Difference: ResearchBioLab (1 vs. 20)

Revision 20
24 Oct 2014 - Main.AngelaLuijf
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  Research description Research leaders .

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The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Information Management focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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The Bioinformatics Laboratory pursues two lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Information Management focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge.
 

Systems biology
Revision 19
31 Jul 2014 - Main.AngelaLuijf
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Medical Bioinformatics

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Research description Research leaders .
Revision 18
22 Jul 2014 - Main.AngelaLuijf
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

Revision 17
18 Jul 2014 - Main.AdminUser
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research description Research leaders .

  The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Information Management focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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Synergy
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Information Management. Similarly, the infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
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Revision 16
18 Jul 2014 - Main.UnknownUser
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

Revision 15
16 Jan 2012 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research leaders
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The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Information Management focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
 

Systems biology
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Synergy
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We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Semantic Biosystems. Similarly, the infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
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We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Information Management. Similarly, the infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
 

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Revision 14
21 Mar 2011 - Main.AngelaLuijf
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research description Research leaders
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Research description Research leaders
 
The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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 Synergy
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Semantic Biosystems. Similarly, the infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
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Revision 13
03 Apr 2010 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Systems biology
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Results from this research will largely facilitate systems biology research (which is a AMC spearhead). For example, Systems Genomics contributes to the reconstruction, analysis and interpretation of biological networks. The biomedical knowledge bases developed within Semantic Biosystems organize biological and clinical information, including high-quality definition of pathways and their relation to specific disorders, which will serve as input of mathematical modeling and simulation. Finally, e-Bioscience will provide the data- and computatinal (Grid) infrastructure that is necessary for systems biology.
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Results from this research will largely facilitate systems biology research (which is a AMC and UvA spearhead). For example, Systems Genomics contributes to the reconstruction, analysis and interpretation of biological networks. The biomedical knowledge bases developed within Semantic Biosystems organize biological and clinical information, including high-quality definition of pathways and their relation to specific disorders, which will serve as input of mathematical modeling and simulation. e-Bioscience will provide the data- and computatinal (Grid) infrastructure that is necessary for systems biology.
 

Synergy
Revision 12
23 Mar 2010 - Main.SilviaOlabarriaga
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research leaders
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The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
>
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The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies advanced IT infrastructures to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
 

Systems biology
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Synergy
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We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
>
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We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Semantic Biosystems. Similarly, the infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
 
Revision 11
01 Feb 2010 - Main.PerryMoerland
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

Revision 10
09 Jan 2010 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Synergy
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We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Biology provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
>
>
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Genomics provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
 
Revision 9
22 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research description
The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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Research description Research leaders
The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
 

Systems biology
Results from this research will largely facilitate systems biology research (which is a AMC spearhead). For example, Systems Genomics contributes to the reconstruction, analysis and interpretation of biological networks. The biomedical knowledge bases developed within Semantic Biosystems organize biological and clinical information, including high-quality definition of pathways and their relation to specific disorders, which will serve as input of mathematical modeling and simulation. Finally, e-Bioscience will provide the data- and computatinal (Grid) infrastructure that is necessary for systems biology.
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Synergy
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Biology provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
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Research leaders
Systems Genomics Perry Moerland
e-Bioscience Silvia Olabarriaga
Semantic Biosystems Andrew Gibson
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Revision 8
21 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

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Research

The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
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Research description
The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.
 

Systems biology
Results from this research will largely facilitate systems biology research (which is a AMC spearhead). For example, Systems Genomics contributes to the reconstruction, analysis and interpretation of biological networks. The biomedical knowledge bases developed within Semantic Biosystems organize biological and clinical information, including high-quality definition of pathways and their relation to specific disorders, which will serve as input of mathematical modeling and simulation. Finally, e-Bioscience will provide the data- and computatinal (Grid) infrastructure that is necessary for systems biology.
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  Synergy
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Biology provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.
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Research leaders
 
Systems Genomics Perry Moerland
e-Bioscience Silvia Olabarriaga
Semantic Biosystems Andrew Gibson
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Under construction
Revision 7
19 Dec 2009 - Main.AntoineVanKampen
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Medical Bioinformatics

Research
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Medical Bioinformatics

Research

The Bioinformatics Laboratory pursues three lines of research in areas important for the AMC. This research is generally implemented with groups within and outside the AMC. Systems Genomics aims at the analysis and interpretation of genome-wide omics data in the context of biological networks. Semantic Biosystems focusses on the use of semantic web technologies for the organisation and integration of biomedical data, information and knowledge. Finally, e-Bioscience develops and applies a Grid middleware infrastructure to facilitate distributed and multi-disciplinary collaborations to advance life sciences.

