Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Identification of hepatic molecular mechanisms of action of alpha-tocopherol using global gene expression profile analysis in rats

The recent discovery that vitamin E (VE) regulates gene activity at the transcriptional level indicates that VE may exert part of its biological effects by mechanisms which may be independent of its well-recognised antioxidant function. The objective of this study was the identification of hepatic vitamin E-sensitive genes and examination of the effects of VE on their corresponding biological endpoints. Two groups of male rats were randomly assigned to either a VE-sufficient diet or to a control diet deficient in VE for 290 days. High-density oligonucleotide microarrays comprising over 7000 genes were used to assess the transcriptional response of the liver. Differential gene expression was monitored over a period of 9 months, at four different time-points, and rats were individually profiled. This experimental strategy identified several VE-sensitive genes, which were chronically altered by dietary VE. VE supplementation down-regulated scavenger receptor CD36, coagulation factor IX and 5-alpha-steroid reductase type 1 mRNA levels while hepatic gamma glutamyl-cysteinyl synthetase was significantly up-regulated. Measurement of the corresponding biological endpoints such as activated partial thromboplastin time, plasma dihydrotestosterone and hepatic glutathione substantiated the gene chip data which indicated that dietary VE plays an important role in a range of metabolic processes within the liver. (C) 2004 Elsevier B.V. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Analysis of gene expression profiles under water stress in tolerant and sensitive sugarcane plants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identification, characterization and expression analysis of hepcidin gene in sheep

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Cloning, sequencing and expression analysis of the equine hepcidin gene by real-time PCR

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of Bothrops jararacussu venomous gland transcriptome focusing on structural and functional aspects: I - gene expression profile of highly expressed phospholipases A(2)

Snake venom glands are a rich source of bioactive molecules such as peptides, proteins and enzymes that show important pharmacological activity leading to in local and systemic effects as pain, edema, bleeding and muscle necrosis. Most studies on pharmacologically active peptides and proteins from snake venoms have been concerned with isolation and structure elucidation through methods of classical biochemistry. As an attempt to examine the transcripts expressed in the venom gland of Bothrops jararacussu and to unveil the toxicological and pharmacological potential of its products at the molecular level, we generated 549 expressed sequence tags (ESTs) from a directional cDNA library. Sequences obtained from single-pass sequencing of randomly selected cDNA clones could be identified by similarities searches on existing databases, resulting in 197 sequences with significant similarity to phospholipase A(2) (PLA(2)), of which 83.2% were Lys49-PLA(2) homologs (BOJU-1), 0.1% were basic Asp49-PLA(2)s (BOJU-II) and 0.6% were acidic Asp49-PLA(2)s (BOJU-III). Adjoining this very abundant class of proteins we found 88 transcripts codifying for putative sequences of metalloproteases, which after clustering and assembling resulted in three full-length sequences: BOJUMET-I, BOJUMET-II and BOJUMET-III; as well as 25 transcripts related to C-type lectin like protein including a full-length cDNA of a putative galactose binding C-type lectin and a cluster of eight serine-proteases transcripts including a full-length cDNA of a putative serine protease. Among the full-length sequenced clones we identified a nerve growth factor (Bj-NGF) with 92% identity with a human NGF (NGHUBM) and an acidic phospholipase A2 (BthA-I-PLA(2)) displaying 85-93% identity with other snake venom toxins. Genetic distance among PLA(2)s from Bothrops species were evaluated by phylogenetic analysis. Furthermore, analysis of full-length putative Lys49-PLA(2) through molecular modeling showed conserved structural domains, allowing the characterization of those proteins as group II PLA(2)s. The constructed cDNA library provides molecular clones harboring sequences that can be used to probe directly the genetic material from gland venom of other snake species. Expression of complete cDNAs or their modified derivatives will be useful for elucidation of the structure-function relationships of these toxins and peptides of biotechnological interest. (C) 2004 Elsevier SAS. All rights reserved.

A comparative expression analysis of gene transcripts in brain tissue of non-transgenic and GH-transgenic zebrafish (Danio rerio) using a DDRT-PCR approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Differential gene expression by Paracoccidioides brasiliensis in host interaction conditions: Representational difference analysis identifies candidate genes associated with fungal pathogenesis

Paracoccidioides brasiliensis causes infection by the host inhalation of airborne propagules of the mycelia phase of the fungus. These particles reach the lungs, and disseminate to virtually all organs. Here we describe the identification of differentially expressed genes in studies of host-fungus interaction. We analyzed two cDNA populations of P. brasiliensis, one obtained from infected animals and the other an admixture of fungus and human blood thus mimicking the hematologic events of the fungal dissemination. Our analysis identified transcripts differentially expressed. Genes related to iron acquisition, melanin synthesis and cell defense were specially upregulated in the mouse model of infection. The upregulated transcripts of yeast cells during incubation with human blood were those predominantly related to cell wall remodeling/synthesis. The expression pattern of genes was independently confirmed in host conditions, revealing their potential role in the infection process. This work can facilitate functional studies of novel regulated genes that may be important for the survival and growth strategies of P. brasiliensis in humans. (c) 2006 Elsevier Masson SAS. All rights reserved.

Analysis of the Nicotiana tabacum Stigma/Style Transcriptome Reveals Gene Expression Differences between Wet and Dry Stigma Species

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comprehensive Analysis of Gene Expression Profiles of the Beet Armyworm Spodoptera exigua Larvae Challenged with Bacillus thuringiensis Vip3Aa Toxin

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)