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  • 1.
    Ali, Muhammad
    Högskolan i Skövde, Institutionen för vård och natur.
    A COMPARATIVE STUDY OF GENE EXPRESSION NETWORKS USINGBIOLAYOUT, GENENET AND DAVID2012Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Deriving clusters of genes by different clustering techniques or finding the statistically significant variations among genes are conventional approaches to study microarray expression data. Nowadays in vitro experiments are being considered to make applications of genetical genomics more widespread in non-model species. Different bioinformatics tools are being used to investigate genetic pathways in the form of correlation based networks. In this study, a comparison was made between in vivo and in vitro gene expression data by using two software: BioLayout and GeneNet. From ten mice, five mice with the wild-type allele and five mice with the gene knock out (KO) for the gene SOCS2, a total of twenty samples were taken: five fresh samples from wildtype mice, five fresh samples from KO mice, five cultured samples from wildtype mice and five cultured samples from KO mice. After obtaining differentially expressed genes from microarray cDNA experiments, network analysis was done using the software BioLayout and GeneNet to make correlation and partial-correlation based networks. The resulting networks, or clusters derived from the networks, were subsequently analyzed for gene set enrichment analysis (GSEA) using the tool DAVID. The results from the GSEA were used to compare all the clusters and networks between the fresh and cultured samples to test for functional overlap. The GSEA results were also used to compare the clusters from BioLayout with the networks from GeneNet to compare overlap between these tools using the same data. When functional enrichment analysis and comparisons were made between the fresh and cultured data set after getting the networks and clusters from BioLayout and GeneNet, only a few functional categories were found in common. This suggested that in vitro samples are unable to give the same biological information as in vivo samples for this particular gene KO. Also the two different network tools showed only limited overlap, suggesting that the correlation based networks from BioLayout show a different type of relationship among the genes than the partial correlations from GeneNet.

    Therefore, the use of different network tools can be recommended to visualize and explore the regulatory pathways among genes.

  • 2.
    Anders, Patrizia
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A bioinformaticians view on the evolution of smell perception2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background:

    The origin of vertebrate sensory systems still contains many mysteries and thus challenges to bioinformatics. Especially the evolution of the sense of smell maintains important puzzles, namely the question whether or not the vomeronasal system is older than the main olfactory system. Here I compare receptor sequences of the two distinct systems in a phylogenetic study, to determine their relationships among several different species of the vertebrates.

    Results:

    Receptors of the two olfactory systems share little sequence similarity and prove to be a challenge in multiple sequence alignment. However, recent dramatical improvements in the area of alignment tools allow for better results and high confidence. Different strategies and tools were employed and compared to derive a

    high quality alignment that holds information about the evolutionary relationships between the different receptor types. The resulting Maximum-Likelihood tree supports the theory that the vomeronasal system is rather an ancestor of the main olfactory system instead of being an evolutionary novelty of tetrapods.

    Conclusions:

    The connections between the two systems of smell perception might be much more fundamental than the common architecture of receptors. A better understanding of these parallels is desirable, not only with respect to our view on evolution, but also in the context of the further exploration of the functionality and complexity of odor perception. Along the way, this work offers a practical protocol through the jungle of programs concerned with sequence data and phylogenetic reconstruction.

  • 3.
    Andersson, Christoffer
    Högskolan i Skövde, Institutionen för kommunikation och information.
    PELICAN: a PipELIne, including a novel redundancy-eliminating algorithm, to Create and maintain a topicAl family-specific Non-redundant protein database2005Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The increasing number of biological databases today requires that users are able to search more efficiently among as well as in individual databases. One of the most widespread problems is redundancy, i.e. the problem of duplicated information in sets of data. This thesis aims at implementing an algorithm that distinguishes from other related attempts by using the genomic positions of sequences, instead of similarity based sequence comparisons, when making a sequence data set non-redundant. In an automatic updating procedure the algorithm drastically increases the possibility to update and to maintain the topicality of a non-redundant database. The procedure creates a biologically sound non-redundant data set with accuracy comparable to other algorithms focusing on making data sets non-redundant

  • 4.
    Andersson, Malin
    Högskolan i Skövde, Institutionen för datavetenskap.
    A method for identification of putatively co-regulated genes2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The genomes of several organisms have been sequenced and the need for methods to analyse the data is growing. In this project a method is described that tries to identify co-regulated genes. The method identifies transcription factor binding sites, documented in TRANSFAC, in the non-coding regions of genes. The algorithm counts the number of common binding sites and the number of unique binding sites for each pair of genes and decides if the genes are co-regulated. The result of the method is compared with the correlation between the gene expression patterns of the genes. The method is tested on 21 gene pairs from the genome of Saccharomyces cerevisiae. The algorithm first identified binding sites from all organisms. The accuracy of the program was very low in this case. When the algorithm was modified to only identify binding sites found in plants the accuracy was much improved, from 52% to 76% correct predictions.

  • 5.
    Benediktsson, Elís Ingi
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Detection and analysis of megasatellites in the human genome using in silico methods2005Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.

  • 6.
    Birkmeier, Bettina
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.