Systems biology
Results from this research will largely facilitate systems biology research (which is a AMC spearhead). For example, Systems Genomics contributes to the reconstruction, analysis and interpretation of biological networks. The biomedical knowledge bases developed within Semantic Biosystems organize biological and clinical information, including high-quality definition of pathways and their relation to specific disorders, which will serve as input of mathematical modeling and simulation. Finally, e-Bioscience will provide the data- and computatinal (Grid) infrastructure that is necessary for systems biology.

Synergy
We also aim to maximize synergy between these research lines and other activities of the lab. For example, the analysis and comparison of biological pathway databases with Systems Biology provides important information for the development of biomedical knowledgebases with Semantic Biosystems. The infrastructure developed in e-Bioscience is currently used for the analysis of next-generation sequence data.

Research leaders
Systems Genomics Perry Moerland
e-Bioscience Silvia Olabarriaga
Semantic Biosystems Andrew Gibson
 

Under construction
Revision 6
09 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="WebHome"
Medical Bioinformatics

Research
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Main research lines

    Sequence analysis

    As part of this research line we develop methods and databases for the processing and analysis of sequence data. Examples of past projects involved the analysis of SAGE tags and the identification of genomic islands in prokaryotes. One of the main projects involve the construction and analysis of genomic maps (e.g., transcriptome maps). Genome maps provide tools to identify candidate disease genes but also provide fundamental insight in the (higher-order) organization of the genome of organisms including human. In particular, genome maps provide further understanding of the regulation of gene expression, which should eventually enable us to understand biological processes in more detail. We study transcriptome maps that link gene expression profiles to the genomic DNA sequence and thereby reveal expression activity along the genome. For example, the human, mouse and drosophila transcriptome maps revealed pronounced gene expression domains with significant increased as compared to the average genome. These domains were named RIDGES (Regions of Increased Gene Expression). We have shown that correlating domains occur for gene density, intron length, GC content and SINE/LINE repeats. Furthermore, first results from the comparison of the mouse and human transcriptome map indicate a possible evolutionary conservation of RIDGES. Currently, we are studying the interaction between repeats and genes and stage/tissue specific expression domains. This research clearly revealed a higher order structure of eukaryotic genomes but we are far from understanding all our observations. We aim to extend our genome maps by incorporating of other types of data (e.g., methylation, histon modification), which should further advance our understanding of (epigenetic) gene regulation in eukaryotic genomes.

    Figure 1. Gene expression profiles for human chromosome 1, 2, 3 and 4. Ridges and anti-Ridges are indicated with red and blue bars respectively.

    Data-driven systems biology


    2a. The reconstruction, analysis and interpretation of biological pathways
    The reconstruction of yet unknown biological networks and the identification of missing proteins and genes in existing networks are essential for a full understanding of living organisms in health and disease. We develop algorithms that allow to predict missing genes and proteins in metabolic networks by combining gene expression data with other types of data. As a second aim we use and improve existing algorithms to identify pathway modules from microarray experiments to identify groups of genes that are co-regulated in specific conditions or tissues.

    2b. The development of biomedical knowledge bases
    We develop a general framework for biomedical knowledge bases. Such knowledge bases and complementary methods guide the analysis and interpretation of experimental data in the context of biological networks. Within the knowledge base we integrate different pieces and details of information and expert knowledge. This research actually represents a data-driven approach towards systems biology to finally describe pathways and living organisms in full detail.


    Figure 2. Example Concept Map from the Peroxisome knowledge base


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Revision 5
01 Dec 2009 - Main.AngelaLuijf
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Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience
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Medical Bioinformatics

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Main research lines
Revision 4
27 Nov 2009 - Main.AngelaLuijf
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META TOPICPARENT name="WebHome"
Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience
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Sequence analysis

