Computational analysis of microRNAs in biomedicine

Katherine Abigail Icay-Rouhiainen

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

All genetic information necessary for creating and maintaining life is stored in DNA and RNA molecules. Gene expression is the process by which sets of DNA (i.e. genes) are encoded into functional gene products. Thus, the state and function of a single cell can be determined by the amount and type of genes expressed: tumour cells can be detected from normal cells, and one functional brain region can be differentiated from another. The discovery of non-coding RNAs like microRNAs (miRNAs) introduced a sophisticated level of gene regulation to our understanding of the flow of genetic information. Strong evidence suggest miRNAs have vital roles in mediating a wide range of biological pathways essential to cell maintenance and tissue-specific function. In complex diseases such as cancer, they show particular promise as candidate biomarkers in prognosis, diagnosis, and treatment. However, we are still uncertain about the precise mechanisms and contributions of miRNAs in regulating gene expression. High-throughput technologies generate molecular data of unprecedented size and depth, providing unique opportunities to study small RNA molecules and complex diseases. Despite exact regulatory mechanisms being uncertain, miRNAs are functionally characterized with high-throughput expression data and the biological pathways annotated to their putative target genes. However, the sheer size of the data generated and to be processed raises challenges in computational resources and in discovering clinically relevant information. This work addresses these challenges with the development and application of two computational tools to better facilitate miRNA research. SePIA is a high-throughput workflow to reliably process sequencing data and perform expression analysis to identify strongly-related miRNAs and their predicted target genes. Director is a visualization package to further the interpretation of molecular interactions and depict the co-regulatory behaviour of miRNAs. The usefulness of these tools is shown in the application of two biomedical studies: in differentiating brain tissue phenotypes, and in determining a role in the chemosensitivity of diffuse large B-cell lymphoma. Sufficient biological context is drawn from the computational results generated by the tools to hypothesize and experimentally validate the role of miRNAs, and propose a set as candidate biomarkers and targets for drug therapy. SePIA and Director are readily available tools developed to improve and make more convenient the computational analysis of miRNAs in biomedical research.
Original languageEnglish
Supervisors/Advisors
  • Hautaniemi, Sampsa, Supervisor
Place of PublicationHelsinki
Publisher
Print ISBNs978-951-51-4433-1
Electronic ISBNs978-951-51-4434-8
Publication statusPublished - 2018
MoE publication typeG5 Doctoral dissertation (article)

Fields of Science

  • Lymphoma, Large B-Cell, Diffuse
  • +genetics
  • Genetic Association Studies
  • Genetic Markers
  • High-Throughput Nucleotide Sequencing
  • Sequence Analysis, RNA
  • 3111 Biomedicine

Cite this

Icay-Rouhiainen, K. A. (2018). Computational analysis of microRNAs in biomedicine. Helsinki: [K. A. Icay-Rouhiainen].
Icay-Rouhiainen, Katherine Abigail. / Computational analysis of microRNAs in biomedicine. Helsinki : [K. A. Icay-Rouhiainen], 2018. 51 p.
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title = "Computational analysis of microRNAs in biomedicine",
abstract = "All genetic information necessary for creating and maintaining life is stored in DNA and RNA molecules. Gene expression is the process by which sets of DNA (i.e. genes) are encoded into functional gene products. Thus, the state and function of a single cell can be determined by the amount and type of genes expressed: tumour cells can be detected from normal cells, and one functional brain region can be differentiated from another. The discovery of non-coding RNAs like microRNAs (miRNAs) introduced a sophisticated level of gene regulation to our understanding of the flow of genetic information. Strong evidence suggest miRNAs have vital roles in mediating a wide range of biological pathways essential to cell maintenance and tissue-specific function. In complex diseases such as cancer, they show particular promise as candidate biomarkers in prognosis, diagnosis, and treatment. However, we are still uncertain about the precise mechanisms and contributions of miRNAs in regulating gene expression. High-throughput technologies generate molecular data of unprecedented size and depth, providing unique opportunities to study small RNA molecules and complex diseases. Despite exact regulatory mechanisms being uncertain, miRNAs are functionally characterized with high-throughput expression data and the biological pathways annotated to their putative target genes. However, the sheer size of the data generated and to be processed raises challenges in computational resources and in discovering clinically relevant information. This work addresses these challenges with the development and application of two computational tools to better facilitate miRNA research. SePIA is a high-throughput workflow to reliably process sequencing data and perform expression analysis to identify strongly-related miRNAs and their predicted target genes. Director is a visualization package to further the interpretation of molecular interactions and depict the co-regulatory behaviour of miRNAs. The usefulness of these tools is shown in the application of two biomedical studies: in differentiating brain tissue phenotypes, and in determining a role in the chemosensitivity of diffuse large B-cell lymphoma. Sufficient biological context is drawn from the computational results generated by the tools to hypothesize and experimentally validate the role of miRNAs, and propose a set as candidate biomarkers and targets for drug therapy. SePIA and Director are readily available tools developed to improve and make more convenient the computational analysis of miRNAs in biomedical research.",
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Icay-Rouhiainen, KA 2018, 'Computational analysis of microRNAs in biomedicine', Helsinki.

