Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset

CONTENT ANALYSIS OF UNIVERSITY LIBRARY WEB PAGES

The main purpose of this research study is to explore the web-based content available on HEC recognized public and private sector university library websites. Furthermore, this study aimed to explore the comparison of the content of HEC recognized public and private sector university library websites. A comprehensive review of related literature was made to understand the research problem in the first phase. Contents of library websites and their related literature were reviewed to understand different aspects of the topic. A checklist was developed on different literature reviewed related to the topic. World-leading university library websites were visited to get new contents details and some new content was added to the checklist. A checklist of 138 contents was developed. The checklist was used as a tool for data collection from library websites. Data was gathered in 2016. Collected data was analyzed through frequencies and percentages. Findings of the study revealed that out of 103 public sector universities 36(34%) were providing access to Home links on every page of the website while 67(65%) were not providing access. Out of 74 private sector universities, 18(24%) were providing access to Home links on every page of the website while 56(75%) were not providing access. Out of 103 public sector universities, 31(30%) were providing access to Phone numbers and postal addresses while 72(69%) were not providing access. Out of 74 private sector university library websites, 16(21%) were providing access to Phone numbers and postal addresses while 58(78%) were not providing access. Out of 177 public and private university library websites, 75(42%) were not providing spelling grammatical, and typing mistakes while 102(57%) did not provide access. Out of 103 public sector universities, 48(46%) were not providing spelling grammatical, and typing mistakes while 55(53%) did not provide access to websites. Out of 74 private sector university library websites, 27(36%) were providing spelling grammatical, and typing mistakes while 47(63%) were not providing access to websites. There is no research conducted on HEC university library websites in detail, in some earlier studies, Qutab and Mahmood (2009) studied some university library websites with a checklist of 71 and 39 items respectively. This study is a complete case study of HEC recognized university library websites (both public and private) using a comprehensive checklist of 138 items

Visualisation of heterogeneous data with the generalised generative topographic mapping

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values

Scrutinizing human MHC polymorphism:supertype analysis using Poisson-Boltzmann electrostatics and clustering

Peptide-binding MHC proteins are thought the most variable proteins across the human population; the extreme MHC polymorphism observed is functionally important and results from constrained divergent evolution. MHCs have vital functions in immunology and homeostasis: cell surface MHC class I molecules report cell status to CD8+ T cells, NKT cells and NK cells, thus playing key roles in pathogen defence, as well as mediating smell recognition, mate choice, Adverse Drug Reactions, and transplantation rejection. MHC peptide specificity falls into several supertypes exhibiting commonality of binding. It seems likely that other supertypes exist relevant to other functions. Since comprehensive experimental characterization is intractable, structure-based bioinformatics is the only viable solution. We modelled functional MHC proteins by homology and used calculated Poisson-Boltzmann electrostatics projected from the top surface of the MHC as multi-dimensional descriptors, analysing them using state-of-the-art dimensionality reduction techniques and clustering algorithms. We were able to recover the 3 MHC loci as separate clusters and identify clear sub-groups within them, vindicating unequivocally our choice of both data representation and clustering strategy. We expect this approach to make a profound contribution to the study of MHC polymorphism and its functional consequences, and, by extension, other burgeoning structural systems, such as GPCRs

High-throughput assessment of small open reading frame translation in Drosophila melanogaster

Hundreds of thousands of putative small ORFs (smORFs) sequences are present in eukaryotic genomes, potentially coding for peptides less than 100 amino acids. smORFs have been deemed non-coding on the basis of their high numbers and their small size that makes it extremely challenging to assess their functionality both bioinformatically and biochemically. The recently developed Ribo-Seq technique, which is the deep sequencing of ribosome footprints, has generated significant controversy by showing extensive translation of smORFs outside of annotated protein coding regions, including putative non-coding RNAs.. Our lab adapted the Ribo-Seq technique by combining it with the polysome fractionation in order to assess smORF translation in Drosophila S2 cells. This thesis provides a high-throughput assessment of smORF translation in Drosophila melanogaster by firstly implementing complementary techniques such as transfection-tagging and Mass spectrometry methods in order to provide an independent corroboration of the S2 cell data (Chapter 3). Secondly, the in order to expand the catalogue of smORFs that are translated, I significantly improve upon the yield and sequencing efficiency of the Poly-Ribo-Seq protocol while adapting it to Drosophila embryos and then implementing it across embryogenesis divided in to Early, Mid and Late stages (Chapter 4). Currently, there is still a lot of debate in the field with regards to Ribo-Seq data analysis, and various computational metrics have been developed aimed at discerning ‘real’ translation events to background noise. Chapter 5 explores some of the metrics developed and establishes a translation cut-off suitable for designating small ORFs as translated. Altogether, the improvements introduced to the protocol and my data analysis shows the translation of 500 annotated smORFs, 500 smORFs in long non-coding RNAs and 5,000 uORFs, of which only one-third of each type of smORF has previous evidence of translation. These findings strengthen the establishment of smORFs as a distinct class of genes that significantly expand the protein coding complement of the genome

