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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Longitudinal Research02:20

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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PyIOmica: longitudinal omics analysis and trend identification.

Sergii Domanskyi1, Carlo Piermarocchi1, George I Mias1,2,3

  • 1Department of Physics and Astronomy, East Lansing, MI 48824, USA.

Bioinformatics (Oxford, England)
|November 29, 2019
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PyIOmica is a new open-source Python package for integrating and analyzing longitudinal multi-omics data. It offers tools for data normalization, annotation, visualization, and network analysis of temporal trends.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Analyzing longitudinal multi-omics data presents challenges in integration and temporal trend characterization.
  • Existing tools may lack comprehensive functionalities for diverse omics data types.

Purpose of the Study:

  • To introduce PyIOmica, an open-source Python package designed for seamless integration and analysis of longitudinal multi-omics datasets.
  • To provide a unified platform for characterizing and categorizing temporal trends within complex biological data.

Main Methods:

  • Implementation of bioinformatics tools for data normalization, annotation, and categorization.
  • Integration of visualization techniques, including visibility graphs for time-series network representation.
  • Support for enrichment analysis of gene ontology terms and pathways.

Main Results:

  • PyIOmica facilitates the integration of diverse longitudinal omics data.
  • The package enables robust characterization and categorization of temporal trends.
  • Visualization tools aid in understanding complex time-series data as networks.

Conclusions:

  • PyIOmica offers a valuable, open-source solution for researchers working with longitudinal multi-omics data.
  • The package enhances the ability to uncover temporal patterns and biological insights from complex datasets.