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Signal Processing for Genomics, 2006. The Institution of Engineering and Technology Seminar on

Date Nov. 2006

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Displaying Results 1 - 17 of 17
  • Covers

    Publication Year: 2006 , Page(s): C1
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  • The Institution of Engineering and Technology Seminar on: Signal Processing for Genomics

    Publication Year: 2006 , Page(s): nil2
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  • Copyright page

    Publication Year: 2006 , Page(s): nil3 - nil4
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  • Announcements

    Publication Year: 2006 , Page(s): nil5
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  • Contents

    Publication Year: 2006 , Page(s): nil6
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  • Programme

    Publication Year: 2006 , Page(s): nil7
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  • Grid -Based Signal Processing and Search: Prospects for `Omic Data Analysis'

    Publication Year: 2006 , Page(s): 1
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    First Page of the Article
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  • Modeling Gene Expression Data Using Probabalistic Boolean Networks (PSNs)

    Publication Year: 2006 , Page(s): 3 - 20
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    This paper models gene regulatory network from gene expression data using probabilistic Boolean network. View full abstract»

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  • Employing Bayesian Analysis in Pathway Modelling

    Publication Year: 2006 , Page(s): 21 - 22
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    Biochemical pathway modelling and analysis relies on the development of mechanistic models describing the kinetics of the main chemical species implicated in the overall function of the pathway. Such mechanistic models are highly parameterised representations of the biological system where the model parameters correspond to, for example, actual kinetic reaction rates. Such kinetic rates for biochemical reactions are mostly unknown, and only few can be identified using in vitro assays. Even for the rates that can be measured, biological systems tend to demonstrate a wide range of population variability. However the increasing availability of data obtained from various experimental protocols suggests the adoption of inferential methods in model identification and subsequent analysis. In particular it is argued that the Bayesian viewpoint is ideally suited for biochemical pathway modelling as issues surrounding system identification, sensitivity analysis, experimental design and model comparison are all straightforwardly addressed within this inferential framework. This talk will give an example of Bayesian analysis in modelling of the extracellular signal-regulated kinase (ERK) pathway where there are currently two working hypotheses as to how ERK is stimulated by extracellular growth factor (EGF) signalling. By modelling the pathway based on each hypothesis and employing the Bayesian inferential methodology we are able to objectively assess which hypothesis is best supported by the currently available experimental evidence. View full abstract»

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  • Bioinformatic Approaches to Improve the Identification of Peptides from Proteomics Experiments

    Publication Year: 2006 , Page(s): 23 - 45
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    The accurate analysis of the proteome using mass spectrometry plays an important role in the understanding of many of the physiological processes that occur in an organism and has become a standard tool used in the identification of proteins. This identification of proteins is a challenging one and relies upon bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. An important problem associated with the accurate identification of peptides from mass spectrometry is whether or not a particular peptide is likely to be detected in a standard proteomics experiment, this can be dependant on a number of factors including the physiochemical properties of the peptide itself as well as the mass spectrometer used in the experiment. A machine learning approach was applied to find peptide fragmentation patterns based on different properties of the peptide sequence and we are able to predict which peptide(s) are likely to be detected in a standard proteomics experiment. The task of protein identification is made even more challenging by the occurrence of partial enzymatic protein cleavage, resulting in peptides with internal missed cleavage sites, as proteases frequently fail to digest proteins to their limit peptides. Typically, up to 1 of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using rules derived from information theory, we were able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines. View full abstract»

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  • From the DNA Sequencer to a Sequence Assembly

    Publication Year: 2006 , Page(s): 47 - 60
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    Modern DNA sequencing instruments produce a variety of raw and processed output formats. Traditional Sanger style sequencing is well represented by the ABI sequencing instruments and so this is where most of the talk with be focused, however new emerging technologies from 454 Life Sciences and Solexa are discussed briefly too. It is not the aim to provide in-depth information on any one specific topic, but rather to present a variety of signal processing mechanisms employed by both sequencing machine manufacturers and downstream processing by researchers alike. This covers progressing from the raw data collected off the sequencing instruments, through tidying up the data and base-calling, to SNP detection methods. View full abstract»

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  • Funding Opportunities in BBSRC

    Publication Year: 2006 , Page(s): 61 - 83
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3428 KB)  