  • 7.
    Borgmästars, Emmy
    Högskolan i Skövde, Institutionen för biovetenskap.
    Functional analysis of circulating microRNAs in pancreatic cancer2018Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
  • 8.
    Borgognone, Alessandra
    Högskolan i Skövde, Institutionen för biovetenskap.
    Investigating the human colorectal cancer microbiome from paired formalin-fixed paraffin embedded and fresh-frozen specimens by 16S rRNA sequencing2019Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The gastrointestinal tract harbors a large population of microorganisms that play a pivotal role in host immune response and metabolic functions. In recent years, several studies have attempted to uncover the relationships between resident microbiota and the development of gastrointestinal diseases, including colorectal cancer. The advent of next-generation sequencing technologies has enabled the characterization of microbial composition and alterations in patients with colorectal cancer. In this context, historical tissues stored in biobanks represent an invaluable resource for clinical genomic studies. However, the impact of tissue preservation on the microbiome architecture has not been completely explored. In this study, we compared the microbial composition in two groups of paired CRC tissues stored as fresh frozen (FF, 10 samples) and formalin-fixed, paraffin-embedded (FFPE, 10 samples) by using 16S rRNA gene sequencing (V3-V4 region). After sequencing on an Illumina MiSeq platform, DADA2 software was used for data processing. Analysis of alpha and beta diversity showed a significant difference between the two groups. In addition, analysis of relative abundances revealed systematic differences in terms of taxa distribution between FF and FFPE samples. We hypothesize that variations between matched pairs of FF and FFPE samples might derive from the fragmented nature of FFPE DNA. Our findings demonstrated that sample preservation influences the characterization of CRC biopsies, suggesting that microbial profiles of FF samples cannot be directly inferred from FFPE specimens and viceversa.

  • 9.
    Candelli, Tito
    Högskolan i Skövde, Institutionen för vård och natur.
    NOVEL APPROACH TO STORAGE AND STORTING OF NEXT GENERATION SEQUENCING DATA FOR THE PURPOSE OF FUNCTIONAL ANNOTATION TRANSFER2012Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The problem of functional annotation of novel sequences has been a sigfinicant issue for many laboratories that decided to apply next generation sequencing techniques to less studied species. In particular experiments such as transcriptome analysis heavily suer from this problem due to the impossibility of ascribing their results in a relevant biological context. Several tools have been proposed to solve this problem through homology annotation transfer. The principle behind this strategy is that homologous genes share common functions in dierent organisms, and therefore annotations are transferable between these genes. Commonly, BLAST reports are used to identify a suitable homologousgene in a well annotated species and the annotation is then transferred fromthe homologue to the novel sequence. Not all homologues, however, possess valid functional annotations. The aim of this project was to devise an algorithm to process BLAST reports and provide a criterion to discriminate between homologues with a biologically informative and uninformative annotation, respectively. In addition, all data obtained from the BLAST report isto be stored in a relational database for ease of consultation and visualization. In order to test the solidity of the system, we utilized 750 novel sequences obtained through application of next generation sequencing techniques to Avena sativa samples. This species particularly suits our needs as it represents the typical target for homology annotation transfer: lack of a reference genome and diculty in attributing functional annotation. The system was able to perform all the required tasks. Comparisons between best hits asdetermined by BLAST and best hits as determined by the algorithm showed a significant increase in the biological significance of the results when thealgorithm sorting system was applied.

  • 10.
    Chawade, Aakash
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Inferring Gene Regulatory Networks in Cold-Acclimated Plants by Combinatorial Analysis of mRNA Expression Levels and Promoter Regions2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    Understanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.

  • 11.
    Chen, Lei
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Construction of Evolutionary Tree Models for Oncogenesis of Endometrial Adenocarcinoma2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    Endometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible

    protein genes provided supportive evidence for the reliability of results.

  • 12.
    Deo, Ameya
    Högskolan i Skövde, Institutionen för vård och natur.
    Normalization of microRNA expression levels in Quantitative RT-PCR arrays2010Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Background: Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study.

    Results: In this study, different normalization methods were tested, which are available in the R packages Affy and qpcrNorm for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that qpcrNorm Quantile normalization method performs best for all methods tested.

    Conclusions: The qpcrNorm Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.

  • 13.
    Dnyansagar, Rohit
    Högskolan i Skövde, Institutionen för vård och natur.
    Investigation of phylogenetic relationships using microRNA sequences and secondary structures2010Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    MicroRNAs are important biomolecules for regulating biological processes. Moreover, the secondary structure of microRNA is important for its activity and has been used previously as a mean for finding unknown microRNAs. A phylogenetic study of the microRNA secondary structure reveals more information than its primary sequence, because the primary sequence can undergo mutations that give rise to different phylogenetic relationships, whereas the secondary structure is more robust against mutations and therefore sometimes  more informative.

    Here we constructed a phylogenetic tree entirely based on microRNA secondary structures using tools PHYLIP (Felsenstein, 1995) and RNAforester (Matthias Höchsmann, 2003, Hochsmann et al., 2004), and compared the overall topology and clusters with the phylogenetic tree constructed using microRNA sequence. The purpose behind this comparison was to investigate the sequence and structure similarity in phylogenetic context and also to investigate if functionally similar microRNA genes are closer in their structure-derived phylogenetic tree.

    Our phylogenetic comparison shows that the sequence similarity has hardly any effect on the structure similarity in the phylogenetic tree. MicroRNAs that have similar function are closer in the phylogenetic tree based on secondary structure than its respective sequence phylogeny. Hence, this approach can be very useful in predicting the functions of the new microRNAs whose function is yet to be known, since the function of the miRNAs heavily relies on its secondary structure.

