Extracting information from high-throughput gene expression data with pathway analysis and deconvolution

Modern technologies allow for the collection of large biological datasets that can be utilised for diverse health-related applications. However, to extract useful information from such data, computational methods are needed. The field that develops and explores methods to analyse biological data is called bioinformatics. In this thesis I evaluate different bioinformatic methods and introduce novel ones related to processing gene expression data. Gene expression data reflects how active different genes are in a set of measured biological samples. These samples can be for example blood from human individuals, tissue samples from tumours and the corresponding healthy tissue, or brain samples from mice with different neural diseases. This thesis covers two topics, pathway analysis and deconvolution, related to downstream analysis of gene expression data. Notably, this summary does not repeat in detail the same points made in the original publications, but aims to provide a comprehensive overview of the current knowledge of the two wider topics. The original publications focus on comparing and evaluating the available methods as well as presenting new ones that cover some previously untouched features. While the terms ’pathway analysis’ and ’deconvolution’ have been used with alternative definitions in other fields, in the context of this thesis, pathway analysis refers to estimating the activity of pathways, i.e. interaction networks body uses to react to different signals, based on given gene expression data and structural information of the relevant pathways. I focus on different types of analysis methods and their varying goals, requirements, and underlying statistical approaches. In addition, the strengths and weaknesses of the concept of pathway analysis are briefly discussed. The first two original publications I and II empirically compare different types of pathway methods and introduce a novel one. In the paper I, the tested methods are evaluated from different perspectives, and in the paper II, a novel method is introduced and its performance demonstrated against alternative tools. Many biological samples contain a variety of cell types and here, deconvolution means computationally extracting cell type composition or cell type specific expression from bulk samples. The deconvolution sections of this thesis also focus on a general overview of the topic and the available computational methodology. As deconvolution is challenging, I discuss the factors affecting its accuracy as well as alternative wet lab approaches to obtain cell type specific information. The first original publication about deconvolution (publication III) introduces a novel method and evaluates it against the other available tools. The second (publication IV) focuses on identifying cell type specific differences between sample groups, which is a particularly difficult task

Computational approaches in high-throughput proteomics data analysis

Proteins are key components in biological systems as they mediate the signaling responsible for information processing in a cell and organism. In biomedical research, one goal is to elucidate the mechanisms of cellular signal transduction pathways to identify possible defects that cause disease. Advancements in technologies such as mass spectrometry and flow cytometry enable the measurement of multiple proteins from a system. Proteomics, or the large-scale study of proteins of a system, thus plays an important role in biomedical research. The analysis of all high-throughput proteomics data requires the use of advanced computational methods. Thus, the combination of bioinformatics and proteomics has become an important part in research of signal transduction pathways. The main objective in this study was to develop and apply computational methods for the preprocessing, analysis and interpretation of high-throughput proteomics data. The methods focused on data from tandem mass spectrometry and single cell flow cytometry, and integration of proteomics data with gene expression microarray data and information from various biological databases. Overall, the methods developed and applied in this study have led to new ways of management and preprocessing of proteomics data. Additionally, the available tools have successfully been used to help interpret biomedical data and to facilitate analysis of data that would have been cumbersome to do without the use of computational methods.Proteiineilla on tärkeä merkitys biologisissa systeemeissä sillä ne koordinoivat erilaisia solujen ja organismien prosesseja. Yksi biolääketieteellisen tutkimuksen tavoitteista on valottaa solujen viestintäreittejä ja niiden toiminnassa tapahtuvia muutoksia eri sairauksien yhteydessä, jotta tällaisia muutoksia voitaisiin korjata. Proteomiikka on proteiinien laajamittaista tutkimista solusta, kudoksesta tai organismista. Proteomiikan menetelmät kuten massaspektrometria ja virtaussytometria ovat keskeisiä biolääketieteellisen tutkimuksen menetelmiä, joilla voidaan mitata näytteestä samanaikaisesti useita proteiineja. Nykyajan kehittyneet proteomiikan mittausteknologiat tuottavat suuria tulosaineistoja ja edellyttävät laskennallisten menetelmien käyttöä aineiston analyysissä. Bioinformatiikan menetelmät ovatkin nousseet tärkeäksi osaksi proteomiikka-analyysiä ja viestintäreittien tutkimusta. Tämän tutkimuksen päätavoite oli kehittää ja soveltaa tehokkaita laskennallisia menetelmiä laajamittaisten proteomiikka-aineistojen esikäsittelyyn, analyysiin ja tulkintaan. Tässä tutkimuksessa kehitettiin esikäsittelymenetelmä massaspektrometria-aineistolle sekä automatisoitu analyysimenetelmä virtaussytometria-aineistolle. Proteiinitason tietoa yhdistettiin mittauksiin geenien transkriptiotasoista ja olemassaolevaan biologisista tietokannoista poimittuun tietoon. Väitöskirjatyö osoittaa, että laskennallisilla menetelmillä on keskeinen merkitys proteomiikan aineistojen hallinnassa, esikäsittelyssä ja analyysissä. Tutkimuksessa kehitetyt analyysimenetelmät edistävät huomattavasti biolääketieteellisen tiedon laajempaa hyödyntämistä ja ymmärtämistä

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

'BioNessie(G) - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations

BioNessie - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations

How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Computational methods in cancer gene networking

In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table