40% of human and mouse promoters contain Alu or B1/B2 elements within -1000 to +200 bp interval relative to transcription start site (TSS). Most of these SINEs is associated with distal segments of promoters (-1000 to -200 bp relative to TSS) indicating that their insertion at distances >200 bp upstream of TSS is tolerated during evolution. Distribution of SINEs in promoters correlates negatively with the distribution of CpG sequences. Using analysis of abundance of 12-mer motifs from the B1 and Alu consensus sequences in genome and DBTSS it has been confirmed that some subsegments of Alu and B1 elements are poorly conserved which depends in part on the presence of CpG dinucleotides. One of these CpG-containing subsegments in B1 elements overlaps with SINE-associated B-box and it shows better conservation in DBTSS compared to genomic sequences. It has been also studied conservation in DBTSS and genome of the B-box containing segments of old (AluJ, AluS) and young (AluY) Alu repeats and found that CpG sequence of the B-box of old Alu is better conserved in DBTSS than in genome. This indicates that Bbox- associated CpGs in promoters are better protected from methylation and mutation than B-box-associated CpGs in genomic SINEs. These results are consistent with the view that potential TFIIIC binding motifs in SINEs associated with human and mouse promoters may be functionally important. These motifs may protect promoters from repressive histone modifications which spread from adjacent sequences. This can potentially explain well known clustering of SINEs in GC-rich gene rich genome compartments and existence of unmethylated CpG islands.

Keywords: Retroelement, promoter, CpG island, DNAmethylation.

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Authors: Chien-Hung Huang, Chia-Wei Weng, Chang-Chih Chiang, Shih-Hua Wu, Chih-Hsien Huang, Ka-Lok Ng

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MicroRNAs (miRNAs) are a class of non-coding RNAs that hybridize to mRNAs and induce either translation repression or mRNA cleavage. Recently, it has been reported that miRNAs could possibly play an important role in human diseases. By integrating miRNA target genes, cancer genes, miRNA and mRNA expression profiles information, a database is developed to link miRNAs to cancer target genes. The database provides experimentally verified human miRNA target genes information, including oncogenes and tumor suppressor genes. In addition, fragile sites information for miRNAs, and the strength of the correlation of miRNA and its target mRNA expression level for nine tissue types are computed, which serve as an indicator for suggesting miRNAs could play a role in human cancer. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/mirna_target/index.html.

Keywords: MicroRNA, miRNA expression profile, mRNAexpression profile, cancer genes, oncogene, tumor suppressor gene

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Authors: Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muhlhauser

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The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, generalized trees, graph alignment, DNA microarray data, cervical cancer.

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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: M. Raissy, M. Shahrani

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The present study was done to evaluate the presence of tetracycline resistance genes in Lactococcus garvieae isolated from cultured rainbow trout, West Iran. The isolates were examined for antimicrobial resistance using disc diffusion method. Of the 49 strains tested, 19 were resistant to tetracycline (38.7%), 32 to enrofloxacin (65.3%), 21 to erythromycin (42.8%), 20 to chloramphenicol and trimetoprim-sulfamethoxazole (40.8%). The strains were then characterized for their genotypic resistance profiles. The results revealed that all 49 isolates contained at least one of the tetracycline resistance genes. Tet (A) was found in 89.4% of tetracycline resistant isolates and the frequency of other gene were as follows: tet (E) 42.1%, tet (B) 47.3%, tet (D) 15.7%, tet (L) 26.3%, tet (K) 52.6%, tet (G) 36.8%, tet (34) 21%, tet (S) 63.1%, tet (C) 57.8%, tet (M) 73.6%, tet (O) 42.1%. The results revealed high levels of antibiotic resistance in L. garvieae strains which is a potential danger for trout culture as well as for public health.

Keywords: Lactococcus garvieae, rainbow trout, tetracycline resistance genes.

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

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Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that all genes are not important in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. Here a novel approach is proposed Hybrid K-Mean-Quick Reduct (KMQR) algorithm for gene selection from gene expression data. In this study, the entire dataset is divided into clusters by applying K-Means algorithm. Each cluster contains similar genes. The high class discriminated genes has been selected based on their degree of dependence by applying Quick Reduct algorithm to all the clusters. Average Correlation Value (ACV) is calculated for the high class discriminated genes. The clusters which have the ACV value as 1 is determined as significant clusters, whose classification accuracy will be equal or high when comparing to the accuracy of the entire dataset. The proposed algorithm is evaluated using WEKA classifiers and compared. The proposed work shows that the high classification accuracy.

Keywords: Clustering, Gene Selection, K-Mean-Quick Reduct, Rough Sets.

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Authors: Michael Netzer, Michael Seger, Mahesh Visvanathan, Bernhard Pfeifer, Gerald H. Lushington, Christian Baumgartner

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Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.

Keywords: lung cancer, micro arrays, data mining, feature selection.

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Authors: I. Naeem, R. Naz

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The group invariant solution for Prandtl-s boundary layer equations for an incompressible fluid governing the flow in radial free, wall and liquid jets having finite fluid velocity at the orifice are investigated. For each jet a symmetry is associated with the conserved vector that was used to derive the conserved quantity for the jet elsewhere. This symmetry is then used to construct the group invariant solution for the third-order partial differential equation for the stream function. The general form of the group invariant solution for radial jet flows is derived. The general form of group invariant solution and the general form of the similarity solution which was obtained elsewhere are the same.

Keywords: Two-dimensional jets, radial jets, group invariant solution.