     

  • 14.
    Dodda, Srinivasa Rao
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Improvements and extensions of a web-tool for finding candidate genes associated with rheumatoid arthritis2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    QuantitativeTraitLocus (QTL) is a statistical method used to restrict genomic regions contributing to specific phenotypes. To further localize genes in such regions a web tool called “Candidate Gene Capture” (CGC) was developed by Andersson et al. (2005). The CGC tool was based on the textual description of genes defined in the human phenotype database OMIM. Even though the CGC tool works well, the tool was limited by a number of inconsistencies in the underlying database structure, static web pages and some gene descriptions without properly defined function in the OMIM database. Hence, in this work the CGC tool was improved by redesigning its database structure, adding dynamic web pages and improving the prediction of unknown gene function by using exon analysis. The changes in database structure diminished the number of tables considerably, eliminated redundancies and made data retrieval more efficient. A new method for prediction of gene function was proposed, based on the assumption that similarity between exon sequences is associated with biochemical function. Using Blast with 20380 exon protein sequences and a threshold E-value of 0.01, 639 exon groups were obtained with an average of 11 exons per group. When estimating the functional similarity, it was found that on the average 72% of the exons in a group had at least one Gene Ontology (GO) term in common.

  • 15.
    Engerberg, Malin
    Högskolan i Skövde, Institutionen för datavetenskap.
    Development of database support for production of doubled haploids2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats (Examensarbete)
    Abstract [en]

    In this project relational and Lotus Notes database technology are evaluated with regard to their suitability in providing computer-based support in plant breeding in general and specifically in the production of doubled haploids. The two developed databases are compared based on a set of requirements produced together with the DH-group which is the main users of the databases. The results indicate that both Lotus Notes and the relational databases are able to fulfil all needs documented in this project, although both systems have their limitations. An often expressed opinion is that it is difficult to combine biology and databases. The experience gained in this project however suggests that it does not need to be the case in instances where data is not as complicated as often discussed. Observations made during this project indicate that data warehousing with integrated data mining and OLAP tools are surprisingly similar to how the DH-group at Svalöf Weibull works and could be a suitable solution for the production of doubled haploids.

  • 16.
    Fehrenbach, Stefan
    Högskolan i Skövde, Institutionen för biovetenskap.
    A modular approach to identify differentially expressed genes between men and women for inflammatory diseases2016Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Inflammatory diseases show large differences in susceptibility between men and women. In previous study, genes that showed different expression patterns between patients and healthy controls in males and females were identified using modules in disease-gene interaction networks. In this work, genes were identified using different methods based on gene expressions in public available data sets. By counting the occurrences of genes identified in the interaction network in our results, we showed that they greatly overlap with genes identified by our methods and that the disease gene-interaction networks are able to identify genes that can be identified in a gene expression based analysis as well. Gene expression analysis was implemented in an automatic pipeline, which was designed for a general use. Thereby, future research with similar problems can be simplified. The Rpackages limma and WGCNA were used to identify genes that showed differences in males and females and GO terms and KEGG pathways were used to search for enriched functions of those genes. Further, a difference between males and females was found for systemic lupus erythematosus and Sjögren’s syndrome data sets in the expression of genes belonging to interferon signaling. Interferons are currently examined as drug targets for SLE and a difference between men and women could lead to different results of such a medication. However, the identified genes showed changes in expressions between patients and controls for both men and women. This supports a beneficial effect of such drugs in men and women.

  • 17.
    Geirardsdottir, Kristin
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Identifying and analysing alternative splice variants by aligning ESTs and mRNAs to the genomic sequence2005Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Questions have been raised about the genomic complexity of the human genome, since it was reported that it only consisted of 32,000 genes. Alternative splicing is considered the explanation of the enormous difference between the number of genes and the number of proteins. Aligning expressed sequence tags (ESTs) to the genomic sequence has become a popular approach for gene prediction, revealing alternative splice variants. The aim in this thesis is to identify and analyse splice variants of the adhesion family of G protein-coupled receptors using EST data. 75% of the genes in the data set of 33 sequences were found to have a total of 51 splice variants. About half of the variants were considered functional.

  • 18.
    Genheden, Samuel
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A fast protein-ligand docking method2006Självständigt arbete på grundnivå (kandidatexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    In this dissertation a novel approach to protein-ligand docking is presented. First an existing method to predict putative active sites is employed. These predictions are then used to cut down the search space of an algorithm that uses the fast Fourier transform to calculate the geometrical and electrostatic complementarity between a protein and a small organic ligand. A simplified hydrophobicity score is also calculated for each active site. The docking method could be applied either to dock ligands in a known active site or to rank several putative active sites according to their biological feasibility. The method was evaluated on a set of 310 protein-ligand complexes. The results show that with respect to docking the method with its initial parameter settings is too coarse grained. The results also show that with respect to ranking of putative active sites the method works quite well.

  • 19.
    Ghannoum, Salim
    Högskolan i Skövde, Institutionen för biovetenskap.
    Characterizing sub-populations of myxoid liposarcoma cells using a multi-algorithmic pipeline for analyzing single-cell RNA sequencing data2018Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    All tumors are characterized by intratumor heterogeneity at varying degrees. Cancer stem cells have been put forward to be an essential element that promotes heterogeneity. Myxoid liposarcoma, which is a lipogenic cancer that develops in deep soft connective tissues, is characterized by intermediate intratumor heterogeneity. Despite recent therapeutic advances, the post-treatment recurrence rate remains relatively high. Identifying sub-populations of myxoid liposarcoma tumors can help in characterizing their molecular signatures and tumorigenic capabilities leading to developing better therapeutics. Single-cell transcriptomic approaches can highlight deviations in gene expression patterns among different subpopulations within the tumor. In this study, a multi-algorithmic pipeline was developed to make a fast, simple and efficient process for characterizing cellular sub-populations of cancer cells and gain insight about the molecular signature of the cancer stem sub-population. This pipeline consists of four successive steps, read counts’ pre-processing, cellular clustering and pseudotemporal ordering, defining differential expressed genes and defining biomarker genes. The results showed a harmonic integration between the algorithms that constitute the backbone of the proposed pipeline leading to a reduction in the limitations of some of these algorithms. The outcome of this study is a panel of 33 genes nominated as possible biomarkers for stemness and aggressiveness. To optimize and validate these biomarker candidates, further investigations are required. Moreover, additional functional coupling analysis is necessary to nominate biomarkers for each of the sub-populations based on the defined differential expressed genes.

