Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Analysis Pipeline

The rapidly advancing high-throughput and next generation sequencing technologies facilitate deeper insights into the molecular mechanisms underlying the expression of phenotypes in living organisms. Experimental data and scientific publications following this technological advancement have rapidly accumulated in public databases. Meaningful analysis of currently available data in genomic databases requires sophisticated computational tools and algorithms, and presents considerable challenges to molecular biologists without specialized training in bioinformatics. To study their phenotype of interest molecular biologists must prioritize large lists of poorly characterized genes generated in high-throughput experiments. To date, prioritization tools have primarily been designed to work with phenotypes of human diseases as defined by the genes known to be associated with those diseases. There is therefore a need for more prioritization tools for phenotypes which are not related with diseases generally or diseases with which no genes have yet been associated in particular. Chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) is a method of choice to study the gene regulation processes responsible for the expression of cellular phenotypes. Among publicly available computational pipelines for the processing of ChIP-Seq data, there is a lack of tools for the downstream analysis of composite motifs and preferred binding distances of the DNA binding proteins. This thesis is aimed to address the gap existing in the tools available to process high-throughput ChIP-Seq data to provide rapid analysis and interpretation of large lists of poorly characterized genes. Additionally, programs for the analysis of preferred binding distances of transcription factors were integrated into the pipeline for expedited results. A gene prioritization algorithm linking genes to non-disease phenotypes described by meaningful keywords was developed. This algorithm can be used to process candidate genetic targets of a transcription factor produced by a computational pipeline for ChIP-Seq data analysis

Application of the biologically inspired network for electroencephalogram analysis

Architecture of a neural network combining automatic feature extraction with the minimized amount of network training acquired by means of employing of a multistage training procedure is investigated. The network selects prototypical signals and calculates features based on the similarity of a signal to prototypes. The similarity is measured by the prognosis error of the linear regression model. The network is applied for the meaningful paroxysmal activity vs. background classification task and provides better accuracy than the methods using manually selected features. Performance of several modifications of the new architecture is being evaluatedKauno technologijos universitetasTaikomosios informatikos katedraVytauto Didžiojo universiteta

PhenotypeLinksREADME.doc

Update on PhenotypeLinks project. This project on figshare.com intended to refer to bioinformatics tools developed for PhD thesis "Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Processing Pipeline", by Erinija Pranckeviciene, Ottawa University, January 2015. This project is moved to github. Link to the tool and tool's code: https://github.com/erinijapranckeviciene/phenotypelink

On classification models of gene expression microarrays: The simpler the better

NRC publication: Ye

Liknon feature selection for microarrays

Many real-world classification problems involve very sparse and high-dimensional data. The successes of LIKNON - linear programming support vector machine (LPSVM) for feature selection, motivates a more thorough analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. Robust feature/model selection methods are desirable. LIKNON is claimed to have such robustness properties. Its feature selection operates by selecting the groups of features with large differences between the resultants of the two classes. The degree of desired difference is controlled by the regularization parameter. We study the practical value of LIKNON-based feature/model selection for microarray data. Our findings support the claims about the robustness of the method.Peer reviewed: YesNRC publication: Ye

Supervised recognition of the definite visual objects in aerial photographs

In automated production of a digital map and the geodetic data from aerial photograph human operators perform several intermediate image-processing stages. The most complicated and less automated stage is an extraction of the boundaries of the visual objects - roads, urban areas, buildings, landmarks etc. A method of supervised recognition of definite image objects, which facilitates an automation of the extraction of the object's boundaries, is presented. [...]Kauno technologijos universiteta

Liknon feature selection: behind the scenes

NRC publication: Ye

README document of PhenotypeLinks project

<p>Document explaining content of PhenotypeLinks project developed for PhD thesis "Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Processing Pipeline". </p><p>Supporting software moved to github.com/erinijapranckeviciene</p><p><br></p

Classification of regions in CT images of early brain ishemic stroke

The problem of the automatic recognition of the brain stroke in the early stage is addressed. Each pixel of a CT scan is classified either to ishemic stroke class or the rest of the image using supervised classification strategy. The classes of the image regions are described by the prototypical gray level histograms. For feature extraction the training images are transformed to the new similarity space, where the training of a classifier and classification is performed.[...]Kauno medicinos universitetasKauno technologijos universiteta

Control of sparseness for feature selection

In linear discriminant (LD) analysis high sample size/feature ratio is desirable. The linear programming procedure (LP) for LD identification handles the curse of dimensionality through simultaneous minimization of the L1 norm of the classification errors and the LD weights. The sparseness of the solution \u2013 the fraction of features retained \u2013 can be controlled by a parameter in the objective function. By qualitatively analyzing the objective function and the constraints of the problem, we show why sparseness arises. In a sparse solution, large values of the LD weight vector reveal those individual features most important for the decision boundary.Peer reviewed: YesNRC publication: Ye