{
  "abstract": "Theoretical methods for predicting the performance of assembly algorithms have the potential to guide experimental design on a scale that simulations cannot. A fundamental question of experimental design is how deep to sequence in order to obtain an assembly with long-enough contigs. More precisely, given a certain read length and coverage, what is the expected contig length that an assembler would produce on a given genome? The foundational work of Lander and Waterman tackled this question, but their method is not applicable to modern high-throughput sequencing. In this paper, we derive what we call a Z-value, which is a formula that takes as input the read length, coverage, and basic properties of the genome, and returns a prediction of the mean contig length produced by a simple single-k assembler on error-free data. We show on a benchmark of 14 genomes that the Z-value is highly accurate, with a mean absolute prediction error of less than 2 percent. We further show how this formula can be used to determine the best choice of k for the assembler and how to choose a coverage level that gives contig lengths within a desired fraction of the optimal. Finally, we show how the Z-value can also predict the performance of a more complicated assembler like Velvet and how it can be used to guide the choice between instruments when there is a tradeoff between coverage and read length and the budget is limited.",
  "abstract_shown": true,
  "affiliations": [
    "The Pennsylvania State University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "M. H. Abrar"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Medvedev"
    },
    {
      "name": "A. Novikov"
    },
    {
      "name": "K. Sahlin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1tww18x2cw5fw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.05.764522",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764522v1.full",
  "id": "1tww18x2cw5fw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764522v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764522v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1tww18x2cw5fw.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T00:21:14Z",
      "native_id": "10.64898/2026.11.05.764522",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764522v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Theoretical prediction of assembly contiguity",
  "title_text": "Theoretical prediction of assembly contiguity",
  "type": "preprint",
  "updated_at": "2026-11-08T00:21:05Z",
  "url": "https://research.draconitic.com/paper/1tww18x2cw5fw",
  "venue": "bioRxiv",
  "versions": []
}