  • 20.
    Gusenleitner, Daniel
    Högskolan i Skövde, Institutionen för vård och natur.
    In silico modeling for uncertain biochemical data2009Självständigt arbete på avancerad nivå (magisterexamen), 15 poäng / 22,5 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Analyzing and modeling data is a well established research area and a vast variety of different methods have been developed over the last decades. Most of these methods assume fixed positions of data points; only recently uncertainty in data has caught attention as potentially useful source of information. In order to provide a deeper insight into this subject, this thesis concerns itself with the following essential question: Can information on uncertainty of feature values be exploited to improve in silico modeling? For this reason a state-of-art random forest algorithm is developed using Matlab R. In addition, three techniques of handling uncertain numeric features are presented and incorporated in different modified versions of random forests. To test the hypothesis six realworld data sets were provided by AstraZeneca. The data describe biochemical features of chemical compounds, including the results of an Ames test; a widely used technique to determine the mutagenicity of chemical substances. Each of the datasets contains a single uncertain numeric feature, represented as an expected value and an error estimate. Themodified algorithms are then applied on the six data sets in order to obtain classifiers, able to predict the outcome of an Ames test. The hypothesis is tested using a paired t-test and the results reveal that information on uncertainty can indeed improve the performance of in silico models.

  • 21.
    Gustafsson, Sara
    Högskolan i Skövde, Institutionen för datavetenskap.
    Evaluation of analysis methods for identification of differentially expressed genes in oligonucleotide microarray data2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels four thousands of genes simultaneously. The problem is now to make sense of the resulting massive data set. In this thesis the results from five different methods for differential analysis of oligonucleotide microarray data are evaluated. The methods are simple classic t-test and Mann-Whitney U test, the software GeneSpring and Significance Analysis of Microarrays (SAM) and the use of Affymetrix software in combination with a scoring system. The methods are used to analyse two different microarray data sets with different number of replicates. These data sets are further divided in different ways to examine different questions that still are unsolved problems in the microarray technology. The aim of the evaluation is to examine the reliability of the results obtained from differential analysis of microarray data.

  • 22.
    Helgadóttir, Hanna Sigrún
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Using semantic similarity measures across Gene Ontology to predict protein-protein interactions2005Självständigt arbete på grundnivå (kandidatexamen)Studentuppsats
    Abstract [en]

    Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.

  • 23.
    Hettne, Kristina
    Högskolan i Skövde, Institutionen för datavetenskap.
    Using nuclear receptor interactions as biomarkers for metabolic syndrome2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Metabolic syndrome is taking epidemic proportions, especially in developed countries. Each risk factor component of the syndrome independently increases the risk of developing coronary artery disease. The risk factors are obesity, dyslipidemia, hypertension, diabetes type 2, insulin resistance, and microalbuminuria. Nuclear receptors is a family of receptors that has recently received a lot of attention due to their possible involvement in metabolic syndrome. Putting the receptors into context with their co-factors and ligands may reveal therapeutic targets not found by studying the receptors alone. Therefore, in this thesis, interactions between genes in nuclear receptor pathways were analysed with the goal of investigating if these interactions can supply leads to biomarkers for metabolic syndrome. Metabolic syndrome donor gene expression data from the BioExpressä, database was analysed with the APRIORI algorithm (Agrawal et al. 1993) for generating and mining association rules. No association rules were found to function as biomarkers for metabolic syndrome, but the resulting rules show that the data mining technique successfully found associations between genes in signaling pathways.

  • 24.
    Hillerton, Thomas
    Högskolan i Skövde, Institutionen för biovetenskap.
    Predicting adverse drug reactions in cancer treatment using a neural network based approach2018Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
  • 25.
    Huque, Enamul
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Shape Analysis and Measurement for the HeLa cell classification of cultured cells in high throughput screening2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Feature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.

  • 26.
    Jacobsson, Annelie
    Högskolan i Skövde, Institutionen för datavetenskap.
    Comparing NR Expression among Metabolic Syndrome Risk Factors2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The metabolic syndrome is a cluster of metabolic risk factors such as diabetes type II, dyslipidemia, hypertension, obesity, microalbuminurea and insulin resistance, which in the recent years has increased greatly in many parts of the world. In this thesis decision trees were applied to the BioExpress database, including both clinical data about donors and gene expression data, to investigate nuclear receptors ability to serve as markers for the metabolic syndrome. Decision trees were created and the classification performance for each individual risk factor were then analysed. The rules generated from the risk factor trees were compared in order to search for similarities and dissimilarities. The comparisons of rules were performed in pairs of risk factors, in groups of three and on all risk factors and they resulted in the discovery of a set of genes where the most interesting were the Peroxisome Proliferator Activated Receptor - Alpha, the Peroxisome Proliferator Activated Receptor - Gamma and the Glucocorticoid Receptor. These genes existed in pathways associated with the metabolic syndrome and in the recent scientific literature.

