[dipl] Perustieteiden korkeakoulu / SCI

Permanent URI for this collectionhttps://aaltodoc.aalto.fi/handle/123456789/21

Browse

Browsing [dipl] Perustieteiden korkeakoulu / SCI by Department "BIT-tutkimuskeskus"

Filter results by typing the first few letters
Now showing 1 - 11 of 11
  • Analysis of downstream targets of the aPKC-ERK signalling pathway
    (2012) Joshi, Naveen
     School of Science | Master's thesis
  • Computational analysis of heart transcriptome in genetically modified rats
    (2012) Rajaraman, Sitaram
     School of Science | Master's thesis
    The transcriptome represents the DNA in the form of mRNA and it encodes proteins. Studying the transcriptome has always given scientists insights into the proteins synthesised in the various regions of the body and its functions. This project deals with the computational analysis of the heart transcriptome of an over-expressing rat and a knockout rat, the outcome of which will shed light on the factors influencing the functioning of the heart. The project was split into three parts. The first two parts dealt with RNASeq analysis of the over-expressing rat where the transcriptome was subjected to differential expression analysis at the gene level and at the exon level respectively. The outcome provided some interesting genes and exons at the top of the list. The gene level analysis was extended to Gene Set Enrichment Analysis and a number of pathways were obtained. The third part of the project dealt with micro array data analysis of knockout rats. The final output showed the obvious differential expression of the knockout gene while the rest of the genes showed minimum variation under normal conditions. Future directions for this project would involve further analysis on the biological aspects of the pathways obtained from the RNASeq data analysis to shed light on the impact of the transgene on them and unravelling other functionalities of the gene while also identifying new and unknown transcripts using tools like Cufflinks and annotating the exons.
  • Elucidating the transcriptional regulatory network controlling the TPO1 response to benzoic acid in yeast
    (2012) Alasoo, Kaur
     School of Science | Master's thesis
    Multidrug resistance (MDR) is the simultaneous acquisition of resistance to wide range of structurally and functionally unrelated cytotoxic chemical compounds that has severe consequences in cancer therapy, agriculture and food industry. Saccharomyces cerevisiae is a well-established model organism used to study the mechanisms of MDR. In yeast and other related organisms, MDR is often caused by drug-efflux pumps that are able to export a wide range of unrelated chemicals. Tpo1, a drug:H+ antiporter of the major facilitator superfamily, is one such drug-efflux pump. In the current work, our aim was to characterize the transcriptional regulatory network controlling TPO1 response to benzoic acid. We have employed two complementary approaches to achieve this aim. First, we have used RT-PCR to measure the transcript levels of Tpo1 and five of its known and putative regulators (GCN4, STP1, STP2, PDR1, PDR3) over a time course in wild type and respective deletion mutants. We have subsequently used this information to construct a logical model of TPO1 regulation. In the second part, we have developed a computational approach that combines data from multiple public sources to predict novel regulators for TPO1 and we have verified some of the prediction experimentally using, ß-galactosidase assays. Our results indicate that in benzoic acid stress, Pdrl/Pdr3 seem to play no role in regulating TPO1 and instead, a complex interplay between Gcn4, and Stp1 is responsible for the up regulation of TP01. Screening for new regulators revealed Hal9 and Ash1 that seem to be repressing TPOl expression in control conditions or in benzoic acid stress, respectively. Furthermore, multiple transcription factors previously implicated in pseudohyphal growth also have a small effect on TPOl expression.
  • Information Visualisation in a Peer Support Application
    (2012) Heikkilä, Antti Mikael
     Perustieteiden korkeakoulu | Master's thesis
    Using visualisations to present multidimensional data may help to understand complex relations and to make better decisions. This thesis presents methods for visualising peers based on their similarity. The purpose of the visualisation is to help users of an online peer support service to browse and find relevant peers that are most similar to them. Four nonlinear dimensionality reduction methods are used to produce visualisations from multidimensional data. The Neighbour Retrieval Visualiser (NeRV), Multidimensional Scaling (MDS), the Self-Organising Map (SOM) and the Generative Topographic Mapping (GTM) are presented and compared quantitatively. The results from the comparison suggest that any one of the four methods could be used in such a peer support service. The methods are then used to visualise data in a hypothetical peer support service called the Stress Map. To further test the methods, the visualisations are subjected to a user study. The visualization based on the NeRV algorithm performs best, whereas the visualisations made with the SOM and the GTM are judged less appealing.
