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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data.

Yohan Lefol1,2, Tom Korfage3, Robin Mjelle4

  • 1Institute of Clinical Medicine, University of Oslo, PO Box 1171, Blindern 0318, Norway.

NAR Genomics and Bioinformatics
|March 7, 2023
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Summary
This summary is machine-generated.

A new pipeline, Time Series Analysis (TiSA), simplifies the analysis of longitudinal transcriptomic data. It integrates differential gene expression, clustering, and functional enrichment for comprehensive insights.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology

Background:

  • Advancements in transcriptomic sequencing enable longitudinal studies, generating vast datasets.
  • Existing methods lack comprehensive approaches for analyzing longitudinal transcriptomic experiments.

Purpose of the Study:

  • To introduce the Time Series Analysis (TiSA) pipeline for analyzing longitudinal transcriptomic data.
  • To provide a unified framework combining differential gene expression, clustering, and functional enrichment.

Main Methods:

  • TiSA performs differential gene expression analysis along temporal and conditional axes.
  • Clustering of differentially expressed genes is followed by functional enrichment analysis for each cluster.
  • The pipeline supports analysis of both microarray and RNA-seq data, accommodating various dataset sizes and missing data points.

Main Results:

  • TiSA successfully analyzed diverse longitudinal transcriptomic datasets, including cell lines and COVID-19 patient data.
  • The pipeline demonstrated robustness across different data complexities and formats.
  • Included custom visualizations aid biological interpretation, such as PCA, MDS plots, and heatmaps.

Conclusions:

  • TiSA offers the first dedicated and comprehensive solution for longitudinal transcriptomic data analysis.
  • The pipeline facilitates easier biological interpretation of complex temporal gene expression patterns.
  • TiSA is a valuable tool for researchers working with time-series gene expression data.