    BBSRC is a research council that aims to further the understanding of biological processes at the level of molecules and cells, as well as improve the knowledge of how organisms work and how they interact with each other in populations and ecological systems. This paper gives an overview how the council is structured, what are its funding opportunities and how to get funding from the council. View full abstract»

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  • Microarray Analysis - Problems and Potential Solutions

    Publication Year: 2006 , Page(s): 85 - 115
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    The explosion in sophisticated experimental techniques and hence in the potential biological information they provide has led to a paradigm shift in molecular biology. Pre-genomic biology followed, in general, a traditional hypothesis-driven approach, in which qualitative methods were applied to small sub-systems. In contrast to this modern post-genomic molecular biology takes a more holistic approach which is intrinsically data-driven and increasingly relies on quantitative methods. In this sea of data bioinformatics has emerged as an essential part of making sense of information in molecular life-sciences. At the very deepest levels, biology is changing from a qualitative to a quantitative science. This talk will concentrate on analysis; and on analysis of gene microarrays in particular. We will consider this as a case study in data-driven inference. We look at the issues of representation, gene selection and inference. View full abstract»

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  • Bayesian Hidden Markov Models for Detecting Regions of Deletion and Duplication in the Human Genome using Illumina BeadChip Arrays

    Publication Year: 2006 , Page(s): 117 - 137
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    The association of CNVs with disease is of interest in medical genetics. This paper makes use single nucleotide polymorphism (SNP) to infer copy number variation (CNV). A microrray technology known as SNP-CGH (SNP-comparative genomic hybridization) is used to detect DNA CNV. Current SNP-CGH arrays contain > 100, 000 probes. The Illumina BeadChip has been designed for whole genome SNP genotyping applications. It can probe thousands of SNPs on one array and up to 30 replicates per SNP. The study shows that fluoresescence intensity signals are proportional to the amount of gDNA. SNPs in deleted or duplicated regions show decreased/elevated signals relative to normal regions. Signal processing using Bayesian hidden Markov model has been applied to BeadChip data to find CNVs. View full abstract»

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  • Strategies and Tools for Multivariate Biology: Tackling High Dimensional Postgenomic Data

    Publication Year: 2006 , Page(s): 138 - 155
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    Postgenomic biology is characterised by large and diverse datasets presenting many technical challenges in data handling, analysis and integration. Further, there are significant challenges in converting the data into biological knowledge to support model building and hypothesis generation. A basic strategy is to find structure in the data (clusters) representing the coordinated response of system probes (e.g. biomolecules) to given stimuli. Clusters may then be characterised by reference to knowledgebases (such as pathway genome databases). A complementary strategy is to select probes based on extant knowledge (e.g. related by terms in an ontology) and explore the character of their biological response. We have developed an interactive software tool, Vitamin-B (visual, interactive tool for the analysis and mining of bioinformatics data), which provides utilities for both supervised and unsupervised inquiry and links to external resources to promote knowledge acquisition in support of model construction and hypothesis generation. The focus in the tool's design and development is on usability: aiming to lower the barriers for both expert and novice users to interactive exploratory and targeted data analysis through a full complement of analysis methods coupled to linked dynamic graphics. The tool is based on the proven components of R and ggobi and provides enhanced functionality through a custom user interface, flexible data selection, linkage to external resources, undo functionality and the production of summary reports. A key feature of the project is the central role of HCI principles in the interface. Evidence for the effectiveness of the tool (and its components) will be acquired by user evaluation in illustrative goal-driven scenarios. View full abstract»

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  • Finding Characteristic Biology Patterns in Cancer Microarrays

    Publication Year: 2006 , Page(s): 156 - 166
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    Genetic and environmental differences are known to affect gene expression. The natural variance of expression of many genes affects the control system in any tissue. A finite set of controls must respond to these perturbations, causing regular patterns of altered gene expression characteristic of the system. In order to examine these ideas, a simple method the summarise and test the observed patterns is presented. In this paper, a classified microarray data is used to find relationships between genes. The quantitative data from microarrays can be classified as up or down, allowing estimation of significance by Monte Carlo methods. Spectral analysis and singular value decomposition were also used to study the gene expression pattern. Successive SVD vectors can identify obvious clusters of related genes. View full abstract»

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  • Covers

    Publication Year: 2006 , Page(s): nil11
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