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.

Regulatory hotspots are associated with plant gene expression under varying soil phosphorus supply in Brassica rapa

Gene expression is a quantitative trait that can be mapped genetically in structured populations to identify expression quantitative trait loci (eQTL). Genes and regulatory networks underlying complex traits can subsequently be inferred. Using a recently released genome sequence, we have defined cis- and trans-eQTL and their environmental response to low phosphorus (P) availability within a complex plant genome and found hotspots of trans-eQTL within the genome. Interval mapping, using P supply as a covariate, revealed 18,876 eQTL. trans-eQTL hotspots occurred on chromosomes A06 and A01 within Brassica rapa; these were enriched with P metabolism-related Gene Ontology terms (A06) as well as chloroplast-and photosynthesis-related terms (A01). We have also attributed heritability components to measures of gene expression across environments, allowing the identification of novel gene expression markers and gene expression changes associated with low P availability. Informative gene expression markers were used to map eQTL and P use efficiency-related QTL. Genes responsive to P supply had large environmental and heritable variance components. Regulatory loci and genes associated with P use efficiency identified through eQTL analysis are potential targets for further characterization and may have potential for crop improvement.

Relationships Among Murine CD11chigh Dendritic Cell Subsets as Revealed by Baseline Gene Expression Patterns

The functional relationships and properties of different subtypes of dendritic cells (DC) remain largely undefined. To better characterize these cells, we used global gene analysis to determine gene expression patterns among murine CD11c(high) DC subsets. CD4(+), CD8alpha(+), and CD8alpha(-) CD4(-) (double negative (DN)) DC were purified from spleens of normal C57/BL6 mice and analyzed using Affymetrix microarrays. The CD4(+) and CD8alpha(+) DC subsets showed distinct basal expression profiles differing by >200 individual genes. These included known DC subset markers as well as previously unrecognized, differentially expressed CD Ags such as CD1d, CD5, CD22, and CD72. Flow cytometric analysis confirmed differential expression in nine of nine cases, thereby validating the microarray analysis. Interestingly, the microarray expression profiles for DN cells strongly resembled those of CD4(+) DC, differing from them by <25 genes. This suggests that CD4(+) and DN DC are closely related phylogenetically, whereas CD8alpha(+) DC represent a more distant lineage, supporting the historical distinction between CD8alpha(+) and CD8alpha(-) DC. However, staining patterns revealed that in contrast to CD4(+) DC, the DN subset is heterogeneous and comprises at least two subpopulations. Gene Ontology and literature mining analyses of genes expressed differentially among DC subsets indicated strong associations with immune response parameters as well as cell differentiation and signaling. Such associations offer clues to possible unique functions of the CD11c(high) DC subsets that to date have been difficult to define as rigid distinctions.

Histological and global gene expression analysis of the ‘lactating’ pigeon crop

Background: Both male and female pigeons have the ability to produce a nutrient solution in their crop for the nourishment of their young. The production of the nutrient solution has been likened to lactation in mammals, and hence the product has been called pigeon ‘milk’. It has been shown that pigeon ‘milk’ is essential for growth and development of the pigeon squab, and without it they fail to thrive. Studies have investigated the nutritional value of pigeon ‘milk’ but very little else is known about what it is or how it is produced. This study aimed to gain insight into the process by studying gene expression in the ‘lactating’ crop.
Results: Macroscopic comparison of ‘lactating’ and non-’lactating’ crop reveals that the ‘lactating’ crop is enlarged and thickened with two very obvious lateral lobes that contain discrete rice-shaped pellets of pigeon ‘milk’. This was characterised histologically by an increase in the number and depth of rete pegs extending from the basal layer of the epithelium to the lamina propria, and extensive proliferation and folding of the germinal layer into the superficial epithelium. A global gene expression profile comparison between ‘lactating’ crop and non-’lactating’ crop showed that 542 genes are up-regulated in the ‘lactating’ crop, and 639 genes are down-regulated. Pathway analysis revealed that genes up-regulated in ‘lactating’ crop were involved in the proliferation of melanocytes, extracellular matrix-receptor interaction, the adherens junction and the wingless (wnt) signalling pathway. Gene ontology analysis showed that antioxidant response and microtubule transport were enriched in ‘lactating’ crop.
Conclusions: There is a hyperplastic response in the pigeon crop epithelium during ‘lactation’ that leads to localised cellular stress and expression of antioxidant protein-encoding genes. The differentiated, cornified cells that form the pigeon ‘milk’ are of keratinocyte lineage and contain triglycerides that are likely endocytosed as very low density lipoprotein (VLDL) and repackaged as triglyceride in vesicles that are transported intracellularly by microtubules. This mechanism is an interesting example of the evolution of a system with analogies to mammalian lactation, as pigeon ‘milk’ fulfils a similar function to mammalian milk, but is produced by a different mechanism.

