Academic literature on the topic 'Bioinformatics tool'

<p>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.</p>

<p>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.</p><p>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.</p><p>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.</p>

<p>The heart is a complex three-dimensional structure with mechanical properties that are inhomogeneous, non-linear, time-variant and anisotropic. These properties affect major physiological factors within the heart, such as the pumping performance of the ventricles, the oxygen demand in the tissue and the distribution of coronary blood flow.</p><p>During the cardiac cycle the heart muscle tissue is deformed as a consequence of the active contraction of the muscle fibers and their relaxation respectively. A mapping of this deformation would give increased understanding of the mechanical properties of the heart. The deformation induces strain and stress in the tissue which are both mechanical properties and can be described with a mathematical tensor object.</p><p>The aim of this master's thesis is to develop a visualization tool for the strain tensor objects that can aid a user to see and/or understand various differences between different hearts and spatial and temporal differences within the same heart. Preferably should the tool be general enough for use with different types of data.</p>

Complex diseases such as cancer or obesity are thought to be caused by abnormalities in multiple  genes and cannot be derived to one specific location in the genome. It has been shown that  identification of disease associated genes can be made through looking at interaction patterns in a  protein‐protein interaction network, where the disease associated genes are represented in clusters,  or disease modules. There are several algorithms developed to infer these disease modules, but  studies have shown that the reliability of the results increase if multiple algorithms are used and a  consensus module is derived from them. MODifieR is an R package developed to combine the results  of multiple  disease module inferring algorithms and has proven to provide a stable result. To  increase usability of the R package and make it available not only for users with programmatic skills,  MODifieR Web was developed as a web based tool with a graphical user interface. The tool was built  using Angular and .NET core, invoking the MODifieR R package in the backend. The interface requires  input in the form of an expression matrix and a probe map from the user, easily uploadable in a  drag‐and‐drop  interface.  It  gives  the  user  the  possibility  to  analyze  data  using  seven  different  algorithms and provide results as gene lists and visualizes the consensus module in a network image.  MODifieR Web is a first version of an application that is a novel contribution to the existing tools for  identification of disease modules, although in need of further improvements to be able to serve a  greater  pool  of  users  in  a  more  effective  way.  The  tool  is  available  to  try  out  at   http://transbioinfo.liu.se/modifier#/home and the source code is released as an open‐source project  in Github (https://github.com/emmape/MODifieRProject).

This thesis is divided into two distinct sections which can be combined under the broad umbrella of structural bioinformatics studies related to drug discovery. The first section involves the establishment of an online South African natural products database. Natural products (NPs) are chemical entities synthesised in nature and are unrivalled in their structural complexity, chemical diversity, and biological specificity, which has long made them crucial to the drug discovery process. South Africa is rich in both plant and marine biodiversity and a great deal of research has gone into isolating compounds from organisms found in this country. However, there is no official database containing this information, making it difficult to access for research purposes. This information was extracted manually from literature to create a database of South African natural products. In order to make the information accessible to the general research community, a website, named “SANCDB”, was built to enable compounds to be quickly and easily searched for and downloaded in a number of different chemical formats. The content of the database was assessed and compared to other established natural product databases. Currently, SANCDB is the only database of natural products in Africa with an online interface. The second section of the thesis was aimed at performing structural characterisation of proteins with the potential to be targeted for antimalarial drug therapy. This looked specifically at 1) The interactions between an exported heat shock protein (Hsp) from Plasmodium falciparum (P. falciparum), PfHsp70-x and various host and exported parasite J proteins, as well as 2) The interface between PfHsp90 and the heat shock organising protein (PfHop). The PfHsp70-x:J protein study provided additional insight into how these two proteins potentially interact. Analysis of the PfHsp90:PfHop also provided a structural insight into the interaction interface between these two proteins and identified residues that could be targeted due to their contribution to the stability of the Hsp90:Hop binding complex and differences between parasite and human proteins. These studies inspired the development of a homology modelling tool, which can be used to assist researchers with homology modelling, while providing them with step-by-step control over the entire process. This thesis presents the establishment of a South African NP database and the development of a homology modelling tool, inspired by protein structural studies. When combined, these two applications have the potential to contribute greatly towards in silico drug discovery research.