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Authors: N. Umasuthan, S. D. N. K. Bathige, W. S. Thulasitha, I. Whang, J. Lee

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The MyD88 is an evolutionarily conserved host-expressed adaptor protein that is essential for proper TLR/ IL1R immune-response signaling. A previously identified complete cDNA (1626 bp) of OfMyD88 comprised an ORF of 867 bp encoding a protein of 288 amino acids (32.9 kDa). The gDNA (3761 bp) of OfMyD88 revealed a quinquepartite genome organization composed of 5 exons (with the sizes of 310, 132, 178, 92 and 155 bp) separated by 4 introns. All the introns displayed splice signals consistent with the consensus GT/AG rule. A bipartite domain structure with two domains namely death domain (24-103) coded by 1^st exon, and TIR domain (151-288) coded by last 3 exons were identified through in silico analysis. Moreover, homology modeling of these two domains revealed a similar quaternary folding nature between human and rock bream homologs. A comprehensive comparison of vertebrate MyD88 genes showed that they possess a 5-exonic structure.In this structure, the last three exons were strongly conserved, and this suggests that a rigid structure has been maintained during vertebrate evolution.A cluster of TATA box-like sequences were found 0.25 kb upstream of cDNA starting position. In addition, putative 5'-flanking region of OfMyD88 was predicted to have TFBS implicated with TLR signaling, including copies of NFkB1, APRF/ STAT3, Sp1, IRF1 and 2 and Stat1/2. Using qPCR technique, a ubiquitous mRNA expression was detected in liver and blood. Furthermore, a significantly up-regulated transcriptional expression of OfMyD88 was detected in head kidney (12-24 h; >2-fold), spleen (6 h; 1.5-fold), liver (3 h; 1.9-fold) and intestine (24 h; ~2-fold) post-Fla challenge. These data suggest a crucial role for MyD88 in antibacterial immunity of teleosts.

Keywords: MyD88, Innate immunity, Flagellin, Genomic analysis.

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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: 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: Zeinep A. Berkimbayeva, Almagul T. Mansharipova, Elmira M. Khussainova, Leyla B. Djansugurova

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It is believed that DNA damaging toxic metabolites contributes to the development of different pathological conditions. To prevent harmful influence of toxic agents, cells developed number of protecting mechanisms, such as enzymatic reaction of detoxification of reactive metabolites and repair of DNA damage. The aim of the study was to examine the association between polymorphism of GSTT1/GSTM1 and XRCC1/3 genes and coronary artery disease (CAD) incidence. To examine a polymorphism of these genes in CAD susceptibility in patients and controls, PCR based genotyping assay was performed. For GST genes, frequency of GSTM1 null genotype among CAD affected group was significantly increased than in control group (P<0.001). Frequencies of the GSTT1 null and positive alleles are almost equal in both groups (P>0.1). We found that neither XRCC1 Arg399Gln nor XRCC3 Thr241Met were associated with CAD risk. Obtained data suggests that GSTM1 null genotype carriers are more susceptible to CAD development.

Keywords: Cardiovascular disease, DNA reparation, gene polymorphism, risk factors, xenobiotic detoxification.

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Authors: Achitphol Chookaew, Paramee Thongsukhsai, Patamarerk Engsontia, Narongwit Nakwan, Pritsana Raugrut

Abstract:

Lung cancer is one of the most common malignancies and primary cause of death due to cancer worldwide. Non-small cell lung cancer (NSCLC) is the main subtype in which majority of patients present with advanced-stage disease. Herein, we analyzed differentially expressed genes to find potential biomarkers for lung cancer diagnosis as well as prognostic markers. We used transcriptome data from our 2 NSCLC patients and public data (GSE81089) composing of 8 NSCLC and 10 normal lung tissues. Differentially expressed genes (DEGs) between NSCLC and normal tissue and between early-stage and late-stage NSCLC were analyzed by the DESeq2. Pairwise correlation was used to find the DEGs with false discovery rate (FDR) adjusted p-value £ 0.05 and |log2 fold change| ³ 4 for NSCLC versus normal and FDR adjusted p-value £ 0.05 with |log2 fold change| ³ 2 for early versus late-stage NSCLC. Bioinformatic tools were used for functional and pathway analysis. Moreover, the top ten genes in each comparison group were verified the expression and survival analysis via GEPIA. We found 150 up-regulated and 45 down-regulated genes in NSCLC compared to normal tissues. Many immnunoglobulin-related genes e.g., IGHV4-4, IGHV5-10-1, IGHV4-31, IGHV4-61, and IGHV1-69D were significantly up-regulated. 22 genes were up-regulated, and five genes were down-regulated in late-stage compared to early-stage NSCLC. The top five DEGs genes were KRT6B, SPRR1A, KRT13, KRT6A and KRT5. Keratin 6B (KRT6B) was the most significantly increased gene in the late-stage NSCLC. From GEPIA analysis, we concluded that IGHV4-31 and IGKV1-9 might be used as diagnostic biomarkers, while KRT6B and KRT6A might be used as prognostic biomarkers. However, further clinical validation is needed.

Keywords: Bioinformatics, differentially expressed genes, non-small cell lung cancer, transcriptomics.

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Authors: Minsoo Lee, Yearn Jeong Kim, Yun-mi Kim, Sujeung Cheong, Sookyung Song

Abstract:

Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.

Keywords: Ant Colony System, DNA chip data, Classification.

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Authors: M. Anandhavalli Gauthaman

Abstract:

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: C. Gunavathi, K. Premalatha

Abstract:

Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.

Keywords: F-Test, Gene Expression, Genetic Algorithm, k- Nearest-Neighbor, Microarray, Signal-to-Noise Ratio, Support Vector Machine, T-statistics, Tumor Classification.

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