  • 27.
    Jadhav, Trishul
    Högskolan i Skövde, Institutionen för vård och natur.
    Knowledge Based Gene Set analysis (KB-GSA): A novel method for gene expression analysis2010Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

  • 28.
    Jantscher, Yvonne
    Högskolan i Skövde, Institutionen för vård och natur. Högskolan i Skövde.
    Identification of miRNAs and their target genes in stem cell derived cardiomyocytes2011Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Stem cell research, especially the one dealing with human embryonic stem cells, is a major topic nowadays. In the last few years studies about human embryonic stem cell derived cardiomyocytes highlighted the importance of those, as their characteristics are almost identical as of the cardiomyocytes in the heart (i.e. the contraction of those cells). The studies concentrate on the ability of using cardiomyocytes in the drug development for cardiac diseases or in regenerative medicine and cell replacement therapies. In contrast some researchers concentrate on microRNAs (miRNAs) as regulators in the development of cardiomyocytes. This study combines both research topics as it deals with stem cells and miRNAs (as well as their target mRNAs). A main objective is to find differentially expressed genes by using Significance Analysis of Microarrays (SAM) as method. Furthermore miRNA target prediction is applied and the identified targets are compared with the ones found by SAM. With an intersection approach we derived 41 targets of up-regulated miRNAs and 25 targets of down-regulated miRNAs, which can be the basis for further studies (i.e. knock-out experiments).

  • 29.
    Karjalainen, Merja
    Högskolan i Skövde, Institutionen för datavetenskap.
    Analysing subsets of gene expression data to find putatively co-regulated genes2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    This project is an investigation of whether analysing subsets of time series gene expression data can give additional information about putatively co-regulated genes, compared to only using the whole time series. The original gene expression data set was partitioned into subsets and similarity was computed for both the whole timed series and subsets. Pearson correlation was used as similarity measure between gene expression profiles. The results indicate that analysing co-expression in subsets of gene expression data derives true-positive connections, with respect to co-regulation, that are not detected by only using the whole time series data. Unfortunately, with the actual data set, chosen similarity measure and partitioning of the data, randomly generated connections have the same amount of true-positives as the ones derived by the applied analysis. However, it is worth to continue further analysis of the subsets of gene expression data, which is based on the multi-factorial nature of gene regulation. E.g. other similarity measures, data sets and ways of partitioning the data set should be tried.

  • 30.
    Keller, Jens
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Clustering biological data using a hybrid approach: Composition of clusterings from different features2008Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.

    The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.

  • 31.
    Kristinsson, Vilhelm Yngvi
    Högskolan i Skövde, Institutionen för kommunikation och information.
    The effect of normalization methods on the identification of differentially expressed genes in microarray data2007Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    In this thesis the effect of normalization methods on the identification of differentially expressed genes is investigated. A zebrafish microarray dataset called Swirl was used in this thesis work. First the Swirl dataset was extracted and visualized to view if the robust spline and print tip loess normalization methods are appropriate to normalize this dataset. The dataset was then normalized with the two normalization methods and the differentially expressed genes were identified with the LimmaGUI program. The results were then evaluated by investigating which genes overlap after applying different normalization methods and which ones are identified uniquely after applying the different methods. The results showed that after the normalization methods were applied the differentially expressed genes that were identified by the LimmaGUI program did differ to some extent but the difference was not considered to be major. Thus the main conclusion is that the choice of normalization method does not have a major effect on the resulting list of differentially expressed genes.

  • 32.
    Lagervik Öster, Alice
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Probability calculations of orthologous genes2005Självständigt arbete på grundnivå (kandidatexamen)Studentuppsats
    Abstract [en]

    The aim of this thesis is to formulate and implement an algorithm that calculates the probability for two genes being orthologs, given a gene tree and a species tree. To do this, reconciliations between the gene tree and the species trees are used. A birth and death process is used to model the evolution, and used to calculate the orthology probability. The birth and death parameters are approximated with a Markov Chain Monte Carlo (MCMC). A MCMC framework for probability calculations of reconciliations written by Arvestad et al. (2003) is used. Rules for orthologous reconciliations are developed and implemented to calculate the probability for the reconciliations that have two genes as orthologs. The rules where integrated with the Arvestad et al. (2003) framework, and the algorithm was then validated and tested.

  • 33.
    Lantz, Mikael
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A targeted evaluation of OpenEye’s methods for virtual ligand screens and docking2005Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The process of drug discovery is very slow and expensive. There is a need for reliable in silico methods; however the performance of these methods differs.

    This work presents a targeted study on how the drug discovery methods used in OpenEye’s tools ROCS, EON and FRED perform on targets with small ligands. It was examined if 12 compounds (markers) somewhat similar to AMP could be detected by ROCS in a random data set comprised of 1000 compounds. It was also examined if EON could find any electrostatic similarities between the queries and the markers. The performance of FRED with respect to re-generation of bound ligand modes was examined on ten different protein/ligand complexes from the Brookhaven Protein Data Bank. It was also examined if FRED is suitable as a screening tool since several other docking methods are used in such a way. Finally it was also examined if it was possible to reduce the time requirements of ROCS when running multiconformer queries by using a combination of single conformer queries coupled with multiconformer queries.

    The conclusions that could be drawn from this project were that FRED is not a good screening tool, but ROCS performs well as such. It was also found that the scoring functions are the weak spots of FRED. EON is probably very sensitive to the conformers used but can in some cases strengthen the results from ROCS. A novel and simple way to reduce the time complexity with multiconformer queries to ROCS was discovered and was shown to work well.

  • 34.
    Linde, Jörg
    Högskolan i Skövde, Institutionen för vård och natur.
    Ranking the relevance of genes targeted by cancer-associated MiRNAs2008Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    MicroRNAs control the expression of their target genes by translational repression.

    They are involved in various biological processes including cancer progression.

