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Related Concept Videos

Diversity of Antigen Receptors01:28

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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
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Immune2vec: Embedding B/T Cell Receptor Sequences in ℝ Using Natural Language Processing.

Miri Ostrovsky-Berman1,2, Boaz Frankel1,2, Pazit Polak1,2

  • 1Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.

Frontiers in Immunology
|August 9, 2021
PubMed
Summary

Immune2vec, a novel natural language processing method, effectively analyzes B-cell receptor (BCR) repertoire sequencing data. This technique creates low-dimensional embeddings that preserve key biological information, aiding in clinical condition stratification.

Keywords:
BCR repertoireNLPbiological sequence embeddingcomputational immunologyword2vec

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • The adaptive immune system relies on diverse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires for pathogen recognition and memory.
  • Analyzing the vast, multi-dimensional data from immune repertoires presents significant challenges in extracting meaningful biological insights.
  • Vector-space embedding of DNA and amino acid sequences is crucial for developing advanced analytical methods for immune repertoire data.

Purpose of the Study:

  • To introduce Immune2vec, an adaptation of natural language processing (NLP) embedding techniques for analyzing B-cell receptor (BCR) repertoire sequencing data.
  • To validate the efficacy of Immune2vec in representing immune sequencing data at various levels, from amino acid n-grams to entire repertoires.
  • To demonstrate the utility of Immune2vec embeddings in machine learning applications for stratifying distinct clinical conditions.

Main Methods:

  • Adaptation of a natural language processing (NLP)-based embedding technique, termed Immune2vec, for processing BCR repertoire sequencing data.
  • Validation of Immune2vec using amino acid 3-gram sequences, progressively longer BCR sequences, and complete immune repertoires.
  • Application of Immune2vec embeddings with machine learning algorithms for feature extraction and exploratory data analysis on patient data.

Main Results:

  • Immune2vec provides a reliable, low-dimensional representation of immune sequencing data.
  • The embedding technique successfully preserves critical information, including n-gram properties and IGHV gene family classification.
  • Machine learning models utilizing Immune2vec embeddings effectively stratified patients based on distinct clinical conditions.

Conclusions:

  • Immune2vec is a powerful tool for analyzing complex BCR repertoire sequencing data.
  • The generated embedding space is valuable for feature extraction and exploratory data analysis in immunological studies.
  • Immune2vec facilitates the stratification of clinical conditions, highlighting its potential in precision medicine and disease research.