  • Integration of genomics and transcriptomics; Analysis of Streptococcus pneumoniae wild-type and ΔccpA strains
    (2012) Upadhyaya, Bimal Babu
     School of Science | Master's thesis
    In many Gram-positive bacteria, the transcription regulator catabolite control protein A (CcpA) has a global regulatory effect in response to carbohydrate availability, lying at the core of catabolite control mechanisms. In a previous study, whole-genome microarray analysis comparing the human pathogen Streptococcus pneumoniae D39 with its isogenic ccpA mutant, grown in chemically defined medium (CDM) containing two different carbon sources, glucose and galactose, was obtained at mid-exponential and transition to stationary phases of growth. In this work, we resorted to this previous analysis to perform in silico differential expression, clustering, Biclustering and functional analysis of the data in order to narrow down the genome-wide analysis to study specific cellular processes. Besides, an integrative approach was adopted to analyse genomics and transcriptomics data. Our data showed that CcpA regulates the expression of genes involved in many cellular processes as determined by comparing gene ontology associated biological processes and Clusters of Orthologous Groups (COGs) of proteins databases. Carbohydrate transport and metabolism were the most affected functions and, accordingly, defence mechanisms based on carbohydrate availability. In order to predict catabolite responsive element (cre) sites in S. pneumoniae D39, a probabilistic motif inference analysis was performed using the known consensus cre sequences of other Gram-positive bacteria, namely Bacillus subtilis, Bacillus megaterium and Lactobacillus lactis. Remarkably, this analysis revealed the presence of 211 putative targets of CcpA. These motifs, over-represented in differentially expressed genes (Fisher's Exact Test), were further analysed in terms of the distribution of their position related to the promoter region. Integration of genomic information with transcriptomic analyses revealed that a high number of genes from different functional categories were directly affected, thus providing a promising strategy to infer gene regulatory networks.
  • Making microarray and RNA-seq gene expression data comparable
    (2012) Uziela, Karolis
     Master's thesis
    Measuring gene expression levels in the cell is an important tool in biomedical sciences. It can be used in new drug development, disease diagnostics and many other areas. Currently, two most popular platforms for measuring gene expression are microarrays and RNA-sequencing (RNA-seq). Making the gene expression results more comparable between these two platforms is an important topic which has not yet been investigated enough. In this thesis, we present a novel method, called PREBS, that addresses this issue. Our method adjusts RNA-seq data computational processing in a way that makes the resulting gene expression measures more similar to microarray based gene expression measures. We compare our method against two other RNA-seq processing methods, RPKM and MMSEQ, and evaluate each method's agreement with microarrays by calculating correlations between the platforms. We show that our method reaches the highest level of agreement among all of the methods in absolute expression scale and has a similar level of agreement as the other methods in differential expression scale. Additionally, this thesis provides some background on gene expression, its measurement and computational analysis of gene expression data. Moreover, it gives a brief literature review on the past microarray{RNA-seq comparisons.
  • Modeling drug response in cancer cells by combining multiple drug and cell line views
    (2012) Ammad-ud-din, Mohammad
     School of Science | Master's thesis
  • Modeling dynamics of influenza with antigenic change
    (2012) Xu, Jiang
     School of Science | Master's thesis
    The main objective of the thesis is to implement a mathematical epidemic model developed by Koelle et al. 2009 and apply it to influenza A virus to simulate the disease dynamics. The influenza A virus is notable for annual epidemics and antigenic drift dynamics, where many closely related strains co-circulate. The model is evolved from a status-based multi-strain model with the innovation of incorporating the method of modeling the emergence event of newly evolved strains. Reactions between the strains in this status-based multi-strains model are modelled by introducing cross-immunity. The core idea of the model developed by Koelle is to model from the virus's perspective instead of the host's. Instead of simulating a virus's genetic evolution, this model incorporates a way to simulate its antigenic evolution. There are two ways of modeling antigenic changes in Koelle's work, the gradual and the punctuated model, while only the gradual model of antigenic change is applied to the influenza A epidemic in this thesis work. Gradual model consider each amino acid variant to be antigenically unique and the antigenic change occurs gradually. And there are two models of gradual antigenic changes: one-dimensional model and unconstrained infinite-dimensional model. The simulation results for both models of gradual antigenic changes managed to reproduce some behaviours of influenza's transmission: annual outbreaks, antigenic variant's coexistence, replacement and limited viral diversity. However, they exhibit some notable difference with Koelle's result. The reason might be that there are some hidden or forgotten assumptions not given a full account in the paper by Koelle.
