High-throughput immune repertoire analysis with IGoR
By Q. Marcou, Thierry Mora, Aleksandra M. Walczak. 2017
239 citations recorded by the source corpus.
High-throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology. Successful implementations of these methods require a correct characterization, analysis, and interpretation of these data sets. We present IGoR (Inference and Generation Of Repertoires)—a comprehensive tool that takes B or T cell receptor sequence reads and quantitatively characterizes the statistics of receptor generation from both cDNA and gDNA. It probabilistically annotates sequences and its modular structure can be used to investigate models of increasing biological complexity for different organisms. For B cells, IGoR returns the hypermutation statistics, which we use to reveal co-localization of hypermutations along the sequence. We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization. B and T cell receptor diversity can be studied by high-throughput immune receptor sequencing. Here, the authors develop a software tool, IGoR, that calculates the likelihoods of potential V(D)J recombination and somatic hypermutation scenarios from raw immune sequence reads.
Subfields: Immunology, Molecular Biology
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In Molecular Biology
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- Modern Hopfield Networks and Attention for Immune Repertoire Classification (2020, background): “, 2020), further fine-tuning of ground-truth benchmarking immune receptor datasets (Weber et al., 2020; Olson et al., 2019; Marcou et al., 2018), increased GPU memory, and increased computing power, it will be possible to identify discriminating immune receptor motifs for many diseases, potentially ”