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      Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome

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          Abstract

          IgA, the most highly produced human antibody, is continually secreted into the gut to shape the intestinal microbiota. Methodological limitations have critically hindered defining which microbial strains are targeted by IgA and why. Here, we develop a new technique, Metagenomic Immunoglobulin Sequencing (MIG-Seq), and use it to determine IgA coating levels for thousands of gut microbiome strains in healthy humans. We find that microbes associated with both health and disease have higher levels of coating, and that microbial genes are highly predictive of IgA binding levels, with mucus degradation genes especially correlated with high binding. We find a significant reduction in replication rates among microbes bound by IgA, and demonstrate that IgA binding is more correlated with host immune status than traditional microbial abundance measures. This study introduces a powerful technique for assessing strain-level IgA binding in human stool, paving the way for deeper understanding of IgA-based host microbe interactions.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              Matplotlib: A 2D Graphics Environment

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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                21 November 2023
                : 2023.11.21.568153
                Affiliations
                [1 ]Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
                [2 ]Chan Zuckerberg Biohub, San Francisco, CA, USA.
                [3 ]Center for Human Microbiome Studies, Stanford University School of Medicine, Stanford, CA, USA.
                [4 ]Division of Gastroenterology and Hepatology, Stanford School of Medicine, Stanford, CA, 94305, USA
                Author notes
                [*]

                These authors contributed equally to this work

                Contributions

                M.R.O, S.P.S, and J.L.S. designed the study. S.P.S. led development of MIG-Seq protocol. M.R.O. performed bioinformatic analysis. M.R.O, S.P.S, and E.L. performed wet lab experiments. M.R.O. wrote the manuscript and all authors contributed to manuscript revisions.

                []Corresponding author: jsonnenburg@ 123456stanford.edu
                Author information
                http://orcid.org/0000-0001-5540-350X
                http://orcid.org/0000-0002-7328-5399
                Article
                10.1101/2023.11.21.568153
                10690254
                38045399
                e80ed550-fe51-475f-8454-022b047a0e39

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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