    To uncover the biological role of microRNAs it is necessary to identify their

    target genes. The small number of experimentally validated target genes makes

    computer prediction methods very important. However, state of the art prediction

    tools result in a great number of putative targets. The number of false positives

    among those putative targets is unknown. This report proposes, investigates and

    analyses two ways of ranking the biological relevance of putative targets of miRNAs

    which are associated with breast cancer.

    One approach characterises values of network properties of the putative microRNA

    targets in the human Protein-Protein Interaction network and compares

    them to network property values of validated microRNA targets. Using these results

    we suggest a simple approach for ranking the relevance of putative targets.

    The approach consists of testing if a network property value of a putative target

    differs from the mean value of the network. In addition we study which network

    property contributes most to ranking using this approach.

    The second approach identifies commonly overrepresented Gene Ontology categories

    among putative microRNA targets, validated targets and known breast

    cancer genes. We investigate possibilities to use the occurrence of a putative target

    in these categories to rank its biological relevance.

    Finally we present a number of genes with interesting features considering both

    approaches. These genes might play a role in breast cancer progression and might

    be worth investigating further.

  • 35.
    Lindefelt, Lisa
    Högskolan i Skövde, Institutionen för datavetenskap.
    Predicting gene expression using artificial neural networks2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Today one of the greatest aims within the area of bioinformatics is to gain a complete understanding of the functionality of genes and the systems behind gene regulation. Regulatory relationships among genes seem to be of a complex nature since transcriptional control is the result of complex networks interpreting a variety of inputs. It is therefore essential to develop analytical tools detecting complex genetic relationships.

    This project examines the possibility of the data mining technique artificial neural network (ANN) detecting regulatory relationships between genes. As an initial step for finding regulatory relationships with the help of ANN the goal of this project is to train an ANN to predict the expression of an individual gene. The genes predicted are the nuclear receptor PPAR-g and the insulin receptor. Predictions of the two target genes respectively were made using different datasets of gene expression data as input for the ANN. The results of the predictions of PPAR-g indicate that it is not possible to predict the expression of PPAR-g under the circumstances for this experiment. The results of the predictions of the insulin receptor indicate that it is not possible to discard using ANN for predicting the gene expression of an individual gene.

  • 36.
    Lindlöf, Angelica
    Högskolan i Skövde, Institutionen för datavetenskap.
    Deriving Genetic Networks from Gene Expression Data and Prior Knowledge2001Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In this work three different approaches for deriving genetic association networks were tested. The three approaches were Pearson correlation, an algorithm based on the Boolean network approach and prior knowledge. Pearson correlation and the algorithm based on the Boolean network approach derived associations from gene expression data. In the third approach, prior knowledge from a known genetic network of a related organism was used to derive associations for the target organism, by using homolog matching and mapping the known genetic network to the related organism. The results indicate that the Pearson correlation approach gave the best results, but the prior knowledge approach seems to be the one most worth pursuing

  • 37.
    Liu, Oscar H.
    Högskolan i Skövde, Institutionen för biovetenskap.
    RNAseq Analysis of Gastric Bacteria in Helicobacter pylori-Associated Carcinogenesis2014Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Helicobacter pylori infects more than half of the world's population, and is known to be involved in several diseases including gastric cancer. Its close interactions with the stomach and host immune system serves as a good model to study the co-adaptation and co-evolution of the organisms in the stomach micro-environment. In this project, we utilized RNA-seq and data analysis tools to investigate differentially expressed genes by H. pylori in patients at different stages of early gastric cancer development. We also investigated the abundance and diversity of bacterial genera other than H. pylori, and looked for correlations with H. pylori presence and number. For differential gene expression of H. pylori, one gene was differentially expressed between samples of corpus atrophy without metaplasia vs. samples of antrum gastritis, and eight genes were found to be differentially expressed between samples of corpus atrophy with metaplasia vs. samples with pan-gastritis. When samples were clustered into different groups based on the expression data, 52 genes (shared or unique to the specific comparison groups) were found to be differentially expressed, but no apparent patterns were observed that could be explained by medical or sample collection data. For bacterial diversity and abundances, we found several genera colonizing the stomach, of which some have been previously identified. While most of these bacteria colonize regardless of the presence of H. pylori, the abundance of three genera, Wolinella, Campylobacter, and Veillonella, seem to be correlated with the presence of H. pylori.

  • 38.
    Liu, Oscar H.
    Högskolan i Skövde, Institutionen för biovetenskap.
    RNAseq Analysis of Gastric Bacteria in Helicobacter pylori-Associated Carcinogenesis2014Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Helicobacter pylori infects more than half of the world's population, and is known to be involved in several diseases including gastric cancer. Its close interactions with the stomach and host immune system serves as a good model to study the co-adaptation and co-evolution of the organisms in the stomach micro-environment. In this project, we utilized RNA-seq and data analysis tools to investigate differentially expressed genes by H. pylori in patients at different stages of early gastric cancer development. We also investigated the abundance and diversity of bacterial genera other than H. pylori, and looked for correlations with H. pylori presence and number. For differential gene expression of H. pylori, one gene was differentially expressed between samples of corpus atrophy without metaplasia vs. samples of antrum gastritis, and eight genes were found to be differentially expressed between samples of corpus atrophy with metaplasia vs. samples with pan-gastritis. When samples were clustered into different groups based on the expression data, 52 genes (shared or unique to the specific comparison groups) were found to be differentially expressed, but no apparent patterns were observed that could be explained by medical or sample collection data. For bacterial diversity and abundances, we found several genera colonizing the stomach, of which some have been previously identified. While most of these bacteria colonize regardless of the presence of H. pylori, the abundance of three genera, Wolinella, Campylobacter, and Veillonella, seem to be correlated with the presence of H. pylori.