As part of this research line we develop methods and databases for the processing and analysis of sequence data. Examples of past projects involved the analysis of SAGE tags and the identification of genomic islands in prokaryotes. One of the main projects involve the construction and analysis of genomic maps (e.g., transcriptome maps). Genome maps provide tools to identify candidate disease genes but also provide fundamental insight in the (higher-order) organization of the genome of organisms including human. In particular, genome maps provide further understanding of the regulation of gene expression, which should eventually enable us to understand biological processes in more detail. We study transcriptome maps that link gene expression profiles to the genomic DNA sequence and thereby reveal expression activity along the genome. For example, the human, mouse and drosophila transcriptome maps revealed pronounced gene expression domains with significant increased as compared to the average genome. These domains were named RIDGES (Regions of Increased Gene Expression). We have shown that correlating domains occur for gene density, intron length, GC content and SINE/LINE repeats. Furthermore, first results from the comparison of the mouse and human transcriptome map indicate a possible evolutionary conservation of RIDGES. Currently, we are studying the interaction between repeats and genes and stage/tissue specific expression domains. This research clearly revealed a higher order structure of eukaryotic genomes but we are far from understanding all our observations. We aim to extend our genome maps by incorporating of other types of data (e.g., methylation, histon modification), which should further advance our understanding of (epigenetic) gene regulation in eukaryotic genomes.
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  Figure 1. Gene expression profiles for human chromosome 1, 2, 3 and 4. Ridges and anti-Ridges are indicated with red and blue bars respectively.

Data-driven systems biology

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  2b. The development of biomedical knowledge bases
We develop a general framework for biomedical knowledge bases. Such knowledge bases and complementary methods guide the analysis and interpretation of experimental data in the context of biological networks. Within the knowledge base we integrate different pieces and details of information and expert knowledge. This research actually represents a data-driven approach towards systems biology to finally describe pathways and living organisms in full detail.

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  Figure 2. Example Concept Map from the Peroxisome knowledge base


Revision 3
16 Nov 2009 - Main.AngelaLuijf
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META TOPICPARENT name="WebHome"
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Bioinformatics Laboratory
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Bioinformatics Laboratory
  Medical Bioinformatics and e-Bioscience
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Main research lines
Revision 2
10 Nov 2009 - Main.AngelaLuijf
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META TOPICPARENT name="VacanciesBioLab"
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META TOPICPARENT name="WebHome"
  Bioinformatics Laboratory
Medical Bioinformatics and e-Bioscience
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Research

Main research lines

    Sequence analysis

    As part of this research line we develop methods and databases for the processing and analysis of sequence data. Examples of past projects involved the analysis of SAGE tags and the identification of genomic islands in prokaryotes. One of the main projects involve the construction and analysis of genomic maps (e.g., transcriptome maps). Genome maps provide tools to identify candidate disease genes but also provide fundamental insight in the (higher-order) organization of the genome of organisms including human. In particular, genome maps provide further understanding of the regulation of gene expression, which should eventually enable us to understand biological processes in more detail. We study transcriptome maps that link gene expression profiles to the genomic DNA sequence and thereby reveal expression activity along the genome. For example, the human, mouse and drosophila transcriptome maps revealed pronounced gene expression domains with significant increased as compared to the average genome. These domains were named RIDGES (Regions of Increased Gene Expression). We have shown that correlating domains occur for gene density, intron length, GC content and SINE/LINE repeats. Furthermore, first results from the comparison of the mouse and human transcriptome map indicate a possible evolutionary conservation of RIDGES. Currently, we are studying the interaction between repeats and genes and stage/tissue specific expression domains. This research clearly revealed a higher order structure of eukaryotic genomes but we are far from understanding all our observations. We aim to extend our genome maps by incorporating of other types of data (e.g., methylation, histon modification), which should further advance our understanding of (epigenetic) gene regulation in eukaryotic genomes.

    Figure 1. Gene expression profiles for human chromosome 1, 2, 3 and 4. Ridges and anti-Ridges are indicated with red and blue bars respectively.

    Data-driven systems biology


    2a. The reconstruction, analysis and interpretation of biological pathways
    The reconstruction of yet unknown biological networks and the identification of missing proteins and genes in existing networks are essential for a full understanding of living organisms in health and disease. We develop algorithms that allow to predict missing genes and proteins in metabolic networks by combining gene expression data with other types of data. As a second aim we use and improve existing algorithms to identify pathway modules from microarray experiments to identify groups of genes that are co-regulated in specific conditions or tissues.

    2b. The development of biomedical knowledge bases
    We develop a general framework for biomedical knowledge bases. Such knowledge bases and complementary methods guide the analysis and interpretation of experimental data in the context of biological networks. Within the knowledge base we integrate different pieces and details of information and expert knowledge. This research actually represents a data-driven approach towards systems biology to finally describe pathways and living organisms in full detail.


    Figure 2. Example Concept Map from the Peroxisome knowledge base


 
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