Computational analysis of microRNAs in biomedicine. / Icay-Rouhiainen, Katherine Abigail.

Helsinki : [K. A. Icay-Rouhiainen], 2018. 51 p.

Research output: ThesisDoctoral ThesisCollection of Articles

TY - THES

T1 - Computational analysis of microRNAs in biomedicine

AU - Icay-Rouhiainen, Katherine Abigail

N1 - M1 - 51 s. + liitteet

PY - 2018

Y1 - 2018

N2 - All genetic information necessary for creating and maintaining life is stored in DNA and RNA molecules. Gene expression is the process by which sets of DNA (i.e. genes) are encoded into functional gene products. Thus, the state and function of a single cell can be determined by the amount and type of genes expressed: tumour cells can be detected from normal cells, and one functional brain region can be differentiated from another. The discovery of non-coding RNAs like microRNAs (miRNAs) introduced a sophisticated level of gene regulation to our understanding of the flow of genetic information. Strong evidence suggest miRNAs have vital roles in mediating a wide range of biological pathways essential to cell maintenance and tissue-specific function. In complex diseases such as cancer, they show particular promise as candidate biomarkers in prognosis, diagnosis, and treatment. However, we are still uncertain about the precise mechanisms and contributions of miRNAs in regulating gene expression. High-throughput technologies generate molecular data of unprecedented size and depth, providing unique opportunities to study small RNA molecules and complex diseases. Despite exact regulatory mechanisms being uncertain, miRNAs are functionally characterized with high-throughput expression data and the biological pathways annotated to their putative target genes. However, the sheer size of the data generated and to be processed raises challenges in computational resources and in discovering clinically relevant information. This work addresses these challenges with the development and application of two computational tools to better facilitate miRNA research. SePIA is a high-throughput workflow to reliably process sequencing data and perform expression analysis to identify strongly-related miRNAs and their predicted target genes. Director is a visualization package to further the interpretation of molecular interactions and depict the co-regulatory behaviour of miRNAs. The usefulness of these tools is shown in the application of two biomedical studies: in differentiating brain tissue phenotypes, and in determining a role in the chemosensitivity of diffuse large B-cell lymphoma. Sufficient biological context is drawn from the computational results generated by the tools to hypothesize and experimentally validate the role of miRNAs, and propose a set as candidate biomarkers and targets for drug therapy. SePIA and Director are readily available tools developed to improve and make more convenient the computational analysis of miRNAs in biomedical research.

AB - All genetic information necessary for creating and maintaining life is stored in DNA and RNA molecules. Gene expression is the process by which sets of DNA (i.e. genes) are encoded into functional gene products. Thus, the state and function of a single cell can be determined by the amount and type of genes expressed: tumour cells can be detected from normal cells, and one functional brain region can be differentiated from another. The discovery of non-coding RNAs like microRNAs (miRNAs) introduced a sophisticated level of gene regulation to our understanding of the flow of genetic information. Strong evidence suggest miRNAs have vital roles in mediating a wide range of biological pathways essential to cell maintenance and tissue-specific function. In complex diseases such as cancer, they show particular promise as candidate biomarkers in prognosis, diagnosis, and treatment. However, we are still uncertain about the precise mechanisms and contributions of miRNAs in regulating gene expression. High-throughput technologies generate molecular data of unprecedented size and depth, providing unique opportunities to study small RNA molecules and complex diseases. Despite exact regulatory mechanisms being uncertain, miRNAs are functionally characterized with high-throughput expression data and the biological pathways annotated to their putative target genes. However, the sheer size of the data generated and to be processed raises challenges in computational resources and in discovering clinically relevant information. This work addresses these challenges with the development and application of two computational tools to better facilitate miRNA research. SePIA is a high-throughput workflow to reliably process sequencing data and perform expression analysis to identify strongly-related miRNAs and their predicted target genes. Director is a visualization package to further the interpretation of molecular interactions and depict the co-regulatory behaviour of miRNAs. The usefulness of these tools is shown in the application of two biomedical studies: in differentiating brain tissue phenotypes, and in determining a role in the chemosensitivity of diffuse large B-cell lymphoma. Sufficient biological context is drawn from the computational results generated by the tools to hypothesize and experimentally validate the role of miRNAs, and propose a set as candidate biomarkers and targets for drug therapy. SePIA and Director are readily available tools developed to improve and make more convenient the computational analysis of miRNAs in biomedical research.

KW - Lymphoma, Large B-Cell, Diffuse

KW - +genetics

KW - Genetic Association Studies

KW - Genetic Markers

KW - High-Throughput Nucleotide Sequencing

KW - Sequence Analysis, RNA

KW - 3111 Biomedicine

M3 - Doctoral Thesis

SN - 978-951-51-4433-1

PB - [K. A. Icay-Rouhiainen]

CY - Helsinki

ER -

Icay-Rouhiainen KA. Computational analysis of microRNAs in biomedicine. Helsinki: [K. A. Icay-Rouhiainen], 2018. 51 p.