Gene expression in placentation of farm animals: An overview of gene function during development

Eutherian mammals share a common ancestor that evolved into two main placental types, i.e., hemotrophic (e.g., human and mouse) and histiotrophic (e.g., farm animals), which differ in invasiveness. Pregnancies initiated with assisted reproductive techniques (ART) in farm animals are at increased risk of failure; these losses were associated with placental defects, perhaps due to altered gene expression. Developmentally regulated genes in the placenta seem highly phylogenetically conserved, whereas those expressed later in pregnancy are more species-specific. To elucidate differences between hemotrophic and epitheliochorial placentae, gene expression data were compiled from microarray studies of bovine placental tissues at various stages of pregnancy. Moreover, an in silico subtractive library was constructed based on homology of bovine genes to the database of zebrafish - a nonplacental vertebrate. In addition, the list of placental preferentially expressed genes for the human and mouse were collected using bioinformatics tools (Tissue-specific Gene Expression and Regulation [TiGER] - for humans, and tissue-specific genes database (TiSGeD) - for mice and humans). Humans, mice, and cattle shared 93 genes expressed in their placentae. Most of these were related to immune function (based on analysis of gene ontology). Cattle and women shared expression of 23 genes, mostly related to hormonal activity, whereas mice and women shared 16 genes (primarily sexual differentiation and glycoprotein biology). Because the number of genes expressed by the placentae of both cattle and mice were similar (based on cluster analysis), we concluded that both cattle and mice were suitable models to study the biology of the human placenta. (C) 2011 Elsevier B.V. All rights reserved.

Systematic Variation in the Pattern of Gene Paralog Retention between the Teleost Superorders Ostariophysi and Acanthopterygii

Teleost fish underwent whole-genome duplication around 450 Ma followed by diploidization and loss of 80-85% of the duplicated genes. To identify a deep signature of this teleost-specific whole-genome duplication (TSGD), we searched for duplicated genes that were systematically and uniquely retained in one or other of the superorders Ostariophysi and Acanthopterygii. TSGD paralogs comprised 17-21% of total gene content. Some 2.6% (510) of TSGD paralogs were present as pairs in the Ostariophysi genomes of Danio rerio (Cypriniformes) and Astyanax mexicanus (Characiformes) but not in species from four orders of Acanthopterygii (Gasterosteiformes, Gasterosteus aculeatus; Tetraodontiformes, Tetraodon nigroviridis; Perciformes, Oreochromis niloticus; and Beloniformes, Oryzias latipes) where a single copy was identified. Similarly, 1.3% (418) of total gene number represented cases where TSGD paralogs pairs were systematically retained in the Acanthopterygian but conserved as a single copy in Ostariophysi genomes. We confirmed the generality of these results by phylogenetic and synteny analysis of 40 randomly selected linage-specific paralogs (LSPs) from each superorder and completed with the transcriptomes of three additional Ostariophysi species (Ictalurus punctatus [Siluriformes], Sinocyclocheilus species [Cypriniformes], and Piaractus mesopotamicus [Characiformes]). No chromosome bias was detected in TSGD paralog retention. Gene ontology (GO) analysis revealed significant enrichment of GO terms relative to the human GO SLIM database for growth, Cell differentiation, and Embryo development in Ostariophysi and for Transport, Signal Transduction, and Vesicle mediated transport in Acanthopterygii. The observed patterns of paralog retention are consistent with different diploidization outcomes having contributed to the evolution/diversification of each superorder.

Gene copy number and differential gene expression in haploid and diploid males of the stingless bee, Melipona quadrifasciata

Complementary sex determination in Hymenoptera implies that heterozygosity at the sex locus leads to the development of diploid females, whereas hemizygosity results in haploid males. Diploid males can arise through inbreeding. In social species, these pose a double burden on colony fitness, from significant reduction in its worker force and through being less viable and fertile than haploid males. Apart from being "misfits", diploid males are of interest to assess molecular correlates for possibly ploidy-related bionomic differences. Herein, we generated suppression subtractive cDNA libraries from newly emerged haploid and diploid males of the stingless bee Melipona quadrifasciata to enrich for differentially expressed genes. Gene Ontology classification revealed that in haploid males more DEGs were related to stress responsiveness, biosynthetic processes, reproductive processes and spermatogenesis, whereas in diploid ones differentially expressed genes were associated with cellular organization, nervous system development and amino acid transport were prevalent. Furthermore, both libraries contained over 40 % ESTs representing possibly novel transcripts. Quantitative RT-PCR analyses confirmed the differential expression of a representative DEG set in newly emerged males. Several muscle formation and energy metabolism-related genes were under-expressed in diploid males. On including 5-day-old males in the analysis, changes in transcript abundance during sexual maturation were revealed.

