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Related Experiment Video

Updated: Feb 9, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Published on: June 27, 2013

RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy.

Wahiba Chaara^1,2, Ariadna Gonzalez-Tort¹, Laura-Maria Florez¹

¹Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.

Frontiers in Immunology

|June 6, 2018

Summary

High-throughput sequencing (HTS) of T cell receptor (TCR) repertoires requires careful consideration of experimental conditions. Optimizing cell numbers, sampling, and sequencing depth is crucial for accurate analysis of immune cell diversity.

Keywords:

TCR repertoire bioinformatics diversity normalization sampling

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Area of Science:

Immunology
Genomics
Bioinformatics

Background:

High-throughput sequencing (HTS) offers potential for T cell receptor (TCR) repertoire analysis.
Understanding TCR repertoire dynamics is key for identifying disease biomarkers.
Experimental conditions significantly impact TCR repertoire data quality.

Purpose of the Study:

To assess the impact of experimental conditions on TCR repertoire diversity assessment.
To determine the representativeness and robustness of TCR repertoire data.
To provide insights for optimizing HTS-based TCR repertoire studies.

Main Methods:

Comparative analysis of experimental datasets.
Computer simulations of TCR repertoire sequencing.
Evaluation of factors like cell numbers, sampling, and sequencing depth.

Main Results:

Small samples may yield more clonotypes than cells, even after singleton removal.
High sequencing depth in small samples can distort clonotype distributions, correctable with Shannon entropy filtering.
Multiple sequencing runs are superior to single high-depth runs for covering repertoire richness in polyclonal populations.

Conclusions:

Experimental conditions critically influence TCR repertoire diversity assessment via HTS.
Awareness of these limitations is essential for accurate data interpretation.
Development of advanced computational tools is needed for robust TCR repertoire modeling.