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Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

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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.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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B Cell Activation and Differentiation01:24

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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
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T Cell Activation and Clonal Selection01:22

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
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tRNA Activation02:26

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Aminoacyl-tRNA synthetases are present in both eukaryotes and bacteria. Though eukaryotes have 20 different aminoacyl-tRNA synthetases to couple to 20 amino acids, many bacteria do not have genes for all of these aminoacyl-tRNA synthetases. Despite this, they still use all 20 amino acids to synthesize their proteins. For instance, some bacteria do not have the gene encoding the enzyme that couples glutamine with its partner tRNA. In these organisms, one enzyme adds glutamic acid to all of the...
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Related Experiment Video

Updated: Oct 25, 2025

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
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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
This summary is machine-generated.

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.