A UML profile for the OBO relation ontology

Background: Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain. Results: We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO). Conclusions: The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

Gene expression profile of osseointegration of a hydrophilic compared with a hydrophobic microrough implant surface

OBJECTIVES: To compare the gene expression profile of osseointegration associated with a moderately rough and a chemically modified hydrophilic moderately rough surface in a human model. MATERIAL AND METHODS: Eighteen solid screw-type cylindrical titanium implants, 4 mm long and 2.8 mm wide, with either a moderately rough (SLA) or a chemically modified moderately rough (SLActive) surface were surgically inserted in the retromolar area of nine human volunteers. The devices were removed using a trephine following 4, 7 and 14 days of healing. The tissue surrounding the implant was harvested, total RNA was extracted and microarray analysis was carried out to identify the differences in the transcriptome between the SLA and SLActive surfaces at days 4, 7 and 14. RESULTS: There were no functionally relevant gene ontology categories that were over-represented in the list of genes that were differentially expressed at day 4. However, by day 7, osteogenesis- and angiogenesis-associated gene expression were up-regulated on the SLActive surface. Osteogenesis and angiogenesis appeared to be regulated by BMP and VEGF signalling, respectively. By day 14, VEGF signalling remains up-regulated on the SLActive surface, while BMP signalling was up-regulated on the SLA surface in what appeared to be a delayed compensatory response. Furthermore, neurogenesis was a prominent biological process within the list of differentially expressed genes, and it was influenced by both surfaces. CONCLUSIONS: Compared with SLA, SLActive exerts a pro-osteogenic and pro-angiogenic influence on gene expression at day 7 following implant insertion, which may be responsible for the superior osseointegrative properties of this surface.

Gene expression profiling reveals consistent differences between clinical samples of human leukaemias and their model cell lines

Microarray gene expression profiles of fresh clinical samples of chronic myeloid leukaemia in chronic phase, acute promyelocytic leukaemia and acute monocytic leukaemia were compared with profiles from cell lines representing the corresponding types of leukaemia (K562, NB4, HL60). In a hierarchical clustering analysis, all clinical samples clustered separately from the cell lines, regardless of leukaemic subtype. Gene ontology analysis showed that cell lines chiefly overexpressed genes related to macromolecular metabolism, whereas in clinical samples genes related to the immune response were abundantly expressed. These findings must be taken into consideration when conclusions from cell line-based studies are extrapolated to patients.

Primaquine-induced differential gene expression analysis in mice liver using DNA microarrays

Primaquine (PQ). a clinically important derivative of 8-aminoquinoline used against the hepatic stages (hypnozoites) of Plasmodium vivax and Plasmodium ova Ie. was studied to evaluate and compare between mRNA expression. and biochemical and histological parameters of hepatic stress in adult Swiss mice (Mus musculus). Following single oral dose of PQ (40 mglkg. bw). alanine aminotransferase (ALT) and aspartate aminotransferase (AST) along with hematoxylin and eosin stained liver sections did not show any signs of hepatic stress at 6. 12 and 24 h except for ALT activity at 6 h. However. analysis at RNA transcript level revealed consistent and significant deregulation (p<0.01 and twofold) of 16 probes corresponding to important cellular processes such as protein transportation. transcription regulation. intracellular signaling. protein synthesis, hematopoiesis, cell adhesion and cell proliferation. Pathway analysis identified large number of affected genes corresponding to 40 Gene Ontology terms having a z score greaibr than 2. These results indicate that PQ at high doses may affect gene expression in liver and may produce undesirable outcomes if consumed for longer durations.

Towards the identification of a tumor suppressor gene on chromosome 3p12: Physical mapping and candidate gene screening

Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney. Characterization of RCC tumors indicates that the most frequent genetic event associated with the initiation of tumor formation involves a loss of heterozygosity or cytogenetic aberration on the short arm of human chromosome 3. A tumor suppressor locus Nonpapillary Renal Carcinoma-1 (NRC-1, OMIM ID 604442) has been previously mapped to a 5–7 cM region on chromosome 3p12 and shown to induce rapid tumor cell death in vivo, as demonstrated by functional complementation experiments. ^ To identify the gene that accounts for the tumor suppressor activities of NRC-1, fine-scale physical mapping was conducted with a novel real-time quantitative PCR based method developed in this study. As a result, NRC-1 was mapped within a 4.6-Mb region defined by two unique sequences within UniGene clusters Hs.41407 and Hs.371835 (78,545Kb–83,172Kb in the NCBI build 31 physical map). The involvement of a putative tumor suppressor gene Robo1/Dutt1 was excluded as a candidate for NRC-1. Furthermore, a transcript map containing eleven candidate genes was established for the 4.6-Mb region. Analyses of gene expression patterns with real-time quantitative RT-PCR assays showed that one of the eleven candidate genes in the interval (TSGc28) is down-regulated in 15 out of 20 tumor samples compared with matched normal samples. Three exons of this gene have been identified by RACE experiments, although additional exon(s) seem to exist. Further gene characterization and functional studies are required to confirm the gene as a true tumor suppressor gene. ^ To study the cellular functions of NRC-1, gene expression profiles of three tumor suppressive microcell hybrids, each containing a functional copy of NRC-1, were compared with those of the corresponding parental tumor cell lines using 16K oligonucleotide microarrays. Differentially expressed genes were identified. Analyses based on the Gene Ontology showed that introduction of NRC-1 into tumor cell lines activates genes in multiple cellular pathways, including cell cycle, signal transduction, cytokines and stress response. NRC-1 is likely to induce cell growth arrest indirectly through WEE1. ^

Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling

Thesis (Ph.D.)--University of Washington, 2016-06

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.