This thesis is divided into three parts, united under the general theme of bioinformatics tool development and variation analysis. Part 1 describes the design and development of the Job Management System (JMS), a workflow management system for high performance computing (HPC). HPC has become an integral part of bioinformatics. Computational methods for molecular dynamics and next generation sequencing (NGS) analysis, which require complex calculations on large datasets, are not yet feasible on desktop computers. As such, powerful computer clusters have been employed to perform these calculations. However, making use of these HPC clusters requires familiarity with command line interfaces. This excludes a large number of researchers from taking advantage of these resources. JMS was developed as a tool to make it easier for researchers without a computer science background to make use of HPC. Additionally, JMS can be used to host computational tools and pipelines and generates both web-based interfaces and RESTful APIs for those tools. The web-based interfaces can be used to quickly and easily submit jobs to the underlying cluster. The RESTful web API, on the other hand, allows JMS to provided backend functionality for external tools and web servers that want to run jobs on the cluster. Numerous tools and workflows have already been added to JMS, several of which have been incorporated into external web servers. One such web server is the Human Mutation Analysis (HUMA) web server and database. HUMA, the topic of part 2 of this thesis, is a platform for the analysis of genetic variation in humans. HUMA aggregates data from various existing databases into a single, connected and related database. The advantages of this are realized in the powerful querying abilities that it provides. HUMA includes protein, gene, disease, and variation data and can be searched from the angle of any one of these categories. For example, searching for a protein will return the protein data (e.g. protein sequences, structures, domains and families, and other meta-data). However, the related nature of the database means that genes, diseases, variation, and literature related to the protein will also be returned, giving users a powerful and holistic view of all data associated with the protein. HUMA also provides links to the original sources of the data, allowing users to follow the links to find additional details. HUMA aims to be a platform for the analysis of genetic variation. As such, it also provides tools to visualize and analyse the data (several of which run on the underlying cluster, via JMS). These tools include alignment and 3D structure visualization, homology modeling, variant analysis, and the ability to upload custom variation datasets and map them to proteins, genes and diseases. HUMA also provides collaboration features, allowing users to share and discuss datasets and job results. Finally, part 3 of this thesis focused on the development of a suite of tools, MD-TASK, to analyse genetic variation at the protein structure level via network analysis of molecular dynamics simulations. The use of MD-TASK in combination with the tools developed in the previous parts of this thesis is showcased via the analysis of variation in the renin-angiotensinogen complex, a vital part of the renin-angiotensin system.

Sepsis is a life-threatening condition that is caused by a dysregulated host response to infection. Timely detection of sepsis and antibiotic treatment is important for the patient’s recovery from sepsis. Usually, when sepsis is detected, immediate antibiotic treatment is started with broad-spectrum antibiotics as it takes time to determine the correct antibiotic susceptibility. To overcome this problem, next-generation sequencing is seen as one possible development in clinical diagnostics in the future. Automated bioinformatics pipelines could be used initially for surveillance purposes but eventually for rapid clinical diagnosis. Therefore, the results of 1928 Diagnostics, an automated pipeline for whole-genome sequencing (WGS) data analysis, were compared with the results of an in-house developed pipeline for manual data processing by analyzing sepsis-associated Escherichia coli (SEPEC) WGS data. The pipelines were compared by assessing their predicted antimicrobial resistance (AMR) genes, virulence genes and epidemiological relatedness. In addition, the predicted resistance genes were compared to phenotypic antimicrobial susceptibility testing (AST) data from the clinical microbiology laboratory. All the results obtained from the 1928 Diagnostics and in-house pipeline were similar but differed in the number of virulence/predicted AMR genes, AMR gene variants, detection of species and epidemiologically related E. coli samples. Moreover, the predicted AMR genes from both pipelines did not show a good overall relation to the phenotypic AST result. More studies are needed to make predictions of genes from the WGS analysis more reliable so that WGS analysis can be used as a diagnostics tool in clinical laboratories in the future.

Thesis (M.S.)--George Mason University, 2008.<br>Vita: p. 29. Thesis director: Jennifer Weller. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics. Title from PDF t.p. (viewed July 7, 2008). Includes bibliographical references (p. 28). Also issued in print.