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Related Experiment Video

Updated: Nov 2, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Inferring transcriptomic cell states and transitions only from time series transcriptome data.

Kyuri Jo¹, Inyoung Sung², Dohoon Lee²

¹Department of Computer Engineering, Chungbuk National University, Cheongju, 28644, Korea. kyurijo@chungbuk.ac.kr.

Scientific Reports

|June 16, 2021

Summary

This summary is machine-generated.

This study introduces TRAnscriptomic Cellular States (TRACS), a new computational framework. TRACS identifies hidden cellular states and transitions using only time series transcriptome data, advancing molecular-level biological process analysis.

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Last Updated: Nov 2, 2025

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Cellular states are crucial for understanding biological processes but current characterization methods are limited.
Time series transcriptome data offers potential for molecular-level cell state and transition inference.
Existing methods often require prior biological knowledge, limiting their scope.

Purpose of the Study:

To develop a novel computational framework for inferring transcriptomic cellular states (TRACS) solely from time series transcriptome data.
To overcome limitations of existing methods by not requiring prior cell-type information.
To enable the discovery of previously unknown cellular states and transitions.

Main Methods:

Developed a time series clustering framework integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm.
Applied the TRACS framework to both single-cell and bulk RNA sequencing data.
Utilized gene expression data clustering to identify patterns indicative of cellular states.

Main Results:

TRACS successfully inferred cellular states and transitions from time series transcriptome data without prior knowledge.
Generated cluster networks that accurately reflected key biological process stages.
Demonstrated applicability to diverse RNA sequencing data types (single-cell and bulk).

Conclusions:

TRACS provides a powerful tool for uncovering hidden cellular states and dynamic transitions at the molecular level.
The framework expands the utility of time series transcriptome data for biological discovery.
TRACS is available as an open-source Python implementation for broader research use.