  • 39.
    Lorentzon, Fredrik
    Högskolan i Skövde, Institutionen för datavetenskap.
    Data modelling and implementation of a chemical compounds database2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In this project a relational DBMS has been designed, developed and implemented. The RDBMS handles information on cellular behaviour in response to many different chemical compounds. The RDBMS is accessed with a Common Gateway Interface (CGI) programmed with Perl and the new system replaces the previous system that was used at the biotech company Neuronova. The results indicate that the developed RDBMS system will improve the work, especially for the Lead Discovery department. The data is structured in a stricter way. The complete system offers the benefit of integration with other databases at the company including systems for EST sequences and for target discovery.

    The full text is not available due to confidetial parts in it.

  • 40.
    Lundell, Simon
    Högskolan i Skövde, Institutionen för datavetenskap.
    Modelling Gene Expression during Ontogenetic Differentiation2001Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Various types of recurrent neural networks have been used as models for the regulatory relationships between genes. The neural network is trained on the data from micro-array techniques, each gene corresponds to a neuron in the network. The data from the micro-array technologies has numerous genes, but usually involves few samples, this makes the network heavily under-determined. In this work we will propose a method that can cope with the poorness of the data. We will use a Hopfield-type neural network to model the ontogenetic differentiation of female honeybees. A method that identifies the genes that determine the castes is proposed.

  • 41.
    Martinez Maestre, Andreu
    Högskolan i Skövde, Institutionen för biovetenskap.
    PVC: Proximity Value Clustering: A new clustering method without human interaction2018Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
  • 42.
    Mathew, Sumi
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A method to identify the non-coding RNA gene for U1 RNA in species in which it has not yet been found2007Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background

    Non coding RNAs are the RNA molecules that do not code for proteins but play structural, catalytic or regulatory roles in the organisms in which they are found. These RNAs generally conserve their secondary structure more than their primary sequence. It is possible to look for protein coding genes using sequence signals like promoters, terminators, start and stop codons etc. However, this is not the case with non coding RNAs since these signals are weakly conserved in them. Hence the situation with non coding RNAs is more challenging. Therefore a protocol is devised to identify U1 RNA in species not previously known to have it.

    Results

    It is sufficient to use the covariance models to identify non coding RNAs but they are very slow and hence a filtering step is needed before using the covariance models to reduce the search space for identifying these genes. The protocol for identifying U1 RNA genes employs for the filtering a pattern matcher RNABOB that can conduct secondary structure pattern searches. The descriptor for RNABOB is made automatically such that it can also represent the bulges and interior loops in helices of RNA. The protocol is compared with the Rfam and Weinberg & Ruzzo approaches and has been able to identify new U1 RNA homologues in the Apicomplexan group where it has not previously been found.

    Conclusions

    The method has been used to identify the gene for U1 RNA in certain species in which it has not been detected previously. The identified genes may be further analyzed by wet laboratory techniques for the confirmation of their existence.

    4

  • 43.
    McCoy, Daniel
    Högskolan i Skövde, Institutionen för biovetenskap.
    Comparing consensus modules using S2B and MODifieR2019Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    It is currently understood that diseases are typically not caused by rogue errors in genetics but have both molecular and environmental causes from myriad overlapping interactions within an interactome. Genetic errors, such as that seen by a single-nucleotide polymorphism can lead to a dysfunctional cell, which in turn can lead to systemic disruptions that result in disease phenotypes. Perturbations within the interactome, as can be caused by many such errors, can be organized into a pathophenotype, or “disease module”. Disease modules are sets of correlated variables that can represent many of a disease’s activities with subgraphs of nodes and edges. Many methods for inferring disease modules are available today, but the results each one yields is not only variable between methods but also across datasets and trial attempts. In this study, several such inference methods for deriving disease modules are evaluated by combining them to create “consensus” modules. The method of focus is Double-Specific Betweenness (S2B), which uses betweenness centrality across separate diseases to derive new modules. This study, however, uses S2B to combine the results of independent inference methods rather than separate diseases to derive new modules. Pre-processed asthma and arthritis data are compared using various combinations of inference methods. The performance of each result is validated using Pathway Scoring Algorithm. The results of this study suggest that combining methods of inference using MODifieR or S2B may be beneficial for deriving meaningful disease modules.

  • 44.
    Morland, Sara
    Högskolan i Skövde, Institutionen för biovetenskap.
    Exploring qpcr data with weighted gene coexpression network analysis (WGCNA)2015Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Differently expressed genes e.g. in a disease may play a role in the etiology or progression of the disease. The traditional approach of finding differentially expressed genes is to compare the expression levels in the groups, and produce a list of differentially expressed candidate genes. With many pairwise comparisons, the risk of introducing type I and type II errors is high. One solution is to group together genes that are co-expressed into modules. Weighted gene coexpression network analysis (WGCNA) uses a topological overlap module approach and has been proved to find patterns that have been undetected by gene-to-gene comparison methods. qPCR has high sensitivity and specificity, and advances in technology has increased its throughput. The goal of the project was to construct WGCNA modules from qPCR data and evaluate the WGCNA method in five previously published qPCR data sets. There was little overlap between the differentially expressed genes found in the published articles and the candidates found by WGCNA. In three data sets WGCNA failed to produce any significant genes. In one of the data set significant genes were found where the original article failed. In one data set, 19 out of 60 genes that are top-ranked by the original authors were found in significant WGCNA modules. The biggest challenge with this type of comparison is to determine whether results that differ from the published studies are more or less biologically relevant. It is difficult to draw conclusions on whether the method is suitable for use for analysis of qPCR data based on this study.