  • Projection microstereolithography equipment
    (2013) Lehtinen, Pekka A.
     Perustieteiden korkeakoulu | Master's thesis
    Stereolithography (SL) is one of the most successful additive manufacturing technologies. In SL a three-dimensional object is fabricated directly from a CAD model. The product is manufactured layer-by-layer by curing liquid resin. These layers, which are built on one another, form the product. In projection stereolithography (PSL) the laser light source and scanner system commonly used in SL, are replaced with a digital micromirror device (DMD) or a liquid crystal display (LCD). Thus, each layer is cured at once in one exposure according to the pattern on the DMD or LCD. Projection microstereolithography (PµSL) operates on the same principle as PSL, but the fabrication resolution is significantly higher. In this study, a PµSL setup with a DMD chip is constructed and a computer code is written to control the entire manufacturing process.
  • RNA-seq analysis of stem cells for differential gene expression and alternative splicing
    (2012) Malonzo, Maia Aisha
     School of Science | Master's thesis
  • The use of RNA-seq data for re-annotation of transcriptomes
    (2012) El Hadidi, Mohamed
     School of Science | Master's thesis
    Recently, demands for whole genome sequencing have been greatly increased for many applications, including the study of SNPs and their role in phenotypic diversity in nature. However, whole genome sequencing using high throughput sequencing methods remains an expensive task, only suitable to large consortium of researchers funded by strong agencies. As an alternative, RNA-seq seems to be an appropriate alternative for many reasons. First, while genome sizes can differ by as much as 5 orders of magnitude, transcriptome sizes differ by less than 2 orders of magnitude even between yeast and polyploid plants. Second, coding sequences are more conserved and have less repetitive elements than non-coding sequences. Finally, RNAseq allows not only the identification of coding polymorphism but also characterization of expression differences, both of which have been shown to underlie phenotypic diversity. In this study, we have developed a pipeline for annotating transcriptomes for species without an available direct reference genome, based mainly on RNA-seq data and a closely related reference genome. Benchmarking studies were performed to decide software components of the pipeline. Among three programs; AUGUTSUS gene prediction tool incorporated with RNA-seq data, Cufflinks transcriptome reconstruction tool and Trinity denovo transcriptome assembler, AUGUSTUS proved to be the most accurate software in terms of sensitivity and specificity. We have used published gene models of Col accession of Arabidopsis thaliana as a reference annotation and compared it with the software generated gene models. The performance of the pipeline in the absence of an available direct reference genome was tested. In such case, a pseudo-reference genome was constructed by incorporating accession-specific SNPs into the closest reference genome. RNA-seq reads were mapped against both the published A. thaliana (Ler accession) and the Ler pseudo-reference genome, which is constructed by incorporating Ler SNPs into Col accession of A. thaliana. The two gene models gave highly similar results when compared with the published Ler gene models. Finally, the pipeline was applied on four different tomato species; S. lycopersicum var. m82, S. pennellii, S. pimpinellifolium and S. habrochaites. Among the four species, only S. lycopersicum var. m82 has a reference genome of S. lycopersicum var. Heinz from which we have constructed pseudoreference genomes for the four species using the available RNA-seq data. AUGUSTUS with RNA-seq guidance was applied to predict genes models from the four constructed pseudoreference genomes. In order to monitor the effect of incorporating species-specific SNPs on annotation, we compared each of the four generated annotations with the published ITAG S. Iycopersicum var. Heinz annotation. Results showed variation in the values of sensitivity and specificity between pairs of compared gene models. We illustrated that evolutionary distances between the four tomato species and the values of sensitivity and specificity are inversely correlated with each others.