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MADaM, an accurate and fast unsupervised algorithm for genotyping of short sequencing reads
  • Thomas Goeury,
  • José Manuel Nunes,
  • Alicia Sanchez-Mazas
Thomas Goeury
University of Geneva

Corresponding Author:[email protected]

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José Manuel Nunes
University of Geneva
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Alicia Sanchez-Mazas
University of Geneva
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We present here MADaM (Multiplexed Amplicon Data Miner), an original algorithm designed to de-novo genotyping of small sequencing reads that do not require assembly step. It performs a classification of the reads based on an original set of features using t-SNE’s and clustering with the DBSCAN algorithm. The algorithm is applied to three different approaches and datasets showing that this software is fully suitable for fastly genotyping highly variable regions such as MHC-HLA exons 2 without any priors such as SNP positions or already known alleles.
14 Feb 2024Submitted to Molecular Ecology Resources
15 Feb 2024Assigned to Editor
15 Feb 2024Submission Checks Completed
22 Feb 2024Reviewer(s) Assigned