  • 45.
    Muhammad, Ashfaq
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and

    management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.

  • 46.
    Naswa, Sudhir
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Representation of Biochemical Pathway Models: Issues relating conversion of model representation from SBML to a commercial tool2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm the conclusions drawn by experimental studies of biological networks and guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool.

    Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML.

    Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.

  • 47.
    Nordström, Rickard
    Högskolan i Skövde, Institutionen för datavetenskap.
    3DPOPS: From carbohydrate sequence to 3D structure2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In this project a web-based system called 3DPOPS have been designed, developed and implemented. The system creates initial 3D structures of oligosaccharides according to user input data and is intended to be integrated with an automatized 3D prediction system for saccharides. The web interface uses a novel approach with a dynamically updated graphical representation of the input carbohydrate. The interface is embedded in a web page as a Java applet. Both expert and novice users needs are met by informative messages, a familiar concept and a dynamically updated graphical user interface in which only valid input can be created.

    A set of test sequences was collected from the CarbBank database. An initial structure to each sequence could be created. All contained the information necessary to serve as starting points in a conformation search carried out by a 3D prediction system for carbohydrates.

  • 48.
    Olsson, Elin
    Högskolan i Skövde, Institutionen för datavetenskap.
    Deriving Genetic Networks Using Text Mining2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    On the Internet an enormous amount of information is available that is represented in an unstructured form. The purpose with a text mining tool is to collect this information and present it in a more structured form. In this report text mining is used to create an algorithm that searches abstracts available from PubMed and finds specific relationships between genes that can be used to create a network. The algorithm can also be used to find information about a specific gene. The network created by Mendoza et al. (1999) was verified in all the connections but one using the algorithm. This connection contained implicit information. The results suggest that the algorithm is better at extracting information about specific genes than finding connections between genes. One advantage with the algorithm is that it can also find connections between genes and proteins and genes and other chemical substances.

  • 49.
    Padvitski, Tsimafei
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. CECAD University of Cologne.
    Integrative analysis of age-related changes in the transcriptome of Caenorhabditis elegans2015Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Ageing is difficult to study because of the complexity and multi-factorial nature of traits that result from a combination of environmental, genetic, epigenetic and stochastic factors, each contributing to the overall phenotype. In light of this challenge, transcriptomic studies of aging organisms are of particular interest, since transcription is an intermediate step that links genotype and phenotype.

    In recent years microarrays have been widely used for elucidation of changes that occur with age in the transcriptome in Caenorhabditis elegans. However, different microarray studies of C. elegans report sets of differentially expressed genes of varying consistence, with different functional annotations. Failures to find a consistent set of transcriptomic alterations may reflect the absence of a specific genetic program that would guide age-related changes but may also, to some extent, be a consequence of a small sample sizes and a lack of study power in transcriptomic researches. To tackle this issue we analyzed RNA sequences of samples from a time-series experiment of normal aging of C. elegans, performing the first, to our knowledge, NGS-based study of such kind. As a result, evidences were collected that promote a union of two competing theories: the theory of DNA damage accumulation and the theory of programmed aging.

    Next, we applied two alternative methods, namely the Short Time-series Expression Mining and the Network Smoothing algorithm, in order to obtain and analyze sets of genes that represent distinct modules of age-related changes in the transcriptome. Besides characterization of age-related changes, we were also interested in assessment and validation of the Network Smoothing algorithm. Generally, results of clustering of smoothed scores are consistent with results of short time-series clustering, allowing robust elucidation of functions that are perturbed during aging.

    At the last phase of the project we questioned if observed changes in the transcriptome can be controlled by specific transcription factors. Thus we used Chip-seq data to predict plausible transcription factor regulators of gene sets obtained using time series clustering and Network smoothing. On the one hand, all predicted transcription factors had documented relevance to aging. On the other hand, we did not achieve gene set specific prediction of transcription factors. In fact, genes with the opposite dynamics were predicted to respond to the same transcription factors. 

    To summarize, we characterized in details age-related changes in the transcriptome of C. elegans, validated the performance of the Network Smoothing algorithm and showed that integration of gene expression with Chip-seq data allows to predict transcription factors that are capable to modulate the lifespan of C. elegans.

  • 50.
    Paramonov, Ida
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Deriving a refined set of housekeeping genes in differentiating human embryonic stem cells2008Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    In this thesis project housekeeping genes in differentiating human embryonic stem cells were investigated. Housekeeping genes are involved in basic functions in the cells and are assumed to be expressed at relatively constant levels across different cell types and experimental conditions. Based on these features, housekeeping genes are frequently used as controls in calibration of gene expression data. Commonly used housekeeping genes in somatic tissues have shown to vary notably in human embryonic stem cells and are therefore inappropriate as reference genes in this unique cell type. In the present work a novel set of gene expression data obtained by profiling of undifferentiated and early differentiating cardiac cells, was analyzed. Stably expressed genes were identified in this data set and were subsequently intersected with a previously proposed set of 292 stable genes in human embryonic stem cells. A resulting set of 73 genes show stability across all investigated cell lines and experimental conditions. These genes are suggested as a more reliable set of reference genes in differentiating human embryonic stem cells than frequently used housekeeping genes in somatic tissue. In addition, a novel set of 20 genes was identified as very stably expressed during the differentiation towards the cardiac lineage. After further validation of stability with RT-PCR, these genes could be useful as controls in studies of human embryonic stem cells that differentiate towards the cardiac lineage.

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