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<p>In this Research Note, we introduce Intronomics-MIP, a snakemake-based pipeline for the automated analysis of multi-locus intron polymorphisms (MIPs) using intron-targeted amplicon sequencing. Building on established methodologies, our pipeline integrates tools such as Cutadapt, FLASH, and SeekDeep to efficiently process and analyze highly variable intron regions. These MIPs serve as powerful multiple-allelic markers, primarily useful for distinguishing species, identifying cryptic species, disentangling species complexes and detecting hybridization, but can also be informative for assessing population structure without prior species knowledge. Our pipeline enhances reproducibility and scalability, making it adaptable to a wide range of taxa, with a specific demonstration on teleost species. We provide a comprehensive overview of the pipeline’s design, along with performance assessments using representative datasets.</p>

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Intronomics-MIP: a snakemake pipeline for analyzing multilocus intron polymorphisms in species identification and population genomics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月6日

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  • A. Scapolatiello,
  • E. Boscari,
  • L. Schiavon,
  • N. Vitulo,
  • L. Congiu

摘要

In this Research Note, we introduce Intronomics-MIP, a snakemake-based pipeline for the automated analysis of multi-locus intron polymorphisms (MIPs) using intron-targeted amplicon sequencing. Building on established methodologies, our pipeline integrates tools such as Cutadapt, FLASH, and SeekDeep to efficiently process and analyze highly variable intron regions. These MIPs serve as powerful multiple-allelic markers, primarily useful for distinguishing species, identifying cryptic species, disentangling species complexes and detecting hybridization, but can also be informative for assessing population structure without prior species knowledge. Our pipeline enhances reproducibility and scalability, making it adaptable to a wide range of taxa, with a specific demonstration on teleost species. We provide a comprehensive overview of the pipeline’s design, along with performance assessments using representative datasets.