Role of FDI and Foreign Remittances in Boosting and Economic Growth: Evidence from Brazil

The paper has two main objectives; (i) Firstly, to investigate the relationship between economic growth and economic indicators of Brazil economy, (ii) Secondly, to examine the impact of FDI on Gross domestic product of Brazil economy. The study employed time series data from 1986 to 2014 of the remittance, FDI, capital formation and domestic savings to know the impact on Gross domestic product of Brazil. The paper analyzed the growth and economy nexus by applying econometric models such as; Granger causality test, OLS methods and unit root test (both ADF and PP). According to empirical estimations, foreign remittances and capital formation has significant and positive contribution towards economic growth for Brazil. However, foreign direct investment and savings show insignificant response towards gross domestic product of Brazil economy. The Brazil economy needs massive reforms to properly utilize the foreign and domestic investments on industrial, agriculture, and technology sector. In addition, the capital formation is playing an important role in the economic development due to positive impact on economic growth. The study proposed some fruitful policy implication for Brazil to boost the economic growth and living standard of people

Reinvestigating the Role of Coal Consumption in Indian Economy: An ARDL and Causality analysis

The present study investigates the role of coal consumption on the economic growth in India from 1980-2016. We use panel data estimation techniques for long run and VECM Granger causality for short run analysis to find the direction of causality between coal consumption and economic growth. The model specification also incorporates the urbanization, labor, fiscal deficit and service sector value added. In long-run analysis, the results confirm the unidirectional relationship from economic growth to coal consumption. On contrary, in short-run, bidirectional causal relationship between coal consumption and Indian economic growth confirms that higher coal consumption supports the Indian economy through energy. Furthermore, bidirectional causality is confirmed between economic growth and population; besides, the results show the unidirectional causality from service sector to economic growth

Role of FDI and Foreign Remittances in Boosting and Economic Growth: Evidence from Brazil

The paper has two main objectives; (i) Firstly, to investigate the relationship between economic growth and economic indicators of Brazil economy, (ii) Secondly, to examine the impact of FDI on Gross domestic product of Brazil economy. The study employed time series data from 1986 to 2014 of the remittance, FDI, capital formation and domestic savings to know the impact on Gross domestic product of Brazil. The paper analyzed the growth and economy nexus by applying econometric models such as; Granger causality test, OLS methods and unit root test (both ADF and PP). According to empirical estimations, foreign remittances and capital formation has significant and positive contribution towards economic growth for Brazil. However, foreign direct investment and savings show insignificant response towards gross domestic product of Brazil economy. The Brazil economy needs massive reforms to properly utilize the foreign and domestic investments on industrial, agriculture, and technology sector. In addition, the capital formation is playing an important role in the economic development due to positive impact on economic growth. The study proposed some fruitful policy implication for Brazil to boost the economic growth and living standard of people

Reinvestigating the Role of Coal Consumption in Indian Economy: An ARDL and Causality analysis

The present study investigates the role of coal consumption on the economic growth in India from 1980-2016. We use panel data estimation techniques for long run and VECM Granger causality for short run analysis to find the direction of causality between coal consumption and economic growth. The model specification also incorporates the urbanization, labor, fiscal deficit and service sector value added. In long-run analysis, the results confirm the unidirectional relationship from economic growth to coal consumption. On contrary, in short-run, bidirectional causal relationship between coal consumption and Indian economic growth confirms that higher coal consumption supports the Indian economy through energy. Furthermore, bidirectional causality is confirmed between economic growth and population; besides, the results show the unidirectional causality from service sector to economic growth