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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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A comprehensive platform for analyzing longitudinal multi-omics data.

Suhas V Vasaikar1, Adam K Savage2, Qiuyu Gong2

  • 1Allen Institute for Immunology, Seattle, WA, 98109, USA. suhas.vasaikar@alleninstitute.org.

Nature Communications
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

PALMO is a new platform for analyzing complex longitudinal multi-omics data, addressing challenges in biological and clinical research. It offers tools to understand data variations, identify key features, and detect outliers across time and participants.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics
  • Multi-omics Data Analysis

Background:

  • Longitudinal omics data (bulk and single-cell) is crucial for biological and clinical research.
  • Analyzing such data is complex due to inherent variations across time and individuals.
  • Existing tools often struggle to comprehensively address these multi-faceted variations.

Purpose of the Study:

  • To introduce PALMO, a novel platform for analyzing longitudinal multi-omics data.
  • To provide a unified framework for dissecting sources of variation in complex datasets.
  • To facilitate the identification of stable/variable features, temporal marker changes, and sample outliers.

Main Methods:

  • Development of PALMO, a platform with five distinct analytical modules.
  • Modules include variation decomposition, feature stability analysis, differential marker identification, and outlier detection.
  • Validation using a complex longitudinal multi-omics dataset (five modalities) and six external datasets.

Main Results:

  • PALMO effectively analyzes longitudinal bulk and single-cell multi-omics data from various perspectives.
  • The platform successfully decomposes sources of variation and identifies dynamic biological features.
  • Demonstrated utility across diverse datasets, highlighting its robustness and applicability.

Conclusions:

  • PALMO offers a comprehensive solution for the analysis of challenging longitudinal multi-omics data.
  • The platform enhances understanding of biological processes over time and across individuals.
  • PALMO and the associated dataset serve as valuable resources for the scientific community.