ArticleScientific reports2017
A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Challenges and future directions of AIRR-seq-based diagnostics.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- IgG Biomarkers in Multiple Sclerosis: Deciphering Their Puzzling Protein A Connection.Biomolecules · 2025Review
- Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls.Frontiers in immunology · 2021Article
- Immunoglobulin gene analysis as a tool for investigating human immune responses.Immunological reviews · 2018Review
- Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.Frontiers in immunology · 2018Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced repertoires by contrasting disease and healthy subjects. This technique employs a local-search graph-theory approach to discover novel motifs in patient data. We apply the Haystack Heuristic to nine million B-cell receptor sequences obtained from nearly 100 individuals in order to elucidate a new motif that is significantly associated with multiple sclerosis. Our results demonstrate the effectiveness of the Haystack Heuristic in computing possible biomarker candidates from high throughput sequencing data and could be generalized to other datasets.
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Registered trials
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